Great read. It also raises an interesting question: as LLMs dramatically accelerate strategy generation, do you think the industry's competitive edge shifts from idea generation to validation infrastructure? In a world where everyone can generate signals, proving they're real may become the true differentiator.
I agree. My guess is regulators won't focus on how a strategy was generated—they'll focus on whether you can demonstrate it's robust, reproducible, and behaves predictably under stress. Validation may become a regulatory requirement, not just a research discipline.
Do you think we'll eventually see something analogous to "model certification," where firms are expected to demonstrate not only performance but also data lineage, stress-test coverage, and ongoing drift monitoring before deploying AI-driven strategies?
The regulators will mostly demand that models be interpretable, since in a large financial institution many stakeholders are involved and you should be able to explain AI output to them.
But how to make an AI model interpretable, I don't know. But slowly people will come up with guidelines.
It reminds me of how financial regulation evolved after the Global Financial Crisis.
The emphasis gradually shifted from evaluating individual trades to evaluating the governance surrounding them—stress testing, capital planning, model risk management, auditability, and board oversight all became central.
AI may follow a similar trajectory. Explainability will matter, but regulators are also likely to ask: Can this system be validated, monitored, challenged, and audited throughout its lifecycle?
That feels more like a systems engineering problem than a machine learning problem.
This is the part many backtests skip. A Sharpe ratio should be discounted when dozens of variations were tested, and walk-forward results should be shown across different regimes. A small honest edge is more useful than a perfect historical curve.
Great read. It also raises an interesting question: as LLMs dramatically accelerate strategy generation, do you think the industry's competitive edge shifts from idea generation to validation infrastructure? In a world where everyone can generate signals, proving they're real may become the true differentiator.
yeah, I think this is where we’re going.
Also the industry will be more regulated, to what degree I don’t know
I agree. My guess is regulators won't focus on how a strategy was generated—they'll focus on whether you can demonstrate it's robust, reproducible, and behaves predictably under stress. Validation may become a regulatory requirement, not just a research discipline.
Do you think we'll eventually see something analogous to "model certification," where firms are expected to demonstrate not only performance but also data lineage, stress-test coverage, and ongoing drift monitoring before deploying AI-driven strategies?
The regulators will mostly demand that models be interpretable, since in a large financial institution many stakeholders are involved and you should be able to explain AI output to them.
But how to make an AI model interpretable, I don't know. But slowly people will come up with guidelines.
It reminds me of how financial regulation evolved after the Global Financial Crisis.
The emphasis gradually shifted from evaluating individual trades to evaluating the governance surrounding them—stress testing, capital planning, model risk management, auditability, and board oversight all became central.
AI may follow a similar trajectory. Explainability will matter, but regulators are also likely to ask: Can this system be validated, monitored, challenged, and audited throughout its lifecycle?
That feels more like a systems engineering problem than a machine learning problem.
This is the part many backtests skip. A Sharpe ratio should be discounted when dozens of variations were tested, and walk-forward results should be shown across different regimes. A small honest edge is more useful than a perfect historical curve.