Once Experiment Cost Approaches Zero, What Does Research Itself Become?

Thu, 20 Aug 2026 12:00:00 +0800

30-second version: What AI really lowers isn’t the cost of writing code — it’s the search cost across the entire hypothesis space. An idea that used to have a 20% success rate and take two days to test wasn’t worth trying before; now it is. So the bottleneck moves from execution to selection: when you can run two hundred experiments a day, the hard part is which two are worth looking at. And alpha discovery has gotten cheap — alpha validation hasn’t. As the search space grows, multiple testing, overfitting, and selection bias all get worse; you can always fish a pretty equity curve out of pure noise. So the researcher’s job shifts from “finding one good result” to “ranking a pile of results by strength of evidence.” What’s scarce is no longer the ability to search, but the prior knowledge of where to search, and the discipline to actually kill a signal.

When Writing Code Is No Longer the Bottleneck in Quant Research

Thu, 20 Aug 2026 00:00:00 +0800

30-second version: AI agents can now read code, run experiments, and fix bugs on their own — writing code is no longer the bottleneck in quant research. So what’s scarce is no longer “how many experiments can I run,” but what’s worth trying, and what results are worth believing. The researcher’s role isn’t just “the one who asks questions” either — asking is just as cheap now; what’s genuinely hard is designing experiments that can distinguish between competing explanations. Once execution gets cheap, you’re not facing an empty ocean — you’re facing an ocean that suddenly has a few hundred extra boats in it. The problem shifts from “how do I find something” to “how do I know what I found isn’t just glass shards.”

Quant Research on WM's Blog

Once Experiment Cost Approaches Zero, What Does Research Itself Become?

When Writing Code Is No Longer the Bottleneck in Quant Research