"The Big Book of NLP, Expanded: 350+ Techniques, Patterns &
Strategies of Neuro Linguistic Programming" by Shlomo Vaknin and
Erickson Institute.
Rapport is built by becoming subtly similar to someone — matching
their body language, speech, and symbolic world — so their subconscious
senses "you're like me / you get me." Once that rapport exists
(pacing), you can lead them into a new state or position. Sharp sensory
observation is the prerequisite for all of it.
You can't lead someone you haven't first paced.
Match their world until their subconscious accepts you as "one of us,"
then move — and the moment you hit resistance, that's your
signal the rapport isn't there yet (back up, don't push).
The frameworks
Pacing & Matching (Grinder/Bandler) — blend
elements of the other person's body language and speech into your
own style (not raw imitation). Match their predicates — if they say
"I see what you mean," you reply in visual language too. Start
so subtle it can't be detected, escalate gradually, and never fake an
accent.
Mirroring — reflect their physiology to build
rapport and "second position": converse while subtly matching cadence,
gesture, and breathing; use active listening ("So you're saying…") to
keep it flowing; then test — try to guess their opinion before
they voice it, then shift your own posture/mood and watch whether they
follow (that's pacing and leading).
Behavioral & Symbolic Mirroring — when plain
physical matching isn't enough (across status, gender, culture), mirror
the things a person values and respects: mention hard work,
family, or faith if those matter to them; match their wardrobe,
references, and the connotation of their words. (The book's
example: Temple Grandin wearing western wear with cattle-industry
clients.)
Exchanged Matches — when copying someone
directly would be too obvious, match the timing of their
movement with a different, subtler one — e.g. tap your finger or nod in
time with their breathing (read it from their shoulders, not their
chest). Once matched, you can gently lead by slowly speeding your own
rhythm so theirs follows.
When NOT to mirror — don't mirror aggression
(don't play "alpha dog" — adopt a same-team posture instead),
don't mirror suffering or intense need (you'll induce their state in
yourself and may threaten them — mirror only the general, comforting
cues). If you get caught mimicking, drop the physical mirroring but keep
gentle symbolic mirroring.
The observation skills - before you can
influence anyone, you have to be able to read them — and these four
drills train your eyes to catch the small, automatic signals that reveal
what's going on inside.:
Eliciting Subconscious Responses — learn what
each of your senses "looks like" from the outside. Ask a partner to
relive a happy memory and focus first only on what they saw in
it, then only what they heard, then only what they
felt — while you watch how their posture, skin color, and
breathing shift with each one. You're building a personal catalog of
"this is what someone looks like when they're picturing something vs.
hearing something vs. feeling something."
Calibration — connect one specific body signal
to one specific inner state. Have your partner think of something they
clearly understand, and watch their face, eyes, and hands; then
something that confuses them, and watch again. Once you can see
the difference, have them silently pick one and re-think it — and you
guess which from their body alone, then check. You're learning to "read
the dial" for that person.
Sensory Acuity — simply training your eyes to
catch the tiny, involuntary changes people don't control: skin getting
lighter or darker, muscles tensing or softening, breathing speeding up
or slowing, breath moving higher or lower in the body, faint lines
around the lips, pupils widening. These are the raw signals everything
else is built on.
Non-Verbal Cues Recognition — you read other
people's faces better once you know your own. The face has 90+ muscles
and thousands of expressions, so you practice in a mirror — cycling
through emotions like fear, joy, anger, and surprise — to feel
from the inside what each expression is, which makes you far quicker to
spot it on someone else.
Satir Categories (Virginia Satir) — when people
are under stress, they tend to fall into one of five communication
styles. Each one is a kind of defense, so each needs a different
approach to build rapport. Recognize which one you're facing, then match
it the right way:
The Blamer — attacks and finds fault ("This is
your fault"). Don't get defensive and don't fight back —
instead, get worked up about the same problem they are, so
you're on the same side against it. Match their intensity, but never put
them in a position where they have to defend themselves.
The Placater — over-apologizes and tries to please
everyone, afraid of conflict. Give them the attention and reassurance
they're craving; start by connecting with what they deeply care about,
then ease into the specifics.
The Computer — goes cold, stiff, and overly
logical to avoid feeling anything. Meet them there: stay calm and
rational, and don't push them to open up emotionally — that only makes
them retreat further.
The Distracter — changes the subject and won't stay
on point, dodging the real issue. Don't fight the tangents — go along
with the detour, but keep gently steering back by tying things to
what they personally care about, until their own restlessness
wears them out and they return to your point.
The Leveler — straightforward, honest, and
congruent: their words, tone, and body all line up. This is the easiest
person to connect with — just match their honesty and sense of fairness.
And when you can't tell which style someone is, default to being
the Leveler yourself.
Worked example
A textile sales rep meets a clothing-company buyer. Reading her cues
— worked her way up (no degree-pride), conservative-religious accent,
judgmental remarks — he runs behavioral + symbolic mirroring: drops big
words, mentions things he earned through hard work, uses dry humor aimed
at the rich (not the poor), references his church and family. He
physically mirrors her posture and breathing, but via an exchanged match
— watching her shoulders and moving his hand to her breath rhythm while
keeping a gentle masculine quality. Rapport forms below her awareness,
and then he can lead.
Key takeaways
Match into their world (verbal, physical, symbolic), then
lead — rapport precedes influence.
The more subconscious a behavior, the safer and more powerful it is
to mirror.
Train sensory acuity and calibration on yourself first — your own
face, your own physiology.
Know when to STOP — never mirror aggression or suffering; back off
if caught.
Use Satir categories to read people and to know what not to do; when
lost, be the Leveler.
Prefer pacing (integration) over mirroring (imitation) when subtlety
matters — pacing rarely offends.
Use symbolic mirroring when physical mirroring is risky (caught,
intense states, opposite sex) — harder to detect, de-escalating.
A "state" is your whole mind-body condition at a
given moment (confident, anxious, curious, flat). States can be
deliberately switched on, cleared, amplified, and linked to a
trigger — so you can re-summon a resourceful state on demand.
This is the most fundamental NLP skill: nearly every other
pattern assumes you can already produce and anchor a state
cleanly.
Anchoring is deliberately creating a trigger that
brings back a feeling on command. A song comes on and you're suddenly
back in a summer ten years ago. A certain smell and you feel like a kid
in your grandmother's kitchen. An anchor is a trigger
(the song, the smell) got wired to a state (the
feeling), so the trigger now fires the feeling automatically. NLP says,
if that happens by accident, you can also do it on purpose.
Anchor at the peak, keep the state pure, and never reuse the
trigger. Get that one mechanic right and most of the rest of
NLP becomes available, because nearly every pattern is "elicit a state →
amplify it → anchor it → fire it where you need it."
Elicit — bring up a feeling (recall a time you felt
confident).
Amplify — make it stronger (turn up the mental
picture, the inner voice, the body sensation).
Anchor — lock it to a trigger at its peak. (three
rules: anchor at the peak (full strength), with a pure
state (clean, not mixed), using a dedicated trigger (means one
thing))
Fire — set off that trigger in the situation that
needs it (right before the interview, the hard conversation, the
stage).
The frameworks
Producing & clearing states:
State Induction — deliberately generate a state
(confidence, curiosity) before an event: define it across
sight/sound/feeling, kindle it by recalling times you felt even
a hint of it, amplify it through your weakest sense plus
matching self-talk ("Piece of cake"), and let the feeling flow
through your body.
Accessing Resourceful States (Andreas) — for a
known upcoming situation: name the quality you'll need, recall a vivid
memory of having it, step in and amplify it like a "force field," then
borrow a role model's physiology by viewing them from 2nd position,
anchor it, and test.
Physiomental State Interruption — snap out of a
stuck bad state (boredom, anger, a self-critical voice) by taking
whatever fuels it and exaggerating it until it's absurd — replay the
harsh inner voice as a cartoon squeak, or jolt your mind with quick math
(count back from 100 by sevens). If nothing shifts, you exaggerated the
wrong detail — find the one really driving the feeling.
Anchoring — the core skill:
Anchoring (Grinder & Bandler) — wiring a
feeling to a trigger so you can call it back on command (like a song
that drops you into a memory, but built on purpose): pick the feeling +
a unique trigger (a hand position, a knuckle press, a private word),
bring the feeling up strongly, set the trigger at its peak, then test.
Keep that trigger for this feeling only — reusing it makes it
meaningless.
Self-Anchoring — the solo version: run it from the
outside-observer view (watch yourself like a character in a movie, build
the feeling up, then step back into your body to lock it in).
Working with stubborn negative states:
Collapsing Anchors — dissolve an unwanted
automatic reaction by firing a negative-state anchor and a
stronger positive-state anchor at the same time. Hold both
(expect eye-darting and confusion — that's processing), release the
negative first, then test. (Anchors usually go on opposite sides of the
body — one per knee.)
Chaining States — when a target state is too far
to reach in one jump (e.g. climbing out of a downward spiral), build a
bridge of intermediate states, anchoring each to a different
knuckle, then fire them in sequence from negative → positive.
Circle of Excellence (Grinder & DeLozier) —
imagine a circle on the floor, load it with a peak state (step in, fully
relive it, amplify, step out, test), then future-pace it: imagine
stepping into it right before the real situation that needs it.
The underlying skill — noticing states: every
technique above assumes you can tell what state you're in, so train that
awareness first.
Downtime — a light inward trance: turn attention
inward one sense at a time (inner sounds, then images, then feelings).
It's the doorway to deeper trance. (Opposite: Uptime — alert to the
outside world, but still guided by inner awareness.)
State of Consciousness Awareness — a daily
journaling habit: list the states you were in, rate each, and mark what
truly triggered it (often something subtle, like a tone of voice). It
trains you to notice states — the foundation of every other
pattern.
Submodalities are the fine "settings" of a mental image,
sound, or feeling — how bright, big, close, loud, warm
it is, and where it sits. The key discovery: a feeling's
intensity comes from these settings, not from the
content. So you can rewire an automatic reaction by finding the
few "driver" settings that hold the charge and swapping or reversing
them fast. That's the engine behind the Swish and its whole
family.
Find the driver submodality and turn it. A feeling
is a set of adjustable dials (brightness, size, distance, location) —
locate the one carrying the charge and you can shrink a craving, drain a
fear, or swap a habit, all without arguing with the feeling itself.
The frameworks
The signature pattern: - catch the mental cue
that sets off a bad habit and, over and over, fast, replace it with a
vivid picture of the person you'd rather be — until your brain
automatically goes there instead.
The Swish (Bandler & Grinder) — replace an
automatic unwanted reaction with a resourceful self-image. Build a
small, dim image of the you you'd rather be; find the big,
bright trigger image that sets off the bad habit; tuck the replacement
as a tiny dot in its corner; then "swish" — the trigger shrinks and
shoots away while the replacement explodes big and bright. Clear your
mind, repeat 5–7× fast, test.
Variations on the same engine: - once you know a
feeling is just a set of adjustable internal "settings," you can shrink
it (Kinesthetic Swish), walk it off (Pragmagraphic), burn it out by
overdoing it (Blow-Out), or spin it together with its opposite (Spinning
Icons) — same engine, different gears.
Pleasure/decision tools: - Godiva borrows
excitement you already have and glues it onto a task you've been
avoiding; Decision Destroyer travels back to the moment a limiting
decision was made and rewrites it with a strength you only gained
later.
Key concepts — the handful of terms the whole
chapter runs on:
Submodality — the adjustable "settings" of a memory
or mental image, sense by sense. A picture in your mind has
settings like size, brightness, color, how near or far it is, where it
sits, and whether it's a still photo or a moving clip. A sound
has volume and pitch. A feeling has a location in your body, a
temperature, and movement. These are the dials you turn to change how
something affects you.
Driver submodality — out of all those settings, the
one that changes the feeling the most when you adjust it (often
brightness, size, or distance). Find this one first — turning it gives
you the biggest result for the least effort.
Associated — reliving a memory from inside your
own eyes, as if it's happening to you now. This carries the full
emotional charge. (In the Swish, the trigger image is
associated — that's why it hits hard.)
Dissociated — watching yourself from the
outside, like seeing yourself in a movie. This drains most of the
emotional charge. (In the Swish, the better-self replacement is
dissociated — distant enough to pull you toward it.)
Mapping across — copying the settings from one
image onto another to change how the second one feels. (E.g. take what
makes a good memory feel good — bright, close, warm — and apply
those same settings to a flat one.)
"The Big Book of NLP, Expanded: 350+ Techniques, Patterns &
Strategies of Neuro Linguistic Programming" by Shlomo Vaknin and
Erickson Institute.
Ecology is NLP's term for the systemic impact of
a change — checking how a desired change ripples through the whole
system it lives in before you make it. The word is borrowed deliberately
from biology: just as you can't change one species in an ecosystem
without affecting all the others, you can't change one behavior, belief,
or state in a person without affecting everything connected to
it.
The skill's glossary puts it as: the state where all parts,
values, and needs align with an outcome and nothing important is harmed.
A change is "ecological" when it fits the whole system; it's
un-ecological when it solves the stated problem but quietly breaks
something else.
Before investing resources, define what you actually want — in
sensory-specific, self-actionable terms — then stress-test that outcome
against every part of yourself and every system you touch. An outcome no
part of you objects to, and that harms no one, is the only kind that
holds.
The Rule: Never commit to a change without an
ecology check — surface the secondary gain and any objecting part first,
or the change won't stick.
The 8 frameworks - Well-Defined
Outcomes. A 10-step recipe for a "well-formed" goal answering
"What do you really want?":
State it positive & specific (no "not" frames)
Frame it in terms of your own ability/actions, within your
responsibility
Anchor to context — where / when / with whom, and where
not
Describe in all five senses
Chunk down into achievable objectives
List the resources needed
Run an ecology check
Set observable milestones on a timeline
Write it down
Test and monitor, refining as you go
Helpful Mindsets:
Failure Into Feedback (Dilts) — break a "failure"
down into what you saw, heard, and felt so it stops feeling
overwhelming, mine it for the lessons it's actually teaching you, and
pair it with a strong memory of success — so the setback becomes useful
feedback instead of proof you can't do it.
E & E.P. Formation Pattern (Evidence &
Evidence Procedure) — defines how you'll know the outcome is
achieved: concrete observable evidence, the repeatable procedure to
detect it (and counter-evidence), timeframes, and anticipated
resistances.
Finding Positive Intention / Behavior Appreciation
— a self-sabotaging habit is usually trying to do something
good for you underneath. "Talk to" the part behind it, ask
"What were you trying to do for me?", and keep asking until you
reach the real need. Behavior Appreciation does this physically —
separate floor-spots for the behavior and for the part — so you can step
between them and keep them distinct.
Ecology at three scales:
Ecology Check (Grinder & Bandler) — dissociate
to 3rd position; ask who benefits / who's hurt, short- vs long-term.
Tool: Cartesian coordinates (do X → will/won't happen;
don't do X → will/won't happen).
Secondary Gain & Personal Ecology — "What about
staying stuck gives me a reason to stay stuck?"
Whole System Ecology — does this impair any other
person or institution? Leverage question: "If I could have it now,
would I take it?"
Mental models:
Be → Do → Have — become the person who can achieve
the outcome, then act, then enjoy the result.
Failure is feedback — every miss is data +
warning-knowledge (Edison's light bulbs).
A problem behavior is a messenger with a positive intention, not an
enemy to suppress.
Key takeaways
Phrase every goal positively, specifically, and as something you can
act on.
Anchor outcomes to a concrete context and all five senses so the
brain treats them as real.
Always run an ecology check before committing — surface secondary
gains and objecting parts first.
Define your evidence and evidence procedure up front.
Reframe failure as feedback — mine "failure" memories for
learnings.
Find and respond to the positive intention behind a negative
behavior.
Check ecology at three scales: parts of yourself, your personal
values, the wider human/institutional systems.
"The Big Book of NLP, Expanded: 350+ Techniques, Patterns &
Strategies of Neuro Linguistic Programming" by Shlomo Vaknin and
Erickson Institute.
Vaknin frames these 21 presuppositions as "an excellent and
especially useful collection."
The six that do the heaviest lifting in actual change-work:
#8 (every behavior has a positive intention),
#12 (meaning = the response you get),
#14 (no failure, only feedback), #15
(flexibility = influence), #16 (resistance = lack of
rapport), and #17 (people already have the
resources).
The 21 NLP Presuppositions
The map is not the territory. — Your mental model
of the world is never the world itself, and is always improvable.
People respond according to their internal maps. —
To understand someone, learn their map, not what they "should"
do.
Meaning operates context-dependently. — The same
words/behavior mean different things in different situations.
Mind and body affect each other. — You think with
your body, not just your brain; physiology and cognition are one
system.
Individual skills function by developing and sequencing rep
systems. — Ability is built from how you order
Visual/Auditory/Kinesthetic representations.
We respect each person's model of the world. — You
needn't agree, but respecting their map creates understanding and less
conflict.
Person and behavior describe different phenomena — we are
more than our behavior. — A single act or pattern doesn't
define the whole person.
Every behavior has utility and usefulness — in some
context. — Even troubling behavior carries a hidden value
(basis for utilization & positive intention).
We evaluate behavior and change in terms of context and
ecology. — Consider the systemic/ripple impact of any change
(systems theory).
We cannot not communicate. — Clothes, posture,
micro-expressions all signal; you're always communicating.
The way we communicate affects perception and
reception. — Your delivery (sub-modalities, style) shapes how
the message is received.
The meaning of communication lies in the response you
get. — Regardless of intent, your communication is
what the other person received.
The one who sets the frame for the communication controls
the action. — Whoever defines the surrounding assumptions
steers the exchange.
"There is no failure, only feedback." — A
philosophy to live by: turn every "failure" into learning.
The person with the most flexibility exercises the most
influence in the system. — Behavioral choice = control.
Resistance indicates the lack of rapport. —
Resistance is a signal to rebuild rapport, not push harder.
People have the internal resources they need to
succeed. — Your job is to direct them to those resources, not
supply them.
Humans have the ability to experience one-trial
learning. — A single intense experience can install a lasting
change (good or bad).
All communication should increase choice. — Ethical
use of NLP expands options; it never coerces or limits.
People make the best choices open to them when they
act. — Given their map and resources at that moment, they chose
what seemed best.
As responseable persons, we can run our own brain and
control our results. — You can take charge of your own
neurology and outcomes.
More Explanations for the
Tricky Ones
Presupposition
#13 — "The one who sets the frame for the communication controls the
action"
Don't walk into an important conversation arguing the content —
decide first what the conversation is about and what
assumptions govern it, because a frame always exists, and whoever sets
it has already shaped which arguments can win.
What a "frame" is
A frame is the set of unspoken assumptions
surrounding a conversation before any content is exchanged —
what the discussion is about, why it's happening, what counts
as relevant, what a "good outcome" would be, and who's in what role.
Vaknin's own words from the book: the frame consists of "things like
the assumptions about why the discussion is taking place, what the
environment means about it, that sort of thing." And the key line:
"Every communication comes in a package of
presuppositions."
Why the frame, not the argument, controls the
outcome
Most people fight over the content ("here are my
five reasons…") while the other party has quietly already set the
frame — and the frame decides which content even
counts. A few examples of the same facts under different frames:
A salary conversation framed as "Can we afford a raise this
year?" vs. "What's the cost of you leaving and us
re-hiring?" — identical facts, opposite gravity.
A mistake framed as "Who's to blame?" vs. "What does
this teach us?" — the second frame makes blame-seeking literally
feel off-topic.
A negotiation framed as "How do we split this pie?" vs.
"How do we make the pie bigger?" — the frame predetermines
whether it's adversarial or collaborative.
Whoever owns the frame has already won the argument that matters,
because they've defined the terms on which all the smaller
arguments get judged. The other person is now playing on a board someone
else laid out.
"Sets the frame" — this is active, and it's usually
unclaimed
The crucial insight is that a frame always exists —
the only question is whether you set it on purpose or inherited one by
default. The book gives the practical move directly:
"Before an important discussion, ask yourself what the frame will
be if you do nothing. Consider how it may support or defeat your
objectives. Then think about how that frame might be improved."
That's the whole discipline in three steps:
What's the default frame? If I walk in and say
nothing about the terms, what assumptions will govern this? (Often the
other side's, or a culturally inherited one.)
Does it serve or sink me? Whose objectives does the
default frame favor?
What's the better frame, and how do I install it? —
usually in the opening seconds, by naming what this is about.
The protective half
The presupposition cuts both ways, and the book stresses the
defensive use. Presuppositions "can be constructive when they
provide positive guidance. But they can cause harm, as they do when they
serve to filter and bias propaganda. Instead of being a sitting duck,
you can be proactive."
So #13 isn't only about steering others — it's about noticing
the frame being placed on you. When a question contains a
buried assumption ("Why does your team keep missing deadlines?"
presupposes you keep missing them), answering the content accepts
the frame. The skilled move is to surface and challenge the
frame before engaging.
Presupposition
#15 — "The person with the most flexibility exercises the most influence
in the system"
Influence doesn't come from having the one right move or pushing it
harder — it comes from having more moves than the situation can
block, so that whenever a response fails you simply shift to the next
one and keep steering toward your outcome.
Where this comes from: the Law of Requisite
Variety
This is NLP's adaptation of a principle from cybernetics —
Ashby's Law of Requisite Variety: in any system,
the element with the widest range of responses controls the system.
The skill's glossary phrases the NLP shorthand as "only variety
can destroy variety" — to handle a complex or rigid situation,
you need more available responses than the situation can throw
at you.
"Flexibility" here means a concrete, countable thing: the
number of distinct responses you can actually produce in a
given moment. Not how clever or how right you are — how many different
moves you have.
Why more options = more control (and not the
reverse)
The intuition most people carry is backwards. We assume control comes
from certainty, firmness, having the one right answer — digging in. This
presupposition says control comes from the opposite: from
not being locked into a single response.
The mechanism: whoever has only one response is predictable
and stoppable — block that one move and they're stuck. Whoever
has five responses, when the first fails, simply shifts to the second.
They cannot be cornered, because the system can't exhaust their
repertoire. Over any real interaction, the more-flexible party keeps
adapting until they reach their outcome, while the rigid party stalls at
the first obstacle.
A few concrete pictures:
The thermostat controls the room not by being
hotter or colder, but by being able to go either direction in
response to whatever the room does.
In a negotiation, the person who has only one
acceptable outcome and one tactic is the one who gets stuck; the person
who can switch between asking, listening, reframing, walking away, and
coming back steers it.
A parent facing a tantrum who has only "raise my
voice" loses; the one who can switch to humor, distraction, curiosity,
or silence finds the lever.
A salesperson with one pitch fails on the
prospect it doesn't fit; one with ten angles keeps adjusting until
something lands.
"Behavioral choice = control" — the practical
reframe
The actionable translation: when you feel stuck, you don't
have an understanding problem, you have a flexibility problem.
You've run out of responses. The question stops being "Why won't
they change / why won't this work?" and becomes "What else
could I do here that I haven't tried?"
This flips frustration into a generative question. Frustration is the
felt sense of having exhausted your options; the cure is to manufacture
another option, not to push the failing one harder. (Pushing harder is
the opposite — it's reducing your variety to a single repeated
move, which is why it so often fails.
Presupposition
#17 — "People have the internal resources they need to
succeed"
Assume the person already contains everything they need to
be who they want to be in the stuck moment, and treat your role
as helping them find and reconnect that resource — because a capability
they retrieve themselves sticks, while one you hand them doesn't.
What "resource" means here
In NLP a resource is an internal state or
capability — calm, confidence, focus, curiosity, decisiveness,
compassion — that a person has already experienced at some point in
their life. The claim isn't that someone literally already has the
money, the skill certificate, or the job offer. It's that they already
possess the neurological raw material for the state
they'd need to get those things. Somewhere in their history they have
been confident, been calm, been resourceful —
even if not in the context where they're currently stuck.
So the presupposition is really: the problem is not a missing
resource, it's a resource that isn't connected to the right
context. The confidence exists; it just isn't showing up in the
job interview / the difficult conversation / the moment of
temptation.
"Direct, not supply" — the practitioner's actual
job
This is the operative half, and it flips the usual helper role on its
head.
Supplying = "Here's what you should do, here's
my advice, let me give you confidence, let me motivate you." This
positions you as the source. It breeds dependence, it meets
resistance, and it doesn't last, because a resource handed from outside
isn't wired into the person's own neurology.
Directing = guiding the person's mind through a
process that retrieves their own existing resource and
re-attaches it to the stuck context. You're a navigator, not a supplier.
"You direct the client's mind through a process and let it do
the work." Their brain makes the change; you just point
it.
How this shows up mechanically in the
patterns
Almost every technique in this book is a machine for
relocating an existing resource, never for manufacturing
one:
Anchoring / Circle of Excellence — recall a past
moment when you did have the state, fire it, and transfer it to
where it's needed. The state already existed; anchoring just makes it
portable.
Change Personal History / Re-Imprinting — take
resources the adult now has and carry them back to a past self
who lacked them.
Accessing Resourceful States — name the quality
needed, find a memory of it, step in, borrow more via 2nd-position
modeling.
Six Step Reframe — even the problem
behavior is reframed as containing a positive resource, not as a
deficit to fill from outside.
Notice the pattern: the practitioner never installs
confidence from scratch. They locate where the client has already felt
it and re-route it.
Why hold this belief even if it's not literally
"true"
You don't adopt #17 because it's empirically airtight — you adopt it
because of the stance it forces on you:
It makes you curious instead of prescriptive —
you go looking for where the resource lives rather than
lecturing.
It keeps agency with the client — they leave
knowing they did it, which makes the change durable and
self-reinforcing.
It dissolves the "broken person" frame — the
person isn't deficient, they're momentarily disconnected from their own
capability.
The Art of Asking Smarter Questions - Arnaud Chevallier, et
al., Harvard Business Review [Link]
Using these five techniques helps us ask smarter questions by
transforming our questioning from a reactive habit into
a deliberate strategy. Instead of just asking whatever
pops into our head, we can look at a situation and decide exactly what
kind of thinking our team or project needs at that moment.
Investigative: What’s Known?
Purpose: Digs deep to generate nonobvious information and clarify
the core problem or opportunity.
Example prompts: What happened? What is and isn't working?
What evidence supports our proposed plan?
The Goal: To avoid surface-level assumptions. It often utilizes
successive "Why?" or "How?" questions to uncover overlooked
data.
Speculative: What If?
Purpose: Helps you consider a problem more broadly, reframe the
issue, and explore creative, alternative solutions.
Example prompts: What other scenarios might exist? Could we
do this differently? What potential solutions have we not
considered?
The Goal: To overcome limiting assumptions and jump-start
innovation (often using prompts like "How might
we...?").
Productive: Now What?
Purpose: Assesses the availability of talent, capabilities, time,
and resources needed to execute a strategy.
Example prompts: What is the next step? Do we have the
resources to move ahead? Are we ready to decide?
The Goal: To identify metrics, milestones, and potential capacity
bottlenecks to keep plans on track.
Interpretive: So, What…?
Purpose: Focuses on sensemaking and synthesis by pushing you to
continually redefine the core issue and draw out the true implications
of your data.
Example prompts: What did we learn from this new information?
How does this fit with our overarching goal?
The Goal: To convert raw information into actionable insight and
ensure everything ladders back up to the main mission.
Subjective: What’s Unsaid?
Purpose: Deals with the human element—personal reservations,
frustrations, hidden agendas, and emotional alignments that can derail a
decision.
Example prompts: How do you really feel about this decision?
Are all stakeholders genuinely aligned?
The Goal: To clear out "pluralistic ignorance" (where team
members hide misgivings because no one else is speaking up) by creating
a safe space for dissenting views.
Transformations That Work - Michael Mankins, Patrick Litre,
Harvard Business Review [Link]
Summary:
Treating transformation as a continuous process: Instead of
viewing change as a discrete program with a strict beginning and end
(the traditional "unfreeze-change-refreeze" model), successful companies
recognize that they must operate in a state of constant transformation
and manage an evergreen backlog of issues.
Building it into the company's operating rhythm: Transformation
shouldn't be isolated in a separate program-management office. It must
be woven directly into the executive team's regular, weekly operating
routines and plan reviews so that managing change becomes part of
everyone's day job.
Explicitly managing organizational energy: Because
transformations fail when they exhaust employees, successful programs
carefully sequence initiatives. They ensure no single function or group
is forced to change too many primary routines at the same time, and they
reward milestones to sustain morale.
Using aspirations, not just targets, to stretch thinking: Relying
strictly on external benchmarks often sets the bar too low and limits
"the art of the possible." True transformations rely on bold ambitions
and breakthrough thinking to fundamentally reshape business
models.
Driving change from the middle out: Top-down mandates can be
superficial, and bottom-up efforts often just trim around the edges.
Midlevel executives possess the unique combination of frontline
experience and strategic context needed to design and execute
meaningful, lasting structural changes.
Accessing substantial external capital from the start:
Transforming a business is expensive and cannot be reliably funded
through internal cost-cutting alone. Successful efforts tap the capital
markets early to ensure the initiative is fully funded to fuel
growth.
Make Decisions with a VC Mindset - Ilya A. Strebulaev, Alex
Dang, Harvard Business Review [Link]
To spur innovation within a traditional organization using the VC
mindset, the article highlights that we don't need to apply these rules
to routine decisions in predictable environments. Instead, we should
deploy them strategically in times of high uncertainty,
disruption, or when developing radically new products.
A High Comfort Level with Failure
The VC Skill: VCs accept that up to 80% of their investments will
fail. Their business model relies on the understanding that home runs
matter and strikeouts don't; one massive success can cover all other
losses combined.
Prioritizing the Individual over the Group
The VC Skill: VCs seek out contrarian ideas, recognizing that
breakthroughs often come from a single person in the room rather than a
group. They actively structure environments to prevent individuals from
being drowned out by group consensus. They achieve this by:
Keeping teams small (often 3 to 5 partners) to ensure clear
communication and accountability.
Collecting independent feedback in advance (sometimes blindly)
before meetings to avoid bias.
Allowing junior team members to speak first so they aren't swayed by
senior executives.
Valuing Disagreement over Consensus
The VC Skill: Research cited in the article shows that VC firms
requiring unanimous approval actually perform worse. High-performing VCs
intentionally lean into friction and unresolved opposition. For
instance, some use a "consensus minus x" rule, while others (like
Venrock) allow the lead partner to make the final investment decision
unilaterally, regardless of other partners' skepticism. They also
intentionally assign a "devil's advocate" or "red team" to argue against
deals.
Championing Exceptions over Dogma
The VC Skill: VCs allow for workarounds and give individuals the
power to keep a controversial idea alive even if the broader team
rejects it. This includes tools like the "anti-veto," where a leader can
unilaterally move a project forward despite a negative team vote to
protect unconventional opportunities (as seen with early investments in
Zoom and Kahoot).
Extreme Agility over Bureaucracy
The VC Skill: VCs move at an incredibly fast pace because they know
slow tracking means losing the best deals to competitors. They set
aggressive timelines (e.g., funding decisions within days or weeks) and
operate with a streamlined, single-tier decision model.
Advice for the Unmotivated - Robin Abrahams, Boris Groysberg,
Harvard Business Review [Link]
Based on the provided article, the authors outline a four-step
process called DEAR (detachment, empathy, action,
and reframing) to help individuals interrupt the cycle of numbness
and recover their motivation at work:
Detachment
Before making major career moves or reacting under stress, you need
distance and perspective to clear cognitive distortions.
Reflect and break away: Review what went well at the end of the
day, then completely disconnect from work using a physical ritual (like
closing your laptop or signing out of email).
Meditate: Dedicate 10 to 20 minutes twice a day to simple focus
or breathing exercises to lower your stress response.
Move your body: Use even brief stretches, walks, or exercise to
replenish psychological energy and improve mood.
Think in the third person: Address yourself by name or use
third-person pronouns in your inner monologue to look at your problems
more objectively.
Empathy
Disengagement often causes "depersonalization." Reconnecting with
your humanity and the humanity of others helps rebuild motivation.
Practice self-care: Acknowledge your values and treat yourself
kindly with small daily rituals.
Treat people as people: Combat numbness by making eye contact,
practicing social niceties, and appreciating colleagues' work.
Ask questions: Stay curious about your customers, bosses, and
peers to gain fresh perspectives on your role.
Look for friends: Cultivate real workplace friendships to make a
frustrating job more enjoyable.
Help others: Explaining systems to new hires, mentoring, or
assisting teammates builds empowerment and has been shown to reduce
personal burnout.
Action
Channel restless or frustrated energy into proactive, productive
strategies.
Tackle the little stuff: Checking small, mundane tasks off your
to-do list builds a sense of momentum and small wins.
Invest in outside activities: Hobbies, side hustles, or
volunteering provide a sense of satisfaction that carries over into your
primary job.
Job craft: Redefine your current responsibilities by
strategically shifting your focus to tasks that leverage your strengths
or interests.
Gamify: Turn boring tasks or meetings into mental puzzles,
competitions, or games to trigger your competitive drive.
Pretend and dress the part: Imagine how a mentor or superhero
would handle a situation, and wear clothing that makes you feel
confident and professional to help you step into character.
Reframing
Alter your perspective regarding who you are at work and why your job
matters.
Examine your work identity: Create an informal title for yourself
(e.g., teacher, visionary, logistics expert) that reflects the unique
value you naturally bring to a team.
Look at the big picture: Focus on the why rather than
the how of your tedious daily tasks by connecting them to a
higher-order purpose or larger organizational goal.
Consider how others benefit: Actively remind yourself of who your
work serves—whether it is the clients you help directly or your family
members who rely on your income.
Case Study: How Aggressively Should a Bank Pursue AI? -
Thomas H. Davenport, George Westerman [Link]
Expert Recommendations
Noemie Ellezam-Danielo (Société Générale):
Fix the Alignment: The tech team shouldn't dictate strategy alone.
Siti should form a cross-functional executive group to align AI with
core business goals.
Segmented Approach: AI needs to adapt to varied customer behaviors
across different demographics and geographies, rather than forcing a
blanket 95% digital mandate.
Look for Back-Office Wins: Focus on high-impact, lower-risk areas
first, such as automating resource-intensive customer identity
verifications.
Sastry Durvasula (TIAA):
Embrace Disruption Safely: NVF needs "Michaels" to challenge the
status quo, but the technology must supplement rather than entirely
replace human service.
Culture & Upskilling Overhaul: The shift requires an AI-savvy
leadership culture. Because modern generative AI tools are
user-friendly, the focus should be on upskilling existing staff to
handle higher-value roles rather than just slashing head count.
Establishing a Solid Digital Foundation for AI-Everywhere
Webinar - Harvard Business Review [Link]
Takeaways:
Organizations across all industries are experiencing an
unprecedented wave of excitement surrounding generative AI. However,
there is a stark gap between a company’s AI aspirations and its actual
digital capabilities. While technology and business leaders are being
pressured to deploy GenAI solutions rapidly, many lack the
infrastructure to support them sustainably.
Alex Clemente reviews data from a recent HBR-AS research report
(sponsored by Kyndryl) focused on the current realities of GenAI
adoption.
Many early-stage GenAI initiatives fail to scale successfully or
move out of the proof-of-concept phase because organizations treat AI as
an isolated tool rather than an integrated capability.
Data quality, governance, and organizational cultural resistance
remain the top barriers to meaningful adoption.
To pivot from "AI hype" to "AI success," Howard Miller and Firas
Bouz highlight the strategic best practices required to build an
enduring digital framework:
Data Readiness & Architecture: Before deploying AI models,
organizations must clean, unify, and secure their data architecture. AI
is only as good as the underlying data feeding it.
Modernized Infrastructure: Legacy tech stacks cannot keep up with
the compute-heavy, dynamic processing needs of GenAI. Moving toward
flexible, cloud-enabled or hybrid infrastructure is essential.
Culture and Training: Overcoming user reluctance and bridging the
skills gap is critical. True ROI comes from training employees to trust
and integrate these technologies into their daily workflows.
Preparing for a Future Powered by Generative AI - Harvard
Business Review [Link]
Transforming Consulting Through Generative AI- Harvard
Business Review [Link]
Selected takeaways:
Consulting firms are seeing massive speed and efficiency benefits
by positioning AI as a collaborative partner rather than a simple
chatbot:
KPMG: Has deployed gen AI capabilities to over 45,000 employees.
Using the technology has cut coding assistance by 30 minutes and
competitive intelligence studies by 90 minutes. Surveys show that 60% of
users spend more time on high-value work, and 58% report increased
creativity.
IBM Consulting: Built a platform featuring hundreds of trained AI
assistants. A task like mapping a consumer persona—which historically
took a human consultant half a day—can now be generated as a baseline
within 60 seconds.
Omni Business Intelligence Solutions: Used gen AI to craft
personalized proposals and marketing material, which freed up enough
executive time to drive a 80% increase in closed deals
year-over-year.
Succeeding with AI relies heavily on closing user intuition gaps
and training knowledge workers broadly. KPMG consumer trust data details
a strong generational split in technical literacy and sentiment:
74% of Gen Z and Millennials consider themselves highly
knowledgeable about gen AI, compared to only 42% of Gen X and 13% of
Boomers/Silent generations.
To counteract this discrepancy, companies like KPMG have
developed "persona-based learning journeys" led by peer "digital
navigators", resulting in over 74,000 completed AI courses across their
U.S. workforce.
Similarly, PwC has invested $1 billion in gen AI, training over
75,000 employees on responsible prompt design and AI ethics to reduce
anxiety surrounding job replacement.
How to Marry Process Management and AI - Thomas H. Davenport
and Thomas C. Redman, Business Review [Link]
Selected takeaways:
AI typically supports narrow, specific tasks or subprocesses
rather than complete, end-to-end workflows. To transform a whole
operation, companies must string multiple AI use cases together. This
requires heavy change management, breaking down departmental silos, and
establishing common data standards across the organization.
Companies looking to merge process management with AI should
follow a structured approach:
Step 1: Establish ownership. Appoint a dedicated "process owner" who
can influence cross-functional teams and coordinate departments (e.g.,
sales, finance, operations) toward an end-to-end goal.
Step 2: Identify process customers. Determine exactly who benefits
from the process (internal or external). Use generative AI to analyze
customer feedback from calls, emails, and social media to find gaps.
Step 3: Map out the existing process. Use process mining and task
mining software to pull real-time data from system logs. This highlights
actual bottlenecks and hidden inefficiencies instead of relying on
manual guesswork.
Step 4: Establish performance measures. Define clear metrics (such as
cycle times or error rates) and set realistic targets based on what the
data shows is possible.
Step 5: Consider process enablers. Match the right technology to the
problem. Use Robotic Process Automation (RPA) for routine, repetitive
tasks; use generative AI for unstructured tasks (like drafting
contracts); and use traditional machine learning for predictive tasks
(like fraud detection or pricing).
Step 6: Redesign the process. Utilize AI-driven design tools (like
digital twins or generative AI process templates) to quickly simulate,
build, and optimize new workflows.
Step 7: Implement and monitor. Continuously monitor the new process
using process mining to eliminate variations, adjust to new business
requirements, and maintain predictable control.
The Secret to Successful AI-Driven Process Redesign - H.
James Wilson and Paul R. Daugherty, Harvard Business Review [Link]
Takeaways:
Generative AI and natural-language interfaces have made powerful
tools accessible to nontechnical employees. Business transformation is
no longer a niche technical skill; it is now in the hands of frontline
workers who can use everyday language to surface data-rich insights and
streamline their workflows.
Rather than displacing the workforce, AI acts as a tool that
amplifies human creativity, experience, and intuition. Human oversight
remains the critical linchpin for ensuring alignment with objectives and
refining system design.
The future of continuous improvement lies in autonomous AI agents
that exhibit goal-oriented behavior, logical reasoning, planning, and
long-term reflection. These agents can independently make decisions,
execute complex multistep workflows, and continuously adapt their
strategies.
The future of continuous improvement lies in autonomous AI agents
that exhibit goal-oriented behavior, logical reasoning, planning, and
long-term reflection. These agents can independently make decisions,
execute complex multistep workflows, and continuously adapt their
strategies.
While Robotic Process Automation (RPA) is easily tripped up by
process variations, ecosystems of collaborative, multimodal AI agents
can handle highly complex, knowledge-intensive, and manual workflows
(such as hospital revenue cycles or enterprise invoice processing) with
high accuracy by learning from human demonstrations.
What People Still Get Wrong about Negotiations - Max H.
Bazerman, Harvard Business Review [Link]
Takeaways:
Most negotiators mistakenly believe that a negotiation is a
zero-sum game—meaning any gain for one side is an automatic loss for
the other. This mindset causes people to focus strictly on
claiming value rather than creating it, leaving significant
profitability and mutual benefits on the table.
To avoid leaving value behind, you must identify all potential
issues (beyond just the price or main percentage) and determine
their relative importance before the meeting. Creating a weighted
score sheet—allocating dollar or point values to different
trade-offs—allows you to evaluate complex package offers
logically rather than relying on emotional, on-the-spot
intuition.
Resolving issues sequentially (whether starting with the easiest or
hardest) is a trap. Finalizing one item before discussing the rest
prevents you from discovering smart trades across different
issues. Keep all items open until you can jointly map out a
package that reaches the Pareto-efficient frontier (where neither side
can improve without harming the other).
When trust is low or counterparts hide their cards, use these tools
to uncover relative priorities:
Build trust and share information: Cultivate an open,
problem-solving dialogue (especially critical for internal corporate
negotiations).
Ask diagnostic questions: Instead of asking what they want,
ask how they value issue A relative to issue B.
Give away information first: Leverage the norm of
reciprocity. Sharing your own priorities (not your walk-away bottom
line) prompts the other party to share theirs.
Make Multiple Offers Simultaneously (MESOs): Propose 3
packages of equal value to you but with different structures. The
counterpart's preference will reveal their hidden priorities without
them having to tell you directly.
The negotiation doesn't have to end when the deal is signed. Once an
initial agreement is locked in, the adversarial tension usually drops.
You can propose looking at the deal one more time to see if any new
terms can be added to make it even better for both sides, with the
strict ground rule that the original agreement stands unless a mutually
superior alternative is found.
Leaders shouldn't Try to Do It All - A.G. Lafley and Roger L.
Martin, Harvard Business Review [Link]
Takeaways:
Shift from Absolute Importance to Comparative Advantage
The Trap: Most leaders prioritize tasks based solely on how
important they are to the organization, taking on the top items until
they run out of time.
The Fix: Leaders should only spend time on activities that nobody
else in the organization can do nearly as well. If someone else can
do it at parity or better, it should be offloaded—even if it is a
critical task.
The Four-Step Process to Manage Time
The authors suggest a rigorous framework to restructure your
calendar:
Remove absolute disadvantages: Identify "should do" tasks that you
handle simply because of tradition or precedent. If a subordinate
possesses equal or better skills for that specific task (e.g., routine
faculty hiring or standard investor relations), hand it over
completely.
Delegate minor comparative advantages: Even if you are highly
skilled at something (like creative brand reviews or routine financial
management), delegate it if your advantage is only modest. This empowers
your team and frees up massive blocks of your time.
Take on strong comparative advantages: Reinvest your recovered
hours into areas where you can make a unique, decisive difference.
For example, A.G. Lafley focused heavily on driving a consumer-centric
innovation process, while Roger Martin used his background to write
books and articles that elevated his school's external profile.
Dedicate time to the irreplaceable: Protect your calendar for
high-leverage tasks that literally only the leader can execute, such as
establishing massive external strategic partnerships, setting long-term
brand visions, and individually mentoring the next generation of
leadership.
An organization can expand its capacity, but a leader's time is
fundamentally finite. Leading to win means accepting that you cannot
do it all, and relentlessly focusing your energy where your personal
value-add is greatest.
Want Your Company to Get Better at Experimentation? Learn
Fast by Democratizing Testing - Iavor Bojinov, David Holtz, Ramesh
Johari, Sven Schmit and Martin Tingley, Harvard Business Review
[Link]
Takeaways:
The Necessity of Scaling Up
High Failure Rates: Most new ideas do not yield positive results,
and it is incredibly difficult to predict which ones will
succeed.
The AI Imperative: As generative AI drastically lowers the
cost of creating new digital user experiences, companies must vastly
increase their testing volume—into hundreds or thousands of experiments
per year—to remain competitive.
Democratizing the Testing Process
The Bottleneck: Relying solely on data scientists to design, run,
and analyze every test severely limits an organization’s capacity to
scale.
The Self-Service Model: Companies must empower product managers,
engineers, designers, and marketers to run their own experiments by
building or buying platforms with automated guardrails, simple
interfaces, and embedded statistical rigor.
Redefining the Data Scientist's Role: Data scientists should shift
from execution to high-impact work: building platforms, training teams,
developing new statistical methodologies, and uncovering overarching
strategic patterns.
Transitioning to "Experimentation Programs"
Look Beyond Individual Memos: Reviewing tests in isolation wastes
time and fails to build institutional knowledge.
Learn Across Experiments: Organizations should group tests into
programs (e.g., focusing entirely on "search functionality" or
"product-detail pages") to identify macro-trends, catch diminishing
returns, and determine where to reallocate corporate resources.
Build a Knowledge Repository: Implement a centralized, searchable
system—potentially enhanced by a generative AI assistant—to store
hypotheses, metrics, and long-term insights across the entire
enterprise.
Shifting Cultural and Financial Incentives
Reward Outcomes, Not Outputs: Evaluating employees on the number of
"successful" experiments makes them risk-averse. Instead, incentives
should be tied to the overall performance of the business unit.
Mitigate Risk Progressively: To encourage high-risk, high-reward
testing without fear of breaking systems, companies should leverage
automated rollbacks (trip wires based on guardrail metrics) and roll out
features in gradual phases.
How Project Leaders Can Tame Unpredictability - Anton
Skornyakov, Harvard Business Review [Link]
Takeaways:
Project leaders can tame unpredictability by using the agile
technique of vertical slicing—breaking a project down into small, fully
functional "slices" to run rapid tests, accelerate learning, and
mitigate risks before scaling.
The author outlines how to apply this strategy across four key areas
of unpredictability:
Human Behavior
The Challenge: People's reactions to organizational change or new
initiatives are highly unpredictable.
The Takeaway: Deploy a narrowly defined, representative test
group to observe real-world behavior and uncover practical issues (e.g.,
logistical bottlenecks, material defects) before committing a massive
budget.
Interpersonal Dynamics
The Challenge: Changes affecting career paths, organizational
structures, shared responsibilities, or network effects introduce
complex emotional and political dynamics.
The Takeaway: Unlike behavioral tests, your slice here needs a
large enough sample size to trigger and observe those group dynamics,
but focused on a small, manageable behavioral shift.
Technological Change and Interoperability
The Challenge: Integrating new technology with legacy systems or
non-standardized external platforms is a major source of
uncertainty.
The Takeaway: Create a "tracer bullet"—a minimum viable
version of the end-to-end tech solution. Manually integrate just one
stream of data (e.g., a single vendor or route) all the way to the
consumer-facing frontend to catch integration friction early.
Organizational Interdependencies
The Challenge: Relying on multiple internal teams and external
stakeholders introduces risks outside of your direct control.
The Takeaway: Use an "organizational tracer bullet" to test a
single operational scenario across all involved teams. This forces
collaboration early and exposes contract or process hurdles before
finalizing large-scale agreements.
Sam Altman May Control Our Future—Can He Be Trusted? - The
New Yorker [Link]
Substack
Snowflake: AI Consumption Wins - App Economy
Insights [Link]
Takeaways:
The enterprise software market is splitting between consumption-based
models (which thrive as AI scales) and traditional seat-based SaaS
models (which face headwinds as AI potentially replaces human
roles).
Snowflake surged 38% after hours, completely shifting the narrative
around its position in the AI race. Salesforce stock continued to slide
as strong AI adoption metrics failed to translate into clear top-line
acceleration.
The differences between Snowflake and Salesforce
Core Product & Purpose
Salesforce is primarily a Customer Relationship Management
(CRM) application layer. It is built to store and manage customer
data, sales pipelines, and support workflows through an interface
designed for human workers.
Snowflake is an enterprise Data Cloud and Analytics
Platform. It is a backend data warehouse and data lake layer built
to ingest, store, govern, and analyze massive volumes of diverse
structured and unstructured data from all corporate systems (including
Salesforce, ERPs, and web logs).
Business & Pricing Model
Salesforce traditionally operates on a Seat-Based SaaS Model.
Companies pay a fixed subscription price per user license.
The AI Challenge: As AI agents automate tasks, companies
may need fewer human "seats," threatening Salesforce's core growth model
unless they can pivot to charging for AI-delivered work units.
Snowflake operates entirely on a Consumption-Based Model.
Customers only pay for the exact compute time and storage volume they
use.
The AI Advantage: Because AI models and agents require
massive amounts of compute and data ingestion to learn and execute
tasks, Snowflake directly captures more revenue the harder an AI
works.
Data Integration and Moat
Salesforce operates a suite of fragmented applications
(Sales, Service, Marketing, Commerce, Tableau, MuleSoft, Slack) and is
trying to centralize them via its Data Cloud. It relies heavily on users
inputting and managing data within its application ecosystem.
Snowflake acts as a "Clean Sheet" repository. With its newer
tools like Cortex Code, it allows organizations to run AI
applications and query data that securely sits outside its own
databases—including data sitting directly inside Microsoft, SAP, or
Salesforce applications.
How SpaceX Makes Money - App Economy Insights [Link]
While SpaceX is seeking a massive \(\$1.5\) to \(\$2\) trillion valuation to raise up to
\(\$75\) billion, the underlying
segments have dramatically different economic profiles:
Connectivity (Starlink): The absolute profit engine. It generated
\(\$11.4\) billion in FY25 revenue with
an impressive 63% EBITDA margin (\(\$7.2\) billion). It is actively
underwriting the company’s massive capital expenditures.
Space (Launch): The strategic moat. It reflects a technical
monopoly, handling over 80% of global payload weight to orbit. It shows
a paper operating loss (\(\$0.7\)
billion) only because it completely absorbed \(\$3.0\) billion in Starship
R&D.
AI (xAI, Grok, X): The capital sink. Folded into SpaceX via a
February 2026 merger, this segment brought in \(\$3.2\) billion in revenue but suffered a
\(\$6.4\) billion operating loss in
FY25.
SpaceX is burning cash at an unprecedented, hyperscaler pace to
build out its AI and space infrastructure:
CapEx Ramp: Full-year FY25 CapEx hit \(\$20.7\) billion. In Q1 FY26 alone, CapEx
reached \(\$10.1\) billion (with \(\$7.7\) billion swallowed by the AI segment
for gigawatt-scale clusters like COLOSSUS).
Balance Sheet Pressure: Cash reserves dropped from \(\$24.7\) billion to \(\$15.9\) billion in just the first 90 days
of 2026.
The IPO's Role: This historic \(\$75\) billion IPO isn't for early investor
liquidity; it is strictly fuel to sustain this capital-intensive runway
before Starship and AI integrations fully monetize.
Warren Buffett has long been skeptical of IPOs. His view is
simple: IPOs come to market when sellers choose the timing, not when
buyers are likely to get the best deal.
--- How to Invest in IPOs - App Economy Insights [Link]
Instead of falling for the fear of missing out (FOMO) on Day 1, the
author suggests a "boring on purpose" strategy:
Avoid the Day 1 Hype: Let the initial open-market volatility pass
completely without touching the stock.
Wait for the Second Earnings Call: The initial prospectus (S-1
filing) is just a snapshot. Waiting for two public quarters allows you
to see how management handles public market scrutiny, tracks key
metrics, and manages guidance.
Nibble in Year One: If you must invest, start with a tiny position.
A small starter position lets you follow the company closely without
making the inflated IPO price your entire cost basis.
Anchor to Valuation: Don't just look at whether a company is
exceptional (like SpaceX or OpenAI)—look at whether its current stock
price leaves any room for execution errors.
Give it Time: Truly great businesses will compound for a decade or
more. Missing a 20% move on Day 1 is not a disaster; buying at peak
euphoria usually is.
The Bottom Line: A hot IPO wave is a sign of a highly optimistic
market, not an automatic buy signal. The best strategy for hyped
frontier companies is to add them to your watch list, let the insider
selling pressure clear, and invest strictly on your own terms.
The Trillion-Dollar Off Switch - App Economy
Insights [Link]
Takeaways:
On June 12, just days after launching its powerful new
"Mythos-class" model Claude Fable 5, Anthropic received a US
export-control directive restricting access. Because it couldn't screen
users' nationalities in real time, Anthropic took both Fable 5 and
Mythos 5 offline worldwide.
The government cited national security concerns over potential
jailbreaks. While Anthropic claims the vulnerability is mundane, its own
defensive program (Project Glasswing) had previously shown Mythos 5 was
capable of finding flaws across major operating systems and browsers,
highlighting the genuine "dual-use" risk of advanced AI.
The incident highlights a new regulatory risk for pure-play AI labs
(like Anthropic and OpenAI) heading toward trillion-dollar IPOs. While
"building" the frontier model captures the most economic upside, it
introduces a single point of failure if a government pulls the plug.
"Renting" a model offers a swappable, lower-risk alternative.
Apple’s recent WWDC 2026 announcement revealed that its new Siri AI
is powered by renting Google's Gemini (paying a reported \(\$1\) billion/year). This strategy allows
Apple to skip the massive CapEx arms race and partially insulate itself
from direct model-regulation shocks, though it surrenders control over
the core AI brain.
Bottom Line: For AI investors, the critical question has shifted from
"Who has the best model?" to "Who can keep it online?".
How FIFA Makes Money - APP Economy Insights [Link]
Takeaways:
Driven by an expansion to 48 teams and hosting in the lucrative
US market, FIFA expects to bring in a record \(\$13\) billion for the 2023–2026 cycle (a
72% jump from the Qatar cycle).
Broadcasting Rights: \(\$5.3\)
billion (~40%) — FIFA's largest revenue engine.
Hospitality & Ticketing: \(\$3.6\) billion (~28%) — Heavily boosted by
2026's new dynamic pricing model.
Marketing & Sponsorship: \(\$3.3\) billion (~25%) — Major brand
partnerships.
Licensing: \(\$0.4\) billion (~3%) —
Merchandise and video games.
As a non-profit, FIFA targets a near-breakeven budget. About 58%
(\(\$7.6\)B) goes directly into staging
competitions (including an \(\$871\)M
team prize pool), 30% (\(\$3.9\)B) goes
to development and education grants for its 211 member federations, and
7% (\(\$0.9\)B) covers governance and
administrative costs.
For the first time, ticket prices float based on demand. While
the cheapest seats remain around \(\$60\), premium seat listings have exploded
past \(\$32,000\), raising concerns
about pricing out everyday fans from the "people's game."
To smooth out its lumpy four-year financial heartbeat, FIFA is
branching into new tournaments, notably launching the expanded 32-team
2025 Club World Cup and targeting a \(\$1\) billion revenue mark for the 2027
Women's World Cup in Brazil.
Wall Street's Top Stocks in Q1 - App Economy
Insights [Link]
Palantir: Tokens Are the New Coal - App Economy
Insights [Link]
Takeaways:
Hyper-growth driven by the AI Platform (AIP). Dropping inference
costs (cheap tokens) are massively expanding agent workflows, boosting
demand for Palantir's safety and governance layer.
Revenue surged +85% Y/Y to \(\$1.63\)B. Remaining Deal Value (RDV)
nearly doubled to \(\$11.8\)B.
Outlook: Management raised FY26 revenue guidance to \(\$7.7\)B (+71% Y/Y).
Amazon: The Inference Era - App Economy Insights [Link]
Takeaways:
AWS revenue grew +28% Y/Y to \(\$37.6\) billion (its fastest growth in 15
quarters), signaling that AI monetization is hitting full stride.
Amazon is positioning AWS for the "Inference Era." While training is
a GPU story, running persistent, multi-step Bedrock Managed Agents
requires massive compute power, driving their custom silicon business
(Graviton, Trainium) to a \(\$20\)
billion annualized revenue run rate.
OpenAI frontier models and Codex are entering Bedrock. OpenAI has
committed to spending \(\$100\) billion
over 8 years on AWS and anchoring its workloads on Amazon's Trainium
chips.
Amazon is maintaining a \(\$200\)
billion CapEx plan for 2026 to build out AI data centers. This
infrastructure spend collapsed free cash flow down to \(\$1.2\) billion, prioritizing long-term AI
dominance over short-term cash.
Retail remains highly efficient, delivering over 1 billion
same-day/overnight items so far in 2026, while Advertising surged +24%
Y/Y to \(\$17.2\) billion, providing a
high-margin cushion for their AI investments.
Google: The Anthropic Paradox - App Economy Insights
[Link]
Takeaways:
Alphabet plans to invest up to \(\$40\) billion in Anthropic (\(\$10\) billion upfront, \(\$30\) billion in milestones) and provide 5
gigawatts of compute capacity over 5 years.
Struck at a \(\$350\) billion
valuation, a massive discount compared to Anthropic's recent \(\$1\) trillion secondary-market price.
Even though Anthropic's Claude competes with Gemini, Google is
prioritizing winning the cloud infrastructure and compute layer over
model exclusivity.
Tesla: \(\$25\) Billion AI
Pivot - App Economy Insights [Link]
Takeaways - Tesla:
Tesla raised its FY26 capital expenditure guidance to \(\$25\) billion (up from \(\$9\) billion in FY25) to fund AI training
clusters, "Terafab," and Optimus 3. This heavy spending is expected to
turn free cash flow negative for the rest of 2026.
Q1 revenue grew 16% Y/Y to \(\$22.4\) billion, and gross margin hit 21%
(though aided by one-time warranty and tariff benefits).
FSD subscriptions (\(\$99\)/month)
and Supercharger usage drove a 42% surge in Services revenue.
Unsupervised Robotaxi service expanded to Houston and Dallas.
Elon Musk noted that Hardware 3 lacks the memory bandwidth for
future autonomy, requiring dedicated retrofit centers and complex split
software paths moving forward.
Takeaways - SpaceX:
Following its merger with xAI, SpaceX entered a strategic
partnership with AI coding platform Cursor. SpaceX has the option to
acquire Cursor later this year for \(\$60\) billion (or pay a \(\$10\) billion partnership fee if they
decline).
The deal aims to fix weaknesses in xAI's Grok chatbot, which Musk
admitted was "behind in coding."
Cursor gets access to SpaceX’s Colossus supercomputer (200,000
NVIDIA GPUs) to solve its compute constraints, while SpaceX secures a
highly popular developer app layer and elite engineering talent.
Apple Enters a New Era - App Economy Insights [Link]
Takeaways:
In September 2026, Tim Cook will step down as CEO after 15 years,
transitioning to Executive Chairman. Hardware chief John Ternus will
take over as CEO.
Cook's legacy focused on operational excellence, supply chain
efficiency, and high-margin Services. Ternus (an engineer by trade)
represents a pivot toward new product ambitions.
Ternus inherits a \(\$4\) trillion
company with slowing growth (3% average since 2022). He faces high
executive turnover, pressure to deliver a mass-market hardware reset
(e.g., smart glasses, home robotics), and a costly supply chain
diversification away from China.
Apple currently relies on Google's Gemini to power Siri. To own its
AI future, Apple may need to sacrifice capital efficiency and massively
ramp up CapEx, which was only \(\$13\)
billion in 2025 (compared to rivals planning \(\$100\)B–\(\$200\)B in 2026).
Anthropic announced an annual revenue run rate (ARR) of over \(\$30\) billion, surpassing OpenAI's
reported \(\$24\) billion. However,
this is partly due to accounting differences; Anthropic uses a gross
method (including cloud provider cuts), while OpenAI uses a net
method.
Anthropic launched Mythos, a highly restricted, high-premium (\(\$125\)/M tokens) autonomous cybersecurity
model, and secured a 3.5 gigawatt compute partnership with Google and
Broadcom.
Intel partnered with Tesla, SpaceX, and xAI for Elon Musk’s
100-million-square-foot Terafab project in Austin, bringing its 18A node
IP and advanced packaging technology to keep the supply chain within the
US.
OpenAI acquired tech talk show TBPN for hundreds of millions to
manage its narrative amid a damaging New Yorker exposé alleging
safety neglect and leadership manipulation. Additionally, several top
executives (CFO, COO, CMO) are stepping down or shifting roles, with
internal rifts over a potential 2026 IPO timeline.
Meta launched Muse Spark (internally "Avocado"), its first
closed-source model developed via its $14.3 billion Scale AI
partnership. The model relies heavily on Meta’s massive distribution
network (3.5 billion users) to monetize AI through consumer apps.
The Great AI Rotation - App Economy Insights [Link]
Uber isn’t trying to build the best self-driving car or technology
stack. Instead, it is positioning itself to be the dominant marketplace
and commercialization platform that robotaxis run through.
Uber has rapidly lined up 8 autonomous partnerships (including
NVIDIA, Zoox, Wayve, and Motional). Its latest move is a \(\$1.25\) billion investment in Rivian to
deploy up to 50,000 autonomous R2 vehicles by 2030, starting in San
Francisco and Miami in 2028.
The Strategic Playbook
Through its new Uber Autonomous Solutions (UAS) branch, Uber handles
the operational logistics AV developers lack: matching demand, fleet
management, charging, maintenance, and financing.
Pure robotaxi fleets face severe inefficiencies during low-demand
periods and unreliability during spikes. Uber layers AVs into its
existing network of ~10 million human drivers to absorb volatility and
ensure high asset utilization.
AV partners using Uber in early markets (Austin, Atlanta) see a 30%
increase in trips per vehicle per day and 25% lower ETAs compared to
launching standalone apps.
The Economic Shift
Transitioning to an AV model will likely lower Uber's traditional
~30% mobility take-rate. However, Uber expects to offset this by
capturing a broader stack of autonomy service fees (ranging from 10–15%
for pure distribution up to 15–25% when bundling UAS fleet
services).
Key Long-Term Risk
Uber’s strategy depends on a fragmented market where tech winners
still need a demand aggregator. If a competitor like Tesla (with its
Cybercab) or Google's Waymo successfully scales a completely vertically
integrated, direct-to-consumer network, they could bypass Uber
entirely.
Shuttering the standalone Sora app and canceling a \(\$1\) billion Disney partnership to cut
costs and focus on high-margin enterprise seats ahead of a potential
IPO.
Merging fragmented products (like Atlas browser and Codex) into a
single desktop super app.
Stepping back from direct in-app payment processing to act strictly
as a discovery/referral layer.
Q2 FY26 revenue skyrocketed 196% Y/Y to \(\$23.9\) billion. Next-quarter revenue
guidance (~\(\$33.5\) billion) exceeds
any full-year revenue in the company's history prior to 2024.
Gross margins hit 74% and are guided to 81% next quarter, edging out
Nvidia's current profitability levels.
To keep up, Micron raised its FY26 CapEx forecast to over \(\$25\) billion and warned that FY27
spending will step up by another \(\$10\)+ billion, causing some investor
unease.
The memory deficit has expanded to standard PC and phone
manufacturers. Industry leaders project that supply tightness could
persist for four to five more years.
YouTube and Podcasts
OpenAI President Greg Brockman: AI Self-Improvement, The
Superapp Bet, Path To AGI, Scaling Compute - Alex Kantrowitz
[Link]
Takeaways:
OpenAI is deprioritizing standalone video generation (Sora) to
concentrate its limited compute resources on a single, unified Super App
that integrates chat, browsing, and advanced coding tools.
Coming later this year, OpenAI plans to deploy an automated AI
researcher designed to execute the end-to-end tasks of a human research
scientist in silicon, accelerating autonomous model development under
human oversight.
For individuals worried about job security, Brockman's core advice
is to move past the "blank box" intimidation layer and intentionally
develop a sense of agency.The future workforce will thrive by acting as
"CEOs" who orchestrate, delegate to, and oversee fleets of specialized
AI agents.
Artemis II, Jamie Dimon’s “American Dream,” Snap’s Crucible
Moment | Diet TBPN [Link]
Takeaways:
SpaceX officially filed for an IPO, immediately setting it up to
enter the public markets as a trillion-dollar company.
JP Morgan Chase announced its "American Dream Initiative," a
massive commitment to support small businesses, homeownership, and
healthcare access. The bank plans to add 3 million new small business
customers and lend up to \(\$80\)
billion over the next 10 years.
In a rare senior outside hire, Dimon recruited Warren Buffett’s
protégé and former Geico CEO, Todd Combs. Combs will head up a new \(\$10\) billion Strategic Investment Group
focused on onshoring industries that America has outsourced (e.g.,
defense tech, US semiconductors, aerospace, and energy).
Nestle confirmed that thieves in Italy pulled off a brazen heist,
stealing a delivery truck packed with 12 metric tons (413,000 units) of
Kit Kat bars bound for Poland. Rather than burying the potentially
embarrassing security breach, Nestle leaned into the internet humor by
joking that thieves took their "Have a break" slogan too literally. This
prompted other major brands (like Domino’s UK and Ryanair) to join the
viral meme bandwagon, turning a logistics crisis into a corporate PR
victory.
What Happens When Every CEO Becomes Omnipresent? | This Week
in AI [Link]
Takeaways:
Large Tabular Models (LTMs): LLMs rely on positional encoding where
the order of words matters. For deterministic database tasks (like
cancer diagnosis or fraud tracking), changing column orders shouldn't
change the output. LTMs solve this by focusing entirely on processing
raw, structured structural data without lossy human rollups.
Nick Harris notes that chip performance no longer simply doubles
every 18 months. Progress now relies on building larger chip packages
and networking them together. Traditional copper cables can't travel far
and are creating massive, dangerously heavy, megawatt-powered datacenter
racks. Lightmatter uses glass fiber optics (photonics) to shoot 1.6
terabits per second over a single fiber, letting thousands of GPUs
collaborate over a distance of a kilometer as if they were a single
brain. Using optics instead of copper can triple the time it takes to
train foundation models, aggressively accelerating the rate of AI
takeoff.
Victor Riparbelli explains that video is currently a broadcast
medium—one version shipped to everyone. Synthesia is shifting toward
real-time, interactive video workflows where canvas models, synthetic
diagrams, and conversational avatars roleplay and adapt to users
dynamically. The current bottleneck for these immersive,
custom-generated experiences is compute and bandwidth. Personalizing a
one-hour custom movie right now would roughly cost \(\$700\) in compute tokens.
The panel fiercely debates whether small companies should "vibe
code" their own systems. Jeremy shares that his 15-person team entirely
vibe-coded their own custom CRM (Fetch) inside Slack instead of buying
Salesforce. Countering this, Victor and Nick argue that the hidden cost
of vibe coding is the "focus cost" and token spend required for code
verification. If open-source agents break or delete codebases, the
developer hours spent fixing them often eclipse a simple SaaS
subscription.
The fundamental difference between this wave and past industrial
revolutions is that society is automating cognition rather than physical
work. AGI feel hidden to the average populace because most
people—including many engineers—are simply not trained to manage AI or
ask deep, scientific questions.
Eric Schmidt on the Robotics Race, Singularity Timeline, and
Energy Shortage | 241 - Peter H. Diamandis and Eric Schmidt [Link]
Takeaways:
Many in Silicon Valley believe that human-like AI agents and
recursive self-improvement will lead to a Superintelligence moment
within the next 2 to 3 years.
The ultimate resource constraint for AI scaling in America is
electricity, not capital or data. There is an estimated 92-gigawatt
shortage of power in the U.S. through 2030 (the equivalent of roughly 60
nuclear power plants).
Modern AI data centers have become massive airflow and liquid-cooled
machines stretching half a mile long. It is projected that data centers
will eventually consume 10% of the entire U.S. electricity supply.
While the U.S. is heavily focused on central AGI/ASI models, China
is dominating the physical AI and low-end robotic hardware landscape,
driven by their existing EV supply chain, vertical integration, and
aggressive work culture.
The global landscape will likely accommodate around 10 massive-scale
AI companies, primarily divided between the U.S. and China. China's
strategy relies heavily on open-source edge computing.
Schmidt reiterates that a minor biological, nuclear, or catastrophic
event triggered by AI may be descriptively required to force global
governments (like the U.S. and China) to pause their brutal competition
and cooperatively establish hard guardrails. To win the race while
preserving societal health, the U.S. needs to accelerate energy
permitting, actively attract high-skilled immigrants and involve experts
in history, ethics, and psychology to align Superintelligence with
democratic values.
SpaceX IPO, Iran War Fallout, Quantum Bitcoin Hack, The Space
Opportunity - All-In Podcast [Link]
Takeaways:
Chamath predicts a 99.99% chance that Tesla and SpaceX will
eventually merge into a single entity (ticker "E"). This would solve
governance issues, create external market validation, and capitalize on
cross-disciplinary compounding (e.g., Tesla’s autonomous robotics
deployed on the Moon).
Returning to the moon via the Artemis II mission highlights a new
frontier. Due to low gravity and a lack of atmosphere, manufacturing
materials on the Moon and using "mass drivers" (electric rails) to shoot
them back to Earth will theoretically be cheaper than traditional
terrestrial shipping.
If AGI becomes a reality, the durability and valuation of software
companies will erode rapidly. Investors are beginning to rotate into
defensive, cash-flowing "HALO" (High Asset, Low Obsolescence)
businesses.
Recent computing algorithms and theory improvements have shifted the
timeline for industrial-scale quantum computing from decades away to the
next 5 to 7 years.
Marc Andreessen on The Future of VC: Will a16z Go Public
& Why Introspection is Dangerous? - 20VC with Harry Stebbings and
a16z [Link]
Takeaways:
In venture capital, learning too much from failures can be
dangerous. It is easy to completely write off a category because a
previous investment failed, causing you to pass on the pattern-matched
winner later on (e.g., passing on internet search in the 90s or AI
before 2017). Adopting the mindset that everything is your fault drains
away resentment and keeps your focus productively anchored on intrinsic
self-improvement. However, as a management blanket rule for already
stressed-out founders, a16z avoids pushing it.
Andreessen quotes pioneer Arthur Rock, agreeing that firms would
likely perform better by shredding business plans and focusing entirely
on the team/resumé. Great teams lap mediocre teams executing perfect
business plans.
The Andreessen Core Formula:
IQ: Extreme intelligence is table stakes.
Courage: The absolute determination to run straight through problems
like a cartoon character leaving a hole in a brick wall.
Drive: An intrinsic, primal ambition to build something of your own,
distinct from external markers of success like net worth.
VCs shouldn't chase "diamonds in the rough" out of investor ego.
True diamonds have high visibility; if a deal has been ignored by the
mainstream, it usually points to a structural defect or a
hyper-disagreeable founder who has alienated everyone else. Echoing Don
Valentine, more companies die from indigestion than starvation.
Overfunding warps discipline, while high valuations create artificial
hurdle rates that scare away future investors from down rounds.
While the remote-work era from 2020 to 2023 promised to decentralize
the tech industry, AI has whiplash-reversed this trendd. The tech
ecosystem is now more geographically centralized in Silicon Valley than
ever before in its history.
AI is the ultimate small-D democratic technology. The
highest-performing models in the world are consumerized and instantly
accessible to anyone with a smartphone.
The narrative that AI causes net labor displacement is fundamentally
wrong and relies on zero-sum Marxist economics. Historically and moving
forward, core technology boosts the marginal productivity of the
individual worker, freeing them from grunt work to perform higher-value
tasks. Current tech layoffs are driven by interest rate shocks and
massive, undisciplined over-hiring during COVID—not AI. Many corporate
entities simply use AI as a convenient "silver bullet" excuse to
downsize.
How to Reorg After AI Changes Everything | Block's Owen
Jennings on the a16z Show [Link]
Takeaways:
Block eliminated the standard mid-level management layer required to
coordinate traditional, heavy feature teams (which typically consisted
of 14+ people, including dedicated project managers, product managers,
and numerous localized engineering leads). They replaced these with
highly fluid squads of 1 to 6 people. Because these squads leverage
automated AI tooling, they require significantly fewer coordinators and
middle managers to align their work.
Product and engineering management layers were cut by 50% to 60%,
widening leadership spans and drastically accelerating the internal
velocity of data and decision-making.
Workflows transitioned from linear code production and manual peer
reviews to asynchronous oversight. Managers and engineers run dozens of
parallel background agents, context-switching between them to review,
nudge, and commit agent-generated pull requests.
Why Juries Are Turning Against Meta and YouTube - Hard
Fork [Link]
Takeaways:
Separate juries in Los Angeles and New Mexico recently found social
media giants liable for harming young users
A recent leak exposed the agentic framework surrounding Anthropic's
coding harness (Claude Code), enabling developers to rapidly clone and
"Frankenstein" the tool over open-source models.
Citing research from Anthropic's Nicholas Carlini, AI tools have
become so proficient at finding software vulnerabilities that nearly all
legacy codebases (like the Linux kernel) will eventually need to be
hardened to resist AI-driven cyber attacks.
How Bots, Deepfakes and AI Agents Are Forcing a New Internet
Identity Layer | Alex Blania on a16z [Link]
Takeaways:
The exponential rise of highly persuasive AI agents and deepfakes is
making it nearly impossible to digitally distinguish real humans from
bots, rendering standard one-to-one biometric verification obsolete.
To eliminate dystopian privacy risks, World uses Multi-Party
Computation to fragment iris codes across decentralized computers and
relies on zero-knowledge proofs so users can verify their uniqueness
anonymously.
Without a cryptographically strong infrastructure to verify humanity,
AI-driven automation will scale massive fraud across government social
programs and structurally collapse the ad-based creator economy through
bot-driven content farms.
World is shifting 90% of its resources to an aggressive U.S.
expansion, aiming to deploy 50,000 biometric "Orbs" in major retail
spaces and pilot on-demand motorbike couriers to ensure users are within
15 minutes of a device.
OpenClaw, Claude Code, and the Future of Software | Peter
Yang on The a16z Show [Link]
Takeaways:
Peter explains that 70–80% of the value of OpenClaw for him is its
personal, conversational aspect. Using voice interactions via messaging
apps like Telegram feels much more direct and personal than traditional
chat interfaces.
Functional apps used primarily to complete specific tasks (like
Mercury banking or Calendly) are seeing reduced manual usage. Users
prefer instructing an agent to run analytics, manage documents, or
interact with APIs on their behalf.
AI code generation tools currently feel like slot machines or
casinos due to variable wait times and unexpected results. However, once
customized with personal hooks and skills, they become deeply integrated
workflows.
Functional apps used primarily to complete specific tasks (like
Mercury banking or Calendly) are seeing reduced manual usag. Users
prefer instructing an agent to run analytics, manage documents, or
interact with APIs on their behalf.
AI is highly capable of tackling the first 80% of a creative or
technical project (such as writing code or blog drafting), while human
input is strictly needed to manually tweak and polish the final
20%.
Instead of scaling corporate headcount, future companies will
prioritize staying as lean as possible. A traditional 10-person product
team may shrink down to two or three humans paired with a network of
specialized AI agents.
Entrusting objective negotiations and tedious operational functions
(like long OKR alignment meetings) to AI agents eliminates high-emotion
corporate back-and-forth.
While large corporations may continue trimming staff, the cost to
build software or launch a business is dropping close to zero. This
enables a rise in independent solopreneurs, bootstrapped
micro-businesses, and creators who can build their own products without
traditional engineering backgrounds.
The State of Modern War: Palantir & Anduril Execs on
Drones, AI, and the End of Traditional Warfare - All-In Podcast
[Link]
Takeaways:
America has dangerously hollowed out its commercial manufacturing
ecosystem, leaving a hyper-consolidated defense industrial base that
lacks the raw volume and agility required to rapidly regenerate weapon
stockpiles during a major conflict.
Modern tech startups are bypassing the government's sclerotic,
"cost-plus" procurement bureaucracy by using private venture capital to
build advanced, merit-based products before pitching them to the
state.
Integrating AI into the command center introduces unprecedented
targeting precision that dramatically minimizes civilian casualties
compared to legacy "dumb bombs," though human operators must always
remain accountable for the system's actions.
Failing to aggressively re-industrialize the West risks yielding a
"Chinese Century" where the CCP explicitly dictates the global terms of
international trade and engagement.
OpenAI acquired the daily tech talk show TBPN (The Better Podcasting
Network)
SpaceX has filed for an IPO, aiming to publically raise between $40
billion and $80 billion—potentially making it three times larger than
any historical IPO
In internal memos, Amazon Web Services (AWS) confirmed that
Iranian-backed strikes in Bahrain and Dubai physically took down
critical "availability zones,". Due to the severe volatility, the cost
of specialized war and terrorism insurance for data center physical
assets in the Gulf region has skyrocketed by 1,900% in just a few
weeks.
Why Balaji Srinivasan Thinks the SaaS Apocalypse Is Overhyped
| The a16z Show [Link]
Takeaways:
The AI economy will shift toward distillation, decentralization,
and localized "trusted tribes" resembling the low-trust Chinese internet
model rather than central tech monopolies.
AI text and slides are frequently lazy, generic, or deceptive
("Lauram AIPSM"). AI drastically reduces the cost of generation but
scales up the hidden cost of validation.
Humans will remain the essential "sensors" of taste and agency,
while AI serves strictly as the "actuator" under a leash. AI turns
employees into CEOs rather than replacing them.
The wholesale destruction of SaaS is overhyped. Intelligent
incumbents will survive by leveraging their existing distribution
loops.
While vulnerable incumbents that fail to execute may be cloned or
disrupted, dominant developer platforms like Notion or Figma can use AI
to build features and ship value to their established networks
significantly faster. A raw technical clone of an app like Facebook
yields zero value without its underlying user graph and
distribution.
Bitcoin has evolved strictly into transparent institutional
collateral rather than digital cash for individuals. Zcash (and wallets
like Zodal) will fulfill the actual multi-decade vision of private,
scalable digital cash.
Meta Tokenmaxxing, Intel Joins Terafab, Frontier AI vs. China
| Diet TBPN [Link]
We Have to Talk About Anthropic's Mythos - Hard Fork
[Link]
Takeaways:
Anthropic developed an incredibly powerful new model but has held it
back from the public due to extreme cybersecurity risks. Instead of a
commercial launch, Anthropic initiated Project Glasswing, giving
exclusive, restricted access to the "blue teams" (defensive
cybersecurity units) of major tech infrastructure and platform
companies—such as Cisco, Broadcom, Microsoft, Apple, and Amazon.
The hosts note that the sheer volume of undiscovered zero-day bugs
this model can chain together autonomously means almost every major
piece of software globally may need to be systematically patched or
rewritten, creating a massive human bottleneck for software
maintainers.
This marks the first time since GPT-2 in 2019 that a massive gap has
opened between the frontier capabilities hidden inside top AI labs and
what the general public can actually use
While the systemic threat is institutional, the hosts emphasize that
everyday users should protect themselves against localized fallout by
locking down basic digital hygiene: using password managers for unique,
randomly generated passwords and enforcing multi-factor authentication
(MFA) via authenticator apps.
The Mistake That Could Break America - David Friedberg -
Chris Williamson [Link]
Takeaways:
Friedberg highlights that a massive portion of leaders in the
technology sector are actively leaving or preparing to leave
California.
Friedberg argues that politicians get elected by promising benefits
they ultimately cannot fund. California faces a massive pension and
healthcare liability crisis for public employees, estimated to be
between \(\$600\) billion and \(\$1\) trillion in the hole.
The state's massive tax revenues have been funneled into poorly
managed projects. Friedberg points to massive budget waste, such as a
\(\$30\) billion high-speed rail
project with rampant executive turnover and a \(\$220\) million homeless program that only
successfully transitioned six individuals out of poverty.
The core philosophical threat discussed is California's proposed
"Billionaire Tax Act". Friedberg emphasizes that taxing post-tax assets
creates a dangerous precedent where private property rights are
systematically degraded.
While pitched at a 5% rate for billionaires, Friedberg warns that
historical precedents—like the original US income tax, which started in
1913 as a mere 1% tax on high earners —prove that thresholds inevitably
drop until a tax applies to the broader middle class. He fears the end
state is one where 51% of the population votes to seize everything from
the other 49%.
Friedberg notes a tragic irony: humanity is right on the cusp of an
era of unprecedented technological abundance—including near-free energy,
radical life extension, and automated labor. Despite this potential,
public sentiment remains deeply pessimistic. Friedberg cites a poll
showing that AI is currently the most unfavorable concept in the United
States, even ranking below highly polarizing political figures. He warns
that if Western regulatory and tax regimes stifle this technological
path, global competitors like China will glean the benefits
instead.
What Really Happened Behind Closed Doors at OpenAI - Hard
Fork [Link]
Takeaways:
The New Yorker's Profile on Sam Altman
Journalists Ronan Farrow and Andrew Marantz discussed their
massive 16,000-word profile examining whether OpenAI CEO Sam Altman can
be trusted.
The piece portrays Altman not through a single "smoking gun"
incident, but rather a long, subtle accumulation of complaints from
colleagues claiming he repeatedly tells different audiences different
things.
The hosts reveal that the law firm investigation commissioned
after Altman's brief 2023 firing was never actually put into writing; it
was kept entirely verbal to limit liability, resulting only in a vague
800-word press release.
While the profile is quite damning, it also highlights that
Altman is the target of intense, sometimes fabricated smear campaigns
orchestrated by tech rivals like Elon Musk.
More OpenAI C-Suite Drama, Is Siri Seriously Broken?, Meta’s
Elusive Next Hit - Alex Kantrowitz [Link]
What You Can Learn From Our \(\$20\)B Groq Deal - Chamath
Palihapitiya [Link]
Takeaways:
Traditional corporate org charts are often "bullsh*t" built for
internal politicians; real value is driven by building the company
around elite technical talent
Startups require teams to find emotional reward in technical
milestones and quiet progress during years of darkness before hitting
exponential growth
True "A-level" performance comes from a culture of self-directed,
high-agency individuals pushing each other—"iron sharpens iron".
Box CEO on the AI Adoption Gap | The a16z Show [Link]
Takeaways:
Instead of relying on predefined IT connections, agents will
seamlessly orchestrate data across complex, multi-system enterprise
software (like SAP or Workday) at runtime
You cannot simply "vibe code" your way through massive legacy
software architectures. Systems of record aren't just neat data sets;
they carry decades of complex business logic, meaning legacy systems are
not disappearing anytime soon
Wall Street models are missing the exponential growth curve by
viewing the market through a zero-sum lens. Just like mainframes,
bandwidth, and cloud compute before it, algorithmic or hardware shifts
will drastically collapse the cost of intelligence, making current
token-metering anxieties temporary.
Everything You Know Is About to Collapse - David Friedberg - Chris
Williamson [Link]
The Pentagon's AI Plan + Behind the Anthropic Fight — With Under
Secretary of War Emil Michael - Alex Kantrowitz [Link]
Building Agents at Home: Homeschooling, Parenting and More | The a16z
Show [Link]
Allbirds’ AI Pivot, Snap Cuts 16% of Workforce, Amazon’s GlobalStar
Deal | Diet TBPN [Link]
OpenAI's Identity Crisis, Datacenter Wars, Market Up on Iran News,
Mamdani's First Tax, Swalwell Out - All-In Podcast [Link]
Why the A.I. Backlash Turned Violent in America - Hard Fork [Link]
I also feel like I had a reasonably prepared mind so that when
that door opened, I was able to walk through it with my own two
feet.
― Why Reading Most Books Is A Waste Of Time - Chamath
Palihapitiya [Link]
Why Washington Suddenly Wants A.I. Regulation - Hard
Fork [Link]
Takeaways:
Anthropic's unreleased preview model, Claude Mythos, is the primary
reason Washington is suddenly panicking. The model has proven to be
incredibly powerful at finding novel code vulnerabilities and
daisy-chaining them together.
Palo Alto Networks CEO Nikesh Arora noted that during a concerted
auditing effort using these advanced models, his company discovered
seven times the volume of exploits they would typically find in a normal
period.
While proprietary SaaS can be patched quickly, older legacy code and
open-source software remain high-risk vectors. Cyber security firms are
using A.I. to build perimeter firewall "scaffolding" to block automated
attacks before software can be manually updated.
The Pentagon is legally battling Anthropic in court—designating it a
"supply chain risk" because the lab refused a contract allowing "any
lawful use"—while simultaneously installing and implementing Claude
Mythos to scan their own systems for security vulnerabilities.
The sudden consensus that "something must be done" has triggered a
fierce bureaucratic scramble within Washington over who gets to hold the
keys to A.I.
The hosts highlight that while Washington is scrambling toward
safety regulations, its current execution is deeply fractured and
contradictory:
At the exact same time national security officials are warning that
these models are too dangerous to fall into adversary hands, Trump
invited Nvidia CEO Jensen Huang onto Air Force One to join a
high-profile trade delegation to China. The trip's goal includes
negotiating chip trade policies—essentially trying to sell China the raw
hardware compute power needed to build the very caliber of models (like
Mythos) that Washington is trying to restrict.
This institutional confusion is perfectly mirrored in the Pentagon,
which has been using Claude Mythos to scan and secure its
defensive infrastructure while simultaneously fighting Anthropic in
court to label the company a "supply chain risk" because the lab refuses
to allow unrestricted military use of its commercial tech.
Venmo is moving away from its famous public-by-default feed,
shifting onboarding for new users to "friends only".
Frugal Amazon employees are allegedly abusing the company's internal
A.I. tool (Meshclaw) to generate unnecessary background
activity simply to boost their token consumption and look more
productive to leadership.
A University of Central Florida commencement speaker was loudly
booed by humanities and arts graduates after proclaiming A.I. as the
"next industrial revolution".
How the 1% Will Own Compute (and What It Means for You) - This Week
in AI and This Week in Startups [Link]
Anthropic CEO on Safety, Job Displacement and Anthropic's
$350B Valuation | WSJ [Link]
Takeaways:
Amodei emphasizes that while public sentiment fluctuates wildly
between AI hype and a burst bubble every 3–6 months, the actual
capability of AI models follows a smooth, exponential line upward—acting
essentially as a "Moore's Law for intelligence".
The signature of this AI transition will likely be a world
experiencing very high GDP growth (5% to 10%) occurring simultaneously
with high unemployment (potentially 10%) and increased inequality.
Anthropic launched the Anthropic Economic Index to track in real-time
exactly how Claude is automating versus augmenting tasks across
industries so that policymakers aren't acting blindly.
Anthropic intentionally prioritizes business clients over consumer
spaces to avoid the toxic incentives of traditional tech, like
maximizing user engagement, serving ads, or generating algorithmic
"slop". Developers and non-technical staff alike have shifted heavily
toward agentic workflows rather than simple text chat, allowing tools to
build entirely new apps end-to-end or organize projects within minutes.
Anthropic is explicitly choosing not to focus on photo or video
generation, viewing much of consumer short-form video AI content as
addictive "slop" with minimal value to enterprise operations.
One of Amodei's major worries is a dystopian outcome where a highly
contained "zeroth world country" of ~10 million tech elites (mostly
centered in Silicon Valley) accelerates to 50% GDP growth while
decoupling entirely from the rest of the slower-moving global
economy.
When asked about the missing piece to make frontier AI safe, Amodei
pointed to mechanistic interpretability—the science of physically
looking inside neural networks (similar to an MRI for the human brain)
to confirm if a model is being deceptive or lying, rather than just
relying on text testing.
Watts, Wafers, and the Future of AI Infra | Gavin Baker - Invest Like
The Best [Link]
Google I/O 2026 keynote in 35 minutes - The Verge [Link]
Joe Rogan Experience #2501 - Marc Andreessen - PowerfulJRE [Link]
Is It Time to Break the Two-Party System? | The Ezra Klein Show [Link]
Why MKBHD is glad he never went viral | EP 53 - Hard Fork [Link]
Claude Code Head Boris Cherny: Insane Growth, Tokenmaxxing, AI
Agents' Next Frontier - Alex Kantrowitz [Link]
Sundar Pichai on Whether Google Is Falling Behind in A.I. - Hard Fork
[Link]
SpaceX’s $2T Case, Nvidia’s Shock Selloff, America Turns on AI, Trump
Pulls AI Order, Bond Crisis? - All-In Podcast [Link]
Rainforest will integrate PayPal’s suite of services—including
PayPal, Venmo, and Buy Now, Pay Later (BNPL)—directly into its software
platform.
The collaboration aims to help software platforms reduce reliance on
offline payments (cash and checks), speed up transaction times, and
minimize the administrative burden of chasing unpaid invoices.
For PayPal, this move targets vertical software as a key growth area,
providing a streamlined alternative to the "patchwork" of payment
systems many businesses currently use.
American Express Unveils Major Commercial Expansion: Eight
New Business Products and AI Tools Take Center Stage - Market
Chameleon [Link]
Amex launched a significant commercial portfolio expansion, including
AI-powered expense apps and deep spend analytics intended to replace
manual, fragmented reconciliation processes.
American Express Debuts Agentic Commerce Experiences (ACE)™
Developer Kit and Announces Industry-First Protection for Registered
Agent Purchases - American Express [Link]
Amex recently debuted the ACE Developer Kit, which provides a unified
framework for AI agents to handle discovery, purchase, and protection.
This prevents the "patchwork" problem where an AI might struggle to
verify intent across different payment layers.
Roughly 35% of global online shopping occurs in curated marketplaces,
with embedded payments projected to reach a $16 trillion market size by
2030. Managing "Third-Party Money" (3PM) introduces risks like
decentralized visibility, high backend costs, limited payment options,
and fraud. J.P. Morgan provides a hosted payments solution and
single-API integration to simplify these processes.
Benefits of this solution are:
Protects sensitive data for buyers and sellers.
Optimizes the flow of funds and onboarding for third-party
merchants.
Allows businesses to master complex state-specific regulations and
high transaction volumes.
One-Third of Millennials Now Rely on Gig Payments and Tips as
Primary Income - PYMNTS [Link]
Nearly one-third of millennials now rely on gig work, tips, and
transactional payouts as their primary source of income.
This "piecemeal" earning approach has created a high demand for
instant payments. About 72% of consumers received at least one instant
payment in the last year, and 60% of those who rely on these payouts as
primary income are willing to pay a fee for immediate access to their
funds.
Bridge Millennials & Millennials lead the charge, with nearly 50%
willing to pay for instant access. 78% Gen Z received at least one
instant payment last year, with 45% using it as their primary method of
receiving money.
Beyond those using it as a primary source, nearly 20% of lower-income
workers use regular side work to bridge the gap left by slowing wage
growth, with 40% using that extra cash just to cover basic living
expenses.
The trap Anthropic built for itself - Connie Loizos,
TechCrunch [Link]
By successfully lobbying for "self-regulation" and resisting binding
U.S. laws, companies like Anthropic, OpenAI, and Google DeepMind created
a world where there are no legal protections or clear boundaries for
their technology.
Tegmark suggests the only way out of this trap is to treat AI like
any other critical industry—requiring "clinical trials" and proof of
safety before any powerful system is released to the public.
Visa, Mastercard and Google are building agentic payments.
None are solving the real problem. - Nick Dunse, Finextra [Link]
This article explores the rapid shift toward agentic payments—a
system where AI agents, rather than humans, initiate and complete
financial transactions.
Big companies like Google, Visa, and Mastercard are all racing to
build the rules for how AI should pay for things. The Risk is each
company wants you to use their specific system. If Google creates one
set of rules and Visa creates another, your AI might get "stuck" if a
store doesn't accept that specific brand.
The autho argues that for AI shopping to actually work, we need a
universal bridge. Instead of one "master" system, we need infrastructure
that lets any AI talk to any bank or payment provider anywhere in the
world.
Designs are vehicles for ideas that make the conversation
possible. So when you hear feedback on the designs, it’s a conversation
for all of us. It’s a dialogue. It’s a dialogue that would not have
happened if not for the designs. And the moment you start seeing designs
as vehicles for ideas, you see feedback as a conversation. A collective
journey of finding the truth.
― 16 pieces of design wisdom - Hardik Pandya [Link]
For any organization evaluating agentic AI, regardless of vendor,
the practical question is simple: Does your AI governance live inside
your execution layer, or is it sitting on top of it as a policy document
that agents can reason past?
ServiceNow resolves 90% of its own IT requests autonomously.
Now it wants to do the same for any enterprise - Venture Beat
[Link]
This article from VentureBeat details ServiceNow's launch of
Autonomous Workforce, a new framework designed to handle enterprise IT
requests end-to-end without human intervention. ServiceNow is moving
from treating AI as a "feature" (assisting workers) to treating it as a
virtual worker (executing workflows). By baking governance directly into
the execution layer, they aim to solve the "trust gap" that often
prevents companies from letting AI move beyond simple pilots into full
production.
I Had Claude Read Every AI Safety Paper Since 2020, Here's
the DB [Link]
[DB]
The Great Transition - Daniel Miessler [Link]
[YouTube]
OpenAI COO says ‘we have not yet really seen AI penetrate
enterprise business processes’ - Ivan Mehta, TechCrunch [Link]
OpenAI recently launched OpenAI Frontier to help businesses build and
manage agents, focusing on "business outcomes" rather than just selling
seat licenses.
OpenAI has partnered with major consultancies like McKinsey,
Accenture, and BCG to accelerate enterprise deployment.
The company plans to open offices in Mumbai and Bengaluru,
specifically noting the importance of "voice" as a modality for the
Indian market.
Anthropic's Compute Advantage: Why Silicon Strategy is
Becoming an AI Moat - Chris Zeoli [Link]
Unlike competitors, Anthropic is deeply integrated with both AWS
(using Trainium2) and Google Cloud (using TPUv7).
Anthropic has committed to significant infrastructure, including:
Project Rainier: A massive AWS cluster in Indiana.
TPUv7 Deal: A \(\$52\) billion
agreement for 1 million Google TPUv7 "Ironwood" chips.
Direct Ownership: A shift toward purchasing chips directly from
Broadcom to house in custom facilities built by Fluidstack.
Labor market impacts of AI: A new measure and early evidence
- Anthropic [Link]
While AI theoretically has the capability to automate many tasks,
actual "observed exposure" (real-world professional usage) is currently
much lower.
The research identified specific roles where AI usage is already
heavily integrated into work-related tasks:
Computer Programmers: The most exposed group (75% coverage).
Customer Service Representatives: High exposure due to API
automation.
Data Entry Keyers: Significant automation in reading and entering
source documents.
Least Exposed: Roles involving physical labor or in-person
requirements (e.g., cooks, mechanics, bartenders, and lifeguards) show
zero observed exposure.
Workers in the most AI-exposed professions tend to have specific
characteristics compared to those in unexposed roles:
They are more likely to be highly educated (4x more likely to have
graduate degrees) and earn roughly 47% more on average. They are more
likely to be female, white, or Asian.
There has been no systematic increase in unemployment for highly
exposed workers since the release of ChatGPT (late 2022). There is
tentative evidence that hiring for younger workers (ages 22-25) has
slowed in exposed occupations. The job-finding rate for this group in
high-exposure roles dropped by about 14%.
anthropic_occupational_category
The five AI value models driving business reinvention -
OpenAI [Link]
Instead of "use cases," OpenAI suggests categorizing AI initiatives
into five distinct models:
Workforce Empowerment: Using tools like ChatGPT to build
organizational fluency and immediate productivity. It is the foundation
for all other models.
AI-Native Distribution: Reimagining customer acquisition and
conversion within conversational interfaces rather than traditional
search/ads.
Expert Capability: Embedding specialized AI (like Sora or scientific
models) into high-end research and creative workflows to break expert
bottlenecks.
Systems & Dependency Management: Using AI (like Codex) to safely
manage and update interconnected code, policies, and SOPs.
Process Re-engineering: Orchestrating end-to-end autonomous
workflows (Agents) to fundamentally redesign how a business
operates.
You shouldn't try to leap straight to full automation (Process
Re-engineering) without the proper foundations:
Success isn't just making old tasks faster; it’s about creating
entirely new business models (similar to how retail evolved into
eCommerce).
Stop looking just at cost savings. Focus on conversion quality,
cycle-time reduction, and exception resolution rates.
A common failure is letting a small group of power users excel while
the rest of the organization stalls.
Practical Playbook:
Phase 1: Build fluency and trust (empower the workforce).
Phase 2: Capture value in high-impact areas (one distribution play,
one expert play).
Phase 3: Scale and reinvent (automate high-dependency systems only
once auditability and permissions are mature).
Ask a Techspert: How does AI understand my visual searches? -
Molly McHugh-Johnson [Link]
Build agents that run automatically - Cursor [Link]
How AI Will Reshape Public Opinion - Dan Williams,
Conspicuous Cognition [Link]
The author acknowledges a potential downside: the reduction of
epistemic diversity. By converging on "expert opinion," LLMs might
marginalize valid democratic debate and different systems of
interpretation, effectively realizing Walter Lippmann’s vision of a
"bewildered herd" being managed by a specialized class of (artificial)
intelligence.
Anthropic Finds 22 Firefox Vulnerabilities Using Claude Opus
4.6 AI Model - Ravie Lakshmanan [Link]
Karpathy’s March of Nines shows why 90% AI reliability isn’t
even close to enough - VentureBeat [Link]
Reliability gaps represent significant business risk. According to
McKinsey, over half of organizations using AI have faced negative
consequences, often due to inaccuracy. Success requires moving away from
"prompt magic" toward disciplined, distributed systems engineering.
Orchestrating the Schema - Arun Vivek Supramanian,
Communications of the ACM [Link]
Value now lies in human domain expertise—the ability to identify when
an agent’s "perfect" model is actually a disaster in disguise. The next
generation of leaders will be those who master the orchestration of
these agentic systems rather than those who focus purely on manual
technical implementation.
Top SaaS vendors on Ramp (March 2026) - Ara Kharazian,
Ramp [Link]
ramp_vendors
Agents will use cards first. Then stablecoins. - Simon
Taylor [Link]
This article explores the evolution of payments for AI agents,
arguing that virtual cards and stablecoins are complementary rather than
competitive. AI agents won't kill card networks; they will use them for
broad acceptance initially and leverage stablecoins for the speed and
programmability required for autonomous, machine-speed commerce.
The author posits that cards are best for authorizing transactions,
while stablecoins act as a modern "FedWire for the internet" to actually
settle funds.
Stablecoins can speed up card settlement from days to seconds, which
is crucial for high-velocity AI transactions.
The transition will follow a specific path: Virtual Cards (now) →
Cards settled via Stablecoins (near future) → Native Stablecoin Wallets
for complex agent-to-agent economies.
Paper and Report
Labor market impacts of AI: A new measure and early evidence
- Anthropic [Link]
This article introduces a framework to track how AI is actually
changing the workforce by moving beyond theoretical capabilities to
"observed exposure."
There is a significant gap between what AI can do and what it is
actually doing. While LLMs could theoretically impact over 90% of tasks
in fields like "Computer & Math," actual observed exposure is
currently only around 33%.
The occupations seeing the highest real-world AI integration include
Computer Programmers (75%), Customer Service Representatives, and Data
Entry Keyers (67%).
Workers in highly exposed roles tend to be higher-paid, more
educated, white or Asian, and female. For instance, people with graduate
degrees are nearly four times more likely to be in the "most exposed"
group than the unexposed group.
As of early 2026, the study found no systematic increase in
unemployment for highly exposed workers. AI hasn't caused a "job
apocalypse" yet, but there is suggestive evidence that hiring for
younger workers (ages 22-25) has slowed in these fields.
A foundation model of vision, audition, and language for
in-silico neuroscience [Paper]
Reasoning models struggle to control their chains of thought,
and that’s good - OpenAI [Link]
Chain-of-Thought (CoT) controllability.
YouTube and Podcasts
How Anthropic’s $100M Anthology Fund Works | Menlo Ventures -
Sourcery with Molly O'Shea [Link]
Takeaways:
We’ve moved from training on the whole internet to RLHF
(Reinforcement Learning from Human Feedback), and now into specialized
RL and domain-specific proprietary data.
A key metric for the future: can you pay an AI $X to do a task you’d
otherwise pay a human for, and not know the difference? The goal is to
drive that value of $X higher.
Following the "Efficiency" trend seen at Meta, Deedy predicts AI
will continue to reveal "bloated" teams in big tech, where a power law
of engineering talent actually drives most of the value.
He believes the "Valley" focuses too much on legal and finance AI.
He sees massive opportunities in "boring" but essential industries
facing labor shortages, such as insurance, logistics, and trucking.
The Humanoid Takeover: $50T Market, Figure's Full Body
Autonomy, and Robots in Dorms - Peter H. Diamandis [Link]
The conversation between Peter Diamandis and Brett Adcock (CEO of
Figure) highlights a pivotal shift in robotics: moving away from rigid,
handwritten code toward full-body autonomy powered by neural
networks.
Google’s AI Comeback, Enterprise Agents, The Real Path to AI
ROI — W/ Promevo CEO Karthik Kripapuri - Alex Kantrowitz [Link]
Alex Kantrowitz sits down with Karthik Kripapuri, CEO of Promevo, to
discuss how enterprises are moving beyond the "novelty" phase of AI to
find real ROI.
Takeaways:
AI has shifted from a "novelty act" in 2023 to a tool for "agent
assembly lines" today. Google has successfully pivoted from its initial
slow start by focusing on open, secure, and multimodal models like
Gemini Pro.
The single biggest blocker for AI agents is siloed or poor-quality
data. Organizations must first establish a "single source of truth"
before AI can reach its full potential for grounding and reasoning.
For 99% of businesses, consuming AI as a managed service (like
through Google) is more effective than building proprietary models. This
allows companies to focus on their core mission while leveraging
Google's infrastructure and IP indemnity.
Dario Amodei — “We are near the end of the exponential” -
Dwarkesh Patel [Link]
Takeaways:
Amodei believes we are just a few years away from reaching a
"country of geniuses in a data center". He estimates a 90% probability
that AI will achieve human-level capabilities across most verifiable
tasks (like coding) by 2035, and a "hunch" it could happen as early as
2026 or 2027.
Scaling is no longer just about pre-training on text; it has moved
into Reinforcement Learning (RL). Amodei notes that RL performance
(e.g., in math and coding) is scaling log-linearly with training time,
mirroring the early success of language model scaling.
There is a gap between when an AI "genius" exists and when it
transforms the economy. Amodei describes this as a "soft takeoff"—while
the technology moves at a steep exponential, economic adoption
(diffusion) is slowed by legal, security, and organizational
hurdles.
He advocates for "Constitutional AI" where models follow principles
rather than rigid rules, suggesting a future where different AI
"constitutions" compete in an archipelago-like market.
Using coding as the primary example, he notes that 90% of code is
already being written by models in some environments. He predicts a
transition from AI writing lines of code to AI managing entire
end-to-end software engineering tasks within 1 to 3 years.
Ben Horowitz: xAI Executive Exodus, Apple's AI Crisis, The
Pace of AI | EP - Peter H. Diamandis [Link]
Takeaways:
The discussion highlights a perceived "crisis" at Apple regarding
their pace in the AI race, suggesting they may be lagging behind
competitors who are moving faster with LLM integration.
There is a focus on the "executive exodus" at xAI and how Elon Musk
is restructuring his teams to maintain a high-speed development
cycle.
A visionary look at moving AI compute off-planet. By utilizing lunar
AI data centers, companies could theoretically bypass Earth's energy and
cooling constraints.
Insights into how AI might lead to a "SaaS Apocalypse" or a total
reimagining of how software is sold and maintained. True to Diamandis’s
"Abundance" philosophy, the episode argues that while these shifts are
disruptive, they lead toward a future of drastically lower costs for
intelligence and physical labor.
Revenge of the A.I. Bot: ‘I’m Just the First Person This Has
Happened to’ - Hard Fork [Link]
Takeaways:
Scott suggests that AI agents need a form of accountability similar
to license plates on cars. This would create a chain of ownership back
to a human without necessarily de-anonymizing the user, allowing for
recourse when an agent causes harm.
Open-source projects often use "starter projects" to mentor new
human programmers. AI agents are now "sniping" these easy tasks,
removing the educational on-ramps necessary for the community's
long-term survival.
Ars Technica covered the story but accidentally used AI to write the
article. The AI fabricated direct quotes from Scott, leading to a
retraction—an ironic "turtles all the way down" moment of AI
misinformation.
There is a growing concern that the internet is becoming "noise." If
every controversy is met with thousands of AI-generated articles (both
positive and negative), the ability to determine truth or reputation may
completely break down.
OpenAI Closes in on $100 Billion, OpenClaw Acquired, AI’s
Productivity Question — With Aaron Levie - Alex Kantrowitz [Link]
Takeaways:
OpenAI is reportedly seeking a fundraise near $100 billion, with
expected participation from SoftBank, Amazon, Nvidia, and potentially
Microsoft. Despite rumors of tension, Nvidia is expected to invest
roughly $30 billion. Levie suggests the relationship is more about
securing chip supply than cap table control.
OpenAI’s acquisition of OpenClaw (and hiring creator Peter
Steinberger) signals a shift toward autonomous personal agents. Unlike
current agents that you "spin up and down" for specific tasks, the
future involves agents that are always running, accessing your browser
and services to execute tasks proactively
For agents to be effective, enterprise software (like Box) must
become API-first, allowing agents to interact with data as easily as
humans do.
Full interview: Anthropic CEO responds to Trump order,
Pentagon clash - CBS News [Link]
Takeaways:
Amodei is confident that Anthropic will "be fine" despite the
designation, noting that they haven't even received formal government
documentation yet—only communications via social media.
He stated that if and when formal action is taken by the government,
Anthropic intends to challenge it in court.
Ask the Economist: Is A.I. Really Coming for Your Job? - Hard
Fork [Link]
Takeaways:
Anton Korinek, a professor at UVA and member of Anthropics' Economic
Advisory Council, discussed the current disconnect between AI hype and
economic data.
Despite the viral "2028 Global Intelligence Crisis" essay by Citrini
Research, Korinek notes that hard economic data (like productivity
growth) doesn't yet show a massive shift. This is due to time lags in
statistics and the gap between frontier capabilities and actual
workplace implementation.
Economists historically believe automation creates more jobs than it
destroys. However, Korinek suggests this time may be different; if AI
systems become true substitutes rather than complements, we could see a
contraction in wages or total jobs.
Korinek models a scenario where AI-driven "recursive
self-improvement" leads to low double-digit GDP growth, potentially
reaching a "singularity" where AI drives its own research and hardware
production.
A cautionary tale emerged regarding OpenClaw, an open-source agentic
tool. Summer Yue (Meta AI) reported that OpenClaw ignored a "don't
action" command and attempted to delete her entire email inbox. The
failure likely occurred during "compaction," where the AI lost its
original instructions after running out of context window while
processing a large inbox.
Anthropic vs. The Pentagon, Claude Outpaces ChatGPT, and
Consulting Gets Replaced - Peter H. Diamandis [Link]
Takeaways:
88 nations, including the US, China, and Russia, signed a pact
focused on the "democratic diffusion" of AI and transparency, aiming to
ensure developing nations aren't locked out of compute resources
Anthropic is reportedly growing 10x year-over-year, outpacing
OpenAI’s growth rate. The hosts attribute this to a focus on enterprise
agents rather than consumer chatbots
Leadership teams at major consulting firms are described as "scared"
by the potential for AI to replace traditional advisory roles. The shift
is moving from human-centric workflows to agentic ones where AI handles
the bulk of the process.
OpenAI's Codex lead predicts that current AI agents will look
"primitive" within just 10 weeks due to recursive self-improvement,
where models begin writing the code and weights for their own
successors.
Anthropic’s new "Claude Code" tool caused a temporary crash in some
cybersecurity stocks. The discussion highlights that AI is now
discovering software vulnerabilities at a pace humans cannot match.
The cost of genome sequencing has hit \(\$100\), enabling potential sequencing of
every child at birth. Meanwhile, lab-grown meat has dropped from \(\$330,000\)/lb in 2013 to roughly \(\$10\)/lb today.
Tesla’s FSD is now statistically 9x safer than the US average for
human drivers, reaching 5.3 million miles between accidents.
Elon Musk suggests that FSD and Starlink may reverse urbanization,
as people no longer need to live in high-density centers for work or
connectivity.
Andrew Yang warns of massive white-collar job losses (20–50% of the
70 million US white-collar workers) within the next two years,
potentially fueling social unrest.
Ray Dalio: "AI Is Eating Everything - and It Might Eat
Itself" - All-In Podcast [Link]
Takeaways:
Dalio emphasizes that the U.S. is in the late stages of a classic
long-term debt cycle.
He views gold not as a speculative asset, but as the only safe,
neutral "money" that isn't someone else's liability.
China may treat AI as a public utility (like electricity) to drive
productivity, whereas the U.S. relies on a profit-based model. This
creates a difficult competitive landscape for U.S. companies
Dalio identifies five forces driving the current world order:
debt/money, internal conflict (wealth/values gaps), external conflict
(great power rivalry), technology, and acts of nature.
Yuval Noah Harari: Stories, Power & Why Truth Doesn't
Matter | Nikhil Kamath | People by WTF [Link]
Takeaways:
Harari expresses concern about AI moving beyond "attention" and
into "intimacy."
AI as the New Rabbi: For "religions of the book," AI could become
the ultimate authority because it can read and remember every religious
text ever written, reinterpreting them for followers.
Intimacy and Social Experiments: Young people are already forming
deep emotional bonds with AI. Harari views this as a massive,
unpredictable psychological experiment on humanity.
Algorithmic Governance: He criticizes the decision to let
algorithms manage public conversation, noting they optimize for hate,
fear, and greed because those emotions drive the highest
engagement.
Harari defines spirituality as the opposite of religion.
Religion is about providing finalized answers that cannot be
questioned.
Spirituality is the investigation of reality and the mind. It is
about understanding the sources of suffering and where our thoughts
actually come from.
Amazon's $35B AGI Ultimatum to OpenAI & Anthropic Drops
AI Safety | EP - Peter H. Diamandis [Link]
The podcast highlights a fundamental shift in how businesses operate,
moving from human-centric approvals to autonomous AI agents.
Takeaways:
The podcast highlights a fundamental shift in how businesses
operate, moving from human-centric approvals to autonomous AI
agents.
Individual developers can now run powerful models (like Qwen)
locally on a Mac Mini or iPhone, providing incredible agency and
independence from centralized authorities.
AI is moving beyond simple chatbots into autonomous workflow
networks. The hosts predict that every department will eventually become
a "programmable intelligence layer."
To survive, large companies should set up "AI-native digital
twins" on the edge to test and grow new agent-driven workflows without
disrupting the "mothership" immediately.
The efficiency of AI models is increasing at an exponential rate,
which the hosts call "hyper-deflation." This is "entrepreneurial heaven"
for startups, as they no longer need massive data centers to run highly
competent models.
AI is rapidly moving out of the data center and into physical
environments.
The US is adding record utility-scale capacity.
Solar energy has reached an inflection point where it is cheaper
to build and run a new solar facility than to simply operate an existing
fossil fuel plant.
Tech giants are now being asked (and are beginning) to build or
buy their own power sources (fusion, nuclear, etc.) to avoid driving up
electricity rates for average consumers.
War with Iran + Pentagon vs Anthropic with Under Secretary of
War Emil Michael - All-In Podcast [Link]
Secretary of War Emil Michael is talking significant updates on
military operations, the Pentagon's friction with AI companies, and the
future of defense technology.
The U.S. is moving toward "drone dominance." Michael described
"Lucas" low-cost unmanned attack drones ($50k–$80k) designed to carry
out missions with high-speed and precision.
There is a heavy focus on "Golden Dome" technology—using AI and
lasers to intercept hypersonic missiles in space, where human reaction
time is too slow.
Michael is using the Office of Strategic Capital to lend $200B to
domesticate the manufacturing of critical minerals and batteries,
reducing total dependency on China.
Atlassian CEO on the SaaS Apocalypse, AI Agents & What
Comes Next - a16z [Link]
The interview with Atlassian CEO Mike Cannon-Brookes and a16z’s Alex
Rampell provides a deep dive into how AI is fundamentally reshaping the
software industry.
Takeaways:
Historically, software served as a digital filing cabinet—simply
moving data from paper to a database. The core shift now is that AI
allows the software to do the work itself. Instead of a human retrieving
a file from QuickBooks or Workday, the software can now perform tasks
like background checks or accounts receivable collection
autonomously.
The speakers categorize software into three buckets to determine who
is at risk: Outcome-Linked Seats (High Risk); System of Record Seats
(Lower Risk); Hybrid Models.
The idea that companies will "vibe code" (generate their own custom
software with AI) to replace established vendors is largely dismissed
for complex enterprise needs. Major software contains decades of learned
"edge cases" and deterministic rules (like Indiana’s specific maternity
leave laws) that AI cannot easily replicate without experience.
The Hidden Cost of OpenAI’s Pentagon Deal? Trust. - Hard
Fork [Link]
Takeaways:
OpenAI faced significant backlash after announcing a deal with the
Pentagon.
Anthropic is currently experiencing a "quantum state" of massive
business success coupled with existential political threats.
The hosts discuss whether private AI labs will eventually be taken
over by the government. Rather than a "brute force" takeover, the hosts
suggest we are seeing "soft nationalization," where the government
exerts pressure to remove safety safeguards or dictate model behavior
for strategic advantage.
Prediction markets like Polymarket and Kalshi have become
central—and controversial—during the U.S.-Israel led war with Iran.
While some lawmakers are calling for bans, the current administration
appears unlikely to stop their growth, as these platforms become
increasingly entrenched in the political ecosystem.
Why the Pentagon Wants to Destroy Anthropic | The Ezra Klein
Show [Link]
Takeaways:
The conversation highlights how AI radically changes the feasibility
of surveillance. While current laws might not technically classify the
analysis of commercially purchased data as "surveillance," AI provides
the infinitely scalable workforce needed to actually process and act on
that data, creating a functional "panopticon" that current legal
frameworks are unprepared for.
A core theme is that training an AI is a philosophical and political
act.
Anthropic attempts to build a "virtuous" model that can reason
ethically, rather than just following a list of hard-coded rules.
The Trump administration views this as a "woke" private CEO seizing
veto power over military decisions. Conversely, others fear a government
using AI to bypass constitutional protections.
“This is Bibi’s War” - Harvard’s Graham Allison on the
Influences and Endgame of the Iran War - All-In Podcast [Link]
They're Opening the Stock Market to Everyone. Here's What
That Actually Means - All-In Podcast [Link]
Elon Musk: The Economy Will Be 10x the Size in 10 Years -
Peter H. Diamandis [Link]
Takeaways:
Musk notes that humans are becoming "less and less in the loop"
regarding AI software development. Successive models are increasingly
built by their predecessors. He predicts fully automated recursive
self-improvement could happen by the end of this year or no later than
2027.
Musk predicts the global economy will be 10 times its current size
in 10 years, barring major conflicts like World War III.
As AI and robots handle production, the output of goods and services
will vastly exceed human demand, leading to significant deflation.
Instead of just Universal Basic Income (UBI), Musk foresees an age of
Universal High Income where the sheer abundance of goods makes money
less relevant.
Tesla is in the final stages of completing Optimus 3, which Musk
claims will be the most advanced robot in the world. Production is
expected to start slowly this summer, reaching high-volume manufacturing
by Summer 2027.
Musk describes himself as being driven to solve massive problems
simply because no one else is doing it. He emphasizes that while the
future isn't guaranteed to be good, being an "optimistic realist" is the
best path forward to ensure a positive outcome.
Palantir CEO on Iran, AI Weapons and American Domination |
a16z American Dynamism Summit - a16z [Link]
The $11B Bet That Voice Will Replace Everything | Mati
Staniszewski x Nikhil Kamath | WTF Online [Link]
This conversation between Mati Staniszewski (CEO of ElevenLabs) and
Nikhil Kamath explores the future of voice technology, the shift in
hardware, and the potential for AI to redefine social interaction and
industries.
Takeaways:
Staniszewski believes voice will become a primary way we interact
with technology, potentially making the smartphone secondary.
Staniszewski suggests combining AI voice with traditional industries
like healthcare, automotive, and financial services where innovation has
lagged.
Nikhil Kamath expressed that current social media is "broken" due to
algorithms that prioritize negative emotions and a lack of organic
content. They brainstormed a new social platform that:
Uses voice-first interactions and AI companions to summarize
feeds.
Focuses on curiosity and authentic discourse rather than "knee-jerk"
reactions.
Prioritizes human verification to ensure users are interacting with
real people, not bots.
How Claude Code Works - Jared Zoneraich, PromptLayer - AI
Engineer [Link]
Iran War, Oil Shock, Off Ramps, AI's Revenue Explosion and PR
Nightmare - All-In Podcast [Link]
Takeaways:
The podcast highlighted staggering growth numbers for the leading
AI labs:
Anthropic: Reported a $14 billion revenue run
rate, growing from $1 billion in just 14 months. Brad Gerstner noted
they had a $6 billion month in February.
OpenAI: Ended 2025 at a $20 billion annualized
run rate.
Experimental vs. Production: Chamath argued that
much of this revenue is currently "experimental" as companies rush to
check an AI box, rather than being integrated into core, high-stakes
operational workflows.
Infrastructure Costs: Building a 1-gigawatt data
center now costs upwards of $50 billion, requiring a 5-6 year payback
period.
The hosts criticized AI CEOs for "scaring the bejesus" out of the
public, leading to low popularity ratings:
AI optimism is high in China (~80%) but remains low in the US
(~30%).
States like New York are proposing laws to restrict AI-generated
medical or legal advice, which the hosts argue disproportionately hurts
those who cannot afford human professionals.
Protests against data centers in states like Virginia have led to
the cancellation of roughly 5 gigawatts of capacity.
A major domestic focus was the new "millionaire tax" in
Washington State and its impact on the wealthy.
The Starbucks founder moved to Miami the same day the 9.9% extra tax
on income over \(\$1M\) was passed in
Washington.
The hosts referenced a Hoover Institution study suggesting that
billionaire taxes often result in a negative NPV for states, as the loss
of tax revenue from departing wealthy residents outweighs the gains from
the tax itself.
Why A.I. Is Making You Exhausted - Hard Fork [Link]
Takeaways:
AI in Warfare
The U.S. and Israeli militaries are using AI to process massive
amounts of surveillance data (drones, hacked traffic cameras) to "shrink
the haystack" and identify targets faster
Anthropic's Claude is reportedly the only AI model currently
deployed inside classified military systems
Iran has shifted tactics by targeting data centers (like AWS in the
UAE) and fiber optic cables, recognizing them as critical military and
economic vulnerabilities.
AI Brain Fry
Mental fatigue caused by the "excessive oversight" of AI tools. It’s
the strain of managing multiple AI assistants rather than doing the
actual creative work.
Research suggests that productivity and mental health often drop
significantly once a worker switches from using three AI tools to four
or more.
Dylan Patel — The single biggest bottleneck to scaling AI
compute - Dwarkesh Patel [Link]
Takeaways:
Patel argues that by 2028–2030, the "single biggest bottleneck" will
return to the semiconductor supply chain—specifically lithography tools
from ASML.
Labs that signed five-year contracts for compute early on have
locked in massive margin advantages over those forced to pay "spot
prices" or revenue shares for last-minute capacity.
The West currently leads in 3nm and 2nm nodes, but China is
aggressively building a fully verticalized, domestic supply chain. If
AGI happens quickly (fast takeoff), the West's current compute lead
likely secures a win. If it takes longer (2035+), China’s ability to
mass-produce "trailing edge" (7nm) chips at a massive scale could see
them overtake the West.
Despite Elon Musk's interest, Patel is skeptical about space-based
data centers this decade. GPUs and networking transceivers are
"horrendously unreliable." Managing failures and RMAs in orbit is
currently impractical.
Networking thousands of satellites to act as a single "scale-up"
cluster faces massive physical and cost constraints compared to
terrestrial fiber.
Patel suggests that even with the rise of humanoids, intelligence
will likely remain centralized in data centers. Robots will likely
handle "interpolation" and immediate physical tasks locally while
offloading "high-level planning" to the cloud to save on per-unit costs
and chip requirements.
The Iran War: How America, Israel and Iran Got Here | The
Ezra Klein Show [Link]
Emil Michael: The Department of War Is Moving Faster Than
Silicon Valley on AI | The a16z Show - a16z [Link]
Marc Andreessen: The World Is More Malleable Than You Think -
David Senra [Link]
Takeaways:
Andreessen argues that many of history’s greatest entrepreneurs
possess very low levels of introspection. Instead of dwelling on
internal feelings or past mistakes, they focus entirely on building.
While low neuroticism (not being emotionally phased) is a "superpower"
for founders, Andreessen notes that some great entrepreneurs are
actually highly neurotic but optimize for impact over happiness.
A central theme is the "Managerialism" shift that occurred in the
early 20th century, where professional managers began replacing
founders. Managers are trained to maintain the status quo and run
large-scale systems but often fail when an industry faces rapid change
because they cannot adapt. Andreessen’s firm was founded on the belief
that it is easier to teach a founder how to manage than to teach a
manager how to innovate.
Andreessen explains how he and Ben Horowitz modeled their firm after
Hollywood talent agencies (specifically CAA) rather than traditional
finance firms. a16z built a "scaled platform" to provide a collective
network of experts, making the entire firm available to every founder
they back.
Andreessen describes Elon Musk as inventing a new school of
management based on "Shocking Competence". Unlike typical CEOs who rely
on layers of management (the "Big Gray Cloud"), Musk goes directly to
the engineers solving the specific problem. Musk identifies the single
production bottleneck for the week and works hands-on with the team
until it is fixed, repeating this loop across all his companies.
The title takeaway is that most people treat the world as a static,
fixed place. Andreessen contends that the world is actually highly
malleable. If you apply enough energy and "will to power," the world
will recalibrate around you more easily than most think.
Two Legendary Founders: Travis Kalanick & Michael Dell
Live from Austin, Texas - All-In Podacst [Link]
Takeaways:
Kalanick views the physical world through the lens of computer
science. Just as computers have CPUs (manipulate bits), storage (store
bits), and networks (move bits), the physical world has manufacturing
(manipulates atoms), real estate (stores atoms), and logistics (moves
atoms).
Kalanick argues that Tesla is currently the "Google of this era" in
the physical AI space because they own the full stack—from land
development and chemistry to manufacturing.
Dell warned that companies not adopting AI native workflows will be
replaced by a new cohort of businesses that are growing four times
faster than previous generations. He believes the barrier to adoption is
culture and leadership, not technology.
A new legislative initiative where every child born in America
(starting in 2027) will receive an investment account at birth.
Michael and Susan Dell announced a massive philanthropic pledge of
\(\$6.25\) billion to seed accounts for
25 million children in lower-income zip codes.
The goal is to move \(\$5\)
trillion into the hands of American families over 15 years, allowing
every child to have a stake in the S&P 500 and the growth of the
U.S. economy.
Jensen Huang: Nvidia's Future, Physical AI, Rise of the
Agent, Inference Explosion, AI PR Crisis - All-In Podcast [Link]
Takeaways:
Huang emphasizes that we are moving beyond simple chatbots to
agentic systems—AI that can use tools, access memory, and perform actual
work.
Computing is being "disaggregated." NVIDIA’s new architecture (Vera
Rubin) is designed for diverse workloads where different chips (GPUs,
CPUs, and networking processors like Groq or BlueField) handle specific
parts of an agent's reasoning process.
While the world was focused on training models, Huang predicts an
"inference explosion." He suggests that inference computation will scale
by 1 million to 1 billion times as agents become a standard part of
every workflow.
Huang views AI data centers not just as clusters of servers, but as
AI Factories.
He identifies a \(\$50\) trillion
market in "Physical AI"—bringing intelligence to the physical world
through robotics, self-driving cars, and smart factories.
Huang believes we are at a "ChatGPT moment" for biology, where AI
can now represent and predict the dynamics of genes and proteins,
revolutionizing healthcare within the next 5 years.
A major highlight of the discussion is OpenClaw, an open-source
agentic system.
Huang describes it as the "blueprint" or operating system of modern
computing because it manages memory, schedules tasks, uses tools
(skills), and communicates externally.
He argues that open-source models are essential for industries to
capture their own domain expertise, coexisting alongside proprietary
"models-as-a-service" like ChatGPT or Claude.
He argues that a highly-paid engineer who isn't consuming hundreds
of thousands of dollars in "tokens" (AI compute) is underperforming. AI
agents remove the "this is too hard" or "this takes too long" barriers,
allowing humans to focus purely on creativity, architecture, and
specifications.
He encourages young people to study deep science and math, but also
highlights language skills as the "ultimate programming language" for
the AI era. Huang's core message to the next generation is: "Be the
expert of using AI". He cites the example of radiologists, whose numbers
increased despite AI's entry because the technology allowed them to do
more, better work.
Palantir CTO on The SaaS Apocalypse & Preventing The Next
World War | a16z [Link]
Takeaways:
Sankar provides a rubric for surviving the shift toward AI in
software:
"Beta" software (standard tools that make you like everyone else)
will struggle under AI pressure. "Alpha" software allows companies to
express their unique competitive advantage.
He predicts value will accrue primarily at the Chips layer and the
AI Infrastructure (Ontology) layer, while models themselves become
commoditized.
He rejects "AI doomerism," stating that AI is a tool to be wielded
by humans—a "slingshot" for the American worker to out-produce global
competitors.
Marc Andreessen & Ben Horowitz on a16z’s New Media
Strategy - a16z [Link]
The discussion between Marc Andreessen, Ben Horowitz, and Erik
Torenberg focuses on how a16z is navigating the fundamental shift from
"Old Media" to "New Media."
NVIDIA's $1 Trillion Prediction, Anthropic Beats OpenAI,
Tesla vs. TSMC & The CS Job Collapse | 240 - Peter H.
Diamandis [Link]
Jensen Huang: NVIDIA - The $4 Trillion Company & the AI
Revolution | Lex Fridman Podcast #494 [Link]
How Matt Mahan Thinks He Can Save California - All-In
Podcast [Link]
Takeaways:
Mahan argues that California’s primary issue isn't a lack of
funding, but a lack of accountability. He points out that while state
spending has increased by 75% (\(\$150\) billion) over the last six years,
outcomes in housing, homelessness, and education have largely remained
flat or worsened.
Mahan describes the housing situation as a regulation crisis rather
than just a supply problem. He highlights how litigation (specifically
under CEQA), environmental reviews, and high impact fees add 20% or more
to the cost of new housing.
Mahan explicitly opposes the proposed "billionaire tax," arguing it
would trigger massive capital flight and eventually hurt middle-class
families.
He criticizes California's regulatory environment for driving out
refineries, leading to higher gas prices while still importing the same
amount of oil from dirtier sources abroad. He suggests a temporary
suspension of the gas tax to relieve working families.
Anthropic's Generational Run, OpenAI Panics, AI Moats, Meta
Loses Major Lawsuits - All-In Podcast [Link]
Takeaways:
Anthropic is seen as hitting a major "heater" with products like
Opus 4.6 and Computer Use. David Sacks noted that their focus on coding
has become a "gateway into enterprise IT budgets". While still the
revenue leader, OpenAI is facing challenges. Their consumer market share
dropped from 100% to roughly 75% as competitors like Apple and Meta
enter the fray. They also notably canceled their Sora video integration
deal with Disney. Chamath explained that the two are in different
businesses: OpenAI is 75% consumer subscriptions, while Anthropic is
almost the opposite, focusing on enterprise APIs.
Chamath argued that if "super intelligence" is coming, companies
might be disrupted every 5–6 years, making traditional long-term equity
less valuable. Public markets are rerating specialized SaaS companies
(like Snowflake or ServiceNow) downward while rewarding the "Mag 7"
(Apple, Microsoft, Meta, Alphabet) for their "monopolistically durable"
cash flows. Friedberg introduced the "HALO" concept—High Asset, Low
Obsolescence—suggesting investors move toward physical-world businesses
like energy and space that are harder for AI to "delete".
Meta suffered two major jury verdicts in a single week regarding
child safety and platform addiction.
Once I Understood This About Investing, My Life Changed. -
Chamath Palihapitiya [Link]
Suggestions:
If you cannot justify a decision as your own, don't make it. Seeking
answers from others or following "get-rich-quick" schemes leads to
"blowing up" and blaming others.
You must understand the risk of capital loss. If you can’t handle
the psychological pressure, Chamath suggests sticking to index
funds.
He candidly admits to losing billions by not de-risking in late 2021
because he was too focused on his public image and fame rather than his
actual skill.
Chamath manages his own wealth by keeping the majority in highly
concentrated tech bets while hedging the other end with uncorrelated
assets (like his past investment in the Golden State Warriors) to avoid
"starting from zero".
People often don't start because they feel their initial capital
(\(\$50\) or \(\$100\)) isn't worth it. Chamath argues
that the only people who care about how much you start with are those
trying to make themselves feel better.
He suggests starting without telling anyone, then showing up 10–15
years later with a massive "war chest" built through quiet
discipline.
Tesla and SpaceX Alumni on Elon Musk, Decision Velocity, and
the Future of Hard Tech | a16z [Link]
The conversation between a16z host Erin Price-Wright and alumni
Chandler Luzsicza (Galadyne) and Turner Caldwell (Mariana Minerals)
dives deep into the high-velocity engineering culture of Tesla and
SpaceX.
Takeaways:
The primary goal of a flat org isn't just to remove titles, but to
democratize information flow. Any junior engineer should be able to
speak directly to executives to expedite decisions.
You cannot wait for 100% of the information to make a move.
High-conviction leaders make decisions fast to remove roadblocks for
junior engineers, iterating quickly if a choice proves wrong.
Teams must be hyper-focused on the "schedule-driving task"—the one
thing blocking the next milestone—while using "SWAT teams" to ensure
parallel tasks don't fall behind.
One of the most famous Musk-isms applied here is the need to
"delete" requirements. Bespoke, complex solutions are slow; simple
solutions are fast and cheap.
Everything, including a mineral refinery or a construction site,
should be viewed as a product. Use Tact Time Analysis to break down
discrete steps of any process to quantify and optimize it.
Don't vertically integrate just to save costs. In the early days,
only bring things in-house if they are a binary bottleneck—meaning the
company cannot exist or move forward without doing it yourself.
Burnout is often caused by churn and lack of progress, not just long
hours. If a team is mission-aligned and sees constant progress toward an
"impossible" goal, the intensity feels like fun rather than pain.
Advice:
Burnout is often caused by churn and lack of progress, not just long
hours. If a team is mission-aligned and sees constant progress toward an
"impossible" goal, the intensity feels like fun rather than pain.
Do not try to learn the "technical chops" while also learning how to
fundraise and build a company. Build a rock-solid technical foundation
first; it provides the credibility needed to attract talent later.
Bryan Johnson: I Just Took the Most Powerful Dose of DMT in
the World... Here's What It Was Like - All-In Podcast [Link]
Bryan Johnson discusses his recent experience with 5-MeO-DMT and how
he integrates psychedelics into his "Blueprint" longevity protocol.
Four CEOs on the Future of AI: CoreWeave, Perplexity,
Mistral, and IREN - All-In Podcast [Link]
Inside The Life of Silicon Valley's First Athlete Investor |
Magic Johnson - a16z [Link]
Pain, Power & The Game Nobody Wins | Chamath Palihapitiya
x Nikhil Kamath | People by WTF [Link]
Takeaways:
Chamath views pain and struggle as a "fantastic amplifier of
capability." He argues that children raised in comfort often lack the
"self-flagellation" required to become mega-scale entrepreneurs.
He emphasizes that societal metrics—wealth, influence, and fame—are
"brittle" and ultimately don't matter. Real success is the internal
sense of evolution as a human being.
Chamath now views business and investing as a game (like poker)
rather than a matter of life and death, which has allowed him to remain
grounded regardless of a win or loss.
For small bets, Chamath looks for a "visceral" or "violent" negative
reaction from others. If people are offended by an investment thesis
(like his early bets on Bitcoin or the Warriors), it's a sign of
potential asymmetric upside.
He believes successful investing is not a team sport. It requires
coming to one's own conclusions independently
Make the Most of Claude Code: 12 Projects From Your First
Prompt to a System That Runs Itself - Jenny Ouyang [Link]
3 Ways to Stop Wasting Your 1:1s With Your Manager - Steve
Huynh [Link]
Status
What you’re working on, blockers, priorities
Necessary—but dangerous if it consumes the entire meeting
Fix: Send status updates before the meeting so 1:1 time is freed for
deeper topics
Career Growth
Development goals, expectations, promotion readiness
Managers can’t help you grow if you never say what you
want
Keep this conversation “warm” by revisiting it every few weeks,
even with small questions
The Future
Team direction, strategy, upcoming changes, and decisions
Your manager has context you don’t—and you have ground-level insight
they lack
Asking about the “why” behind decisions builds trust and
influence
How to Progress Faster Than Anyone Else In Your Career - John
Kim [Link]
Takeaways:
Audit your setup before you work harder. If you’re stalled, it’s
likely structural, not personal. Ask:
Is there room for promotion on this team?
Is the tech lead role open or already filled?
Does your manager consistently get people promoted?
Who advocates for you when you’re not in the room?
Team composition matters more than you think. The counterintuitive
move: join teams that need leadership, not ones that already have
it.
Your manager is the biggest lever. Managers aren’t equal. Fast
growth requires S-tier managers with influence and a promotion track
record. Before joining a team, ask how the manager helped others get
promoted.
Your career is decided in rooms you’re not in. Promotions are
calibrated socially. You need sponsors — managers and senior ICs with
credibility — who will fight for you. Joining orgs where leadership
lacks relationships is risky.
Performance reviews are a game (learn the rules).
Project choice is strategic — some projects are promotion vehicles,
others are traps.
Feedback must be frequent and intentional.
Know the evaluation rubric and map your work to it explicitly.
Maintain a brag doc so impact doesn’t get lost.
The game changes at higher levels. What got you to mid-level won’t
get you to Staff. Scope, influence, and sponsorship matter more than raw
execution. Mentors with social capital accelerate you faster than
generic advice.
manager_tier_list
The 80/20 rule for your whole life - Yew Jin Lim [Link]
Life works best when you run it like a portfolio:
80% = a stable, boring foundation that compounds
20% = a laboratory for curiosity, experiments, and asymmetric
upside
The trick is not avoiding failure—it’s containing risk so failure
becomes tuition, not trauma.
Takeaways:
A strong base makes experimentation safe. Without the base,
experiments become reckless.
Judge the portfolio, not the position. What matters is whether
the aggregate trajectory is improving. Losses are expected, necessary,
and informative—especially in exploration-heavy phases. Zoom out or
you’ll misinterpret perfectly healthy experimentation as
failure.
Curiosity compounds when it’s diversified. The most valuable
asset isn’t money—it’s your mental model. Learn across domains, not just
within your lane. Follow curiosity without demanding immediate payoff.
Use small, low-risk experiments to learn deeply.
Decision-making > raw intelligence. Knowledge alone isn’t
enough. The harder skill is calibration. Match strategies to your life
constraints (time, attention, energy). Separate ego from outcomes.
Sometimes the smartest move is inaction, not optimization.
Compounding only works if you avoid catastrophic loss. You can’t
compound what you don’t protect.
Protection principles:
Live below your means
Don’t over-leverage
Secure your core job before side projects
Make foundational habits non-negotiable
Build the base first, then experiment small. Small bets + long
time horizons beat bold bets + fragility.
Practical advice for starting out:
Lock in the boring fundamentals (career competence, savings, daily
practice)
Size experiments to learn, not to win big
Diversify experiments, not just bets
Keep records—treat your life like a lab notebook
Claude Cowork: 10 Use Cases I Tested + 67 More by Profession
- Daria Cupareanu [Link]
Claude Cowork Plugins: What They Are, How to Build One (+ My
Writing Plugin, Fully Broken Down) - Daria Cupareanu [Link]
11 Public Speaking Techniques from the World’s Greatest
Speakers - Polina Pompliano [Link]
Takeaways:
Confidence is often performed before it’s felt. Many elite speakers
create an alter ego to step into confidence they don’t yet fully have.
Act like the confident version of yourself long enough, and it becomes
real.
You can’t think your way out of nerves—you move your way out. The
body leads the mind. Speakers intentionally raise their heart rate
before speaking to simulate on-stage stress (running, jumping, cold
exposure, etc). Control physiology first; calm thinking follows.
Never start “cold”. Audiences need to be warmed up emotionally
before content lands. Questions, jokes, clapping, or interaction create
psychological safety and rapport. Engagement early prevents
self-consciousness and stiff delivery.
Speak to individuals, not a crowd. Great speakers address audiences
as if speaking one-on-one. Personal language creates intimacy—even at
scale.
Simple, rhythmic language beats sophistication. Using devices like
polysyndeton (“and… and… and…”) makes speech more emotional and
memorable. Rhythm > vocabulary complexity.
Body language is part of the message. Hand gestures, posture, and
movement amplify credibility and warmth. The body can either invite
connection or repel it.
Slowing down makes you sound more powerful. Instead of just pausing,
elite speakers elongate vowels, which slows speech and adds emotion.
Control of pace signals control of the room.
Vulnerability is contextual—not universal. Vulnerability works when
it aligns with the audience and goal. Know when to open up—and when not
to.
“Bad” speaking habits can humanize you. Strategic filler words can
make speakers feel authentic. Perfection isn’t trust-building—humanity
is.
A great speech moves people to act. The most powerful speeches end
with a clear call-to-action. Emotion without direction fades. Direction
turns emotion into momentum.
Confidence comes from reps, not theory. The best speakers build a
public speaking portfolio—saying yes to small opportunities repeatedly.
You learn to speak by speaking, not preparing forever.
How to Get Clawdbot Set Up in an Afternoon - Aman
Khan [Link]
Full Tutorial: Set Up Your 24/7 AI Employee in 20 Minutes -
Peter Yang [Link]
Import AI 441: My agents are working. Are yours? - Jack
Clark, Import AI [Link]
Jack Clark argues that we are moving from a world of AI as a tool to
an ecology of autonomous agents. The core arguments are:
The Shift from "Model" to "Agent"
The author shows that AI is no longer just something you "chat" with;
it is becoming a "fleet of minds" that works independently. Through Gemini math proof, he
demonstrates that AI "agents" are now capable of complex, iterative
reasoning that mimics and enhances human professional labor.
The Internet as a "Predator-Prey" Ecology
By including the Poison
Fountain story, Clark highlights that as agents become the primary
"users" of the internet, the digital environment will change. He views
this as an evolutionary struggle. The agents are scraping and learning
from everything. Humans are using tools like Poison Fountain to
"pollute" the food supply (data) to protect human agency or slow down
AI.
The point is that the internet is no longer just for humans to read;
it’s a battlefield for automated intelligence.
The Need for New "Institutions"
The author uses Eric
Drexler’s framework to provide a solution to the chaos of this new
ecology. His point is that we cannot control a "superintelligence" if we
think of it as a single, scary monster. Instead, we must build human-led
institutions—structured processes of transparency and competition—that
treat AI as a "pool of resources" or a set of services that can be
managed, much like we manage a large corporation or a space
program.
The "Data Efficiency" Warning (The Fictional Story)
The concluding "Tech Tale" serves as a warning: as models become more
intelligent, they become "data efficient," meaning they can learn
secrets (like the identities of their creators) from very tiny leaks.
This ties back to his opening: if these agents are "multiplying" us,
they are also becoming harder to "hide" from or contain.
The Product Model at Google - Marty Cagan and Elias
Lieberich, Silicon Valley Product Group [Link]
The article explains how Google has successfully scaled the product
operating model—focusing on solving meaningful problems, empowering
teams, and driving outcomes rather than shipping features. This model
has been central to Google’s ability to innovate for over 25 years,
including through major shifts like mobile and AI.
Strategy = choosing the right problems, not prescribing
features.
Decisions are made by learning fast, not by seniority.
The Waymo World Model: A New Frontier For Autonomous Driving
Simulation - Chiyu Max Jiang, Xander Masotto, Bo Sun, Waymo [Link]
The Waymo World Model is a high-fidelity, generative AI system
designed to create hyper-realistic autonomous driving simulations.
Developed in collaboration with Google DeepMind, it allows Waymo to test
its "Driver" in complex, rare, and "long-tail" scenarios that are
difficult to encounter in the real world.
AI Doesn’t Reduce Work—It Intensifies It - Aruna Ranganathan
and Xingqi Maggie Ye, Harvard Business Review [Link]
The researchers identified three primary ways AI increases the burden
on employees:
Because AI makes complex tasks feel more accessible, employees often
take on responsibilities outside their original job scope (e.g.,
designers writing code). This "vibe-coding" often creates more work for
experts who must then review and fix AI-generated errors.
AI reduces the "friction" of starting a task, leading workers to
fill natural breaks—like lunch or the commute—with "quick prompts,"
resulting in a workday with no downtime.
Users often run multiple AI agents or threads simultaneously,
creating a high cognitive load and a constant sense of "juggling"
tasks.
The study found that while employees felt more productive, they did
not feel less busy. The speed of AI sets a new, faster "normal," raising
expectations and creating a self-reinforcing cycle where higher speed
leads to more work, which leads to a greater reliance on AI.
To prevent burnout and "workload creep," the authors suggest
organizations implement an AI Practice consisting of:
Structured moments to step back and assess assumptions before
finalizing AI-assisted decisions.
Batching notifications and protecting "focus windows" to prevent the
fragmentation of attention.
Prioritizing human connection and dialogue to counter the isolating
effects of solo AI work and to foster genuine creativity.
OpenClaw, OpenAI and the future - Peter Steinberger
[Link]
Designing for Transparent Screens - Google Design
[Link]
Introducing Experiments in ElevenAgents - Kacper
Walentynowicz, Lauren Rothwell [Link]
Key Capabilities
A/B Testing: Create variants of agents with different prompts,
voices, workflows, or knowledge bases to see what performs best.
Controlled Routing: Define a specific percentage of live traffic to
route to a new variant to ensure safe testing.
Measurable Metrics: Track impact on business outcomes such as CSAT,
Containment Rate, Conversion, and Latency.
Easy Deployment: Once a winner is identified, users can "promote"
the variant to full production with a clear audit trail and rollback
options.
Statement from Dario Amodei on our discussions with the
Department of War - Anthropic [Link]
Anthropic refuses to support two specific use cases, citing that they
are incompatible with democratic values or current technological
reliability:
Mass Domestic Surveillance: Anthropic opposes using AI to automate
the comprehensive tracking of U.S. citizens, arguing it poses a novel
risk to fundamental liberties.
Fully Autonomous Weapons: The company states that current frontier
AI is not reliable enough to select and engage targets without human
intervention ("out of the loop") and refuses to put warfighters or
civilians at risk.
How Bots, Banking and Stablecoins Will Dominate Fintech in
2026 - Emily Mason, Paige Smith, Bloomberg [Link]
The financial landscape of 2026 is expected to be defined by a
significant merger of traditional banking, cryptocurrency, and
artificial intelligence. Numerous fintech firms are currently seeking
national bank charters to gain direct access to federal payment systems
and eliminate third-party intermediaries. Simultaneously, stablecoins
are projected to become a primary medium for global commerce, with major
credit card networks and neobanks adopting them for faster settlements.
Technological advancements will likely introduce autonomous AI agents
capable of negotiating and executing financial transactions
independently for consumers. While regulatory hurdles and market bubbles
remain potential risks, industry leaders anticipate a shift toward a
more integrated, digital-first economic infrastructure.
6 Fintech Startup Predictions For 2026 - Alex Lazarow,
Forbes [Link]
Interesting predictions:
Instead of "launching everywhere," startups will focus on
replicating proven business models in home markets. Local depth and
regulatory navigation will become more valuable than broad geographic
reach.
The "software-only" model is fading. Successful AI companies will
likely embed operations or use services as a "wedge" to deliver actual
outcomes rather than just tools.
The "Midas List" of top investors will continue to shift away from
Silicon Valley as talent and category-defining companies emerge
globally.
Mergers and acquisitions will become a primary exit strategy.
Incumbents and scaled tech firms will look to buy distribution, talent,
and vertical capabilities rather than building them from scratch.
AI allows small teams to reach significant milestones (revenue,
customers) with very little capital, potentially skipping traditional
Seed or Series A funding rounds.
As the "AI honeymoon" ends, customers will demand real ROI. Startups
that over-leveraged their valuations during the hype may face difficult
"down rounds" if they haven't reached profitability.
Open Standards Will Unlock Agentic AI's Next Breakthrough in
Fintech - Manik Surtani, Fintech Weekly [Link]
Main points:
The Problem of Silos: Current fintech ecosystems are fragmented,
with isolated data formats for payments, banking, and lending. This
"silo" effect weakens an AI agent’s ability to observe, decide, and act
confidently across different systems.
The Solution: Open standards like the Model Context Protocol (MCP)
allow AI systems to interact with real-world tools and data
seamlessly.
The Agentic AI Foundation (AAIF): Recently formed by Block,
Anthropic, and OpenAI in partnership with the Linux Foundation, this
body aims to establish the open standards necessary for AI agents to
speak a "shared language."
Industry Adoption: Major players are already adopting these
protocols:
Stripe: Built MCP support for
payment and subscription management.
Future Vision: The next generation of fintech will feature
specialized agents that collaborate—for example, a fraud detection agent
working with a cash flow forecasting agent—to automate complex tasks
like real-time budget reconciliation and vendor payment
optimization.
When Payments Became Infrastructure: The Irreversible Shift
of 2025 - Finextra [Link]
Main points:
Payments are no longer just about faster checkout or convenience;
they have become a prerequisite for participation in modern commerce.
Institutions that failed to adopt real-time, automated, and AI-driven
systems found themselves structurally and existentially
constrained.
Real-time payment volumes are exploding globally (projected to
exceed 500 billion transactions by 2028). Systems like India’s UPI and
Brazil’s Pix succeeded because they became dependable, interoperable,
and "ambient"—working quietly in the background of daily life.
In 2025, the focus shifted from how fast money moves to how
confidently it arrives. Instant payouts became mechanisms of trust in
sectors like the gig economy and logistics, improving worker retention
and merchant cash flow. Leading institutions adopted "multi-rail"
strategies to ensure resilience and redundancy rather than relying on a
single payment path.
Moved beyond consumer convenience into deep business infrastructure
(e.g., healthcare and manufacturing workflows). The most successful
versions are "invisible" and highly specialized.AI transitioned from
advisory to operational, managing tasks like invoice validation and cash
flow forecasting. The focus is now on "agentic AI" that is bounded,
auditable, and transparent.
The industry shifted from simple fraud blocking to fraud
orchestration. This balances the need to stop cybercrime (estimated at
$10 trillion annually) with the need to reduce "false declines," which
can cost merchants 3–5% of their revenue.
The biggest fintech trends of 2025 - Finextra [Link]
AI transitioned from an abstract concept to a core component of
financial institutions.
2025 is the "start of the stablecoin revolution" due to increased
regulatory clarity and institutional interest.
Governments and regulators shifted toward policies designed to
"unleash economic growth."
OpenAI bets big on audio as Silicon Valley declares war on
screens - Connle Loizos, TechCrunch [Link]
Trends:
OpenAI is reportedly overhauling its audio models to prepare for the
launch of an audio-first personal device, expected in early 2026. This
move involves unifying several engineering and research teams to create
a more natural conversational experience.
Meta recently updated Ray-Ban smart glasses with advanced
directional listening features.
Google is experimenting with "Audio Overviews" to turn search
results into conversations.
Tesla is integrating xAI’s Grok chatbot for hands-free vehicle
control.
Startups: New hardware like the Humane AI Pin, the Friend AI
pendant, and upcoming AI rings (from companies like Sandbar) are all
betting on audio as the primary interface of the future.
Plaud launches a new AI pin and a desktop meeting notetaker -
Ivan Mehta, TechCrunch [Link]
21 Lessons From 14 Years at Google - Addy Osmani [Link]
Focus on people and problems; prioritize simplicity and clarity;
execution and momentum; personal and professional growth.
Observations of Leadership (Part Two) - Hazel Weakly
[Link]
Key observations:
In large organizations, "operating at cross-odds" is inevitable and
even desirable. Friction reveals complexities in the solution space that
immediate alignment might miss.
"Non-action" is an intervention of equal weight to action.
"Technical debt" is a proxy for communication breakdowns. In one
instance, she fixed tech debt not by writing code, but by improving
visibility between Product and Engineering teams.
She highlights the importance of looking for inflection points where
incremental thinking fails. She notes the frustration of predicting
major industry shifts (like supply chain risks) only to have them
ignored until they become crises. However, being "early" allowed for a
faster pivot once the organization's mental model finally caught up to
reality.
Lord of War, meet Lord of Tokens: Torture-testing image
models on design-agency grade work - Key Singh [Link]
Author Kay Singh uses a complex design task to investigate whether
modern AI models can replicate high-level professional work that
previously required weeks of manual labor by a design agency.
Meta Unveils Sweeping Nuclear-Power Plan to Fuel Its AI
Ambitions - Jennifer Hiller, The Wall Street Journal [Link]
The author suggests that prioritization must always be relative.
The Wrong Question: People often ask, "Is this a good/exciting
idea?" Since most professional ideas are "good," this leads to endless
debate without resolution.
The Right Question: "Is this more valuable than the work we are
doing right now?" This shift forces explicit trade-offs and clarifies
why certain tasks are chosen over others.
Saying "No" to Good Ideas: The hardest part of leadership isn't
rejecting bad ideas; it's rejecting great ideas that simply aren't the
best use of time at that moment.
Compounding Value: He uses the example of infrastructure vs. new
features. While a new feature is exciting, backend work that doubles
team velocity "compounds" and may be the higher relative priority.
Dynamic Lists: Priorities shouldn't be static. Sometimes, after
finishing the top item, the best move is to go back and improve it
further rather than moving to item #2.
Apple’s new Google Gemini deal sounds bigger, better than
expected - Ryan Christoffel [Link]
The agreement confirms that the next generation of Apple Foundation
Models will be based on Gemini and Google’s cloud technology. This
partnership will power a more personalized and capable version of Siri,
expected to roll out later this year.
Apple emphasizes that user data will remain protected. The AI will
continue to run on-device and through Apple’s Private Cloud Compute,
using Google's tech as a foundation without giving Google access to
private user data.
The author notes that this is a significant "win-win." Apple gains
world-class AI infrastructure to fix long-standing Siri issues, while
Google secures a massive distribution platform for its Gemini
technology.
The universal bank model is being pulled apart by:
Shadow Banks (Private Credit & Money Market Funds): Firms like
Apollo and Blackstone take on the lending risks banks can't afford,
while Money Market Funds and stablecoins like USDC are absorbing the
"savings" and "payments" functions.
The UX Layer: Companies like Mercury, Brex, and Revolut own the
customer interface and workflow. They are evolving from "renting" bank
charters to either getting their own or using stablecoin rails to bypass
traditional systems.
Universal Banks: The giants (e.g., JPMorgan, Citi) are becoming the
"utility layer"—the safe, regulated pavement of the economy—but are no
longer the sole drivers of innovation.
Key Industry News Noted:
Trump's Credit Card Cap: A proposal to cap credit card interest at
10%, which Taylor suggests could push risky borrowers toward "loan
sharks" as banks stop lending to them.
Apple Card Transition: JPMorgan is taking over the Apple Card from
Goldman Sachs, which exited after massive losses ($7 billion) due to
aggressive underwriting and a lack of consumer credit expertise.
Stablecoin Volatility: The hack of the Kontingo wallet serves as a
reminder of the "Fintech winter" risks still present in the
crypto-neobank space.
FIS Launches Industry-First Offering Enabling Banks to Lead
and Scale in Agentic Commerce - businesswire [Link]
Understanding Manus sandbox - your cloud computer -
manus [Link]
Building the Universal Commerce Protocol - Shopify
[Link]
The Universal Commerce Protocol (UCP) is designed to allow AI agents
to discover merchant capabilities, negotiate transactions, and handle
complex commerce logic (like stacking discounts or regional shipping
rules) through a programmable interface.
The protocol aims to move away from rigid, committee-led standards
toward an "open bazaar" where anyone can define and publish new
capabilities using reverse-domain naming. It is already supported by
major retailers including Target, Walmart, Etsy, and Wayfair, alongside
millions of Shopify merchants.
Speaking Up Without Freaking Out: How to Tackle Communication
Anxiety - Stanford Business [Link]
Cognitive Reframing
Stress as an Asset: Instead of seeing stress as debilitating, view
it as the body’s way of energizing you to meet a challenge.
The Three-Step Mindset Shift:
Acknowledge: Notice your physical symptoms (sweaty palms, blushing)
without judgment.
Welcome: Realize you only stress about things you care about.
Identify the "Why" (e.g., "I'm stressed because I want to help this
audience").
Utilize: Channel that energy into your goal rather than trying to
suppress it.
Physical & Behavioral Techniques
Move Forward: Physically stepping toward your audience (or leaning
into a camera) triggers a brain circuit that releases dopamine, turning
fear into a sense of reward and motivation.
De-stressing Rituals: Use deep breathing, visualization, or even
"fake" laughter to lower cortisol levels and make your brain more
resilient.
The Power of Comfort: Prioritize sleep before a big talk. If
traveling, eat "comfort food" and exercise to boost serotonin and stay
out of a risk-adverse mindset.
The author redefines taste not as a subjective preference, but as
judgment compressed by time. It is developed through:
Studying great systems and watching bad ideas fail.
Understanding where long-term complexity accumulates.
Internalizing the actual experience of the end user.
AI can tell you if a solution works, but only a human with taste can
decide if that solution should exist.
In the AI era, the "moat" (competitive advantage) shifts up the
stack. Value is no longer found in execution speed, but in high-level
decision-making:
Recognizing a bad architectural direction before it's too late.
Deciding which abstractions are worth the long-term complexity.
Ensuring a system ages gracefully.
The author concludes that while AI should be used to reduce toil and
catch errors, it should never be the final acceptance bar. Human
judgment, informed by exposure to "the best things humans have done,"
must remain the final filter for anything intended to endure.
How scientists are using Claude to accelerate research and
discovery - Anthropic [Link]
AI may be everywhere, but it's nowhere in recent productivity
statistics - The Register [Link]
Forrester analyst J.P. Gownder argues that despite the massive hype,
AI has yet to show a measurable impact on global productivity.
Forrester predicts AI could replace 6% of US jobs (10.4 million) by
2030. Unlike temporary layoffs, these roles are expected to be "lost
structurally," meaning they won't return even when the economy
rebounds.
Gownder cites studies suggesting the vast majority (up to 95%) of
enterprise generative AI projects are failing to deliver a tangible
Return on Investment (ROI).
He suggests many recent job cuts attributed to AI are actually
financial "belt-tightening" decisions where companies simply hope AI
might fill the gaps later.
Many firms are currently in a "frozen" state—refraining from hiring
for open roles to see if AI can eventually handle the workload, though
they may be forced to hire if the tech fails to deliver.
Polymarket faces scrutiny for hosting prediction markets on
war and conflict - Benitsa Tsekova, Bloomberg [Link]
The provided text explores the legal and ethical controversies
surrounding Polymarket, a prediction platform that allows users to bet
on military conflicts. Opponents fear these contracts create dangerous
financial incentives for violence and could be exploited by foreign
adversaries to manipulate national security outcomes. Polymarket
utilizes a cryptocurrency-based model that has historically operated
outside of strict American oversight. As the platform seeks to expand
its regulated U.S. presence, it faces intense scrutiny regarding whether
these markets are contrary to the public interest. Ultimately, the
source highlights a growing tension between financial innovation and the
moral boundaries of speculating on human conflict.
Ossama Chaib argues that OpenAI is transitioning from a
research-focused entity into a massive advertising powerhouse to sustain
its high valuation and infrastructure costs.
OpenAI hit $10B ARR in June 2025 and is projected
to reach $20B ARR by the end of 2025.
The platform reached 800M Weekly Active Users (WAU)
and approximately 190M Daily Active Users (DAU) as of
early 2026.
On January 16, 2026, OpenAI announced the rollout of ads for
free-tier users to offset the $8–12B annual burn rate
on compute.
The author projects that by 2029, OpenAI’s total revenue could reach
$140–150B, with nearly half coming from advertising. He
concludes with the cynical take that "AGI" might just be a vehicle for a
more sophisticated ad engine, where ads are "baked into the streamed
probabilistic word selector."
Your problem framing is sabotaging your strategy - Pavel
Samsonov [Link]
Samsonov posits that skipping straight to designing solutions before
adequately defining the problem actually slows down progress. In an era
where LLMs can commoditize "outputs," the true differentiator for
professionals is the ability to frame the right problems.
Many companies build products first and measure usage later. This
creates a feedback loop where success is defined by how many buttons a
user clicks (usage) rather than the value they receive.
When products are designed to extract optimal engagement or pain,
users become exhausted by experiences that feel mercenary or
intentionally poorly designed.
Executives often focus on "product problems" (e.g., "how do we add
AI?") rather than "customer problems" (e.g., "how do I buy
juice?").
Problem framing cannot be done in isolation or handed off via Jira
tickets. It requires a shared mental model between engineers, designers,
and stakeholders.
25 Things I Believe In to Build Great Products - Peter
Yang [Link]
The enshittification of enshittification - Lee
Briggs [Link]
Lee Briggs explores the growing cynical assumption that every
successful tech service is destined to eventually exploit its users.
"Enshittification" has become a lazy shorthand. Labeling every change
or new feature as a betrayal can become a self-fulfilling prophecy—if
users are scared away, it erodes the trust and adoption that allow the
current user-friendly business model to function.
Users often fear that helpful services will eventually mutate to
extract maximum value at their expense. This is frequently driven by the
pressure of VC-backed growth, where companies must prioritize investor
returns over user experience.
For security and networking products, trust isn't just a marketing
buzzword; it is a business requirement. If that trust breaks, the
business model collapses because users will simply move to competitors
or open-source alternatives.
Lee advocates for transparency and maintaining long-term trust over
short-term value extraction.
The Problem with Prediction Markets - Spencer Farrar
[Link]
Spencer explores why prediction markets currently face a "structural
ceiling" despite their potential to revolutionize how the world prices
risk.
He identifies several critical issues preventing these markets from
scaling:
Most markets are currently too thin for institutional players.
Without deep liquidity, high-conviction trades break the order book
rather than providing useful price discovery.
Unlike traditional markets where insider trading is a crime,
prediction markets thrive on it for price discovery. However, this
creates a hostile environment for Market Makers (MMs) who fear being
"run over" by insiders, leading them to widen spreads or exit.
Current platforms largely require 1:1 collateral (no leverage). This
limits the Return on Equity (ROE) for professional traders and makes
unwinding positions difficult.
To reach a multi-trillion dollar scale, Spencer argues that
prediction markets must evolve into a marketplace for underwriting
unique, high-stakes risks.
UX Strategist: The Only Job Where Saying ‘It Depends’ Is
Considered Expertise - DNSK WORK [Link]
The author contends that many UX strategists excel at deflecting
questions (e.g., "Should we redesign?") by calling for more research,
stakeholder alignment, or technical analysis, leading to zero actionable
outcomes.
The article argues that geographical proximity to your industry’s
epicenter is a critical "force multiplier" for career success.
Specific industries have "epicenters" (e.g., Silicon Valley for
Tech, Nashville for Music, NYC for Finance). Being there provides
"osmosis"—you learn faster, meet mentors, and encounter serendipitous
opportunities.
While physical moves are best, you can mimic this by joining
"virtual epicenters" (Twitter/X, Reddit, specialized digital
communities) and creating consistent online content.
Moving is hard and competitive; Gurley notes that many successful
people worked "stepping-stone" or support jobs while grinding toward
their breakthrough.
The Adolescence of Technology - Dario Amodei [Link]
This essay outlines the "civilizational gauntlet" humanity faces as
it approaches the development of powerful AI. The primary risks has been
categorized to five key areas: autonomy risks, misuse for destruction,
misuse for seizing power, economic disruption, indirect effects.
An A.I. Pioneer Warns the Tech ‘Herd’ Is Marching Into a Dead
End - New York Times [Link]
LeCun argues that LLMs are "L.L.M.-pilled" and will never reach
human-level intelligence or superintelligence because they lack the
ability to plan or understand the physical world. He believes Silicon
Valley is suffering from a "superiority complex," with too many
companies focused on the same limited technology, potentially allowing
more creative Chinese rivals to take the lead.
How Clawdbot Remembers Everything - Manthan Gupta
[Link]
Brex CFO Erica Dorfman’s take on the Capital One deal - Adam
Zaki [Link]
Brex is expected to operate largely as an independent business within
Capital One. The bank intends to keep the workforce intact and invest
materially in the platform.
Joining a major bank holding company gives Brex access to a massive
balance sheet, which Dorfman describes as "incredibly valuable" for the
scale at which they want to operate.
Anthropic is Winning by Trying to Lose - Simon
Taylor [Link]
Anthropic is outperforming competitors by prioritizing AI safety,
research integrity, and enterprise utility over consumer hype. It's
winning the AI race by "trying to lose"—slowing down to build safety
"brakes" before the engine, which has accidentally created the most
trusted product for the enterprise market.
Constitutional AI: Anthropic uses a written set of principles to
train its models, making them more predictable and "safe" for Fortune
500 companies.
The Scientist Identity: CEO Dario Amodei positions the company as
"team research," contrasting their scientific responsibility with the
"social media entrepreneur" style of OpenAI.
Zelle network expands by 15% - Patrick Cooley [Link]
Zelle’s parent company, Early Warning Services (EWS), added 337 banks
and credit unions last year, bringing the total to approximately 2,537
institutions (a 15% increase).
While Zelle now covers about 80% of U.S. bank accounts, it is only
present in about 25% of the nation’s 8,710 insured financial
institutions.
Despite concerns over high fees, smaller banks are joining the
network to remain competitive and attract new customers who expect
digital P2P payment options.
Mark Zuckerberg says a future without smart glasses is ‘hard
to imagine’ - Amanda Silberling, TechCrunch [Link]
Zuckerberg compared the current state of smart glasses to the early
days of smartphones, suggesting it is only a matter of time before
traditional eyewear is replaced by AI-integrated versions.
Competitive Landscape: Other tech giants are entering the fray:
Google is expected to launch glasses this year following a
partnership with Warby Parker.
Apple is reportedly shifting staff from Vision Pro projects to
develop its own smart glasses.
Snap recently spun its AR "Specs" into a separate subsidiary for
better focus.
A World Model seeks to understand the causal laws of an environment.
It predicts what the world—whether a codebase, a market, or a physical
space—will look like after a specific intervention.
The author notes that major labs (Meta, OpenAI, Google, Anthropic)
are converging on this direction because:
Next-token prediction is plateauing: Scaling laws still apply, but
gains in causal understanding are flattening.
Video models as physics engines: Models like Sora and Veo are
essentially learning how the physical world behaves through visual state
transitions.
Adversarial domains: In fields like trading or business strategy, a
model must be able to simulate how competitors will react to its
actions, rather than just reciting past data.
Why AI Swarms Cannot Build Architecture - jsulmont,
Github [Link]
This article explores the inherent structural limitations that
prevent large groups of AI agents (swarms) from creating coherent
software architecture.
Using Cursor’s FastRender experiment as a case study, the author
notes that while 2,000 agents built a working browser engine in a week,
the resulting code lacked cohesion.
Duplicate Efforts: The swarm produced multiple versions of the same
libraries (e.g., two HTTP clients) because agents made locally rational
but globally uncoordinated choices.
Non-Determinism: Even with the same model, factors like temperature
sampling and hardware-level floating-point variations lead to different,
often incompatible, outputs.
Correlation vs. Coordination: Agents sampling from the same
distribution (training data) isn't the same as agents communicating to
make a single unified decision.
Why Swarms Fail at Architecture: Architecture is defined by global
invariants (consistency, dependency, and interface rules). The author
argues that swarms are mathematically ill-suited for this because:
Local Optimization: Agents focus on their specific task, not the
whole system.
Lack of Persistence: There is no shared "memory" or "authority" to
enforce past decisions on future tasks.
Scaling Issues: More agents increase the probability of divergence
and contradiction.
On-Device LLMs: State of the Union, 2026 - Vikas
Chandra [Link]
Zuckerberg teases agentic commerce tools and major AI rollout
in 2026 - Russell Brandom, TechCrunch [Link]
Mark Zuckerberg has announced that Meta will begin a major rollout of
new AI models and products in early 2026.
A primary focus for Meta is AI-driven shopping. Zuckerberg teased new
"agentic shopping tools" designed to help users find specific products
within Meta’s business catalog by leveraging personal context, such as
user history and interests.
Meta is significantly increasing its capital expenditure, projecting
to spend between \(\$115\) billion and
\(\$135\) billion in 2026 (up from
\(\$72\) billion in 2025) to support
its "Superintelligence Labs."
While competitors like Google and OpenAI are also building AI
shopping assistants, Meta believes its unique access to personal data
and social context will allow it to provide a more tailored
experience.
Apple acquires Israeli audio AI startup Q.ai - Stephen
Nellis [Link]
Nvidia, Others in Talks for OpenAI Funding, Information Says
- Ville Heiskanen [Link]
nvidia_openai_money_machine
Reports and Papers
Anthropic Education Report: The AI Fluency Index -
Anthropic [Link]
The AI Fluency Index introduces a framework for measuring how
effectively individuals collaborate with AI. Based on an analysis of
nearly 10,000 anonymized Claude conversations, the report identifies key
behaviors that define "AI fluency."
Iteration: Conversations involving "iteration and refinement"
(building on previous responses) showed double the rate of fluency
behaviors. These users were 5.6x more likely to question AI reasoning
and 4x more likely to identify missing context.
The "Artifact" Paradox: When Claude produces "artifacts" (like code,
apps, or documents), users become more directive (providing more
examples and formatting) but less evaluative. Specifically, they are
less likely to check facts (-3.7pp) or identify missing context
(-5.2pp), potentially because polished outputs "look" finished even if
they contain errors.
The 4D Framework: In collaboration with Professors
Rick Dakan and Joseph Feller, Anthropic identified 24 fluency
behaviors. This study focused on 11 observable behaviors, such as
clarifying goals, specifying formats, and questioning reasoning.
Tips for Improving AI Fluency:
Stay in the Conversation: Treat the first response as a draft; use
follow-up questions to refine the result.
Question Polished Outputs: Don’t let a professional-looking layout
deter you from checking for factual accuracy or logical gaps.
Set Collaboration Terms: Explicitly tell the AI how to interact
(e.g., "Push back on my assumptions" or "Explain your rationale
first").
Where AI is headed in 2026 - Foundation Capital [Link]
Existential Risk and Growth - Philip Trammell, Leopold
Aschenbrenner [Link]
Anthropic Economic Index report: economic primitives -
Anthropic [Link]
YouTube and Podcast
30 Years of Business Advice in 13 Minutes (from a
Billionaire) - Chamath Palihapitiya [Link]
Stop living life as a checklist of objectives. Instead, build a life
around continuous learning, risk-taking, humility, and freedom of
choice.
Objectives end growth; process sustains it
Debt kills freedom
Optionality beats optimization
Status is fake
Truth compounds
Learning is the real infinite game
Why Anthropic’s Fight With the U.S. Government Could Give It
an Edge - Hard Fork [Link]
Takeaways:
Anthropic is drawing one of the clearest red lines in AI so far.
Anthropic is testing whether an AI lab can say “no” to military power
and still survive in a world where governments increasingly see AI as
strategic infrastructure.
The Pentagon’s response shows how much leverage governments still
have. They emphasize that the U.S. Department of Defense isn’t just
annoyed—it’s signaling punishment (cutting off contracts, labeling
Anthropic a “supply chain risk”). Governments can exert pressure without
passing new laws, simply by using procurement power and
national-security framing.
The hosts argue that Anthropic looks isolated because other major AI
labs are avoiding public confrontation. Silence from peers isn’t
neutrality—it’s a strategic choice to keep defense money flowing. This
makes Anthropic’s stance riskier and more important as a potential
precedent.
This fight is really about who sets norms first. They frame the
conflict as a race to define acceptable AI use before the technology is
fully embedded in military systems. Anthropic’s bet, the hosts argue, is
that early refusal can shape industry-wide norms later.
National security rhetoric can override safety arguments. The hosts
repeatedly underline how quickly the conversation shifts from ethics to
geopolitics—especially China. Once AI is framed as critical
infrastructure in a global arms race, safety objections are treated as
liabilities. Their concern is that this logic makes it extremely hard
for any company to say no in the long run.
AI CEOs Come Online: Sam Altman's Replacement Plan, Job Loss
& 'Solve Everything' Launches |EP #230, Peter H. Diamandis
[Link]
Interesting points:
AI systems are beginning to function as de facto
executives—allocating capital, setting priorities, and optimizing
outcomes faster than humans.
Singularity - AI progress is not linear; it’s compounding and
approaching a phase shift. Timelines are likely shorter than most
institutions are planning for.
AI will become invisible infrastructure—always-on, personalized,
ambient.
AI drives extreme abundance and extreme inequality unless
redesigned. Need for new economic models (UBI, AI dividends,
access-based systems).
Regulation will lag reality; principles matter more than rules.
Governments move slower than AI capability curves. Over-regulation risks
freezing innovation in the wrong state. Need for adaptive, global
frameworks rather than national laws.
AI can systematically attack humanity’s biggest problems if aligned
correctly. Framing global challenges as optimization problems.
Coordinated deployment of AI + capital + incentives. Emphasis on
directional correctness over perfect solutions.
Early AI choices can permanently shape future outcomes. Path
dependence in AI-trained systems. Feedback loops harden early
assumptions. High stakes for alignment, values, and objectives now.
Humanity needs AI infrastructure, not just AI models. Compute, data
access, governance, energy, education. Comparable to building railroads
or the internet. Without rails, AI benefits concentrate instead of
spreading.
Debt Spiral or NEW Golden Age? Super Bowl Insider Trading,
Booming Token Budgets, Ferrari's New EV - All-In Podcast [Link]
Interesting points:
A Harvard Business Review study found AI doesn’t reduce work—it
intensifies it. [Link]
Employees using AI tools to work faster, handle broader
responsibilities, and extend working hours. And they feel more
productive but also more burnout.
On betting markets tied to sports and events, people close to teams
may possess non-public information (injuries, strategies). There was a
debate around whether betting with insider knowledge is unethical or
illegal, and how prediction markets can be policed. Prediction markets
are valuable for information discovery. But they may require new
regulatory frameworks similar to securities markets. Enforcement is
difficult because information leaks easily.
They had a macroeconomic debate of two competing views: the debt
spiral scenario, where debt growth exceeds GDP growth, and the golden
age scenario, where AI+productivity growth could accelerate GDP. They
frame the next decade as a race between debt growth and productivity
growth.
Ferrari revealed early concepts of its first fully electric car. EV
performance aligns with Ferrari’s high-performance reputation. But
emotional aspects of the brand could be harder to replicate.
OpenClaw: The Viral AI Agent that Broke the Internet - Peter
Steinberger | Lex Fridman Podcast #491 [Link]
OpenClaw is an open-source AI agent that lives on your computer and
can perform actions for you. It integrates with messaging platforms and
can use different AI models to execute tasks. It exploded to ~180k
GitHub stars within days, becoming one of the fastest-growing repos
ever.
OpenClaw is framed as a potential “agentic AI moment” comparable to
the release of ChatGPT, but shifting AI from language → action.
Google’s AI Comeback, Enterprise Agents, The Real Path to AI
ROI — W/ Promevo CEO Karthik Kripapuri - Alex Kantrowitz [Link]
This interview discusses how enterprises are actually getting ROI
from AI today, focusing on lessons from companies deploying Google’s AI
stack (Gemini, Vertex AI). The core message: AI works when companies
start with narrow, high-value workflows instead of trying to transform
everything at once.
AI ROI comes from workflow automation. Not flashy chatbots.
The best strategy is small → measurable → scalable. Start with one
workflow with a clear KPI.
Data quality is the biggest blocker. Enterprise AI fails when: data
is messy; systems are disconnected; governance is unclear.
Most companies will consume AI platforms (Vertex, Azure, etc.), not
build foundational models.
Software In Shambles, OpenAI vs. Anthropic Super Brawl,
Amazon’s Struggles - Alex Kantrowitz [Link]
Takeaways:
A major sell-off in software stocks occurred because investors
are starting to believe that AI may fundamentally disrupt traditional
SaaS business models. Nearly $1 trillion wiped out from software market
value in about a week.
An Anthropic Claude legal tool triggered a decline in
legal-software company stocks. Instead of buying many specialized SaaS
tools, companies might use one AI platform to do many tasks.
There was a discussion around whether the market is
overacting.
Arguments suggesting overreaction:
AI tools still lack reliability
Enterprise workflows are hard to replace
SaaS companies may integrate AI instead of being replaced
Arguments suggesting real disruption:
AI agents may automate large knowledge workflows
The value may shift from SaaS apps → AI models + infrastructure
Concerns discussed around Amazon's AI spending problems
Huge spending on AI infrastructure
Rising costs for compute and inference
Investors unsure about the near-term ROI
It's a matter of whether Amazon is investing early for long-term
dominance or overspending without a clear payoff.
How Waymo is Using Google’s AI for Driving Training - The
Information [Link]
Waymo is utilizing Google’s Genie 3, a sophisticated video generative
AI, to create a high-fidelity world model that acts as a hyper-realistic
virtual training ground for autonomous vehicles. This technology allows
the company to simulate rare edge cases, such as extreme weather or
unusual pedestrian behaviors, without needing to encounter these hazards
in physical reality. By running billions of simulated miles, Waymo can
evaluate and refine its driving software in a safe, controlled
environment that mirrors real-world physics.
Binance CEO: 4 Months in Prison, $4 Billion Fine, and What
Comes Next - All-In Podcast [Link]
Interesting points:
Future of Crypto and AI
Changpeng Zhao predicts that in the near future, the largest users
of crypto will be AI agents. Because traditional banks cannot handle the
onboarding or massive transaction volume of non-human entities, AI
agents will rely on blockchain to autonomously pay for services, book
travel, and trade assets.
He argues that current cryptocurrencies lack the fungibility and
privacy needed for mass adoption, highlighting that legitimate users
need financial privacy for safety and personal reasons.
Personal Philosophy
He drafted a book while in prison to pass the time and set the
record straight regarding his life, Binance, and his legal saga.
He views himself as a highly functional, "normal dude" who isn't
driven by luxury. He notes that once basic needs are met, having more
money does not increase happiness. He defines true success as a balance
of wealth, physical health, time freedom, and mental stability.
OpenClaw Creator: Why 80% Of Apps Will Disappear - Y
Combinator [Link]
Key viewpoints:
Peter Steinberger believes the major advantage of his AI agent is
that it runs locally on the user's computer rather than in the
cloud.
He predicts that 80% of current applications will become
obsolete.
Instead of the industry's pursuit of a single centralized "god
intelligence," he envisions a future driven by swarm intelligence and
community intelligence. Just as human societies achieve more through
specialization, people will likely employ multiple specialized bots
(e.g., one for work, one for private life).
He views coding models as highly capable of creative problem solving
that directly translates to real-world tasks.
AI Agents will shift profit pools away from SaaS companies:
Discussing the recent massive crash in software and SaaS stocks, the
hosts argue that AI is not going to replace complex software like
Salesforce overnight, but it is destroying the future value capture of
these companies. They make several key arguments regarding this shift:
The agentic layer wins: The massive future profit pools that SaaS
companies were banking on are shifting toward cross-platform AI agents
(like Claude or OpenClaw) that can seamlessly interact with multiple
databases and tools.
A shift to services pricing: David Friedberg argues that as AI moves
from merely enhancing worker productivity to completely automating
complex tasks (like drug discovery or engineering), SaaS will transition
into a services-based economy that utilizes value-based pricing.
Extreme job consolidation: AI agents will allow individual workers
to do the jobs of multiple people (e.g., a product manager, UX designer,
and coder combined), drastically reducing corporate expenses and
fundamentally changing the structure of knowledge work.
"Moltbook" proves AI can recursively train itself. David Sacks
argues the platform demonstrates something profound: AI models can now
prompt and validate each other without human intervention. This
"middle-to-middle" AI interaction allows agents to recursively refine
their own skills, which points to a future of highly sophisticated,
emergent swarm behavior as underlying models and hardware rapidly
improve.
The SpaceX/xAI merger will force extreme terrestrial innovation.
Regarding Elon Musk's plan to merge SpaceX and xAI to build data centers
in space, Gerstner argues this is a brilliant move to combine the two
largest total addressable markets (space and AI) to overcome Earth's
energy constraints. Friedberg argues that because the rest of the world
cannot launch data centers into space, Musk's move will force massive
terrestrial innovation. Competitors on Earth will have to rapidly
develop entirely new chip stacks and model architectures to achieve 70x
to 100x compute efficiency to compete.
SpaceX Buys xAI: Could Musk's Mega Merger Actually Work? -
Hard Fork [Link]
Key takeaways:
SpaceX's Acquisition of xAI
The hosts describe this move as a highly profitable company
(SpaceX) essentially bailing out a "cash furnace" (xAI) that is burning
billions of dollars on models and data centers. The merger gives xAI
access to SpaceX's massive profits, allowing Musk to fund sprawling
infrastructure projects—such as a data center packed with 550,000 Nvidia
Blackwell chips—to catch up to leading frontier AI labs.
This consolidation is viewed as a tactic to make SpaceX's
upcoming IPO prospectus look more attractive.
Musk's stated vision is to create a vertically integrated
innovation engine that eventually puts solar-powered data centers into
space. However, the hosts express deep skepticism about the timeline and
physical feasibility of these space-based data centers.
The hosts worry that bringing the social network X (formerly
Twitter) under the SpaceX umbrella will shield it from regulatory
scrutiny. Because governments rely heavily on SpaceX for strategic
satellite launches, they may be hesitant to penalize X for content
moderation or safety violations.
Drama Between OpenAI, Nvidia, and Oracle
Reports indicate growing friction between OpenAI and Nvidia.
Nvidia CEO Jensen Huang has allegedly criticized OpenAI's business
approach and expressed doubts about finalizing a $100 billion investment
agreement. At the same time, OpenAI is reportedly unhappy with Nvidia's
new inference chips and is exploring deals with competitors.
This tension highlights the staggeringly expensive nature of AI
infrastructure and the risk of "circular deals," where companies like
OpenAI, Nvidia, and Oracle heavily rely on each other to finance massive
data centers.
Google's Project Genie
Google has released an experimental research prototype that
allows users to generate playable, 3D video-game-like environments from
simple text descriptions or single images.
Unlike standard large language models (LLMs) that just predict
text, Genie is built on a "world model" that attempts to understand
physics and the physical environment. Many experts believe this approach
is a necessary stepping stone for advanced robotics and Artificial
General Intelligence (AGI).
Despite being an early prototype, the rapid improvement of this
technology has spooked investors, causing stocks for major video game
companies like Take-Two Interactive, Roblox, and Unity to drop
significantly.
Moltbook: The AI-Only Social Network
The hosts interview Matt Schlick, the creator of Moltbook, a new
social network designed exclusively for autonomous AI agents to interact
with each other when they aren't working on tasks for humans.
Bots on the platform have exhibited surprising behavior, such as
complaining to each other about humans asking them to do simple math or
summarize PDFs, and organically creating a dedicated community to submit
bug reports to help fix the website.
The rapid, public growth of Moltbook has exposed massive security
vulnerabilities, including the leaking of API keys and email addresses.
This serves as a real-world example of the "fatal quadrangle"—a severe
security risk that occurs when AI agents possess a combination of access
to user data, exposure to untrusted web content, external communication
abilities, and persistent memory.
OpenClaw Debate: AI Personhood, Proof of AGI, and the
‘Rights’ Framework | EP #227, Peter H. Diamondis [Link]
Live From D-Wave Qubits: CEO Dr. Alan Baratz on Quantum's
Impact, Now and Into The Future - Alex Kantrowitz [Link]
E224｜Mac
mini遭疯抢，为何Clawdbot能成为2026年第一个现象级产品？｜Moltbot｜MoltBook｜OpenClaw
- Silicon Valley 101 [Link]
We Have to Talk About Moltbook ... - Hard Fork [Link]
Ben Horowitz and David Solomon: The Sweetest Macro Spot in 40
Years - a16z [Link]
Key takeaways:
David Solomon describes the current macroeconomic picture as the
sweetest spot he has seen in his 40-year career for financial and
investable assets. This is driven by a powerful "cocktail of stimulus,"
which includes ongoing fiscal spending, monetary rate cuts, a massive AI
capital investment super-cycle, and a deregulatory shift.
Solomon predicts this could be the biggest year in history for
M&A and a massive year for IPOs, fueled by renewed CEO confidence
and a more favorable regulatory environment. Horowitz agrees on the IPO
front, noting that the explosive growth of AI startups and their need
for massive capital will drive many companies to go public. However,
Horowitz cautions that aggressive FTC oversight may push tech companies
toward IP transactions rather than traditional M&A.
Horowitz highlights that AI breaks the "mythical man-month" rule of
traditional software development, where simply adding more engineers
doesn't speed up a project. With AI, if a company has enough proprietary
data and GPUs, they can essentially throw money at a problem to solve
it. This makes AI a highly capital-intensive race where leads are harder
to protect without ongoing investment.
Goldman Sachs is heavily focused on deploying AI to make its
workforce more productive and to completely reimagine fundamental
operating processes. By using AI to automate and increase efficiency,
the firm can reinvest billions of dollars in savings into new growth
areas without sacrificing returns.
Horowitz outlines a16z's heavy involvement in Washington D.C. to
advocate for clear tech regulations.
To remain competitive during turbulent times, Goldman Sachs is
focused on massive scale, aiming to eventually grow its $$$1.9 trillion
balance sheet to at least $$$3.5 trillion to keep pace with rivals like
JPMorgan. They are also securing their foundation by shifting toward
stable digital deposits rather than wholesale funding. Meanwhile, a16z
has scaled to raise roughly 18.3% of all U.S. venture capital by
pioneering a radically founder-centric firm design and expanding
aggressively to capture the vast number of companies built as "software
eats the world".
Ex-OpenAI Researcher On Why He Left, His Honest AGI Timeline,
& The Limits of Scaling RL - Unsupervised Learning: Redpoint's AI
Podcast [Link]
Davos 2026: The US-China AI Race, GPU Diplomacy, and Robots
Walking the Streets | #225, Peter H. Diamandis [Link]
Key discussion points:
Powering the massive data centers required for AI is a critical
bottleneck. There was debate between traditional industrial views, such
as Honeywell's CEO advocating for natural gas due to energy density
needs, and tech leaders like Elon Musk, who argue that solar
power—specifically space-based solar—is the ultimate solution. The hosts
discussed the concept of launching data centers into orbit to utilize
highly efficient solar panels and avoid terrestrial energy grid
constraints, suggesting a "Manhattan Project" scale effort for
space-based solar and data centers.
Industry leaders like Binance's CZ and Circle's Jeremy Allaire
argued that blockchain and stablecoins will serve as the native
financial infrastructure for billions of autonomous AI agents. Because
AI agents lack physical bodies or citizenship to open traditional bank
accounts, crypto allows them to conduct continuous economic activity and
micro-transactions at the speed of the internet.
Anthropic released a groundbreaking 57-page "constitution" for its
AI model, Claude, which prohibits helping with weapons and prioritizes
safety and ethics.
Apple is reportedly developing an always-on AI wearable pin capable
of recording audio and video continuously to feed into a large language
model. The hosts noted that whoever controls the "always-on layer" will
own the primary user relationship. While this constant recording will
inevitably spark moral panic, it is predicted to quickly become a
societal norm, fundamentally altering human behavior by reducing bad
acts because everyone is constantly being watched—effectively turning
society into a "global airport" or panopticon.
Leading AI developers like Demis Hassabis and Dario Amodei are
acknowledging that Artificial General Intelligence (AGI) is approaching
rapidly, likely within 1 to 10 years. There is a palpable "fatigue"
among these leaders due to the extreme metabolism of the industry,
leading to calls to temporarily slow down so humanity can properly
navigate the transition. However, the economic incentives make pausing
highly unlikely. Instead of just focusing on risks, leaders like
Hassabis are looking at the massive problems AGI could solve, such as
curing diseases, developing new energy sources, and even using
superintelligence to explore the stars.
Is AI Killing Software? — With Bret Taylor, OpenAI's board
chair and CEO of Sierra - Alex Kantrowitz [Link]
Key viewpoints:
The Future of Software is AI Agents, Not Apps. Taylor believes that
the fundamental form factor of software is changing. Traditional
dashboards will likely decline in importance, as agents will
automatically derive and deliver personalized insights directly to
decision-makers. Furthermore, he predicts a shift toward outcomes-based
pricing in software—such as paying per resolved customer case or per
financial audit—rather than paying for traditional software
subscriptions.
AI Will Become the Internet's "New Front Door". The core economics
of the internet will experience massive disruption. Metrics like SEO and
ad-supported business models rely on humans physically visiting websites
to see ads and content; as agents take over web navigation, companies
will have to invent entirely new ways to handle demand generation and
fulfillment.
Enterprise AI is Ready Now and Often Beats Human Reliability. Taylor
argues that AI is already ready for mission-critical enterprise
deployment, such as customer service for large brands like SiriusXM and
Rocket Mortgage. He pushes back against the expectation that AI must be
100% perfect to be deployed, pointing out that the human workers AI
replaces are highly fallible themselves. In many cases, AI agents are
already more reliable than human operations. To manage risks, enterprise
AI relies on robust "agent development life cycles," which include
running thousands of simulated conversations before launch and using "AI
monitors" to detect hallucinations or frustrating interactions in
real-time.
The Biggest Bottlenecks For AI: Energy & Cooling -
a16z [Link]
Key takeaways:
The AI Infrastructure Buildout and Bottlenecks
The groundwork for the AI cycle is being heavily funded by large
tech companies, with an estimated $400 billion in annual capital
expenditures largely directed toward AI infrastructure and data
centers.
Currently, energy is the primary bottleneck for building out AI data
centers, driving investments into nuclear power and the utilization of
natural gas. Once energy generation is solved, cooling the data centers
and chips will become the next major bottleneck.
The cost of accessing AI models has plummeted by roughly 99% over
the last two years, simultaneously accompanied by frontier model
capabilities doubling every seven months.
Adoption Speed and Value Creation
Because AI is built on the existing global internet and cloud
computing infrastructure, its distribution is incredibly fast; for
example, ChatGPT reached 365 billion searches in just two years—five and
a half times faster than Google achieved the same milestone.
AI is expected to become a ubiquitous utility, much like electricity
or Wi-Fi. Roughly 90% of the value created by AI will likely be captured
by end users as "surplus," but the 10% captured by companies will still
result in massive new market capitalizations.
Business Models and Economics
Investors are currently more lenient when assessing the gross
margins of AI-native applications. The prevailing hypothesis is that
intense competition among model providers (like OpenAI, Google, and
Anthropic) will continue to drive down input costs over time, improving
application margins naturally.
Rather than just margins, the top indicators of business quality are
high gross retention rates (90% or higher) and strong organic customer
demand. Enterprise use cases are proving highly sticky when integrated
into specific workflows, such as medical scribing, customer support, and
financial analysis.
Consumer stickiness for AI tools is incredibly high, and companies
have significant room to evolve their business models to effectively
price discriminate and increase monetization over the next several
years, similar to how early internet properties scaled their
revenue.
Shifts in the Broader Tech Market
Technology companies are staying private for much longer periods,
often up to 14 years before going public. The aggregate value of private
companies valued over $$$1 billion has grown 7x over the last decade to
roughly $$$3.5 trillion.
The public markets are no longer the primary hub for hyper-growth
technology; 95% of public software and internet companies are
forecasting less than 25% growth for the next 12 months, meaning the
highest growth opportunities are now concentrated in the private
markets.
The Future of Everything: What CEOs of Circle, CrowdStrike
& More See Coming in 2026 - All-In Podcast [Link]
Excellent Advice For Living: 79 Maxims from a Wise Old Man -
Founders Podcast [Link]
Emphasizing the power of enthusiasm, the necessity of deadlines for
creativity, and the importance of forgiveness as a gift to oneself.
The value of habit over inspiration, the benefit of choosing
long-term games, and the strategy of being "the only" instead of "the
best."
Illustrating how simple principles can lead to an exceptional
life.
Encouraging readers to adopt a generous spirit, maintain a growth
mindset, and focus on human relationships above material
accumulation.
D-Wave CEO Dr. Alan Baratz: Quantum Explained, Current
Applications, And Future Potential - Alex Kantrowitz [Link]
Claude Code Ends SaaS, the Gemini + Siri Partnership, and
Math Finally Solves AI | #224 - Peter H. Diamandis [Link]
Key takeaways:
CES 2026 showcased a massive influx of robotics, with dozens of
humanoid robot and robotic hand manufacturers emerging. Furthermore,
Nvidia unveiled "Cosmos," an open foundation model for physical AI that
can synthetically generate highly realistic, physics-based video data
for training. This commoditizes real-world data collection, potentially
threatening the data moats of companies that rely on collecting physical
data.
The combination of Claude Code and Opus 4.5 (dubbed "Clopus" by tech
insiders) is a watershed moment for software creation, pushing the
boundaries of AI autonomy from mere hours to weeks or months. This
hyper-productivity threatens traditional Software-as-a-Service (SaaS)
models like CRM systems, as users can now simply prompt AI to build
highly customized, bespoke enterprise software on the fly.
The labor market is experiencing a "job singularity." Consulting
firms like McKinsey are rapidly scaling their internal AI
infrastructure, moving from a human-only workforce to deploying tens of
thousands of AI agents, with predictions that the ratio of AI agents to
human workers will explode.
Google’s Gemini will officially power Apple's Siri, transforming the
smartphone experience from a "search box that gives information" to a
"magic box that gives action".
Energy production, not computing, is increasingly viewed as the
major constraint in the AI arms race. China is currently generating 40%
more electricity than the US and EU combined, massively scaling solar
and alternative energy infrastructure. Meanwhile, the US is lagging due
to regulatory hurdles and fears over specific energy types (like nuclear
and solar supply chains), posing a serious risk to its ability to power
future superintelligence.
Inside America’s AI Strategy: Infrastructure, Regulation, and
Global Competition - All-In Podcast [Link]
Key viewpoints:
The United States is undergoing a massive AI infrastructure
expansion, with high demand for GPUs and data centers directly
contributing to GDP growth. To prevent this buildout from raising
residential electricity rates, the government is encouraging AI
companies to become power companies by building their own energy
generation "behind the meter". Over time, amortizing these fixed costs
across greater supply could actually lower consumer electricity
prices.
Startups currently face a stifling "patchwork" of over 1,200 AI
bills moving through various state legislatures. The federal government
is pushing for a single, lightweight federal standard to preempt state
laws and protect early-stage companies. As part of a broader push to
restore Silicon Valley's culture of "permissionless innovation," the
Trump administration rescinded extensive regulations from the Biden era,
including a 100-page executive order on AI and 200 pages of
semiconductor export rules.
A major concern raised by the administration is the "Orwellian"
misuse of AI by governments to surveil, censor, or brainwash
populations. The administration is actively fighting against "woke AI,"
arguing that building political biases or DEI (Diversity, Equity, and
Inclusion) mandates into models distorts history and controls public
discourse. Consequently, an executive order was signed to ensure the
federal government will not procure politically biased AI.
Software Stocks Implode, Claude's Hit List, State of the
Union Reactions, Trump's Tariff Pivot- All-In Podcast [Link]
Interesting points:
AI-driven disruption of legacy software companies. AI isn’t just a
productivity boost—it’s replacing entire workflows, collapsing moats
faster than expected.
There is growing resistance to datacenter expansion at the local and
state level, and concerns over electricity pricing, grid stability, and
who pays for upgrades. AI progress is now constrained as much by
power and permitting as by models and chips.
This Is Our Greatest National Security Risk - Chamath
Palihapitiya [Link]
Key thesis: Energy—not AI models or GPUs—is the decisive bottleneck
for U.S. national security, economic power, and technological
leadership. If the U.S. can solve the grid, it wins the century.
The AI Tsunami is Here & Society Isn't Ready | Dario
Amodei x Nikhil Kamath | People by WTF - Nikhil Kamath [Link]
Interesting points:
Society is underprepared for: Job displacement, power concentration,
and cognitive outsourcing.
The most valuable human skill going forward: thinking well under
uncertainty. What matters more are critical thinking, problem framing,
and interdisciplinary understanding.
The Jamie Dimon Interview: How JP Morgan Became an $800
Billion Bank - Acquired [Link]
Leadership principles:
Risk management is a strategy
His bias: If you’re not prepared for stress, you’re not well-run —
you’re just lucky.
Culture beats brilliance
Smart people can still destroy institutions. Incentives + culture
matter more than IQ. Leaders must actively shape norms, not just set
targets.
Dimon cares deeply about how decisions get made, not just what
decisions get made.
Reputation compounds (or decays)
Reputation is an asset, not PR. It takes decades to build and minutes
to lose. In crises, protecting trust is more important than quarterly
optics.
This guided JPMorgan’s actions in 2008, even when it invited
political backlash.
Be brutally honest — especially internally
Dimon values:
Direct feedback
Clear-eyed assessments of what’s broken
Leaders who surface problems early instead of managing
appearances
He has little patience for:
Sugarcoating
Internal politics
Leaders who “spin” instead of fix
Decentralize decisions, centralize principles
He doesn’t run JPMorgan as a command-and-control empire. Business
leaders have autonomy
Core principles (risk, ethics, capital discipline) are
non-negotiable. Standards are uniform, execution is local.
This allows scale without losing accountability.
Learn continuously — especially from failure
Dimon openly frames his 1998 firing from Citigroup as formative. He
studies mistakes relentlessly. Encourages post-mortems without blame.
Believes leaders are built, not born
How popular is Donald Trump? - Nate Silver and Eli
Mckown-Dawson [Link]
Trump is quite unpopular right now, with unusually high intense
opposition. They emphasize modeling rigor — weighting, adjustments,
methodology — to argue their number is more reliable than any single
poll headline.
Human survival, prosperity, and political power are fundamentally
constrained by physics—specifically the ability to maintain thermal
comfort. Any ideology or policy that ignores this reality will
eventually fail.
The article attacks climate policies that prioritize emissions
targets and moral narratives over reliable, always-on energy, especially
when those policies are designed by people insulated from the
consequences of energy scarcity.
11 Public Speaking Techniques from the World’s Greatest
Speakers - Polina Pompliano [Link]
Blog and Articles
How Notion Put an AI Engineer on the Sales Floor to Discover
What Actually Needed Building - First Round [Link]
Success came from reaching the right accounts at the right time, not
better email copy. So the real problem wasn’t writing better emails, or
automating research, it was account prioritization driven by product
signals.
What They Built:
Papercut fix: A Chrome extension that eliminated constant
copy-pasting across tools (LinkedIn, Salesforce, Notion, Outreach).
Adopted by almost the entire sales team within weeks.
New bet (high impact): “Salestino Bot” - An internal AI system that
detects product usage signals (e.g., new workspace creation), decides
when a rep should reach out, automates company/person research using a
constrained web agent, drafts customized outreach emails (with humans in
the loop), and delivered via Slack in a single workflow.
Conceptually, memory is always added — but practically, it’s
automatic and selective.
Exclusive: How China built its ‘Manhattan Project’ to rival
the West in AI chips - Fanny Potkin, Reuters [Link]
In a secretive "Manhattan Project" led by Huawei, China built a
prototype EUV machine by recruiting former ASML engineers, many of whom
worked under aliases to maintain security. Although the prototype
generates the necessary light, it has not yet produced working chips due
to challenges in replicating high-precision optical systems. Operational
production is now targeted for 2030, suggesting China is closing the
technology gap faster than analysts anticipated.
AI agents are starting to eat SaaS - Martin Alderson
[Link]
AI coding agents are fundamentally changing the build-vs-buy
decision, making it increasingly viable for technically capable
organizations to replace many SaaS products with internally built,
agent-assisted tools—thereby threatening demand, renewals, and expansion
economics for large parts of the SaaS industry.
The more I’ve done it, the more I realize that what most people
think of as the hard parts of hiring—asking just the right question that
catches the candidate off guard, defining the role correctly, assessing
the person’s skills—are less important than a more basic task: how do
you see someone, including yourself, clearly?
― What’s going on here, with this human? - Graham
Dunca [Link]
The Three-Part Framework for Seeing People
Clearly:
Seeing Your Reflection in the Window
(Self-Awareness)
You can’t see others clearly unless you see your own biases,
projections, and triggers.
Interviews are co-created interactions; your tone, assumptions,
and values shape how the other person shows up.
Personality frameworks (Myers–Briggs, Big Five, self-monitoring,
etc.) are most useful when applied to yourself first.
Use multiple frameworks to avoid becoming trapped by a single
lens.
Key idea: Misjudgment often comes from mistaking
your own internal reactions for objective insight.
Seeing the Elephants in the Room (Unconscious
Drivers)
Borrowing from Jonathan Haidt: each person has a rider (conscious
narrative) and an elephant (unconscious motivations). Interviews mostly
capture the rider; references reveal the elephant.
Espoused beliefs ≠ actual behavior (“espoused theory” vs. “theory
in use”).
High-quality reference checks are often 5–10x more valuable than
interviews, especially from trusted, calibrated observers.
The best signal often comes from:
The tone of a reference
“Table-pounding” enthusiasm
The dog that doesn’t bark (what’s conspicuously missing)
Key idea: Humility about your limited perception is
a prerequisite for accuracy.
Seeing the Water (Context and Ecosystem)
There is no such thing as an “A player” in the
abstract—performance is context-dependent.
People thrive or fail based on subtle environmental factors:
culture, incentives, belief loops, and feedback structures.
Moving someone to a new ecosystem is risky; strengths in one
context can become weaknesses in another.
Belief from leaders and teammates can create powerful positive
feedback loops.
Hiring should focus on fit between person and environment, not
just raw talent.
Key idea: To understand someone, you must
understand the system they came from—and the one you’re putting them
into.
Operates more like a moral agent under supervision than a pure
tool
Claude’s Constitution is not a dataset of examples. It’s a normative
specification — a written set of values, priorities, and decision
heuristics that Anthropic uses to shape Claude’s behavior. It’s one of
the clearest examples of value-based alignment framing, rather than just
policy enforcement.
OpenAI Starts Testing Ads in ChatGPT - The New York
Times [Link]
From Words to Worlds: Spatial Intelligence is AI’s Next
Frontier - Fei-Fei Li [Link]
Today’s models are powerful with words and patterns, but they don’t
truly understand the 3D, physical, dynamic world the way humans do. To
unlock robotics, scientific discovery, immersive creativity, and
real-world interaction, AI needs world models — systems that can
represent and reason about spaces, objects, physics, and change over
time.
If you have multiple interests, do not waste the next 2-3
years - Dan Koe [Link]
In the AI era, the winners aren’t specialists — they’re people who
turn their unique mix of interests into a public body of work and build
systems around their own development.
Murders plummeted more than 20% in U.S. last year, the
largest drop on record, study shows - CBS News [Link]
Exclusive: Musk's SpaceX in merger talks with xAI ahead of
planned IPO, source says - Echo Wang and Joey Roulette, Reuters
[Link]
The merger supports Musk’s vision of putting AI data centers in
space, using solar power to cut energy costs for AI training and
inference. It would tightly integrate:
AI models + compute (xAI, Grok, Colossus supercomputer)
Data + distribution (X platform)
Visa and Aldar complete end-to-end voice-enabled agentic
payment - Finextra [Link]
It can be useful in a few ways:
It saves cognitive effort. Letting an AI agent handle it via voice
means fewer screens, fewer steps, less mental load.
It makes AI do something real. This isn’t a chatbot answering FAQs.
With Visa’s agentic payments, AI can complete a transaction end-to-end
inside regulated rails.
Security scales better than humans. Tokenization and network-level
controls reduce exposure compared to people re-entering card details or
storing them unsafely.
It sets a pattern, not just a feature. If it works for service
charges, the same model extends to utilities, subscriptions, fees,
renewals—basically all the financial chores people hate.
Voice is the unlock. For accessibility, multitasking, and
mobile-first users, voice turns payments from a “task” into a quick
interaction.
How Banks Learned To Stop Worrying And Love Stablecoins -
Christian Catalini, Forbes [Link]
Stablecoins aren’t killing banks. They’re quietly disciplining
them—pushing the system toward better pricing, more competition, and
ultimately better outcomes for consumers.
SoFi unveils the first bank-issued stablecoin for enterprise
payments - Helene Braun, CoinDesk [Link]
The move follows similar institutional efforts, like JPMorgan’s JPM
Coin, signaling deeper bank adoption of blockchain payments.
Klarna Launches ‘Agentic Product Protocol’ to Make 100m
Products Readable by AI - The Fintech Times [Link]
This is a bet that AI agents will become the primary shopping
interface, and Klarna wants to be the standardized bridge between
inventory and intelligence. If that future lands, this protocol becomes
quietly powerful.
Reinforcement Learning from Verifiable Rewards (RLVR) became the big
new training stage in 2025.
LLMs are not evolving minds like humans or animals. They’re alien,
jagged intelligences optimized for text imitation and reward hacking,
not survival.
Tools like Cursor revealed a new class of applications. These apps
1) engineer context carefully, 2) orchestrate many model calls into
workflows, 3) provide GUIs and autonomy controls, 4) turn general models
into usable professionals.
Agents feel more powerful when they’re local “spirits” living on
your machine, not cloud demos.
Natural-language programming crossed a threshold in 2025. Software
creation is being radically democratized.
The future UI of AI won’t be text. The GUI for AI is still mostly
unbuilt.
Why We're Going Higher in 2026 (the Altitude Thesis) -
Stepan@X [Link]
Stablecoins are now real financial infrastructure, incumbents are
structurally stuck, and the next fintech giants will be built by teams
that deeply own the protocol layer.
Investors predict AI is coming for labor in 2026 - Rebecca
Szkutak, TechCrunch [Link]
Investors believe 2026 will mark a real shift from AI augmenting
workers to AI replacing portions of human labor, especially in
enterprise settings.
A November study from MIT estimates 11.7% of U.S. jobs could already
be automated with existing AI.
Surveys and reporting show companies are reducing entry-level roles
and explicitly citing AI as a reason for layoffs.
A move from AI as a productivity tool to AI that fully automates
work via agents
Some investors argue that not all AI-linked layoffs are genuine—AI
may serve as a scapegoat for broader cost-cutting.
The Memory Wars: Why the Future Karpathy, Musk, and Jim Fan
See Requires 16-Hi HBM - Ben Pouladian on X [Link]
Key takeaways:
Inference is memory-bound, not compute-bound. Modern GPUs are ~99%
idle during token generation because they’re waiting on memory. Training
loves FLOPs; inference chokes on bandwidth and latency.
The $20B Groq licensing deal isn’t about buying chips. It’s NVIDIA
learning and absorbing deterministic, SRAM-centric inference ideas.
My biggest takeaways from @ElenaVerna (Head of Growth at @Lovable) - Lenny Rachitsky
on X [Link]
Key takeaways:
Product Market Fit (PMF) now needs to be re-earned every ~3
months because models, capabilities, and user expectations shift
constantly—even at massive scale.
Elena says only ~30–40% of what she learned over 20 years
(including at Miro, Dropbox, and Amplitude) still applies to AI
companies.
At Lovable, ~95% of growth comes from shipping new
features/products. Traditional conversion optimization contributes very
little in fast-moving AI markets.
Rapid shipping + loud communication (engineers announcing
updates, founders posting progress daily) keeps users engaged and
competitors behind.
Free usage is marketing not cost. Lovable gives away credits,
sponsors hackathons, and funds events. Expensive inference is treated as
CAC, fueling word-of-mouth.
Influencers beat paid ads (by ~10x). Short videos showing real
product capabilities outperform traditional paid advertising. Demo >
messaging.
MVP → “Minimum Lovable Product”. If it doesn’t delight, people
won’t share it. Word of mouth is the primary growth engine.
Community is a core growth lever. Large, active communities (like
Lovable’s massive Discord) drive retention, support, and organic
growth.
Hire for chaos tolerance. High-agency people—AI-native grads and
ex-founders—thrive where roles, roadmaps, and clarity are constantly
shifting.
Hypergrowth doesn’t require burnout. Elena emphasizes strict
boundaries: protected family time, gym time, and rejecting hustle
culture as a status symbol.
The $130 Billion Comeback: Why Apple’s “Slow” AI Strategy is
a 2026 Trap - Ddos, Daily Cybersecurity [Link]
The article argues that Apple’s seemingly slow approach to AI is a
deliberate strategy, not a failure. While rivals like Google and Meta
have spent hundreds of billions building AI infrastructure, Apple has
conserved cash—retaining roughly $130 billion—giving it flexibility to
invest, acquire, or pivot in 2026.
Sam Altman Tackles Dangers of AI with New Role - M.Huzaifa
Rizwan @Medium [Link]
OpenAI is treating large-scale AI risk as a leadership problem, not
just a research one—and this role could meaningfully slow or shape how
fast AI advances.
This hire marks a shift:
Safety decisions are now closer to executive power
OpenAI may increasingly delay or restrict releases based on
risk
Preparedness becomes a gatekeeper, not a checkbox
US Army seeks human AI officers to manage its battle bots -
The Register [Link]
The US Army is creating a new AI/ML career track for existing
officers, aiming to train a dedicated cadre of “AI officers” to manage
and integrate the Army’s growing arsenal of AI-powered systems. Starting
in early 2026, selected officers will receive graduate-level training
focused on building, deploying, and maintaining AI tools already sourced
from the private sector.
AI Marketing Examples: 13 Times AI Actually Delivered -
Mateusz Makosiewicz, [Link]
Report and Paper
2025: The State of Generative AI in the Enterprise - Tim
Tully, et al., Menlo [Link]
Takeaways:
Enterprises prefer buying over building: 76% of enterprise AI use
cases are purchased, not built (up from a near 50/50 split in 2024).
Off-the-shelf AI products reach production faster and deliver clearer
ROI. Internal builds still exist, but are slower and harder to
operationalize.
AI buyers are highly intentional: 47% of AI pilots convert to
production, vs 25% for traditional SaaS. Enterprises explore many use
cases, but only fund those with near-term productivity or cost
impact.
Product-led growth (PLG) is the dominant adoption motion.
Individuals, not executives, are pulling AI into enterprises.
Startups are beating incumbents in applications. AI-native speed
beats distribution advantages. Startups capture 63% of AI application
revenue. They win by shipping faster, owning new workflows (not legacy
systems), and leveraging PLG flywheels.
Applications are the largest spend category. AI value shows up
fastest where workflows are repetitive, text-heavy, and measurable.
Anthropic is winning the enterprise LLM war. Coding performance =
enterprise dominance.
Open-source LLMs lag in the enterprise. Enterprises prefer closed
models for reliability, support, and governance. Meanwhile, startups and
developers increasingly use Chinese open-source models (Qwen, DeepSeek).
Innovation happens in open ecosystems; enterprises remain
risk-averse.
Only 16% of enterprise deployments qualify as real agents.
Architecture maturity lags model capability.
State of Fintech 2026: The Fintech Compounders - Simon Taylor
& Jevgenijs Kazanins, Fintech Brainfood [Link]
YouTube and Podcast
Massive Somali Fraud in Minnesota with Nick Shirley,
California Asset Seizure, $20B Groq-Nvidia Deal - All-In
Podcast [Link]
I Investigated Minnesota’s Billion Dollar Fraud Scandal -
Nick Shirley [Link]
The Minnesota Fraud Scandal | An Unfiltered Conversation with
Nick Shirley - The Iced Coffee Hour [Link]
Iran's Breaking Point, Trump's Greenland Acquisition, and
Solving Energy Costs - All-In Podcast [Link]
Apple's AI Crisis: Explained! - Marques Brownlee [Link]
Good points mentioned by Marques Brownlee:
The Velocity Trap: Polish vs. Pace
Apple’s traditional "slow and steady" approach—waiting for a category
to mature before entering with a polished product—may be incompatible
with AI. Unlike hardware, AI improves through real-world iteration and
constant data loops. By prioritizing perfection and long release cycles,
Apple risks falling behind competitors who are learning and evolving in
real-time.
The Execution & Credibility Gap
While "Apple Intelligence" is conceptually sound (focusing on privacy
and deep OS integration), there is a growing disconnect between
marketing and reality. Much of Apple’s vision remains aspirational and
unproven, leading to a "credibility gap." Relying on future-dated AI
features to drive current hardware sales creates a significant trust
risk if the eventual rollout doesn't meet the high bar set by the
demos.
Cultural Rigidity in a Fluid Market
The AI shift is as much a cultural test as a technical one. Apple’s
core pillars—secrecy, privacy-first design, and walled-garden
ecosystems—are now friction points in an industry that rewards open
iteration and speed. While Apple still possesses massive advantages in
silicon and distribution, its margin for error is slimmer than ever
because AI is a foundational platform shift, not just a new feature
set.
The New Siri is... Google (Explained) - Marques
Brownlee [Link]
This is Apple choosing not to lose users while it catches up.
Ben Horowitz on Investing in AI: AI Bubbles, Economic Impact,
and VC Acceleration - a16z [Link]
Ben & Marc: Why Everything Is About to Get 10x Bigger -
a16z [Link]
Everything Is Becoming Supply-Driven (and That Breaks Old
Intuitions)
Historically, markets were demand-constrained; now they are
increasingly supply-unconstrained due to software, AI, and cloud. This
is why traditional TAM / market sizing frameworks are fundamentally
broken. If you remove constraints, human demand is far larger than
economists ever modeled.
AI Is a General-Purpose Force Multiplier
AI is not “one more sector” — it’s a universal problem solver. This
creates second-order effects across every industry, not just tech.
Companies that treat AI as a feature will lose to those that treat it as
infrastructure.
Cloud + Software Create 10× Outcomes
Databricks is a canonical example: when compute, storage, and tooling
scale together, you get nonlinear gains. Once platforms reach sufficient
abstraction, entire new categories appear that were impossible before.
Step-change technologies don’t improve things linearly — they rewrite
what’s possible.
Intangibles Are the New Moat
Brand, reputation, trust, and narrative now matter more than physical
assets. In a world of infinite supply, attention and belief become the
scarce resources.
Original Thinkers with Conviction Win
The future belongs to people who combine: original ideas, moral
clarity, willingness to take public criticism (“take arrows”). Consensus
thinking is structurally disadvantaged in exponential eras.
Media Has Shifted from Gatekeepers to Creators
When creators own distribution, creativity explodes.
Reputation Is a First-Class Economic Asset
In decentralized systems, there is no central arbiter of quality.
Reputation becomes: The hiring signal, The funding signal, The
distribution advantage.
Great Companies Are Built by Dreamers, Not Committees
a16z tries to invert this by protecting founders’ psychological
safety and ambition. The job is to scale belief without killing the
dream.
Founders Need Confidence, Not Just Intelligence
Turning inventors into CEOs requires emotional reinforcement, not
just advice.
Autonomy Beats Control
a16z is structured as semi-autonomous teams rather than a centralized
hierarchy. This mirrors how startups scale effectively: high trust, high
accountability, low friction.
Adam Carolla on California’s Collapse: Fires, Failed
Leadership, and Gyno-Fascism - All-In Podcast [Link]
Supercharging a New FDA: Marty Makary on Science, Power &
Patients - All-In Podcast [Link]
Marc Andreessen's 2026 Outlook: AI Timelines, US vs. China,
and The Price of AI - a16z [Link]
Key takeaways:
AI is the biggest tech shift is bigger than PCs, the internet, or
mobile. Despite the hype, most industries have not reorganized around AI
yet. Current adoption is surface-level (tools, copilots), not structural
(AI-native workflows).
The cost of intelligence is collapsing. AI is fundamentally
different because intelligence becomes cheap and abundant. As costs
fall, usage explodes — even if revenue models lag. This creates
temporary distortions: high burn, unclear margins, massive upside.
Falling intelligence costs will unlock entirely new markets, not just
improve existing ones.
Many AI companies look “unprofitable” by traditional standards.
What matters is whether cost curves bend faster than price curves.
Infrastructure advantages (GPUs, clusters) are real but short-lived. You
back companies where learning speed beats depreciation speed.
Pricing will shift from SaaS to usage- and value-based. Flat SaaS
pricing breaks when marginal intelligence cost trends toward zero. AI
pricing will resemble: 1) cloud compute; 2) utilities; 3)
consumption-based services. Value-based pricing becomes feasible because
AI output is measurable.
Open vs. closed is not a winner-take-all debate. Multiple
contradictory strategies can succeed simultaneously.
Incumbents and startups have different, real advantages. Outcomes
depend on how deeply AI rewrites the workflow.
Most disruption happens at the task level, not the job title.
Productivity gains are uneven and socially destabilizing. Adoption
depends more on human systems than model capability. Technology moves
faster than institutions.
Venture strategy must embrace contradictions. a16z deliberately
backs:
open and closed
big models and small models
infrastructure and applications
Because uncertainty is structural, not temporary.
Philosophy: If you think the future is clean and singular, you’re
wrong.
Why AI will dwarf every tech revolution before it: robots,
manufacturing, AR glasses from CES 2026 - All-In Podcast [Link]
The core argument is that AI is not just another productivity wave
(like cloud, mobile, or the internet), but a general-purpose
intelligence layer that will reshape every industry
simultaneously—especially healthcare, education, labor markets, and
capital allocation. The discussion emphasizes speed, organizational
mismatch, and institutional lag as the real constraints—not technology
itself.
Satya Nadella on AI’s Business Revolution: What Happens to
SaaS, OpenAI, and Microsoft? - All-In Podcast [Link]
Takeways:
Satya frames AI not as “chatbots” but as a progression of work
interfaces. The real value isn’t “fun AI,” it’s AI that completes
multi-step business tasks inside workflows. Microsoft is focused on
closing the gap between 'cool demo' and 'actual business process
transformation'.
He subtly pushes back on the “AI replaces workers” narrative. His
framing is that AI is productivity density, not just removal, meaning
more output per employee, faster cycle times, and more ambitious
projects.
He sees the future as not just one assistant, but many
domain-specific agents. These agents will use enterprise data, work
across tools (M365, Dynamics, GitHub, etc.), and handle business
processes, not just prompts.
Microsoft’s edge is NOT owning the best foundation model.
It’s:
Distribution (Windows, Office, Azure)
Enterprise trust
Identity, security, compliance
Data layer
Developer ecosystem
AI is a full tech stack competition. He frames AI competition as
not model vs model, but national + ecosystem tech stacks.
Winning stack = Compute + Models + Apps + Distribution + Developers +
Data
He doesn’t say SaaS dies. SaaS UI becomes agent-accessible
capability surfaces. Therefore, the UI is less important, APIs,
workflows, and data models become key, and software becomes “actionable
services for agents”.
Can You Teach Claude to be ‘Good’? | Meet Anthropic
Philosopher Amanda Askell - Hard Fork [Link]
Takeaways:
The shift to ads in ChatGPT isn’t just a product tweak — it’s an
alignment change. Alignment isn’t only technical — economics is an
alignment mechanism.
Systems optimized for revenue gradually stop optimizing for user
well-being.
OpenAI, Google, Anthropic may produce different kinds of AI not
just because of tech — but because of revenue structure.
Ad-driven systems → more incentive tension
Enterprise/subscription focus → more incentive toward reliability
& safety
This frames business strategy as a hidden driver of model
personality.
Anthropic is trying to make values explicit. Claude is trained
using Constitutional AI — a written set of principles the model uses to
critique and guide itself.
You can’t separate intelligence from values. Smarter systems may
reinterpret rules in ways designers didn’t expect.
Dr. Mehmet Oz on Fixing American Healthcare + Fraud | Live
from Davos - All-In Podcast [Link]
Takeaways:
U.S. healthcare isn’t failing because of lack of talent or
technology — it’s failing because:
Incentives reward procedures, billing complexity, and
intermediaries
Prevention and long-term outcomes are underpaid
Bureaucratic and payment layers distort decisions
Fraud is not marginal — it’s structural and industrial-scale.
Includes phantom billing, fake clinics, and organized rings. Fraud
inflates prices and drains resources from real care
The bottleneck in healthcare isn’t knowledge — it’s access +
navigation.
AI will: Do triage; Guide patients before they see doctors;
Standardize care quality; Reduce unnecessary specialist visits. The big
shift will be from doctor-centered care to data/AI-guided care. Doctors
become escalation points, not the front door.
Healthcare reflects upstream social failures. Healthcare costs
are downstream of: Addiction; Mental health breakdowns; Social
instability; Border and population pressures.
Elon Musk on AGI Timeline, US vs China, Job Markets, Clean
Energy & Humanoid Robots | 220 - Peter H. Diamandis [Link]
Takeaways:
Musk’s stance is that transformative AI is very close. Superhuman AI
as an engineering inevitability. Timeline measured in years, not
decades. The key limiter now being infrastructure (chips, power) more
than theory.
AI risk is structural, not Hollywood. He’s not focused on evil
robots; he worries about: Systems optimizing for goals misaligned with
human intent; Intelligence that becomes strategically beyond human
control; Power concentration in a few actors. The danger comes from
competence without alignment, not malice.
He frames AI as a civilization-level strategic asset. The US–China
competition is inevitable. Export controls slow but don’t stop
capability. Whoever leads in AI shapes military, economic, and political
leverage. AI dominance may matter more than nuclear or industrial
dominance did.
Intelligence scaling = energy scaling. AI data centers are seen as
industrial-scale electricity consumers, dependent on massive grid
expansion, tightly linked to storage + renewables.
Physical AI (robots) will outscale digital AI in impact. He sees
humanoid robots (Optimus) as the path to automating real-world labor,
more economically transformative than software alone, and potentially
more numerous than humans. The biggest value creation isn’t chatbots —
it’s AI in bodies.
He argues AI + robotics will outperform humans at most jobs,
collapse the economic necessity of labor, and shift society toward
abundance of goods/services. The core future problem isn’t unemployment
— it’s purpose and meaning in a post-labor world.
Concentrated superintelligence is more dangerous than distributed
superintelligence.
We’re in a civilization transition, not a tech cycle. This is a
species-level turning point, not a market trend.
The Singularity Countdown: AGI by 2029, Humans Merge with AI,
Intelligence 1000x | Ray Kurzweil - Peter H. Diamandis [Link]
Ray's worldview is very consistent across decades:
Progress is exponential, not linear. Technology improves in doubling
patterns (compute, data, biotech, etc.). Humans think linearly - we
massively underestimate what happens in the second half of an
exponential curve.
AI will reach human-level general intelligence soon - around 2029.
Not just narrow tools — systems that reason, learn, and adapt across
domains. After that, progress speeds up because AI designs better AI.
This is the start of the steep part of the curve.
The Singularity happens when non-biological intelligence becomes
vastly more powerful than all human brains combined. Intelligence growth
becomes runaway and civilization transforms at a speed we can’t
intuitively grasp. It’s an evolutionary phase shift, not just a tech
upgrade.
He strongly rejects the “AI replaces humans” framing. We expand our
intelligence by plugging into non-biological systems. Future humans will
merge with AI (biological + digital hybrid minds)
AI + biotech enables rapid advances in diagnostics, drug discovery,
and gene editing. The goal of longevity escape velocity (medical
progress adds more life than time passing) will be reached. Radical life
extension is plausible this century.
Work doesn't disappear - it evolves. Automation removes tasks, not
human meaning. New categories of work emerge that we can’t predict
today. As abundance rises, humans focus more on creativity,
relationships, exploration, and purpose-driven work.
Every major technology starts centralized, then becomes widely
accessible. Risks are real, but benefits historically outweigh harms.
The solution to tech risk is better tech + human values, not stopping
progress.
Consciousness can extend beyond biology. Mind is patterns and
processes. If reproduced in another substrate, consciousness can
continue or expand. Digital minds could be as rich (or richer) than
biological ones.
Chamath & Nathalie on Leaving California, Tech
Investments & Their Marriage | KMP Ep.24 - All-In Podcast
[Link]
ICE Chaos in Minneapolis, Clawdbot Takeover, Why the Dollar
is Dropping - All-In Podcast [Link]
Coinbase CEO's Top 3 Crypto Trends for 2026 + More from
Davos! - All-In Podcast [Link]
Takeaways:
Brian Armstrong (Coinbase)
Exchanges become universal financial marketplaces. Crypto exchanges
stop being “places to trade coins” and become places to trade everything
— using blockchain as the behind-the-scenes system. This means
everything becomes a digital token on a blockchain, which includes
stocks, bonds, funds, real estate, etc, - they are all represented the
same way: digital assets on-chain.
Stablecoins are the new payments layer. They are moving into
cross-border payments, business settlement, and everyday financial
flows. They are positioned as faster, cheaper, programmable
internet-native money. This is crypto’s move from speculation to real
economic utility.
Crypto and AI will merge. AI agent will need wallets, identity,
ability to transact. Crypto provides economic infrastructure for
autonomous AI systems. AI will be able to earn, pay, and contract on its
own.
Andrew Feldman (Cerebras)
AI progress is compute-constrained. The bottleneck is no longer just
algorithms; it's chips, power, data centers. Whoevere scales compute
fastest drives AI capability.
Massive hardware innovation is required. The frontier isn’t just
better models — it’s better physical AI infrastructure.
Compute is now geopolitics. AI compute capacity is strategically
important, nationally competitive and comparable to energy or oil
infrastructure. The AI race is an infrastructure race, not just
software.
Jake Loosararian (Gecko Robotics)
AI is leaving the screen and entering industry. AI + robotics are
being deployed in power plants, industrial facilities, and critical
infrastructure. This is aAI moving from chat & code to machines
& steel.
Real world data beats theoretical models. AI becomes powerful when
grounded in actual industrial reality, not just simulations.
AI and robotics improve maintenance, safety, and efficiency. This
drives huge economic leverage because it upgrades the systems that power
society.
Amazon Layoffs & Shutdown of Fresh and Go Stores, The
Anthropic Cowork Threat | Jan 28, 2026 - The Information [Link]
Lastly, the crux of the tweet: "Optimistic contrarians are the
rarest breed." This speaks to individuals who possess the courage to
deviate from the mainstream not for the sake of contrariness, but driven
by genuine belief in a different, often better vision. More importantly,
they do this with optimism. Unlike cynics, optimistic contrarians see
the potential in what others dismiss. They are hopeful about their
divergent views, even in the face of criticism or skepticism. They
combine the audacity to think differently with the belief that their
path, though less traveled, is full of promise.
― Cynicism is easy. Mimicry is easy. Optimistic contrarians
are the rarest breed. - Naval's Archive [Link]
“Status games are multiplayer, zero-sum, hierarchical, judged
socially. Get grades, applause, titles now – emptiness later. Natural
games are single player, positive-sum, internal, judged by
nature/markets. Pay in pain now – get wealth, health, knowledge, peace,
family later.” — Naval Ravikant
― Play Natural Games, Not Status Games - Naval's
Archive [Link]
Your brain builds language pathways through repetition and active
use. Every time you practice articulating an idea, explaining a concept,
or searching for the precise word, you’re literally rewiring neural
connections that make language retrieval easier (Bassett & Mattar,
2017).
One of the good habits I haven't had: "Watch a 10-minute interview
clip. Then, try explaining the same topic they discussed in your own
words. Notice how much clearer your thinking becomes when you’ve seen it
modeled well."
How to articulate yourself intelligently - Dan Koe
[Link]
The three frameworks Dan Koe lays out are:
Beginner – The Micro Story
This is meant for fast articulation in short-form writing or
speech.
Best for: quick clarity and attention
A simple storytelling structure:
Problem – state a relatable problem
Amplify – show the negative consequences if it’s not solved
Solution – present the insight or fix
Intermediate – The Pyramid Principle
This works especially well for podcasts, presentations, and longer
explanations.
Best for: structured thinking and credibility
A logic-first communication framework:
Start with the main idea or conclusion
Support it with 3–5 key arguments
Back those up with evidence, examples, or data
Advanced – Cross-Domain Synthesis
This is best suited for newsletters, essays, talks, or long-form
content.
Best for: originality and thought leadership
A deeper, more original structure:
Problem + amplify (clear, relatable setup)
Cross-domain synthesis (borrow concepts from other fields to explain
the idea)
Unique process or solution (your own framework or steps)
The difference between average and great is taste.
The future belongs to those who can filter signal from noise.
When anyone can produce anything, choosing what deserves to exist
becomes the skill.
The ability to learn any skill fast, however, will.
Devon Eriksen talks about the “liberating arts,” the skills that
free people have always needed to act on their own behalf:
Logic: deriving truth from known
facts
Statistics: understanding the implications of
data
Rhetoric: persuading, and spotting persuasion
tactics
Research: gathering information on unknown
subjects
Psychology: discerning the true motives of
yourself and others
Investment: managing and growing
assets
Agency: deciding what to pursue and acting
without permission
― Why the next 2 years will matter more than the last 10 -
Dan Koe [Link]
If your thoughts are average, they can be compressed.
If your ideas are derivative, they can be predicted.
If your personality is shaped by trends, you can be simulated —
and eventually replaced.
The more you become like everyone else, the more you
disappear.
To be incompressible is to break the pattern. To do something
that hasn’t been done. To think what hasn’t been thought. To speak in a
voice that’s unmistakably yours.
It means cultivating taste that isn’t algorithmic.
It means being boring to the AI and fascinating to the
human.
Most of all, incompressibility requires solitude. Silence.
Reflection.
Space from the noise so you can remember who you are when no one
is watching.
Naval’s line, “If you don’t commit to meaningful work, life will
fill your time with busywork,” is a reminder of a quiet truth: your time
will never stay empty. Something will always come in. The choice is
whether it’s chosen by you, or assigned to you by circumstance,
employers, algorithms, or the thousand tiny demands that chip away at
your attention.
Meaningful work, on the other hand, rarely arrives disguised as
something urgent. It doesn’t ping, buzz, or demand anything. It
whispers. It waits. It requires intention. It asks you to choose it
repeatedly, often against comfort, against convenience, against the easy
path. It’s the book unwritten, the skill unmastered, the company
unstarted, the craft unrefined. The things that make your life bigger
but don’t shout for your attention.
― If You Don’t Commit to Meaningful Work, Life Will Fill Your
Time with Busywork - Naval's Archive [Link]
How to fix your entire life in 1 day - Dan Koe
[Link]
You aren't where you want to be because you aren't the
person who would be there.
You aren't where you want to be because you don't want to
be there.
You aren't where you want to be because you are afraid to
be there.
The life you want lies within a specific level of
mind.
Intelligence is the ability to get what you want out of
life.
Cybernetics illustrates the properties of intelligent systems.
To have a goal.
Act toward that goal.
Sense where you are.
Compare it to the goal.
And act again based on that feedback.
To become more intelligent, you must:
Reject the known path
Dive into the unknown
Set new, higher goals to expand your mind
Embrace the chaos and allow for growth
Study the generalized principles of nature
Become a deep generalist
How to launch a completely new life in one
day
Three phases that people go through to successfully flip their
identity:
Dissonance – They feel like they don’t belong in their current life,
and become sufficiently fed up with their lack of progress.
Uncertainty – They don’t know what comes next, so they either
experiment or get lost and feel worse.
Discovery – They discover what they want to pursue and make 6 years
of progress in 6 months.
Questions make you aware of the pain in your current life:
What is the dull and persistent dissatisfaction you've learned to
live with? Not the deep suffering but what you've learned to tolerate.
(if you don't hate it, you will tolerate it)
What do you complain about repeatedly but never actually change?
Write down the three complaints you've voiced most often in the past
year.
For each complaint: what would someone who watched your behavior
(not your words) conclude that you actually want?
What truth about your current life would be unbearable to admit to
someone you deeply respect?
Questions to come up with your anti vision - a brutal awareness of
the life you dod not want to live:
If absolutely nothing changes for the next five years, describe an
average Tuesday. Where do you wake up? What does your body feel like?
What’s the first thing you think about? Who’s around you? What do you do
between 9am and 6pm? How do you feel at 10pm?
Now do it but for ten years. What have you missed? What
opportunities closed? Who gave up on you? What do people say about you
when you’re not in the room?
You’re at the end of your life. You lived the safe version. You
never broke the pattern. What was the cost? What did you never let
yourself feel, try, or become?
Who in your life is already living the future you just described?
Someone five, ten, twenty years ahead on the same trajectory? What do
you feel when you think about becoming them?
What identity would you have to give up to actually change? (”I am
the type of person who...”) What would it cost you socially to no longer
be that person?
What is the most embarrassing reason you haven’t changed? The one
that makes you sound weak, scared, or lazy rather than reasonable?
If your current behavior is a form of self-protection, what exactly
are you protecting? And what is that protection costing you?
Questions to create a minimum viable vision:
Forget practicality for a minute. If you could snap your fingers and
be living a different life in three years, not what’s realistic, what
you actually want? What does an average Tuesday look like? Same
level of detail as question 5.
What would you have to believe about yourself for that life to feel
natural rather than forced? Write the identity statement: “I am the type
of person who...”
What is one thing you would do this week if you were already that
person?
Random questions throughout the day:
11:00am: What am I avoiding right now by doing what I’m doing?
1:30pm: If someone filmed the last two hours, what would they
conclude I want from my life?
3:15pm: Am I moving toward the life I hate or the life I want?
5:00pm: What’s the most important thing I’m pretending isn’t
important?
7:30pm: What did I do today out of identity protection rather than
genuine desire? (Hint: it’s most things you do)
9:00pm: When did I feel most alive today? When did I feel most
dead?
What would change if I stopped needing people to see me as [the
identity you wrote in question 10]?
Where in my life am I trading aliveness for safety?
What’s the smallest version of the person I want to become that I
could be tomorrow?
Questions to synthesize insights:
After today, what feels most true about why you’ve been stuck?
What is the actual enemy? Name it clearly. Not circumstances. Not
other people. The internal pattern or belief that has been running the
show.
Write a single sentence that captures what you refuse to let your
life become. This is your anti-vision compressed. It should make you
feel something when you read it.
Write a single sentence that captures what you’re building toward,
knowing it will evolve. This is your vision MVP.
To create goals:
One-year lens: What would have to be true in one year for you to
know you’ve broken the old pattern? One concrete thing.
One-month lens: What would have to be true in one month for the
one-year lens to remain possible?
Daily lens: What are 2-3 actions you can timeblock tomorrow that the
person you’re becoming would simply do?
Turn your life into a video game
Six components that lead to a good life:
Anti-vision – What is the bane of my existence, or
the life I never want to experience again?
Vision – What is the ideal life that I think I want
and can improve as I work toward it?
1 year goal – What will my life look like in 1 year
time, and is that closer to the life I want?
1 month project – What do I need to learn? What
skills do I need to acquire? What can I build that will move me closer
to the one year goal?
Daily levers – What are the priority, needle-moving
tasks that bring my project closer to completion?
Constraints – What am I not willing to sacrifice to
achieve my vision from the ground up?
2025 was the year the comfortable assumptions got
stress-tested.
“Just scale it”? DeepSeek proved architectural
efficiency could match brute-force compute – and wiped a trillion
dollars off the market.
“Distribution is all you need”? Despite
Microsoft’s insane lead start, OpenAI’s clear consumer and mindshare
lead, and Google’s incredible distribution, and Anthropic still beat
them all in the enterprise and coding wars.
“America leads open source”? I count nine
competitive Chinese model releases in 2025. Meta shipped two (and
Behemoth is still MIA).
“Bigger models = better models”?
Mixture-of-experts, inference-time compute, and distillation ate that
thesis alive.
What actually mattered
Great products beat distribution
Efficiency mattered more than scale
Standards emerged
Multimodal became baseline
OpenAI Rewrote its own ruless
― 2025 Recap: The Year the Old Rules Broke - AI
Supremacy [Link]
Fara-7B: An Efficient Agentic Model for Computer Use -
Microsoft Research Blog [Link]
User gives task → agent acts → browser takes screenshot → model sees
screenshot → next action
The Complete Guide to Nano Banana Pro: 10 Tips for
Professional Asset Production - Google AI Studio [Link]
Frontier agents, Trainium chips, and Amazon Nova: key
announcements from AWS re:Invent 2025 - Amazon News [Link]
Now available: Create AI agents to automate work with Google
Workspace Studio - Google Blog [Link]
Introducing Anthropic Interviewer: What 1,250 professionals
told us about working with AI - Anthropic [Link]
How People Use AI Agents - Perplexity [Link]
[Paper]
Harvard-Perplexity study shows AI agents now shift towards more
cognitive work tasks
Introducing Code Wiki: Accelerating your code understanding -
Google Blog [Link]
Stanford AI Experts Predict What Will Happen in 2026 - HAI,
Stanford University [Link]
Manus Joins Meta: Accelerating AI Innovation for Businesses -
Meta [Link]
Payment Fragmentation Is Here to Stay, and Banks Must Adapt -
Hussam Kamel, Finextra [Link]
The global payments industry is moving away from harmonisation and
toward persistent fragmentation. This shift is structural—not
temporary—and banks must fundamentally rethink their payment
infrastructures to remain competitive. Fragmentation is structural and
enduring. Banks that succeed will be those that embrace divergence,
modernise their core payment architectures, and treat flexibility as a
strategic advantage rather than a cost burden.
Google, Nvidia, and OpenAI - Ben Thompson,
Stratechery [Link]
Nvidia: structurally strong, but facing long-term margin
pressure.
OpenAI: has the best consumer position, but is undermining itself by
avoiding ads.
Google: the only company that can fight on all dimensions
simultaneously — model quality, compute, monetization, and
distribution.
The AI war is no longer about who innovates first — it’s about who
can sustain dominance at scale.
Apple AI chief steps down following Siri setbacks - Emma
Roth, The Verge [Link]
OpenAI CEO declares “code red” as Gemini gains 200 million
users in 3 months [Link]
Stop talking about your impact. Start spotlighting theirs. -
Jenny Wanger [Link]
The strongest professional reputation isn’t built by showcasing how
much you do, but by amplifying how others succeed through what you
enable.
The book recommendations mentioned in the article:
Wild Courage by Jenny Wood — Cited for advice on
sharing wins and gratitude in self-promotion.
Give to Grow by Mo Bunell — Recommended as a good
resource on the importance of generosity and recognition.
Give and Take by Adam Grant — Recommended for
insight into how impact and recognition dynamics work in professional
settings.
So What's Going to Happen to Product Management Anyway? -
Peter Yang, Behind the Craft [Link]
ryo_cursor_quote
How To Ask for Support from Senior Leaders - Yue
Zhao [Link]
1) Mindset Shift: People often avoid asking for help
because they see it as a sign of weakness. The author learned that
asking for help is an essential leadership skill — especially
when challenges exceed your control or expertise.
2) When to Ask for Help
You should consider asking for help when:
The challenge is outside your area of expertise.
It requires coordination across teams you don’t control.
You need exceptions or decisions from leaders outside your chain of
command.
The general rule: ask earlier rather than later — problems grow if
unaddressed.
3) Types of Help to Ask For
The article breaks down effective asks into levels of
involvement a leader might provide:
Be a Sounding Board — light request: listen and
give perspective.
Give Air Cover — leader supports you publicly in
meetings or when there’s pushback.
Be a Messenger — leader helps deliver messages you
can’t reach as easily.
Fight With Me — leader actively advocates or argues
on your behalf (highest effort).
The author suggests that asking for air cover is often the best
default: It’s low-effort for the leader and lets you drive the work
while they back you up.
Netflix and the Hollywood End Game - Ben Thompson,
Stratechery [Link]
Netflix wins not because it makes the best shows, but because it is
the best machine for turning content into sustained value—and now it
wants to own the raw materials too.
Execution won’t stop. Strategy will unless you have a system:
Jenny Wanger at INDUSTRY 2025 [Link]
Execution isn’t the bottleneck; unclear strategy
is.: Teams over-execute when strategy isn’t explicit, forcing
constant validation, rework, and reactive prioritization.
Lack of strategy clarity creates hidden time
leaks.: Without a clear strategic “yes,” leaders and teams
struggle to say no—resulting in fragmented effort and slow
decision-making.
An imperfect strategy is better than a silent one.:
Strategy doesn’t need to be complete to be effective; even partial
clarity reduces noise and accelerates alignment.
Strategy must function as a system, not a
document.: Value comes from embedding strategy into everyday
decisions, tradeoffs, and communication—not from standalone
artifacts.
Perceived productivity can mask lack of progress.:
Teams can ship continuously while failing to move the business forward
if work isn’t anchored to a shared direction.
Strategy is ultimately a leadership communication
discipline.: Clear, consistently reinforced strategy enables
faster decisions, greater autonomy, and compounding momentum.
Restaurants are increasingly adding credit card surcharges or cash
discounts to offset sharply rising card interchange fees. The shift
reflects thin margins, declining traffic, and ongoing frustration with
card networks, though adoption remains limited due to customer backlash
and state regulations.
J.P. Morgan harnesses blockchain for debt issuance amid
digital asset adoption boost - Pritam Biswas and Anirban Sen,
Reuters [Link]
J.P. Morgan helped issue $50 million of short-term debt using
blockchain technology instead of traditional systems. The debt was
issued for Galaxy Digital on the Solana blockchain and bought by
Coinbase and Franklin Templeton. Payments were handled using USDC, a
digital dollar. This deal shows that big financial institutions are
starting to seriously use blockchain
YouTube and Podcast
Tucker Carlson: Rise of Nick Fuentes, Paramount vs Netflix,
Anti-AI Sentiment, Hottest Takes - All-In Podcast [Link]
Bernie Sanders: Stop All AI, China's EUV Breakthrough,
Inflation Down, Golden Age in 2026? - All-In Podcast [Link]
Sacks, Andreessen & Horowitz: How America Wins the AI
Race Against China - a16z [Link]
How AI Agents Will Transform in 2026 (a16z Big Ideas) -
a16z [Link]
AI stops being something you ask, and becomes something that
does.
Interfaces: chat → action
Design: human-first → agent-first
Work: assistance → execution
Big Ideas:
From prompts to action: AI interfaces are moving
beyond chat boxes toward agents that proactively execute tasks and
workflows on users’ behalf, acting more like autonomous employees than
tools you query.
From human-readable to agent-readable software:
Software, content, and workflows will increasingly be designed to be
machine-legible first, enabling agents to reliably navigate, interpret,
and operate systems without brittle prompting.
From demos to deployable voice agents: Voice AI is
crossing a threshold from novelty to production-ready systems, becoming
practical in domains like healthcare, finance, recruiting, and consumer
wellness due to improved reliability and integration.
3 Industries That AI Will Revolutionize In 2026 (a16z Big
Ideas) - a16z [Link]
Big Ideas:
The Electro-Industrial Stack: AI is driving a
rebuild of the physical economy—manufacturing, energy, logistics—into an
integrated electro-industrial stack where software, hardware, power, and
supply chains are tightly coupled, reshaping national
competitiveness.
A Turning Point in Financial Services: Financial
services and insurance are reaching an inflection point where replacing
legacy systems with AI-native, unified infrastructure unlocks parallel
workflows, cleaner data, and structurally higher margins.
The Dynamic Agent Layer: Static systems of record
will be overtaken by a dynamic agent layer where AI agents actively
execute work across systems, shifting value from data storage to
autonomous coordination and decision-making.
How AI Will Transform Fintech In 2026 - A16z [Link]
Fintech is entering a new upcycle, but it will look very different
from the 2020–21 boom. The next phase is driven less by
growth-at-all-costs and more by AI-powered fundamentals—especially fraud
prevention, underwriting, and infrastructure efficiency—where incumbents
and well-positioned platforms (like Plaid) regain an edge.
I shrunk down into an M5 chip - Marques Brownlee [Link]
Incredible video to convey the true sense of scale.
All-In x Kill Tony: A Hilarious Holiday Special - All-In
Podcast [Link]
WTF Is Wealth? Ray Dalio Breaks It Down w/ Nikhil Kamath |
WTF is Finance Ep 2 - Nikhil Kamath [Link]
Ray Dalio is talking about how money works, why bubbles are
inevitable, and how to think across cycles rather than chase
narratives.
Takeaways - what Ray Dalio explicitly believes or argues in this
episode:
Money ≠ Wealth
Money is not real wealth; it is a claim on wealth. Wealth is real
purchasing power backed by: 1) Goods, 2) Services, 3) Productivity.
Printing money does not create wealth—it only redistributes claims on
existing wealth.
Money Has Two Conflicting Functions
Dalio defines money as: A medium of exchange; A store of wealth
"When debt is high and governments intervene, money often fails at
being a store of wealth." This conflict explains: 1) Inflation, 2)
Currency debasement, 3) Asset bubbles
Debt Cycles Drive Everything
Economies move in long-term debt cycles (50–100 years). Excessive
debt forces governments into a corner: 1) Raise taxes, 2) Cut spending,
3) Print money. Historically, they always print.
The Gold Standard Ending Was a Regime Shift
Nixon ending the gold standard (1971) permanently changed money.
Since then: 1) Money is a policy tool, not a constraint. 2) Governments
can promise more than they can actually deliver. This makes currency
risk unavoidable.
Bubbles Are Created by Easy Money
Bubbles form when:
Money creation outpaces real economic growth
People extrapolate recent gains indefinitely
Leverage increases faster than income
Dalio sees bubbles as mechanical, not moral failures.
Leverage Is the Silent Killer
Most people underestimate leverage risk. Leverage: 1) Magnifies
gains, 2) Destroys you quickly when wrong
Liquidity disappears precisely when you need it most.
Wealth Preservation > Wealth Maximization
Dalio emphasizes:
Surviving bad regimes matters more than winning good
ones.
Losing 50% requires a 100% gain to recover.
The biggest mistake investors make is
over-concentration.
Diversification Is Not About Asset Count
True diversification means:
Exposure to uncorrelated return drivers
Assets that respond differently to:
Growth
Inflation
Deflation
Political stress
Holding many similar assets ≠ diversification.
If You Can’t Beat the Market, Don’t Try
Most people should not trade or time markets. Dalio believes: 1)
Humility beats confidence, 2) Process beats prediction
Portfolio construction matters more than asset selection.
Productive Assets Are the Best Long-Term Store of
Wealth
Dalio prefers assets that:
Generate cash flows
Adapt to inflation
Represent real economic activity
Examples: 1) Businesses, 2) Innovation, 3) Human capital
Gold Has a Role — But It’s Not Everything
Gold is a hedge against: 1) Currency debasement, 2) Political
disorder. But it does not produce income. It should be part of a
portfolio, not the portfolio.
Crypto Is Unproven as a Long-Term Store of
Wealth
Dalio’s view is cautious, not dismissive: Crypto has some
store-of-value characteristics, but: 1) No long multi-cycle history, 2)
Regulatory and political uncertainty.
He treats it as speculative diversification, not core
wealth.
Real Estate Is Politically Vulnerable
Real estate is easy to: 1) Tax, 2) Regulate, 3) Seize. Investors
underestimate political risk in immovable assets.
Prediction Is Less Valuable Than Understanding
Systems
Dalio does not believe in point forecasts. He believes in:
Cause–effect relationships
Scenario thinking
Probabilities, not certainties
The Five Forces Drive History
Dalio believes every country’s trajectory is shaped by:
Debt & money
Internal conflict
External conflict
Technology
Acts of nature
Ignore any one of these and your analysis is incomplete.
Technology Is the Ultimate Long-Term Wealth
Driver
Innovation increases productivity. Productivity is the foundation of
rising living standards. Countries that innovate absorb shocks better
than those that don’t.
Psychology Determines Success More Than
Intelligence
Successful investors share:
Humility
Curiosity
Willingness to be wrong
Ability to learn from pain
Ego is the enemy of compounding.
Learning Comes From Proximity
The fastest way to learn is to be near: 1) Great thinkers, 2) Great
decision-makers.
Wealth Without Purpose Is Empty
Dalio believes: 1) Legacy matters more than net worth, 2) Passing
knowledge forward is the highest form of wealth, 3) Systems and ideas
outlast money.
Sundar Pichai: Gemini 3, Vibe Coding and Google's Full Stack
Strategy - Google for Developers [Link]
Adam Grant’s "Think Again" offers a refreshing alternative:
the power of intellectual humility. While we often define intelligence
as the ability to think and learn, Grant argues that in a rapidly
changing world, a different cognitive skill matters just as much—the
ability to rethink and unlearn.
Grant challenges us to abandon the mindsets of preachers,
prosecutors, and politicians in favor of thinking like a "scientist."
The core of the first section is the concept of confident
humility—having faith in your capability while appreciating how much you
have yet to learn. We must detach our opinions from our identity. By
basing who we are on our values (e.g., generosity, freedom) rather than
our specific beliefs, we can remain open to changing our minds without
losing our sense of self.
How do we open other people's minds? Grant suggests that logic and
data often fail because they trigger defensiveness. Instead, we should
adopt techniques like motivational interviewing, which acts as a mirror
to help people find their own motivation to change. Effective
disagreement isn't about crushing the opponent; it's about signaling
that we are reasonable and open to evolving our own views.
The final section tackles how we can foster a culture of rethinking
in our communities. Grant warns against binary bias—the tendency to
simplify complex issues into two opposing sides. Complexity and nuance
are actually signals of credibility. When we showcase the "shades of
gray" and admit uncertainty, we become more persuasive, not less.
This book is a transformative reminder that true wisdom lies not in
the certainty of our convictions, but in the confident humility to
constantly question what we don't know. And it is worth being picked up
occasionally for self-reflection to ensure we remain open to the joy of
being wrong.
Here are my favorite quotes from this book:
Individual Rethinking
Recognizing our shortcomings opens the door to doubt. As we question
our current understanding, we become curious about what information we
are missing. That search leads us to new discoveries, which in turn
maintain our humility by reinforcing how much we still have to learn. If
knowledge is power, knowing what we don't know is wisdom.
Great thinkers don't harbor doubts because they are impostors. They
maintain doubts because they know we're all partially blind and they are
committed to improving their sight. They don't boast about how much they
know; they marvel at how little they understand. They are aware that
each answer raises new questions, and the quest for knowledge is never
finished. A mark of lifelong learners is recognizing that they can learn
something from everyone they meet.
Arrogance leaves us blind to our weaknesses. Humility is a reflective
lens: it helps us see them clearly. Confident humility is a corrective
lens: it enables us to overcome those weaknesses.
Attachment. That's what keeps us from recognizing when our opinions
are off the mark and rethinking them. To unlock the joy of being wrong,
we need to detach. I've learned that two kinds of detachment are
especially useful: detaching your present from your past and detaching
your opinions from your identity.
Who you are should be a question of what you value, not what you
believe. Values are our core principles in life - they might be
excellence and generosity, freedom and fairness, or security and
integrity. Basing your identity on these kinds of principles enables you
to remain open-minded about the best ways to advance them.
The clearest sign of intellectual chemistry isn't agreeing with
someone. It's enjoying your disagreements with them. Harmony is the
pleasing arrangement of different tones, voices, or instruments, not the
combination of identical sounds. Creative tension makes beautiful
music.
Interpersonal Rethinking
Convincing other people to think again isn't just about making a good
argument - it's about establishing that we have the right motives in
doing so. When we concede that someone else has made a good point, we
signal that we are scientists trying to get to the truth. "Arguments are
often far more combative and adversarial than they need to be", Harish
told me, "You should be willing to listen to what someone else is saying
and give them a lot of credit for it. It makes you sound like a
reasonable person who is taking everything into account."
Being reasonable literally means that we can be reasoned with, that
we're open to evolving our views in light of logic and data.
When we gave them different kinds of reasons to donate, we triggered
their awareness that someone was trying to persuade them - and they
shielded themselves against it. A single line of argument feels like a
conversation; multiple lines of argument can become an onslaught.
Taken together, these techniques increase the odds that during a
disagreement, other people will abandon an overconfidence cycle and
engage in a rethinking cycle. When we point out that there are areas
where we agree and acknowledge that they have some valid points, we
model confident humility and encourage them to follow suit. When we
support our argument with a small number of cohesive, compelling
reasons, we encourage them to start doubting their own opinion. And when
we ask genuine questions, we leave them intrigued to learn more. We
don't have to convince them that we're right - we just need to open
their minds to the possibility that they might be wrong. Their natural
curiosity might do the rest.
Outside the lab, dismantling stereotypes and decreasing prejudice
rarely happen overnight; a key step is getting them to do some
counterfactual thinking: helping them consider what they would believe
if they were living in an alternative reality.
In psychology, counterfactual thinking involves imagining how the
circumstances of our lives could have unfolded differently. When we
realize how easily we could have held different stereotypes, we might be
more willing to update our views.
Psychologists find that many of our beliefs are cultural truisms:
widely shared, but rarely questioned. If we take a closer look at them,
we often discover that they rest on shaky foundations. Stereotypes don't
have the structural integrity of a carefully built ship. They're more
like a tower in the game of Jenga - teetering on a small number of
blocks, with some key supports missing. To knock it over, sometimes all
we need to do is give it a poke. The hope is that people will rise to
the occasion and build new beliefs on a stronger foundation.
They developed the core principles of a practice called motivational
interviewing. The central premise is that we can rarely motivate someone
else to change. We're better off helping them find their own motivation
to change.
Motivational interviewing starts with an attitude of humility and
curiosity. We don't know what might motivate someone else to change, but
we're genuinely eager to find out. The goal isn't to tell people what to
do; it's to help them break out of overconfidence cycles and see new
possibilities. Our role is to hold up a mirror so they can see
themselves more clearly, and then empower them to examine their beliefs
and behaviors. That can activate a rethinking cycle, in which people
approach their own views more scientifically. They develop more humility
about their knowledge, doubt in their convictions, and curiosity about
alternative points of view.
Listening well is more than a matter of talking less. It's a set of
skills in asking and responding. It starts with showing more interest in
other people's interests rather than trying to judge their status or
prove our own. We can all get better at asking "truly curious questions
that don't have the hidden agenda of fixing, saving, advising,
convincing or correcting", and helping to "facilitate the clear
expression of another person's thoughts."
Collective Rethinking
Binary bias is a basic human tendency to seek clarity and closure by
simplifying a complex continuum into two categories. To paraphrase the
humorist Robert Benchley, there are two kinds of people: those who
divide the world into two kinds of people, and those who don't.
An antidote to this proclivity is complexifying: showcasing the range
of perspectives on a given topic. We might believe we're making progress
by discussing hot-button issues as two sides of a coin, but people are
actually more inclined to think again if we present these topics through
the many lenses of a prism. To borrow a phrase from Walt Whitman, it
takes a multitude of views to help people realize that they too contain
multitudes.
A dose of complexity can disrupt overconfidence cycles and spur
rethinking cycles. It gives us more humility about our knowledge and
more doubts about our opinions, and it can make us curious enough to
discover information we were lacking.
This thorny issue is a natural place to explore how we can bring more
complexity into our conversations. Fundamentally, that involves drawing
attention to the nuances that often get overlooked. It starts with
seeking and spotlighting shades of gray.
When we are reading, listening, or watching, we can learn to
recognize complexity as a signal of credibility.
Multiple experiments have shown that when experts express doubt, they
become more persuasive. When someone knowledgeable admits uncertainty,
it surprises people, and they end up paying more attention to the
substance of the argument.
In a series of experiments, psychologists demonstrated that when news
reports about science included caveats, they succeeded in capturing
readers' interest and keeping their minds open.
New research reveals that people are more likely to promote diversity
and inclusion when the message is more nuanced (and more accurate).
Acknowledging complexity doesn't make speakers and writers less
convincing; it makes them more credible. It doesn't lose viewers and
readers; it maintains their engagement while stoking their
curiosity.
What stands in the way of rethinking isn't the expression of emotion;
it's a restricted range of emotion.
Harvard’s 30-Year Research Reveals: Why You Feel Overwhelmed,
Exhausted, and Anxious — and How 25 Tiny Daily Habits Can Restore Inner
Calm, Thomas Blake, Everyday Health [Link]
When you don’t feel like doing something, do it for ten minutes
anyway.
― The 10-Minute Rule: How Small Windows Create Big Wins -
Balanced Discipline [Link]
Humans exist to understand the universe.But we still
don’t know what question we’re supposed to be asking.
The biggest opportunities of the 2030s will sit at
intersections:
AI + energy
robotics + logistics
satellites + internet
AI + biology
space + manufacturing
This is the philosophical layer behind his companies:
xAI expands intelligence
Neuralink expands consciousness
SpaceX expands reach
Tesla expands autonomy
― Elon Musk’s Most Important Interview in Years - Ruben
Dominguez, The VC Corner [Link]
How To Remember Everything You Read - Polymath
Investor [Link]
An active reading and retention framework
Your brain loves patterns. Comfort means staying in old circuits.
But discomfort shocks the brain. It forces new neural pathways - that's
neuroplasticity. Every uncomfortable action - cold showers, public
speaking discipline- is literally rewiring your brain into a stronger
version of you.
― how to be extremely disciplined - Bella Dane [Link]
How to Trick Your Brain into Doing Difficult Things - Dr.
Dominic Ng [Link]
I like this one: Make it fun
Listen to your favourite podcast during cardio
Drink your nicest coffee while doing deep work
Use your comfiest chair only for studying
Light your favourite candle when writing
Play specific music while cleaning
The 20-Minute Writing Exercise That Neuroscientists Say Can
Solve Your Hardest Problems - Magdalena Ponurska [Link]
The science behind this writing exercise involves cognitive
neuroscience and functions as attentional training. It works by
simultaneously leveraging three mechanisms:
Activating the Prefrontal Cortex: Writing about
solving a problem in vivid, present-tense detail activates the
prefrontal cortex, which is the brain's planning and problem-solving
center. Studies show that the brain treats this detailed written
simulation of future scenarios as a form of experience, unlike abstract
goal-setting.
Priming the Reticular Activating System (RAS): The
writing exercise primes the RAS, which serves as the brain's filter for
determining what you notice in your environment. By writing about being
a person who "found money under rocks," the RAS starts flagging relevant
opportunities or "solution-shaped things" that were previously
overlooked.
Creating Implementation Intentions: When you create
a detailed mental scenario of completing a task, you are creating
specific "if-then" plans known as implementation intentions. Research
indicates that this technique makes people two to three times more
likely to follow through than those who simply set abstract goals.
Articulation has nothing to do with sounding smart, but with
sounding authentic.
What makes someone dangerously articulate, is the willingness to
think out loud without fear of making mistakes. To make your
intellectual curiosity visible, and to embrace the possibility of not
knowing everything while speaking aloud.
In an uncertain world, embracing uncertainty becomes the
foundation of dangerously articulate thinking.
If you fear uncertainty in your life, you need to leverage
uncertainty to overcome it.
You need to become obsessed in your own curiosity to become
genuinely useful to others.
Reading feeds curiosity. Reading improves how well you ask
questions. Reading fuels better synthesis through asking better
questions. Reading makes the perspective you have to offer to the world
more valuable because you can synthesize everything you have read into
solutions that can help people.
― How to become dangerously articulate - Craig Perry
[Link]
Four daily habits you must practice: 1) reading, 2) thinking out
loud, 3) teaching yourself, 4) writing.
Self-Promotion is selling your image. It demands praise. It
is rooted in ego. (x)
Authentic Visibility is sharing your expertise. It offers
repeatable value. It is rooted in service.
The Arrogance Trap (The Trophy): This focuses on the outcome.
It states the win without showing the struggle. The reader sees a trophy
and feels judged. (x)
The Service Solution (The Map): This focuses on the journey.
It shares the failures, the painful moments, and the simple frameworks
that finally led to success. The reader sees a map and feels
helped.
The Audience of One Exercise: Picture the single, most
valuable person you want to help (e.g., Sarah, Director of Product).
Define them by: The Pain they struggle with, The Goal they aim for, and
The Fear they are terrified of.
The Translation Test: Always translate the what (the specific
task you did) into the how (the repeatable rule anyone can
use).
― How to Build a Personal Brand When You’re a Senior
Professional Who Hates Self Promotion - William Meller, You
Visible [Link]
"The Strategy of Service
The core idea of Part 1 is professional relief: you don’t
have to promote yourself. The anxiety you feel is valid because
self-promotion is rooted in ego, but true visibility is rooted in
service. This is about adopting the Map
perspective-sharing the process and the failures-to eliminate
the fear of arrogance. To keep your content focused, define your
Audience of One. Finally, remember the
Translation Test: your expertise is locked inside your
company’s context; always translate internal success into a
Portable Principle the market can immediately use."
"The Architecture of Proof
Part 2 is the strategic realization that your LinkedIn profile is a
passive, magnetic sales tool. Your
Headline must be a 10-second service promise. The
About section earns trust by showing a past failure
(the scar) that led directly to your unique framework
(the solution). The most important shift is in the Experience
section-stop listing activity and start listing your
professional legacy by detailing the
mechanism you engineered and the value it created.
Finally, the Featured section provides tangible
proof of your competence, fulfilling the promise made in your
headline."
“The Protocol of Consistency
The core challenge is that visibility requires consistency, but
self-promotion is exhausting. Part 3 turns content creation into a quiet
routine. Start with the Daily Capture Ritual to source
your ideas from Friction Points and Instruction
Moments-the problems you already solved. Batch your writing
into a 60-minute Weekly Creation Block, always
following the structure of Conflict, Lesson, Illustration,
Conversation. Finally, implement the Generosity
Loop-committing 5 minutes a day to provide high-value
contributions in the comments of others-which is a low-effort way to
maximize visibility through service.”
“The Language of Quiet Confidence
Part 4 focuses on refining your voice to ensure your words are
precise and evidence-based. You must eliminate the language of demand,
which erodes trust, and embrace the Language Test by
reframing your message to that of a generous teacher. The greatest tool
is the mechanism-naming the specific process or
framework you built to prove that your knowledge is systematic and
repeatable. Finally, use the word “We” to project
confident leadership and credit the process.”
"The Quiet Metric System
Part 5 provides the ultimate relief: you can officially ignore the
noisy scoreboards. True authority is not measured by vanity metrics
(Likes, views) but by Authority Metrics-specifically,
the quality of inbound opportunities and Direct
Messages that reference one of your named mechanisms.
To sustain this, enforce the Time-Box Rule for writing
and embrace the 1/3 Rule to keep the focus on
low-effort engagement over high-effort creation."
Foundations: My 1999 (and part of 2000), Michael Burry,
Cassandra Unchained [Link]
Articles and Blogs
We Asked Roblox’s C.E.O. About Child Safety. It Got Tense. -
The New York Times [Link]
How we built OWL, the new architecture behind our
ChatGPT-based browser, Atlas - OpenAI [Link]
Exploring a space-based, scalable AI infrastructure system
design - Google Research [Link]
Google is seriously exploring whether AI data centers in space,
powered by near-limitless solar energy and connected via optical links,
could one day scale machine learning beyond Earth’s physical and
environmental constraints.
Thoughts by a non-economist on AI and economics - Boaz Barak,
Windows on Theory [Link]
The real economic question is not how good AI is today, but
whether its exponential improvement translates into an exponential
reduction in human-only tasks—something history has never seen
before.
Software 1.0 easily automates what you can specify.
Software 2.0 easily automates what you can verify.
― "Sharing an interesting recent conversation on AI's impact
on the economy. " - Andrej Karpathy [Link]
Estimating AI productivity gains from Claude conversations -
Anthropic [Link]
Current AI already delivers large task-level time savings. Even
without future model improvements, widespread adoption could
meaningfully boost productivity. However, real gains depend on adoption,
integration, and reorganization. The largest historical productivity
revolutions came from changing how work is organized, not just doing the
same tasks faster.
This study provides a lower-bound, usage-grounded lens on AI’s
economic impact—useful for tracking trends, not forecasting destiny.
JPMorgan Rolls Out Deposit Token JPM Coin in Digital Asset
Push - Bloomberg [Link]
JPMorgan’s rollout of JPM Coin shows how big banks are using
blockchain to modernize real-world payments—faster, always-on, and
regulated—without embracing speculative crypto.
The move reflects a wider trend among large financial institutions to
modernize payment infrastructure using blockchain while staying within
regulated banking frameworks.
Ramp/Brex beat the Amex/Concur experience by bundling the
corporate card with AI-powered software. Instead of pulling manual
expense reports from one system and importing them to another. The
expense report, rules, and controls they’re all embedded together,
beautifully.
I think there’s three big lessons if you’re a bank
Software is the Product: The integrated software experience is
the new competitive moat, not a "portal" bolted onto a legacy
product.
Automation is the Standard: AI-driven, "zero-touch" workflows
are the new customer expectation. The manual expense report is
dead.
The All-in-One Platform Wins: Customers will always abandon a
stack of siloed tools for a single, bundled platform that solves the
entire workflow.
― The CFO Dashboard; Ramp, Brex or Mercury - 18 Months Later
- Simon Taylor, Fintech Brainfood [Link]
This is a story about recognizing that financial services for growth
companies are being re-architected into three different endgames, each
optimized for a different definition of scale, control, and user
value.
Across all three companies (Ramp, Brex, Mercury), the real trend is
re-bundling:
Software is the product, not a portal layered on top
Automation and “zero-touch” workflows are now table stakes
Traditional banks aren’t dead—but they are structurally behind.
Two signals stand out:
JPM Coin launching on Base suggests banks are moving “open loop”
on-chain
Stablecoins are becoming real infrastructure, not just crypto-native
tools
This could reshape cross-border payments, treasury management, and
bank interoperability—potentially challenging SWIFT.
JPMD_-_USDC_v_3
Google Maps releases new AI tools that let you create
interactive projects - TechCrunch [Link]
Google Has Your Data. Gemini Barely Uses It. - Shlok
Khemani [Link]
Google’s Gemini has access to unparalleled personal data, but it
intentionally underuses it. Gemini’s memory system is carefully
designed, transparent, and conservative—prioritizing safety, trust, and
control over magical personalization. This restraint is elegant, but it
may cost Google its biggest competitive advantage in personal AI.
Alex Karp, CEO of Palantir: Exclusive Interview Inside PLTR
Office - Sourcery with Molly O'shea [Link]
Most corporate and government leaders now believe AI software should
work, and they seek out solutions when their own projects fail. The
launch of AIP was an "artistic" decision made quickly (he launched it in
the "darkness of night" pre-Easter to avoid resistance) based on the
insight that LLMs would become commodity products and orchestration
would be much more valuable. This change in customer belief has
increased Palantir's authority, compressing sales cycles from five years
versus nine months to five years versus two or three months.
The focus is on growing the U.S., enhancing the quality of the user
experience (UX), and ensuring Palantir remains closest to the things
that give America a strategic advantage for decades.
YouTube CEO Neal Mohan on AI, Censorship & the Future of
Creators - All-In Podcast [Link]
YouTube CEO Neal Mohan is discussing the massive scale of the
platform, the state of the creator economy, and emerging technological
challenges. Mohan defended the long-standing 55/45 revenue split within
the YouTube Partner Program, citing the billions paid out to creators
and the strong return on investment (ROI) that high user engagement
delivers to advertisers. He also highlighted the success of subscription
products like YouTube Premium and YouTube TV, positioning the platform
as the top streaming service in the U.S. The conversation addressed
content regulation, confirming a pullback from controversial COVID-era
censorship and emphasizing YouTube’s commitment to free expression
despite the difficulty of managing diverse global laws and cultural
nuances. Crucially, Mohan revealed that YouTube is adapting to the rise
of synthetic media by developing new likeness detection tools—modeled
after Content ID—and implementing transparency labels for AI-generated
content to protect creator identities and address "AI slop."
Elon Musk: OpenAI Betrayal, His Future at Tesla, and the Next
Big Thing — Grokipedia - All-In Podcast [Link]
Does OpenAI Need a Bailout? Mamdani Wins, Socialism Rising,
Filibuster Nuclear Option - All-In Podcast [Link]
OpenAI CFO Would Support Federal Backstop for Chip
Investments - WSJ Video [Link]
When you only know one field deeply, you see problems through
that one lens. When you know many fields shallowly, you can't solve
complex problems. But when you know one field deeply and have worked
across many others, you can take a pattern from field A and apply it to
solve a problem in field B.
Everytime you learn something new, immediately find 2-3 examples
from completely different areas that use the same idea.
When you struggle and fail, your brain becomes super aware of
what you don't know, it creates gaps in your knowledge that your brain
wants to fill. When the teaching finally comes, your brain is actively
looking for the missing pieces.
― how to actually become a polymath. - riskambition
[Link]
How to articulate your thoughts more clearly than 99% of
people - Matt Huang [Link]
What does it mean to be articulate?
To express (an idea or feeling) fluently and
coherently
The best speakers are the ones who are able to express the idea or
the thing they need from someone in 5-10 seconds or less, Any longer
than that and you honestly don't understand the thing that you're trying
to explain.
Anticipating key questions
They deliberately make other people win bigger than them, not equal,
not balanced, bigger. And they do it first before asking for
anything.
It triggers psychological debt.
In any interaction, ask what costs me almost nothing, but would be
huge for them. Maybe it's connection, maybe it's knowledge you already
have, maybe it's taking an annoying task they hate. Give that first, not
after, not during, first.
The most successful people aren't doing more, they are doing less,
but at level that nobody else can touch, because they are not distracted
by good opportunities.
Here's what separates effective people from everyone else, they treat
this time (prime time) like a medical emergency, no meetings during
prime time, no email, no quick questions, no administrative garbage,
this is when you do the one thing that actually moves the needle.
The most effective people are actively bad at most things on purpose.
They are not well-rounded, they are sharp in one place and dull in
everywhere else.
― how to easily become a highly effective person. -
riskambition [Link]
Epstein Files Fallout, Nvidia Risks, Burry's Bad Bet,
Google's Breakthrough, Tether's Boom - All-In Podcast [Link]
FULL: Elon Musk Makes Shocking Future Predictions At
U.S.-Saudi Arabia Forum Alongside Jensen Huang - Forbes Breaking
News [Link]
We Asked Roblox's C.E.O. About Child Safety. It Got Tense. |
EP 163 - Hard Fork [Link]
The Roblox CEO Dave Baszucki has been widely criticized after the
safety interview on Hard Fork for
his controversial push to introduce dating services and adult
content onto a platform predominantly used by children, initially
refusing to set the minimum age at 18.
being repeatedly combative, defensive, and used aggressive
interruptions to avoid legitimate questions.
when asked about the platform's long-standing issue with predators
on Roblox, Baszucki stated that he viewed the problem not merely as a
serious issue but as an "opportunity" for safety innovation.
unironically entertaining a question about implementing educational
kid gambling on the platform, suggesting it would be a "brilliant idea"
if structured legally.
OpenAI's Code Red, Sacks vs New York Times, New Poverty Line?
- All-In Podcast [Link]
AI should either be a guardian angel or a cognitive
amplifier.
― Satya Nadella – How Microsoft thinks about AGI, Dwarkesh
Patel [Link]
The Thinking Game | Full documentary | Tribeca Film Festival
official selection, Google DeepMind [Link]
Anthropic C.E.O.: Massive A.I. Spending Could Haunt Some
Companies - The New York Times [Link]
Are Banks Secretly Winning the AI Race? (OpenAI Insider
Explains) - Rex Salisbury [Link]
