Price the tail
Joined February 2024
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Btw, I’m building Daedalus Research (@DaedalusRsch) with @shawpredicts and other very sharp researchers, working on forecasting models and neo insurance.
If you only know me for the industry gossip and have no idea what we’re building, I’m not building hard enough.
Forward.
Top-tier data vendors frontrun OpenAI and OpenAI frontruns everyone else.
(mom, Nic Carter quoted my post. I made it.)
Erdős problems seem to have overtaken prediction market making bots as the preferred way for larpers to advertise a 150+ IQ. I just wish some of these people actually cared about the mathematics, or had any feel for what makes a proof beautiful.
Was just reading papers from 2006 on risk society and mechanism design.
They go far deeper than almost anything being said about AI alignment and insurance today.
More than five of your data lab’s favorite data labs just liked this post. They seems to have found even more ways to legally commit crime.
This evening I sat across from the CEO of a so-called trading neo lab with over $1B val. He told me their models—built on best-in-class RL environments and online learning algorithms—have produced exceptional pnl and sharpe ratios.
I later learned that the company is, in fact, a data broker LARPing as a frontier lab. Its actual edge is trading stocks and options on insider information of model releases and synthetic-data acquisitions.
It appears I have once again materially underestimated the absurdity of this industry.
In the run-up to 2008, S&P resisted changes to its CDO rating model over fears of losing business. Voluntary AI audits face a similar conflict: when rigorous scrutiny threatens continued access, compromising professional standards can become economically rational.
People worry a lot about third-party auditors being biased because of where they get funding or who their employees are friends with. IMO by far the biggest source of bias is that if they upset an AI company, that company will stop letting them audit.
When I was at METR this dynamic was a way bigger effect than everything else combined. Of course no auditor can avoid this until audits are no longer voluntary.
Reading Buffett’s partnership letter for 1967, particularly the passage on National Indemnity.
Running a great insurance company reminds me of Buckminster Fuller’s idea of precession. Underwriting well and getting paid enough for the risks you take matter more than writing more business.
Profit should be a byproduct of good taste: knowing what to write, what to charge, and when to walk away. When you start chasing profit directly, things tend to go wrong.
I used to think CEOs calling themselves “chief janitor” were just humblebragging. Now I get it. You have to be ready to clean up any mess, at any time. Anything that falls outside everyone else’s job description becomes yours.
This evening I sat across from the CEO of a so-called trading neo lab with over $1B val. He told me their models—built on best-in-class RL environments and online learning algorithms—have produced exceptional pnl and sharpe ratios.
I later learned that the company is, in fact, a data broker LARPing as a frontier lab. Its actual edge is trading stocks and options on insider information of model releases and synthetic-data acquisitions.
It appears I have once again materially underestimated the absurdity of this industry.
Reading the backchannel chatter around TML’s latest round made me realize I’m still thinking far too small about neo lab business models. Compute is rewriting modern monetary theory. Neo labs might be doing the same to corporate finance.
Data centers are a political problem in the US, an energy problem in Europe, and an arms smuggling problem in China.
Btw it's cs153 from Stanford
cs153.stanford.edu/
The disappointment that people who were deep into PoW, consensus design, and cryptography feel about pump dot fun and fomo is almost identical to the disappointment the early information finance / exotic option pricing / Arrow–Debreu security ppls felt about regulated US sportsbooks. Same intellectual cynicism and nostalgia in both cases.
Prediction markets are dead. Long live the prediction markets.
Everyone’s sleeping on control theory and Lyapunov cycles. RL is basically just reinventing optimal control at way higher data dimensionality. And the way we structure agent systems today is almost isomorphic to how mechanical systems get designed with classical control.
