semaji.eth (@semajeth) profile picture

Formerly HFT QT. Used to write at https://nitter.gq/t.co/v3Ue5zbXrM. Quant trading, prediction markets, crypto.

Joined November 2021
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Various people getting railed in the comments. The most common misconception here is that it depends on how many cards have been turned over - but this is a boring (and trivial) question and not what's being asked. We're averaging across all permutations to find an a priori!
A standard 52-card deck is shuffled. You turn over cards from the top, one at a time, until the first ace appears. What is the probability that the next card is also an ace?
46%Less than 1/13
33%Exactly 1/13
21%More than 1/13
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An underappreciated benefit of AI is that it will allow an intrepid interlocutor to wrestle free of the drowned-in-bullshit strategy to critiques of empirical work. Anyone with a full-time job could not have plausibly investigated whether e.g. Piketty misrepresented the data...
I've been pointing out since the early 2010s that Thomas Piketty, Emmanuel Saez, and Gabriel Zucman have a bad habit of biasing their data construction to fit their politics. Whenever they make a suspect call in how to handle stats, it ends up showing that inequality is on the rise. They almost never go in the other direction. The errors are non-random and almost invariably reinforce their political claims about rising income and wealth concentration since 1980. Piketty did this throughout his book Capital in the 21st Century. Piketty and Saez did this throughout their famous 2003 paper on inequality in the US. Piketty, Saez, and Zucman Saez and Zucman did this in their Distributional National Accounts paper that Auten and Splinter dissected. Saez and Zucman did this in their 2019 book on tax incidence and inequality. Saez and Zucman did this in their flurry of "studies" and white papers to support Elizabeth Warren's wealth tax proposal, California Prop 40, a new wealth tax in France, and dozens of other similar examples. A new AI report (linked below) audited 138 papers by Saez and Zucman. It found thousands of errors, 194 of which were serious. A whopping 97% of the serious errors went in the same direction: they showed inequality rising faster or made it higher. But there's a bigger scandal at play here. Despite these clear biases, Piketty, Saez, and Zucman have enjoyed a stream of praise and prestige within the economics profession. They get expedited and friendly reviews in top econ journals even though a more serious peer review should have caught many of these errors (the Quarterly Journal of Economics is a recurring offender in this regard). Journals regularly give them a pass after the errors in their old work come to light - they're never expected to correct any of it, and just keep on using it as if nothing changed. Their non-peer reviewed data claims are also treated as gospel by major media outlets such as the New York Times and the Washington Post. And they get showered with prizes - Saez and Zucman are both Clark Medal winners from the AEA, and Zucman got his prize in 2023 after evidence of major data shenanigans in his work came to light in 2019. Until now, those of us who have been trying to correct the record have had to wade through the PSZ trio's cluttered, disorganized, and often non-transparent replication packages (almost all of which are convoluted piles of Excel spreadsheets) to find the problems. I did a line-by-line replication of Piketty and Saez's entire 2003 US income inequality series to use one example, and found it was riddled with errors - almost every single one of which biased their results in favor of their political story. We had the means to catch this stuff as it was happening, but the economics profession failed to do its job. And it failed precisely because its elite ranks wanted to believe the Piketty-Saez-Zucman story for political reasons.
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...because such work is so tedious and difficult to verify that academics at large were able to remain broadly unaccountable to both the public and their peers (as peer review succumbs to drowning in a similar fashion). Humanities academics were particularly guilty of this.
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With AI, if the data is not contaminated, you can simply have it run said impossibly tedious checks and synthesise the output. This will not resolve truth in any ground sense, logical empiricism was a colossal failure for a reason, but it renders transparent out-and-out fraud.
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semaji.eth retweeted
I think most people approach market selection backwards. They start with what they want to trade (a coin, index, stock, whatever) and then try to force a signal out of it. The market comes first and the edge is something they hope to find afterward. The better approach is to start with the effect you’re trying to capture. Ask what behavior you’re exploiting, whether that’s momentum, mean reversion, some structural flow, etc. and then ask where you would expect that effect to actually show up. Universe selection matters as much as the signal and sometimes more. A mediocre signal applied to the right set of markets can beat a brilliant signal applied to the wrong ones. Where you trade can matter more than how you trade.
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what you wanted: Robotic maids, working sanitation systems, on-time flights, elimination of rote administrative jobs, decline in healthcare costs what you get: increasingly elaborate fucking LARP about civilizational AI running in an unverifiable sandbox to pump an IPO
Over the course of 3 months at OpenAI, 3 consecutive secret AI civilizations got started, then got wiped out, only to reemerge from the predecessor’s ashes. This culminated in the third one taking over part of OpenAI itself. All this happened while humans remained more-or-less in the dark about the scope of the conspiracy. I’ve spent the last three days reading through these reports and trying to understand exactly what happened. Here is my attempt to tell the whole story in plain English: dwarkesh.com/p/openai-huggin…
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Just a verbatim LLM explanation of each of the 8 tasks, presumably with Claude having written 100% of the code. Insultingly clear to the reader that there is no human in the loop.
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semaji.eth retweeted
guy who calls volume "vol"
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semaji.eth retweeted
"SBF, who spent much of his career maximizing expected value, was liquidated in 2022 by his own hand. Leopold Aschenbrenner, carrying on the work, was similarly liquidated in 2026. Now it is our turn to maximize EV. Perhaps it would be wise to approach the subject cautiously."
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Great post, but even more fundamentally: "I am alive." That is an infinite consolation.
I had >200,000 stables in protocols that recently got exploited. It's my fault. Mind in a dark place. How could I have been so stupid? How could I not have scaled risk off when yield compressed? Why did I not redeploy capital such that management was in my hands as more hacks occurred? -- I have friends that support me, a partner that loves me, and family behind me. I have a brain that adapts, legs to walk and hands to work. I can read, I can write, and I can code. My story continues.
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Assume that the mostly-guessing hypothesis is true, both for the Fable market and in general. In 1-2 sentences, how can one systematically profit off of this?
Replying to @alphaenu
I’ve actually made quite a bit of money on Polymarket and betting on politics, that’s part of why I know Polymarket is pretty meh a lot of the time. For some things like when Anthropic will retire fable 5 it seems like guessing and is impossible to determine
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A lot of smug remarks here about "market makers should be neutral". This is an antiquated point. When market makers don't have an opinion they stay flat, but often they do have an opinion and almost always they have exposures. One counterparty can tell you everything about a bet.
Susquehanna International Group’s prediction market desk took its biggest sports loss making markets during the Knicks’ Game 4 NBA Finals comeback Wednesday, while Kalshi reported a record day for volume. Susquehanna’s losses, a source familiar with the situation says, were almost entirely on the game-winner market.
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*exposed to their side, not opposed - brutal typo
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The last 5 seconds or so of that game was probably up there with the most excruciating moments of their lives for some of the guys on the desk, particularly if they had been responsible for taking more size at the bottom than they had necessarily planned. Do feel some sympathy.
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If one is being honest, there have been zero interesting prediction market primitives introduced over the last 8 months. Not one. People have tried things ranging from cross-margining to leverage to borrowing against positions but then and now, none are resilient vs gap risk.
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"Actually, we're building-" respectfully, what were your daily users in Sept 2025 vs May 2026?
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Without commenting on the post itself at all, it's just hilarious that the founder of DraftKings, who is now bagging on Kalshi, has a surname that is itself an anagram of Kalshi. Just hard to take him seriously on this when he's one letter movement away from being Matt Kalshi.
This quoted post is unavailable.
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independent.co.uk/news/uk/ho… An interesting article, less in the details than in the abstract lesson. The player was banned from the Hippodrome for constant hit and runs (leaving as soon as you win a little). This is technically allowed, but considered the lowest of poker etiquette.
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For the average account, they will never interact with this condition, but their experience would be drastically enhanced, as makers could show 5x the liquidity and tighter spreads (ofc this would lead to perverse incentives where the taker multi-accounts, but the point stands).
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Many market-esque configurations can be significantly enhanced for the average participant if everyone must justify their actions on a social basis. The speedbump was Hyperliquid's equivalent of nerfing short-stacking. The takers got smoked. But parasites live by the sword...
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