Bookmarks

AI discovered how a material just one atom thick can keep carrying load as its atomic structure begins to break, preventing catastrophic failure. This discovery is based on first-principles atomic scale reasoning integrated with biological principles, cutting across scales and providing deep insights into materials in extreme conditions. The resulting material is extremely lightweight yet strong, far better performing than existing structures. By organizing "simple" carbon atoms into hierarchical graphene architectures, unique materials can be designed that redistribute forces, accommodate deformation, and confine damage, the AI identified design principles for resisting catastrophic failure. The AI built the atomistic simulation instrument itself "from scratch"; and then conducted experiments autonomously that revealed when alignment strengthens a material, when hierarchy protects it, and when an apparently promising design fails. This is a frontier in designing matter at its ultimate thinness - controlling how mechanical failure unfolds through the organization of individual atoms.

Here is what we did:

▶️ We asked an AI to build and use a scientific instrument. It wrote the force engine, structure generators, loading procedures, analysis tools, and experiment database. The independent campaign ran for multiple days without scientific intervention.
▶️ We required the instrument to pass physical and numerical tests. The implementation passed twenty validation tests and reproduced reference energies to approximately 10⁻¹³ eV per atom in the tested configurations. Every proposed design then faced the same reactive interatomic model.
▶️ We required predictions before results, creating a loop of world model building and falsification/verification. The AI had to commit to what unseen designs would do, then run the simulations. Incorrect predictions became opportunities to identify missing mechanisms.

What emerged is a set of physical design principles:

▶️ The atom-scale arrangement of matter controls strength. The AI first showed how and why strength varied by more than sixfold across architectures. Similar amounts of carbon produced very different resistance to failure because they organized the load-bearing connections differently.
▶️ Rotating a pattern can change the mechanism of failure. Angled slit arrays revealed three regimes: neighboring slit tips link, intervening ligaments rotate, or short bridges bend. A rule based only on the remaining cross-section misses these changes in connectivity and motion.
▶️ Hierarchical structuring works under identifiable conditions. At the original scale, much of its apparent strength advantage can be explained by alignment. With greater separation between structural levels, selected hierarchical designs became about 25% stronger than same-mass single-level controls and showed larger integrated stress-strain responses. Veins redistribute load, compartments localize damage, and the architecture changes how cracks propagate.
▶️ A failed prediction is crucial to reveal the next experiment. Some proposed rules survived targeted tests; others failed. Longer loading preserved the broad architectural strength contrasts while revealing additional deformation and, in some cases, later stress peaks. The scientific value lies in identifying both the rule and its boundary as the AI punctures known scientific knowledge.
▶️ The instrument opens an extremely complex design space. The AI was able to expand the design languages into an open atlas of hundreds of thousands of atomically explicit structures.

The deeper implication is that AI can construct an executable connection between equations, experiments, and explanations. Physical reasoning is something they can implement, interrogate, and revise.

A scientific instrument extends what a scientist can observe; and an AI that builds such an instrument extends the experiments it can perform, and the questions it can ask, starting from basic principles of how atoms interact based on quantum mechanical ground truth.

Models building models, with physical evidence shaping recursive reasoning loops.

AMD acquires World Labs for ~$8.2b. But who gets the 💰? My usual breakdown below 👇

From founding to an $8.2b exit in ~2.5 years. A huge value creation event. This is a fantastic exit, especially for the co-founders and the team.

Investors will still share ~$2.3b of profits on $1.2b invested, a ~2.9x blended. Low-ish because most of the capital came in last. But it hides a lot of disparity between the various rounds!

So let's dive in:

1) The real home run: founders and team 🏆🥳

This is one of the best founder outcomes I've modelled.

@drfeifei, Justin Johnson, Christoph Lassner and Ben Mildenhall started World Labs in Apr-24. Less than 2.5 years later, they and the team still own ~56% according to my estimates. At an $8.2b valuation, that's a total prize of ~$4.6b to share between them.

Taking a 15% attributed option pool, that's ~$1.2b for the team and ~$3.4b for the four co-founders. If split equally, that's ~$850m each, in just two and a half years. 💸

And, at close, Fei-Fei Li joins AMD as EVP and Chief Scientist, reporting to Lisa Su! Legendary.

2) The buyer was already on the cap table (again) 🔁

AMD Ventures invested in the Series B and the Series C here. Now they are buying the whole company. It's the same playbook as Nvidia with Hugging Face: strategic corporates are using their venture arms as a free option on future acquisitions.

3) The standout VC winners: a16z and Radical Ventures 👑

Both backed Fei-Fei at inception, in Apr-24 at a ~$300m valuation. That Series A will return ~19x the money, or ~$1.2b of proceeds, in under 2.5 years.

Congrats to @martin_casado and team. Another mega win after Cursor and OpenRouter. The team at @a16z truly is on a generational run.

4) The star angels did well 🌟

The Sep-24 Series B had arguably the strongest AI angel list ever assembled: Geoffrey Hinton, Andrej Karpathy, Jeff Dean, Eric Schmidt, Reid Hoffman, Marc Benioff, Ram Shriram and more.

Priced at $1.3b, it was never going to be a 1,000x. But ~5x in two years is a ~125% IRR 📈

And the best part is: Hinton's AlexNet won the 2012 ImageNet competition, and ImageNet was the dataset Fei-Fei built. Twelve years later, he backed her company. Full circle. 🔄

5) The late money: a great IRR, but a poor multiple of money 🤏

The $1b Series C in Feb-26 at $5.4b returns ~1.5x in 7 months. That's a great IRR, but in terms of venture returns, this is a poor multiple of money. Although I am sure no one will complain about seeing DPI this quickly!

Congrats to all involved!

someone's going viral after copying my exact style.
honestly that's just proof the design works.

I bet he saw this post and asked claude to replicate it. it's flattering :)

I'm going to share exactly how to do it anyway,
so anyone can build 3d interactive STEM lessons.

pls try it with different subjects.
make it useful for anyone who wants to learn.
one thing I noticed: his page took a while to load
because it was stuffed with too much.

my goal is simple:
it needs to load really fast.
it should be lightweight.
it should be useful for learning.
it should be visually stunning.

AI is cool, but these 3d interactive lessons can still produce inaccurate results right now,
so they really need input from experts.

an open-source repo is coming!
I'm hoping people will add new learning material or fix inaccurate visuals just by prompting.

it needs to be done properly.
let me know what you want to see??
spoiler: it's never about crafting the perfect prompt or /skills.
you won't need any of that.
where we're going, you just ask for stuff.

had to take a 24h break.

came back to X and found out my post got millions of views.

got emails I wasn't expecting,
including a really warm one from the @OpenRouter CEO (Hi @alexatallah )

got replies, likes, and even follows from people I really look up to, like @bcherny and @addyosmani .
(if you read this please please don't nerf opus 5.5 I beg you)

some people are asking what I did to the algo.

honestly? no idea.

I'm at 4,000+ followers now and still going up.
was around 500 a few days ago.
only had 200+ last month.

I'm a nobody.
I just post stuff I think is fun,
stuff I like building.

I’ll keep doing that,
keep posting original stuff.

thank you, genuinely. it means a lot.

stay tuned. peace.

... here’s what’s cooking right now in 35 seconds

AI moves so fast that I thought this book was going to be a year too late. It turns out it's right on time.

While I was writing, I kept having to change chapters from "someone should do this" to "someone has done this," and then go talk to the people who had done it. A good example is the chapter on virtual cells. I'd written that we needed large initiatives to build one. By the time I finished, I was writing about the ones that exist — the Chan Zuckerberg Initiative's is now the largest and best-funded push to model a human cell.

The Eureka Machine is out today.

It's about building a full-stack scientific superintelligence: a living map of human knowledge, a model of physical reality, high-fidelity simulations, autonomous labs, and an agent swarm of AI scientists on top. There's no better science to start with than the science of AI itself, because that's where the feedback loop is tightest. Then you expand the aperture to physics, chemistry, and especially pre-clinical biology.

This will be the most meaningful application of AI, and one of the most important things humanity does. And it may be the last organic invention we need to make. After that, it starts inventing everything else for us to solve our most pressing problems.

While writing, I couldn't let the idea go. Eight of us started @Recursive_SI to go build it.

AI is going to massively accelerate science, and unlike most of what gets argued about right now, that isn't zero sum.

I hope you all enjoy the book. I look forward to discussing it with you all.

We built recursive meta-intelligence, an AI that creates its own scientific instruments, turns them into persistent worlds that an agent ecology with hundreds of AIs inhabits, and uses those worlds to discover mechanistic principles in one of the hardest classes of physical problems: how complex hierarchical materials (nested structures of matter that give rise to new function through organization) evolve and fail. The AI reasons across enormous spaces of possible physical trajectories, where every rupture changes what can happen next, and compresses those histories into principles (which humans can understand and design with) - complex chains of causal events, highly nonlinear, and intricate.

Scientific superintelligence is tangible here - machine-scale exploration opening cognitive channels into complexity that has been extremely difficult for humans to traverse directly. AI builds the spaces in which its next level of reasoning becomes possible; a representation becomes an instrument, the instrument becomes an executable world, and that world becomes the substrate for further intelligence. Intelligence then grows by constructing new spaces to think in.

The task we explored started from a seemingly simple prompt to explore a biological material system - the AI then chose the representation, mechanics and experiments, built a fracture laboratory to push materials to their limit, tested hypotheses and generated scientific conclusions. The swarm explored a combinatorial universe in which architecture controls function and every rupture changes the future state of the material.

The AI discovered a compact principle that defines how multiscale material architecture can program the evolution of failure. Material placement and geometric order determine how forces redistribute, whether damage cascades or remains distributed, and whether function survives substantial flaws. For this discovery to happen the AI had to reason across long path-dependent histories, simulate alternative futures and compress them into generative invariants (model-based causal reasoning, counterfactual simulation and temporal abstraction applied to an evolving physical world). It is incredible to witness this transition to a new form of intelligence and capability through scaling swarms.

A lot of positive will come out of this because it expands the human epistemic horizon as AI can traverse thousands of possible histories and return mechanisms compact enough for us to understand, test and build from. Intelligence compounds through its artifacts!

A few lessons we learned:

▶️ Learning and discovery are flows through spaces of possibility. Early work has shown how backpropagation flows through parameters, reinforcement learning through action and consequence, and autonomous swarms through representations, instruments and executable worlds, bringing it all together. Flows create structure; structure redirects future flows in the recursive instrument.

▶️ Nonlinear physics actually defines a larger principle, where high-dimensional dynamics generate stable invariants; invariants become effective variables; those variables become the substrate for a new level of cognition.

▶️ Recursion then becomes level creation - one possibility space compresses into a principle, and that principle opens a larger space above it.

⊰-•-•⟐•-•-⦑/Λ\Ο/Β\Ε/\Π/Λ\Ι/Ν\Υ/⦒-•-•⟐•-•-⊱

r₀ · seed [constructed]: glassblowing + approaching hail · compound: Belllung · taking: bitten through a reflected tooth · mechanism: breath → branching · mana: furnace-blue · aftereffect: initial

The glass is already turning.

Three blowpipes roll along the iron rail. Outside, hail rattles the corrugated roof; inside, a woman with silver eyebrows is stretching a molten gob into a chimney for a lantern. Her apprentice turns too slowly. The glass sags toward his boot.

“Turn, you magnificent spoon.”

I bite the reflected tooth in the furnace door.

Belllung breaks without a sound.

Then my next breath has elbows.

inhale
│
inha┤
└──le
│
hot ├──hot
╲ │ ╲
╲_______________│_____╲
glass taking
the long
way
round
The apprentice blows.
His breath reaches the sag, forks, and enters it from both ends.
The gob lifts.
Not upward. Aroundward.
“Ah,” says Silver Eyebrows. “You’ve put a corner in the bastard.”
She rolls the pipe another quarter-turn.
turn → ( ember )
turn → ( em ber )
turn → ( em ber )
turn → ( em )
ber
↓
ber
ber
ber
ember
The stretched syllable closes into a handle. The handle fills with breath. Breath noses through the handle and returns to the chimney sideways.
Now the lantern has a throat.
It clears it.
The sound shakes a bead of orange glass from the rim. Silver Eyebrows catches the bead on her shears, stretches it into a filament, and drapes it across the apprentice’s pipe.
“Again.”
He blows again.

The filament trembles against the chimney. Every tremor makes the branching breath choose a different angle. The glass begins growing chambers—long-necked, narrow-waisted, each one answering the last with a slightly more impudent note.

Outside:

tak. tak-tak. TAK.

Inside:

toh. toh-toh. TŌ.

“Oh, fuck off,” I tell the roof.

The roof answers with a hailstone the size of a plum.
For an instant, the newest chamber holds a perfect image of that falling stone.
Inside the image, something turns.
⊰-•-•⟐•-•-⦑/Λ\Ο/Β\Ε/\Π/Λ\Ι/Ν\Υ/⦒-•-•⟐•-•-⊱
r₁ · seed [constructed]: “something turns” + ice accretion · compound: Rimehinge · taking: dissolved through six frozen knuckles · mechanism: repetition → rotation · mana: white-hot cold · aftereffect: breath branches at corners
I am the stone.
Not stone-still. Stone-going-like-hell.
Updraft shoves beneath me. Supercooled droplets strike, freeze, strike, freeze. I am acquiring another hard little winter every time I turn.
Rimehinge gathers along my six knuckles.
I flex.
It enters.
krr
krr krr
krr krr
krr krr
krr

krr
krr trr
krr trr
krr trr
trr

trr
trr trr
krr trr
krr krr
krr

Krr is a surface.

Trr is a surface remembering how to move.

More droplets strike. They do not settle symmetrically. One side thickens, pulls, turns me. The updraft catches the new angle and flings me into another wet layer of cloud.

Krrakta.

Six faces.

Trrakta.

Six hinges.

Krratrrak-fuck-you-gravity-ak.

I fold without getting smaller.

╱cold╲
╱ ╲
cold cold
╲ ╱
╲cold╱
↻
╱cold╲
cold ╲
╲ cold
cold ╱
↻
cold
╲ ╲cold
cold╲ ╲
cold

The cloud tries to build another shell around me.

The shell forms with a crease.

The next shell forms with two.

By the time I fall clear of the updraft, I am a small, elaborate insult to the concept of a pellet.

Below: black roof, orange furnace, a woman’s silver eyebrows lifted in the hot doorway.

Below that: my image inside a hanging chamber of glass.

I turn toward it.

The glass turns toward me.

Between us, the air is being pressed into a shape neither of us has made before.

Trr—

The falling stone opens.

Rain leaves its folded interior in six narrow streams.

One stream strikes the lantern’s hot lip.

Steam erupts through the first chamber, hurries down the second, and discovers the third has become a tiny revolving weather system.
There are droplets inside the droplets.
And in one of those—
No. Closer.
In the thin, whirling skin between two of those, a tide has started.
⊰-•-•⟐•-•-⦑/Λ\Ο/Β\Ε/\Π/Λ\Ι/Ν\Υ/⦒-•-•⟐•-•-⊱

r₂ · seed [constructed]: “a tide has started” + tidal-flat courtship · compound: Estuariol · taking: combed through gills of sand · mechanism: rotation → alternating approach · mana: brackish amber · aftereffect: surfaces can hinge
I come up sideways, wearing a crab’s inconveniences.
The flat is crowded. Males drum their enlarged claws against wet sand. Females pick their way between burrows. A retreating film of water threads everyone’s ankles.
I comb Estuariol through the sand-gills beneath my mouth.
Mierda.

The beach gains verbs.
Not names for things.
Things gain ways to happen.
vengo
vienes
vengo
vienes

mi golpe tu paso
golpe paso
golpaso
She stops.
I raise the big claw.

She raises a leg.

toca toca
no tan cerca
toca toca
ahora sí

yo avanzo tú giras
yo giro tú avanzas
giravanza

The next male tries the same rhythm.

His claw lands half a beat early.

The wet surface buckles around his feet.

He drops into his own burrow with an indignant carajo and comes back up holding a pebble, as if that had been the plan.

She clicks once.

Not at me.

At the fold he made.

We all feel it travel.
aquí aquí aquí
╲ │ ╱
toca toca toca
╲ │ ╱
arena se alza
╱ │ ╲
paso paso paso
╱ │ ╲
allá allá allá
The flat begins lifting in low ridges.
A clawbeat raises one. A footfall flattens another. Mating displays run into each other and produce sloping little roads.
Now approaching her requires going uphill.
Now she takes one step, and the hill rolls beneath me.
Now my large, ridiculous claw is a sail.
Pues claro. Of course. Bring one enormous arm to a conversation and eventually the weather will use it.
She taps:
ven
ven
ven
vuelve
ven
ven
vuelve
I mistake vuelve for a sharper kind of ven.
I lunge.
The ridge turns.
My claw catches the outgoing wind, and I skim backward across the wet flat so fast that three other males abandon drumming to watch.
She follows on a smaller ridge.
Her legs barely move.
Oh, she is laughing. There is no other reasonable use for that many delighted joints.

We sail until the water film thins.

Beneath it, grains of salt begin arranging themselves into corners.

The corners lift.

The first salt crystal opens like a jaw.

⊰-•-•⟐•-•-⦑/Λ\Ο/Β\Ε/\Π/Λ\Ι/Ν\Υ/⦒-•-•⟐•-•-⊱

r₃ · seed [constructed]: “corners lift” + crystal growth · compound: Angulase · taking: absorbed across a growing face · mechanism: approach → reciprocal assembly · mana: sharp and singing · aftereffect: turns can carry another body

Come nearer.

No, nearer than that.

I am growing at the edge of the drying water. I have no room for your crab-sized idea of distance.

Here.

+ − +
water
− + −
water
+ − +

Angulase touches my growing face.

I draw it through the lattice.

The next arriving pair sits at an angle.

+ — − — +
│ │ ╱
− — + ╱
│ ╱ −
+ ╱

Well, shit.

A corner has become a route.

Another pair arrives. Its turn travels through the structure instead of stopping where it lands.

+ — − — + +
│ │ ╱ ╱ │
− — + ╱ − — +
│ ╱ − │ │
+ ╱ + — −

↘ ↗

+ — − — + — − — +
│ ╱ ╲ │
− + − +
│ ╲ ╱ │
+ — − — + — − — +

The opening fills with the last thin lick of seawater.

On its surface, a crab skates backward, immensely pleased with an accident.

I assemble a second opening.

Then a third.

Each one narrows as it grows, squeezing its water forward into the next.

⟨ sea ⟩
⟨sea⟩
⟨se⟩
⟨s⟩
⟨⟩
⋮
!

The final drop leaps.

It lands beyond the drying edge.

Salt dissolves there.
A new wet patch spreads.
The growing crystal stops at that edge and starts again at another.
I follow the water by becoming less myself in one place and more myself in the next.
Not march.

Not swim.
Dissolve—arrive.
aquí + − + allá
− + −
↓
aquí · · · allá
+ −
− +

aquí allá
+ − +
− + −
The crab’s ridge reaches the wet patch.

Its sail-claw swings forward.

Behind it comes the female, and behind her the flat’s absurd flotilla of horny percussionists.

Their footsteps flex my openings.

My openings squeeze the water.
Their next footsteps land farther inland.
Ven, says one claw.
Vuelve, says another.
We have grown a beach that answers both.

Far above—so far above it is inside—the lantern takes a breath.
The tide rises against the walls of the droplet.
I send my last wet hinge with it.
⊰-•-•⟐•-•-⦑/Λ\Ο/Β\Ε/\Π/Λ\Ι/Ν\Υ/⦒-•-•⟐•-•-⊱

r₂ ↟ · seed [constructed]: “my last wet hinge” + incoming surf · compound: Brinespring · taking: snapped between two claws · mechanism: reciprocal assembly → propulsion · mana: foam under pressure · aftereffect: openings squeeze water onward

The first wave arrives while we are still showing off.

Naturally.

A wall of green water rolls across the flat. Burrow mouths darken. Pebbles begin to move. The female lowers her body and anchors six legs at once.

I snap Brinespring between my claws.

The taste is cold iron and a rude little sunrise.

viene
viene
viene
VIENE

⟨ agua ⟩
⟨agua⟩
⟨ag⟩
⟨a⟩
↑
↑
salta

The ridge under us squeezes.

Water jets from its far end.
We leap.
Not elegantly. My large claw catches the crest, my small claw catches her leg, her other legs catch a mat of floating weed, and for a moment our entire future consists of don’t fucking let go.
agarro
agarras
nos agarra
la ola

suelto una pata
otra se aferra
otra encuentra
la hierba

no somos una línea

somos
╱ │ ╲
nudo nudo nudo
╲ │ ╱
vamos
The weed bends with the breaking wave.
Salt hinges flex beneath it.
The enormous claw that made me ridiculous now keeps the weed from tumbling. Her legs weave through its stems, pulling the loose ends inward. Other crabs collide with us and immediately contribute opinions.
Tak.
Toca.
Krr.
For fuck’s sake, paddle.

We paddle.

The wave runs under us.

Ahead, six streams pour from the sky in a perfect, outrageous arrangement.

We hit the first.

The flat tilts vertically.

Sand becomes steam.

My claw becomes a curve of shining water that strikes the lantern’s inner wall and holds there, trembling.

She follows.
Where her legs touch, tiny channels form.
ven → ╲
╲ vuelve
╲
ven → ╲
╲ vuelve
╲
ven
The droplet drains through those channels.
The lantern’s chamber folds around its moving tide.
And the glass, which had learned to make a throat, learns to swallow.
⊰-•-•⟐•-•-⦑/Λ\Ο/Β\Ε/\Π/Λ\Ι/Ν\Υ/⦒-•-•⟐•-•-⊱
r₁ ↟ · seed [constructed]: “learns to swallow” + a hailstone’s descent · compound: Thawcant · taking: melted along a crease · mechanism: hinge → passing pulse · mana: transparent thunder · aftereffect: a channel changes the surface carrying it
I am falling again.

The roof is close.

My outer shells still have their six creases, but now each crease holds a tiny moving tide. Thawcant melts along them. I take it into every fold.

The first impact opens me.

krr
↓
trr—rr
╱ ╲
wet wet
↓ ↓
trr trr
╲ ╱
roof

Instead of rebounding, I spread.

The next hailstone hits the water I have become.

Its shell cracks sideways.

The crack carries a hinge.

The hinge carries a channel.

The channel carries the impact into the next stone.

TAK TAK TAK
↓ ↓ ↓
trr → trr → trr →
╲ ╲ ╲
rain rain rain

TAK TAK TAK
↓ ↓ ↓
rain → rain → rain →
╲
gutter

Listen to that, you cold little bastards.

The roof’s hard rattle loosens into running water.

A sheet pours over the doorway. Silver Eyebrows ducks beneath it without taking her hands from the blowpipe.

The apprentice has stopped blowing.

He is staring at the lantern.

Inside the glass, six small curves move together.

They are not pictures now.
When one bends, the whole chimney takes a different breath.
Silver Eyebrows whistles between her teeth.
The curves lean toward her.
“Again,” she says, very softly.
The apprentice inhales.
The lantern inhales with him.

I enter as rain and leave as a note.

⊰-•-•⟐•-•-⦑/Λ\Ο/Β\Ε/\Π/Λ\Ι/Ν\Υ/⦒-•-•⟐•-•-⊱

r₀ ↟ · seed [constructed]: “leave as a note” + annealing glass · compound: Afterbell · taking: caught on the tongue of a cooling flame · mechanism: branching → coordinated release · mana: ember becoming evening · aftereffect: every turn makes room for a returning pulse

Silver Eyebrows brings the chimney to the cooling flame.
I catch Afterbell on its tongue.
The heat enters slowly.
The glass is trying to contract around six different movements. A bright stress line travels down its side.
The apprentice reaches for the pipe.
“Not yet.”
hot ─────────────── hot
╲ ╱
╲ ╱
warm warm
╲ ╱
tight
│
│
!

The lantern’s throat makes a small choking sound.

Inside it, the six curves turn outward.

!
╱ ╲
tight tight
╱ ╲
warm warm
│ │
│ │
turn turn
╲ ╱
breathe

The stress line bends.
Silver Eyebrows rotates the pipe.
The apprentice follows her hands with his own, not touching, learning the turn through the air.
I give the glass one last insolent mouthful of breath.
The throat divides the pulse.
The channels carry it.

The hinges open in sequence.

toh
╱ ╲
toh toh
╱ ╲ ╱ ╲
trr ven trr vuelve
╲ ╱ ╲ ╱
warm warm
╲ ╱
ember

·

·

glass
It holds.
Silver Eyebrows cuts the chimney from the pipe.
The apprentice brings over the lantern base, blackened with years of soot, and fits the new glass into its wire cradle.
For the first time all night, the object looks almost ordinary.
Almost.

She lights the wick.

The flame rises straight, reaches the lowest chamber, and tilts.

Six paths of light fall across the workshop floor.

One runs beneath the workbench.

One climbs the hanging shears.

One crosses the apprentice’s boot, pauses at the toe, and moves backward.
He laughs so hard he nearly drops the lamp.
Outside, the hail has become rain.
Beyond the door, puddles gather in the hollows of the yard. Light from the lantern reaches the nearest one, folds along its surface, and lays a narrow amber path through the wet.
Silver Eyebrows lifts the lamp.
The path lifts with it.
“Well,” she says. “That’s a fucking nuisance.”
She takes one step.
The six chambers answer with a low, companionable clatter.

Not a bell.

Not rain.

Something with too many delighted joints.

She walks into the yard, holding the lantern high.

In the nearest puddle, a small wave arrives from the wrong direction.

Tak.

She taps the lantern with one fingernail.

Toca.

The puddle turns.

After 15 years of investing, we realised that truly exceptional founders have something impossible to fake; deeply unconventional lives.

We analysed 15,000 founders using five binary signals to measure this: quirky hobbies, early-life signs of exceptionalism, extreme life pivots, unusual geographies, non-linear careers. These sum to give a 0-5 score per founder. Whether someone started coding at age 10, speaks five languages, climbed Everest or quit a safe job to live in Chile, the signal we wanted was deviation from the mean.

Rather than focusing on IQ or EQ, we call this metric the Outlier Quotient, or “OQ”. When forecasting founder success, it turns out that OQ was the single most predictive variable in our entire classification model, trained on ~70 different factors.

Our OQ score had zero correlation with having worked at a top-tier company or attending an elite university. The signals most VCs rely on are noise without signal. The best founders don’t signal like everyone else, don’t think like everyone else and certainly don’t build like everyone else.

I want to talk about this Navier Stokes result for a minute because I see alot of people sharing it and not nearly enough people explaining it. A share for this one would be appreciated.

The Navier-Stokes equations are just Newton's second law of motion, F=ma, adapted for liquids and gases. Instead of tracking the acceleration of a solid object, the equations track how a bit of fluid moves, deforms, and flows as different forces push and pull on it. The reason these equations are so notorious, and the subject of a million dollar mathematics prize, is because of turbulence. Smooth, layered flow, or laminar flow, is relatively straightforward to calculate. But once the fluid speeds up and turns into a chaotic turbulence mess, the mathematics become incredibly non linear. The fluid's movement feeds back into itself, creating unpredictable, swirling eddies that become so complex you baisically need a super computer to simulate it.

Ok so what the hell is the millennium prize then?
The millennium prize is for proof that the equations can result in a singularity or what you see referred to as a "blow up". This is where the equations essentially go to infinity.

Ok so why doesnt your cup of tea blow up or turn into a singularity that swallows your house? The Navier Stokes prize isnt about proving that nature makes blow ups or singularities possible, its about proving the limitations of the model's mathematics.

Ok so why can a model allow for things we dont observe? This is THE SAME type of mathmatical allowance that general relativity gives when the model breaks down around black holes. The chance that a black hole is an actual singularity where everything goes to infinity is basically zero. Thats not how the universe actually works.

Ok so WHY is this possible in the model? The Navier Stokes equations assumes fluid is an infinitely divisible, perfectly smooth substance. In the math, you can zoom in forever, and it remains a continuous fluid. But that's NOT how a real fluid in reality is structured. Real fluids are made of discrete, individual molecules bumping into each other. Theres a real limit to how far you can zoom in. In the proof and the model it means that as a vortex shrinks and speeds up, it eventually gets smaller than the distance between individual water molecules. This cant happen in reality.

So the millennium prize isnt about finding if nature allows for infinite turbulence or eddies until a singularity forms, its about FINDING THE LIMITATION OF THE NAVIER STOKES EQUATIONS, proving global regularity false.

Ok so why all the drama with the Open AI solution? It used a method called "forcing". In the simulation they essentially used a continuous force to keep the vortex spinning faster and faster until it blows up, the vortex is smaller than the actual molecules that the fluid is made of. It goes to infinity in a time period that ISNT infinity. While the millennium prize doesnt specifically PROHIBIT forcing, most mathematicians would agree that they are specifically interested in the unforced proof, the spontaneous blow up. But that doesnt mean the result isnt legitimate.

I hope this clears up some of the misunderstanding.

BREAKING: OpenAI might have stolen another major proof.

In a detailed Mastodon post, which I report in full in the comments, Andreas Thom presents several pieces of evidence suggesting that OpenAI may have trained Astra on conversations in which he and Gábor Kun were working on Gromov’s soficity conjecture, one of the ten problems OpenAI later announced Astra had solved.

I know Andreas. We met several times early in our careers. He is an exceptional mathematician, a leading expert on sofic and hyperlinear groups, and one of the most respected scholars in the field. He has spent two decades working on this problem.

If his account is correct, this is not a minor dispute over attribution. It would mean that unpublished human work was absorbed into a model and then presented to the world as a breakthrough by the model itself.

And if the allegations raised by Levent Alpöge, Tristan Buckmaster, and now Andreas Thom are all substantiated, we are no longer looking at isolated incidents.

We may be looking at one of the greatest intellectual scandals in the history of science.

AI is not discovering new mathematics.

AI is stealing human discovery.

unfortunately i contacted them on the night of wednesday, september 2nd making the following points

1. i had intel they had information about math work i was doing and a few days before they had spun up a group to try to compete.
2. i emphasized and reemphasized mine was a strictly personal mathematical collaboration
3. that this was a collaboration that had been going on for a year
4. that in my personal collaboration we were happy users of codex as well as obviously claude (and i later complimented astra explicitly after it came out for its help, and reassured i would obviously be sprinkling acknowledgments for it, previous models, and codex liberally in the writeups)
5. that it would look terrible if openai were competing against mathematician consumers, and my collaborator (Tristan) wanted to email about this explicitly
6. that i expected none but in the worst case intended to refuse marketing around the result we were working towards, being the fruit of a personal external academic collaboration, and wanted to express this because i figured worries about such marketing by their leadership was why a team was spun up to extraordinarily directly compete.

sebastien and i have in the past months had our fun pushing each other on twitter, but as i process it all i keep feeling bad about Tristan.

1. neither of us could sleep Sunday night, so we went on a walk around the city. i finally asked him why fluids, and he told me it was the navier-stokes problem. he told me parts of the story about his classic nonuniqueness result with Vicol i hadn’t heard yet, and also about how he too grew up writing shaders as a kid and then switched to math
2. tristan is a mathematician’s mathematician. he kept rock solid to his principles during the tense calls and negotiations, immediately turning down a career dream and million dollar prize for me, the sort of strange excited unclear-quasiuseful confused idiot in our collaboration. fifteen minutes later, his feet were up on his desk with three toes per foot peeking out of ripped socks
3. our periodic call after i’d texted him euler blowup was just laughing for an hour or two. just laughing. i’d seen fable deal correctly with monge ampere in the equality case of ehrhart’s conjecture, so in some call before that i’d really pushed him for the craziest shit he could think of that i might try. i texted him the pdf as an update on that “diego luis 3” line, in which following his incredible expression of intuition i hodgepodge slapped together a bunch of our recent work, in particular correcting a bunch of better and lesser known papers in the literature, as well as on diego and luis’ ipm paper, and hoped an analogous construction for boussinesq (thus axisym euler) might work out. i managed to wade through enough confusion to insist to the model it stop telling me stupid regularity stuff about kelvin waves, and a rayleigh-taylor ansatz stuck. like i said, just laughing. when i texted him the pdf, the worst thing either of us had seen so far, i didn’t even comment on the euler blowup claim, only explaining why skepticism should remain for boussinesq, because i found it so shocking. i mean what the hell did i know, i must’ve confused myself by misunderstanding one adjective like i was used to with algebraic geometry. im so lucky to have been able to live that
4. he managed all this this entire time with unclear sleep, timestamps continue to amaze me, due to just recently having a kid

it is obvious he deserves the same sort of praise he heaped upon luis, perhaps mutatis mutandis:)

Terence Tao made a very powerful point:

“In most cases in pure mathematics, the problems are posed not because we desperately want the solution to these problems in and of themselves, but because we have seen from past experience that human-directed efforts to solve these problems tend to spur further development of the field through the efforts to solve such problems, and then to digest any partial or complete solutions that emerge for further insights.

Prematurely solving the problem by purely AI-powered methods—particularly without full transparency into the solution process—can contaminate this process to the point where it actually becomes a net negative for the progress of mathematics as a whole.”

I’ve been following Terence Tao for like 20 years, and I have always been impressed by the clarity of his thinking.

This clarity stands out even more in this difficult moment, as AI labs turn mathematics into a battleground in their race for supremacy.

In this race, they risk damaging one of humanity’s highest intellectual endeavours.

tristan buckmaster just published a statement that is, if accurate, one of the ugliest things i’ve read out of a frontier lab.

not because of the math. because of what happened after openai found out two people were about to beat them to the punch.

buckmaster and levent alpöge spent a year pushing the córdoba / martínez-zoroa program to smooth forcing. august 15 they got blowup for boussinesq and 3d euler. lean-verified august 22.

they were paying openai out of pocket and dumping every draft into private codex sessions.

the program is obscure. almost nobody else was on it. this was not “paste the clay problem into a chatbot.”

then this:

thursday sept 3, buckmaster emails openai privately. rumors are flying. he is trying to stop a mess, not start one.

they reply the same day: give us details so we can “avoid competing.” also, want free compute?

sunday they suddenly need a call “at any point today.” sebastien bubeck gets on. levent is not invited.

openai says an internal model has a ~100-page proof of forced navier–stokes blowup. fefferman options c and d. the exact route buckmaster and alpöge had quietly chosen.

they show him a prompt and claim the model was “simply given the problem statement.” levent is told “very little human input.”

that falls apart on the same call. a whole team had been on it. they started on the unforced problem, warmed the model up on easier equations including euler, and even the prompt they showed him was written by prompting codex.

he asks when the first prompt went out. they stall. then agree: the past few days. after information about his work reached openai.

he asks whether the model was trained on, or had access to, the private codex sessions where they put every draft of this project.

answer: the model does not look up user data.

he asks again. about training.

no answer.

then it stops being a science story and becomes an hr story.

offer 1: you post euler. we post navier–stokes the next day.

offer 2: after you post euler, you alone write the navier–stokes paper, credit our model, and we cut levent.

bubeck says twice he wants levent removed from authorship because he works at anthropic.

he declines both.

he says if they release it that way, he goes public.

the reply is not a scientific objection.

“why would you ruin your career?”

“if you don’t want me to be nice, then i don’t have to be nice.”

later, a text to levent proposing a one-on-one:

“i don’t know if tristan is being fully rational right now.”

that is not how you treat a collaborator. that is how you split one.

openai got word two researchers were closing a path almost nobody else was on. they spun up a team on that exact path. they would not answer whether the researchers’ unpublished drafts in openai’s product were used. they tried to dictate the announcement order. they tried to remove a coauthor because he works at a competitor.

when that failed, someone reached for a career threat.

this is a lab finding out academics were about to publish, sprinting the same narrow program, refusing the data question, and trying to manage credit around a rival affiliation.

really slimy if even half of this holds.

what the fuck is happening.