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AI has broadly become synonymous with LLMs, but most AI/ML models used in quantitative trading are decades old. Boosted trees and PCA still account for more alpha than anything built on transformers. More detail on several ML techniques in quant trading: Gradient boosted trees and random forests: A standard strategy in short-horizon trading is to start with an intuitive set of microstructure features and train a non-linear model on them. Random forests provide a baseline ensemble that’s hard to overfit, while gradient boosted models fit fainter structures at the risk of overfitting. Features can include microprices, characteristics of the present order book, recent returns of related instruments, momentum signals, and orderflow directionality. Pre-transformer natural language processing: Throughout the 2010s, NLP PhDs were a sought-after resource for quantitative hedge funds. A variety of techniques such as Loughran-McDonald finance-specific sentiment dictionaries, TF-IDF and bag-of-words classifiers, and LDA topic modeling were used to extract sentiment from news, earnings reports, SEC filings, and FOMC minutes. Transformers are more generalized tools for language processing but some of the older methods in the toolkit are still used for their simplicity, predictability, and cost-effectiveness. Dimensionality reduction: A statistical arbitrage researcher may have thousands of correlated instruments and only a few years of clean historical data, so the covariance matrix has more parameters than observations to estimate them from. Principal component analysis is the standard remedy. A handful of leading components explain most of the variation in a universe of equities, and hedge funds will frequently neutralize the leading factors and trade the residuals. Autoencoders generalize the same idea to nonlinear structure, but PCA persists because its output is stable, interpretable, and cheap to recompute. Computer vision: From around 2010 to 2017, hedge funds gained a large informational edge by building automated pipelines to extract alternative data from satellite imagery. The models were unremarkable by research standards, mainly derivatives of convolutional neural networks. Some of the most famous applications include counting parked cars in retail lots, gauging construction progress at industrial sites, estimating crop yields from near-infrared reflectance rather than visible light, and reading crude inventory off the shadow cast by a floating tank roof. The trade worked best during a time when imagery was scarce and the engineering was novel. Today image-based alternative data is sold by third-party vendors for relatively inexpensive consumption. The large memory footprint, slow inference, and black-box nature of LLMs make them a poor fit for alpha generation at trading firms. Hand-tuned models based on intuitional feature selection are still the norm. For niche industries like quantitative finance, there is ample room for competition with generalized frontier models.

Open weights don’t need full parity with frontier labs. They only need what I call “pleb parity”: get close enough on high-value, high-volume task at a time, so 20 specialized plebs can divide the work and level the game against one elite. When you have a gang of equally talented, frugal and relentless Chinese AI founders, who stays #1# on the leaderboard matters less. What matters is that whenever one falls behind, another open-weight peer fills the gap very soon My simple mental model for Anthropic/OpenAI’s future ARR is the frontier-call ratio: Across an end-to-end business outcome, what % of steps still MUST call the most expensive frontier model? As plebs pushes that ratio down, pricing power and future ARR growth expectations get eroded. Think of the most elite revenue-generating team inside a company. How many seats truly need to be filled by Ivy/Stanford grads? AI stacks will look the same: frontier intelligence for the key steps where it materially changes the outcome; open weights everywhere else. The only domains I can think of with a real case for unlimited frontier-token budgets 1) Quant trading, mm and pod shops: RenTech, Jane Street, Point72 etc where raw intellectual horsepower can be translated directly into P&L 2) Ultra-high-stakes discovery: next-gen chips and materials, cancer drugs, etc. But even there, the upside/scale is limited by the even more scarce giga brain human minds to define the right problem and know where to tinker, not by token or compute For the rest of the world, and most knowledge-work domains, pleb parity is probably the long-term equilibrium. Long live the plebs. Kudos to every Chinese AI founder and engineer working against the odds to make it happen