I wonder how formal methods can help us move faster with GenAI.

Is it that they can help write formulas faster? That they can help ensure formulas match the system they're modeling faster? If the problem you think formal methods will help with is sloppy code, isn't the verification code going to be sloppy as well, unless some (not sloppy) intelligence carefully confirms that the specification matches the target system, which was the labor that previously made formal methods too expensive? I guess I don't understand how the argument works if code was previously less sloppy and verification was too expensive, and now code is more sloppy, and there's more of it, but somehow the sloppy intelligence will make verification move fast enough to make it worthwhile.

Unless we have some non-sloppy intelligence that's less of a bottleneck on verification than humans, how are we in a better place?

Maybe it's that investing that huge amount of labor of verification by human experts is now worth it because so much code will be produced that uses the verification systems that the investment will now pay off. But that requires creating pretty general verification systems, such as type system verification or something (which is what they seem to be aimed at), rather than individually verifying software systems like the micro-kernel example.

In other words, maybe the play is to invest in reusable verification systems that can be run tons of times on new code and systems. If so, it's surprising that this wasn't always the strategy.

In other words, because GEN AI a lot of code, the idea is to shift human value toward verification. Sometimes I think about what programming really is. In fact, learning programming itself is a huge challenge for a non English speaker like me. I have to rely on machine translation to understand English documents that have no translation. The materials in my language are about 5 to 6 years behind.

Now, since it's impossible to code review the tens of thousands of lines of code that AI produces, I see discussions about establishing an absolute rule like mathematical proofs. Reading this reminds me of Rust's borrow checker. In fact, after writing in Rust a few times, it often leads to bad practices where people use tricks to avoid the borrow checker.

Actually, when mathematical rigor goes too far, humans tend to find ways around it. An undereducated programmer like me is especially prone to that.

Looking back at this kind of attempt, it will probably result in writing code only for specific formalized answers. If it becomes that standardized, I'm not sure it will be able to respond to human needs.

I think these defensive programming attempts are fine, but I want to do offensive programming (I coined that term). You take risks, but you fix things quickly and ship. Believing that over time, it will become good enough. Of course, for established industries where accuracy matters and the scope of work is well defined, like Jane Street, the approach in this article is correct. In other words, because there is enough data to adequately model the market's demands

But for social losers like me trying to make money, constantly moving from place to place looking for gold mines, these kinds of methodologies seem like a luxury.Established businesses with mature modeling need highly educated and specialized personnel to continuously optimize. But I know that realistically, I can't keep up with that demand. So I look for places where modeling is unstructured, but I'm not sure if I can use this approach even then.

I have been testing formal verification methods with multiple products. It will be great to also understand more about what’s tried and how it was done. For example attempting to verify the spec is what I have been trying to implement.

seL4 despite being formally verified contained gnarly bugs, like [1]. Thus, it's not a silver bullet as some people think. Yes, it improves the quality, but only one of the aspects of hardening software, like ASAN, SAST, fuzzing, strict languages, and so on.

[1] https://github.com/seL4/seL4/issues/1199

Relevant keynote I wrote about

Formal methods won, now what? https://muratbuffalo.blogspot.com/2026/04/bugbash26-keynote....

See previous kleppmann post https://martin.kleppmann.com/2025/12/08/ai-formal-verificati..., and yes, obviously anything that you can put in the typesystem or the linter, you should weigh doing so.

Hopefully we get more ergonomic ways to do this? Like of the tools listed in the post, dafny + iris are the closest to being industrial I think. And amzn S3 has a history of TLA use in-house I think. But we probably haven't seen the typescript in this space yet, a zero cost abstraction that drops into existing tools, and people genuinely prefer it to the old way.

(And custom linters are also still pretty bad to write. Like golangci-lint is a painful codebase, haven't tried semgrep but the rules engine seemed intimidating. I've yet to use an AST API that I liked)

I am already seeing the uptake on Spec-Driven Development as the Rational Unified Process revenge.