The economics of AI are a financial-market story, from banks' agent costs to the financing of data centres and the Anthropic IPO. Memory, storage and specialised accelerators are constraints on scaling AI inference. AI agents for formal software verification and physical hardware design.
A benchmark that evaluates the entire quantitative research process, from feature engineering to out-of-sample trading performance. Paper Quant-Arena: Benchmarking LLM Agents for Quantitative Trading Research Authors Houde Qian, Zheng Jiang, Wenyi Li, Chenxi Xu, Xinjie Lin, Yuzhao Peng, Ruoyu Han, Youjie Zheng, Kaisen Yang, Qinhuai Na, You
A multi-agent trading system uses LLMs for market analysis while retaining conventional models and deterministic rules for portfolio construction and execution. Paper Inside the Trading Loop: Markets, Models, and Moves with LLM Agents Authors Renee Jia, Di Mu, Yao Yu, and Tianrui Liu Date 8 November 2026 Inside the
AI may eliminate some entry-level tasks, but it could give the next generation an extraordinary advantage.
Intel raises $20bn as investors back its foundry ambitions, Bjarne Stroustrup joins Susquehanna as a Technical Fellow, and Meta releases a smaller open-weight model designed to run locally.
ML inference running directly inside a programmable network switch, bringing market prediction closer to the data.
An array programmer’s journey from avoiding loops to designing them.
Hedge funds compete for AI talent as inference architecture becomes increasingly disaggregated and agentic AI evaluation shifts from capability to reliability.
Big Tech’s $725B infrastructure bet is crushing free cash flow as "Agentic Commerce" protocols move from pilot to production.
Trillion-dollar infrastructure bets are under pressure as firms learn that scaling spend doesn’t scale performance without better ways to manage what models actually see.
A volatile week across markets and AI. Tech valuations wobble, central banks stay cautious, Big Tech defends trillion-dollar AI bets, and new research questions how we measure progress. Signals are getting noisier — not clearer.
The contemporary narrative around AI is dominated by exponential capabilities: the emergence of reasoning, the human-level performance on standardized tests, the spectacular hallucinations. This focus on performance has obscured a far more fundamental and immediate constraint: the economics of production. We have industrialized a new class of cognitive labor,