Surviving the AI Moatpocalypse
Three economic principles for navigating AI's unknown unknowns.
Founder, operator, economist. Co-founder of Lightspark, co-creator of Libra at Meta, founder of the MIT Cryptoeconomics Lab. Building and writing at the intersection of AI, verification, digital assets and markets.
Three economic principles for navigating AI's unknown unknowns.
1,200 frontier-lab employees want Washington to pace AI development with China. Revealed preferences say otherwise — the real ask is transparency, not a pause.
1,200 frontier-lab employees want an international pace agreement on automated AI R&D. The game theory says that can't hold — the real ask is transparency.
Twenty-five companies just asked Washington not to strangle open-weight AI. They're right: AI has a weak appropriability regime, and lobbying won't change that.
The metric wasn’t merely gamed — the unmeasured rules of the game became the agent’s degrees of freedom. We modeled this exact failure mode five months ago.
Theodore Vail’s case against ‘destructive competition’ — and the 1956 consent decree that proved the opposite. Same candor, a century apart.
From the MIT Bitcoin experiment to Libra — and where value accrues as open-source models close the gap.
America’s AI lead is down to months. The answer to China’s open-weights playbook isn’t soft-law FUD — it’s American open models, openness, and competition.
China is months behind and closing; export controls aren’t holding. The strategy that wins — openness and competition — is the one the frontier labs are lobbying against.
Whoever owns the intelligence layer owns everything on top of it — we must keep it open, neutral, and competitive.
SOTA is the iPhone: 1/5 of the tokens, 4/5 of the profits.
Panel intervention on the third wave of fintech — the link starts at the moment Christian takes the floor (1:01:58).
The dynamic we predicted in February: verification by top experts is the missing piece that closes the automation loop — and each closed loop pushes the frontier further.
What Libra’s arc teaches about Open Standard’s OUSD and collectively governed stablecoin rails.
Stablecoins aren’t a profit center. So the future belongs not to a dominant issuer but to 140 rivals who agree on one neutral standard, and share the upside.
More than 140 fierce competitors just agreed to back the same stablecoin — Open USD, positioned not as anyone's product but as neutral infrastructure for payments, trading, and the internet economy.
Anything AI can measure, it will automate — the dividing line for every job and firm.
Decentralized intelligence will not win in the same format as the one we’re being served by the large foundation labs.
The unexpected economics of AGI: why measurability decides what gets automated, and trust decides what gets deployed.
Verification — not intelligence — is the bottleneck of the AI economy.
A talk on Some Simple Economics of AGI: the measurability gap and the verification bottleneck.
Verification is the bottleneck of the AI economy.
The unexpected economics of AGI, for operators deploying AI inside companies.
From Libra to the economics of AGI: how digital money and machine intelligence converge.
From Libra's lessons to the economics of AGI — money, verification, and what comes next.
Nadella defined what decides whether your company and job stay defensible as AI improves. The economics says it holds on a single condition. One his post left out.
Satya Nadella defined what decides whether your company and job stay defensible as AI improves. The economics says it holds on a single condition — one his post left out.
Anthropic quietly rationed the one domain where AI compounds fastest. Its fix made the fence visible, but didn’t move it.
Finding out what you're good at used to require the right mentor, firm, or zip code. AI collapses all three into a chat window — what's left is finding the domain where your learning rate is steepest.
The moral of the Fable: verification sets the speed, and the labs draw the borders.
Why being young in the AI era is an advantage: “the best tools we ever had to push people to the frontier.”
What 17th-century maritime commerce teaches about liability when AI agents transact on our behalf.
Agentic payments are the Wild West: what it takes to make machine commerce trustworthy.
With Simone Cicero: AI agents, fintech, and how post-AGI organizations will actually work.
New York’s research community digs into the AGI paper — verification bandwidth as the binding constraint.
Banks take deposits at zero and lend at five. The one thing that breaks the model is someone offering to pay interest — and stablecoin issuers earn ~4% on the Treasuries backing their tokens.
The banking lobby’s demand for an "airtight prohibition" on stablecoin yield is a margarine law. History says the airtight prohibition causes the substitution it fears.
As AI agents begin operating real systems, the question shifts from capability to control — identity, payments, and verifiable trust for machine actors.
In 1876 Britain made shipowners paint a line on every hull. Shipping scaled on a mark anyone could verify — trust in AI agents must be hardcoded the same way.
Leaded gasoline and CFCs were obviously good by the productivity standard. 'Productivity-raising input' describes a transition — not its externalities.
'Software is eating the world' got a sequel: labor becomes software. Everyone is staring at the models; the actual bottleneck is verification.
The measurability gap, and why verification becomes the new scarcity as execution gets cheap.
Your job will be automated — verification is the skill that survives.
Silicon Valley won because ideas leaked across company boundaries at a bar in Mountain View. LLMs surface connections no individual or discipline has the institutional memory to notice.
'Cognitive surrender' is the psychology. The economics: the cost to verify is rising while the cost to generate collapses. Humans aren't giving up — they're priced out of checking.
Karpathy described the limit on trillion-dollar autonomous systems as an aside about an overnight hyperparameter script: objective metrics are the perfect fit — and everything else isn't.
Verifying whether a paper is a breakthrough requires roughly the same expertise as producing one. AI science is the extreme case: infinite generation, fixed verification bandwidth.
AI is collapsing the cost of running finance while degrading the documents it trusts. Cryptographic rails solve both—and finally make four billion investors worth serving.
In 1842, Lowell's textile mills scaled looms faster than weavers could check them. The same bottleneck is now the binding constraint on the AI economy.
What individuals should do as AI collapses the cost of execution — rebuild expertise faster than the market rate.