M37R10N
Research
Field notes on China A-share quantitative factor research, LLM information extraction, and the historical reality of technological systems.
Why We Pivoted Our Revenue Back to Humans, Without Dropping a Single Line of MCP Code
Reading Feng Ruohang’s recent teardown, AI Penetration Might Not Be As High As You Think, hit me with a visceral sense of resonance—and a cold sweat. As a founder building in the trenches of AI infrastructure and tooling, the numbers felt all too real.
In quant land, everyone presents their stack as Swiss watchmaking. Every gear meshes to the microsecond, every timestamp observes strict point-in-time (PIT) hygiene, look-ahead bias has been exorcised, and backtested alpha curves climb as smoothly as a sovereign bond yield.
Then you put large language models in charge of turning millions of exchange disclosure transcripts into entities, relations, and event atoms.
The Swiss watch instantly turns into a bowl of jelly.
A leaked draft of Anthropic’s rumored S-1 registration statement recently surfaced, presenting a surreal collision of corporate bureaucracy and existential dread. Across its 261-page text, Risk Factors consume roughly 80 pages, easily dwarfing the 48 pages dedicated to the company’s actual business operations.
For research whitepapers, historical point-in-time factor feeds, and bespoke dataset methodology.