Tom J. Espel

Ex-Goldman Oil Derivatives Trader Simran Singh (@0xturbanurban) on the day oil went to -$37.63, why every trading strategy is momentum or mean reversion, & losing $3.12M in a single day. Simran Singh (Goldman Sachs oil derivatives trader, on the desk when WTI went negative | GSR Head of DeFi Trading | Now CEO & co-founder of @MonacoTrading) "You can't touch an interest rate curve, but you can smell a barrel of oil." We cover: - Pricing oil options at 300-400% vol the day WTI went negative - How Goldman's oil desk monetizes client flow (80-85% was producers hedging) - How producer hedges turned a market-risk problem into a credit problem in 2020 - Why market making is really a bet on mean reversion - The perp funding trade: anyone can do it, very few can do it in size (15% carry even on the CME) - "My size your price, or your size my price" - Who's printing money right now: OTC hedging in power, compute & AI - Why there's no venue to trade electricity, and how Monaco wants to fix that Highlights: 00:00 Intro 00:58 The day oil went negative: April 2020 06:07 How Goldman oil traders monetize client flow 18:30 Every strategy is momentum or mean reversion 22:53 Goldman to GSR 28:26 The perp funding trade 34:48 Sell-side to buy-side 42:27 The AI complex & agentic trading 48:21 The Monaco Exchange thesis 53:13 Decentralizing futures listings

I just did a studio podcast and had dinner with with @ethanrkho. He's an impressive fellow, far more than I realized before meeting him in person. Ethan's podcast has **by far** the highest signal to noise ratio of any hedge fund podcast today.

Ex-Balyasny PM Ying Hua (@huaxying) on why automation will increase demand for hedge fund talent, the quant/fundamental convergence, & why quant is blackjack but fundamental is poker. Ying Hua (PM @ Balyasny — built & led a quantamental team covering US insurance, capital markets & fintech | ~5 yrs @ Citadel running a long/short insurance book | Equity research @ Goldman Sachs | MS in Data Science @ UC Berkeley | Now founder & CEO of Implied @impliedinsights) "One of the best-kept secrets: fundamental investors are not good at sizing. Quant funds are really good at sizing." We cover: - The only real line between quant and fundamental: historical pattern matching vs. "how is this time different" — and the alpha neither group is looking at - Why she rebuilt her process so every model updated within 2 minutes of a print - Scraping highway patrol data from 15 states to track auto insurance losses live, every single day - The Malibu wildfire: mapping burned mansions from celebrity tweets to estimate losses before any industry consultant published a number - Her automation math: data gathering ~100% automatable, processing ~80%, judgment still 100% human - AI is quant for words — next-token prediction is pattern matching, which makes this just the next automation wave after quant and indexing - The proof differentiated views pay more: insurance stocks moved 2-3% on earnings in 2010; by the time she left, 15-20% intraday - Why "hook Claude Code up to data and let it rip" fails: BloombergGPT losing to a smaller open-source model, & why horizontal models are college grads - Quant is blackjack with card counting; multi-manager investing is poker — your hand, others' perception of it, your seat, everyone's stack - Most PMs are playing the wrong game: the positioning game hiding inside "fundamental" sectors with no new money coming in - Her hiring bar at BAM: every fundamental analyst learns Python — and the one skill she says can't be trained - The only two truly meritocratic jobs: hedge fund PM & sales Highlights: (00:00) Intro (00:40) How a quantamental PM actually puts on a position (02:05) The only real line between quant and fundamental (04:45) Why quantamental lowers the burden on your brain (06:40) Scraping 15 states of highway patrol data to nowcast insurance losses (09:25) The Malibu wildfire: estimating losses from celebrity tweets (11:55) How much of fundamental investing can be automated (13:45) Quantifying intuition: when a CFO's filler words jump 8% to 20% (16:25) The contrarian case: automation expands demand for talent (18:15) Earnings vol exploded — differentiated views pay more (20:25) Why Claude Code can't run your book (23:40) Horizontal models are college grads with no domain knowledge (30:50) Why chat is the wrong interface for investors (36:20) Will AI make markets more or less efficient? (39:10) Two things every fundamental PM should do today (41:50) The moat that expands: talent, redefined (45:50) Sometimes the game is positioning, not fundamentals (48:25) Blackjack vs. poker vs. surfing: matching the game to your horizon (52:00) Should young analysts chase the hottest sector? (57:55) Munger vs. Musk: two philosophies of wealth (1:01:05) Self-awareness in investing is bimodal (1:07:05) The only two truly meritocratic jobs: hedge funds & sales (1:08:50) The one skill for every regime: reconstruct the narrative

Jesse Livermore turned speculation into legend. But his life ended in tragedy. Listen to the FT's Robin Wigglesworth and Robert Armstrong dissect the extraordinary life of the greatest day trader in history. ft.trib.al/2bnysyX

Some observations on Kimi: 1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run. 2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China. 3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex. 4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business. 5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this. 6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.

BonkDAO was the target of a malicious governance proposal resulting in an estimated $20M worth of BONK tokens being drained from the BonkDAO treasury. During the investigation, BonkDAO identified the exchange wallets used to purchase BONK ahead of the proposal. BonkDAO is currently actively working with exchanges, bridges and Solana Foundation to best manage the situation. Law enforcement has been notified. BonkDAO continues to work with relevant parties to recover funds and identify those responsible.

We’ve automated every single thing we can @every with AI agents. And yet there’s way more human work to do than ever. We’ve gone from 4 -> 30 human employees since GPT-3. I wrote a report on the structural reasons: how AI makes expert competence cheap, why that drives up demand for experts, and why the dynamic only intensifies as we approach AGI. After Automation: every.to/p/after-automation

"The bottom 40% of hedge funds lose money every year." Tom Costello (@tcoste110) ex-Tudor PM, ex-Caxton, ex-Moore Capital — explains: "Hedge fund returns are Pareto distributed. Like a chart of wealth — Elon at the top, a bunch of other billionaires, then it gets flat pretty quickly." "In the hedge fund industry, that distribution falls right around the 40% mark." "60% profitable. The bottom 40% lose money every year." "25% of the bottom 25% will go out of business and be replaced by another 25% pretty much every year." "There's nothing we can do to help them. They're asking the wrong questions." "AI's not going to help them. Machine learning's not going to. They misunderstand the markets in a fundamental way. They can't be saved."