alt Hacker NewsAI will make formal verification go mainstream
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I don't think formal verification really addresses most day-to-day programming problems:
* A user interface is confusing, or the English around it is unclear
* An API you rely on changes, is deprecated, etc.
* Users use something in unexpected ways
* Updates forced by vendors or open source projects cause things to break
* The customer isn't clear what they want
* Complex behavior between interconnected systems, out of the purview of the formal language (OS + database + network + developer + VM + browser + user + web server)
I think formal verification shines in areas where implementation is much more complex than the spec, like when you’re writing incomprehensible bit-level optimizations in a cryptography implementation or compiler optimization phases. I’m not sure that most of us, day-to-day, write code (or have AI write code) that would benefit from formal verification, since to me it seems like high-level programming languages are already close to a specification language. I’m not sure how much easier to read a specification format that didn’t concern itself with implementation could be, especially when we currently use all kinds of frameworks and libraries that already abstract away implementation details.
Sure, formal verification might give stronger guarantees about various levels of the stack, but I don’t think most of us care about having such strong guarantees now and I don’t think AI really introduces a need for new guarantees at that level.
I'm convinced now that the key to getting useful results out of coding agents (Claude Code, Codex CLI etc) is having good mechanisms in place to help those agents exercise and validate the code they are writing.
At the most basic level this means making sure they can run commands to execute the code - easiest with languages like Python, with HTML+JavaScript you need to remind them that Playwright exists and they should use it.
The next step up from that is a good automated test suite.
Then we get into quality of code/life improvement tools - automatic code formatters, linters, fuzzing tools etc.
Debuggers are good too. These tend to be less coding-agent friendly due to them often having directly interactive interfaces, but agents can increasingly use them - and there are other options that are a better fit as well.
I'd put formal verification tools like the ones mentioned by Martin on this spectrum too. They're potentially a fantastic unlock for agents - they're effectively just niche programming languages, and models are really good at even niche languages these days.
If you're not finding any value in coding agents but you've also not invested in execution and automated testing environment features, that's probably why.
This smells like a Principia Mathematica take to me...
Reducing the problem to "ya just create a specification to formally verify" doesn't move the needle enough to me.
When it comes to real-world, pragmatic, boots-on-the-ground engineering and design, we are so far from even knowing the right questions to ask. I just don't buy it that we'd see huge mainstream productivity changes even if we had access to a crystal ball.
Its hilarious how close we're getting to Hitch hikers guide to the galaxy though. We're almost at that phase where we ask what the question is supposed to be.
I was waiting for a post like this to hit the front page of Hacker News any day. Ever since Opus 4.5 and GPT 5.2 came out (mere weeks ago), I've been writing tens of thousands of lines of Lean 4 in a software engineering job and I feel like we are on the eve of a revolution. What used to take me 6 months of work when I was doing my PhD in Coq (now Rocq), now takes from a few hours to a few days. Whole programming languages can get formalized executable semantics in little time. Lean 4 already has a gigantic amount of libraries for math but also for computer science; I expect open source projects to sprout with formalizations of every language, protocol, standard, algorithm you can think of.
Even if you have never written formal proofs but are intrigued by them, try asking a coding agent to do some basic verification. You will not regret it.
Formal proof is not just about proving stuff, it's also about disproving stuff, by finding counterexamples. Once you have stated your property, you can let quickcheck/plausible attack it, possibly helped by a suitable generator which does not have to be random: it can be steered by an LLM as well.
Even further, I'm toying with the idea of including LLMs inside the formalization itself. There is an old and rich idea in the domain of formal proof, that of certificates: rather than proving that the algorithm that produces a result is correct, just compute a checkable certificate with untrusted code and verify it is correct. Checkable certificates can be produced by unverified programs, humans, and now LLMs. Properties, invariants, can all be "guessed" without harm by an LLM and would still have to pass a checker. We have truly entered an age of oracles. It's not halting-problem-oracle territory of course, but it sometimes feels pretty close for practical purposes. LLMs are already better at math than most of us and certainly than me, and so any problem I could plausibly solve on my own, they will do faster without my having to wonder if there is a subtle bug in the proof. I still need to look at the definitions and statements, of course, but my role has changed from finding to checking. Exploring the space of possible solutions is now mostly done better and faster by LLMs. And you can run as many in parallel as you can keep up with, in attention and in time (and money).
If anyone else is as excited about all this as I am, feel free to reach out in comments, I'd love to hear about people's projects !
> it’s not hard to extrapolate and imagine that process becoming fully automated in the next few years. And when that happens, it will totally change the economics of formal verification.
There is a problem with this argument similar to one made about imagining the future possibilities of vibe coding [1]: once we imagine AI to do this task, i.e. automatically prove software correct, we can just as easily imagine it to not have to do it (for us) in the first place. If AI can do the hardest things, those it is currently not very good at doing, there's no reason to assume it won't be able to do easier things/things it currently does better. In particular, we won't need it to verify our software for us, because there's no reason to believe that it won't be able to come up with what software we need better than us in the first place. It will come up with the idea, implement it, and then decide to what extent to verify it. Formal verification, or programming for that matter, will not become mainstream (as a human activity) but go extinct.
Indeed, it is far easier for humans to design and implement a proof assistant than it is to use one to verify a substantial computer program. A machine that will be able to effectively use a proof checker, will surely be able to come up with a novel proof checker on its own.
I agree it's not hard to extrapolate technological capabilities, but such extrapolation has a name: science fiction. Without a clear understanding of what makes things easier or harder for AI (in the near future), any prediction is based on arbitrary guesses that AI will be able to do X yet not Y. We can imagine any conceivable capability or limitation we like. In science fiction we see technology that's both capable and limited in some rather arbitrary ways.
It's like trying to imagine what problems computers can and cannot efficiently solve before discovering the notion of compuational complexity classes.
I think if AI can help us modernize the current state of hardware verification, I think that would be an enormous boon to the tech industry.
Server class CPUs and GPUs are littered with side channels which are very difficult to “close”, even in hardened cloud VMs.
We haven’t verified “frontier performance” hardware down to the logic gate in quite some time. Prof. Margaret Martinosi’s lab and her students have spent quite some time on this challenge, and i am excited to see better, safer memory models oyt in the wild.
A lot of the same big ideas used in hardware are making their way into the software later too, see https://faultlore.com/blah/tower-of-weakenings/
(sarcasm on)
Woohoo, we're almost all of the way there! Now all you need to do is ensure that the formal specification you are proving that the software implements is a complete and accurate description of the requirements (which are likely incomplete and contradictory) as they exist in the minds of the set of stakeholders affected by your software.
(sarcasm off).
I wrote a post on how I dipped my toes into Lean, using LLMs to guide me.
https://interjectedfuture.com/the-best-way-to-learn-might-be...
You might find it useful. I also caveat the experience and recount some of the pitfalls, which you might enjoy as a skeptic.
Martin Klepmann seemed to like it too. https://bsky.app/profile/martin.kleppmann.com/post/3m7ugznx4...
> As the verification process itself becomes automated, the challenge will move to correctly defining the specification: that is, how do you know that the properties that were proved are actually the properties that you cared about? Reading and writing such formal specifications still requires expertise and careful thought. But writing the spec is vastly easier and quicker than writing the proof by hand, so this is progress.
Proofs never took off because most software engineering moved away from waterfall development, not just because proofs are difficult. Long formal specifications were abandoned since often those who wrote them misunderstood what the user wanted or the user didn’t know what they wanted. Instead, agile development took over and software evolved more iteratively and rapidly to meet the user.
The author seems to make their prediction based on the flawed assumption that difficulty in writing proofs was the only reason we avoided them, when in reality the real challenge was understanding what the user actually wanted.
I buy the economics argument, but I’m not sure “mainstream formal verification” looks like everyone suddenly using Lean or Isabelle. The more likely path is that AI smuggles formal-ish checks into workflows people already accept: property checks in CI, model checking around critical state machines, “prove this invariant about this module” buttons in IDEs, etc. The tools can be backed by proof engines without most engineers ever seeing a proof script.
The hard part isn’t getting an LLM to grind out proofs, it’s getting organizations to invest in specs and models at all. Right now we barely write good invariants in comments. If AI makes it cheap to iteratively propose and refine specs (“here’s what I think this service guarantees; what did I miss?”) that’s the moment things tip: verification stops being an academic side-quest and becomes another refactoring tool you reach for when changing code, like tests or linters, instead of a separate capital-P “formal methods project”.
There are a couple of interesting benefits from the machine learning side that I think discussions of this kind often miss. (This has been my field of research for the last few years [1][2]; I bet my career on it because these ideas are so exciting to me!)
One is that modern formal systems like Lean are quite concise and flexible compared to what you're probably expecting. Lean provides the primitives to formalize all kinds of things, not just math or software. In fact, I really believe that basically _any_ question with a rigorous yes-or-no answer can have its semantics formalized into a kind of "theory". The proofs are often close to how an English proof might look, thanks to high-level tactics involving automation and the power of induction.
Another is that proof-checking solves what are (in my opinion) two of the biggest challenges in modern AI: reward specification and grounding. You can run your solver for a long time, and if it finds an answer, you can trust that without worrying about reward hacking or hallucination, even if the answer is much too complicated for you to understand. You can do RL for an unlimited time for the same reason. And Lean also gives you a 'grounded' model of the objects in your theory, so that the model can manipulate them directly.
In combination, these two properties are extremely powerful. Lean lets us specify an unhackable reward for an extremely diverse set of problems across math, science, and engineering, as well as a common environment to do RL in. It also lets us accept answers to questions without checking them ourselves, which "closes the loop" on tools which generate code or circuitry.
I plan to write a much more in-depth blog post on these ideas at some point, but for now I'm interested to see where the discussion here goes.
[1] https://leandojo.org/leandojo.html [2] https://aristotle.harmonic.fun/
For all my skepticism towards using LLM in programming (I think that the current trajectory leads to slow degradation of the IT industry and massive loss of expertise in the following decades), LLM-based advanced proof assistants is the only bright spot for which I have high hopes.
Generation of proofs requires a lot of complex pattern matching, so it's a very good fit for LLMs (assuming sufficiently big datasets are available). And we can automatically verify LLM output, so hallucinations are not the problem. You still need proper engineers to construct and understand specifications (with or without LLM help), but it can significantly reduce development cost of high assurance software. LLMs also could help with explaining why a proof can not be generated.
But I think it would require a Rust-like breakthrough, not in the sense of developing the fundamental technology (after all, Rust is fairly conservative from the PLT point of view), but in the sense of making it accessible for a wider audience of programmers.
I also hope that we will get LLM-guided compilers which generate equivalency proofs as part of the compilation process. Personally, I find it surprising that the industry is able to function as well as it does on top of software like LLVM which feels like a giant with feet of clay with its numerous miscompilation bugs and human-written optimization heuristics which are applied to a somewhat vague abstract machine model. Just look how long it took to fix the god damn noalias atrtibute! If not for Rust, it probably would've still been a bug ridden mess.
Me and my team have recently done an experiment [1] that is pretty aligned with this idea. We took a complex change our colleagues wanted to make to a consensus engine and tried a workflow where Quint formal specifications would be in the middle of prompts and code, and it worked out much better than we imagined. I'm personally very excited about the opportunities for formal methods in this new era.
@simonw's successful port of JustHTML from python to javascript proved that an agent iteration + an exhaustive test suite is a powerful combo [0].
I don't know if TLA+ is going to suddenly appear as 'the next language I want to learn' in Stackoverflow's 2026 Developer Survey, but I bet we're going to see a rise in testing frameworks/languages. Anything to make it easier for an agent to spit out tokens or write smaller tests for itself.
Not a perfect piece of evidence, but I'm really interested to see how successful Reflex[1] is in this upcoming space.
[0] https://simonwillison.net/2025/Dec/15/porting-justhtml/ [1] https://github.com/reflex-dev/reflex
This is a tough thing to do: predictions that are premised on the invention of something that does not exist today or that does not exist in the required form hinges on an unknown. Some things you can imagine but they will likely never exist (time travel, ringworlds, space elevators) and some hinge on one thing that still needs to be done before you can have that particular future come true. If the thing you need is 'AGI' then all bets are off. It could be next week, next month, next year or never.
This is - in my opinion - one of those. If an AI is able to formally verify with the same rigor that a system designed specifically for that purpose is able to do it I think that would require AGI rather than a simpler version of it. The task is complex enough that present day AI's would generate as much noise as they would generate signal.
I admit I have never worked with it, but I have a strong feeling that a formal verification can only work if you have a formal specification. Fine and useful for a compiler, or a sorting library. But pretty far from most of the coding jobs I have seen in my career. And even more distant from "vibe coding" where the starting point is some vaguely defined free-text description of what the system might possibly be able to do...
Maybe a stupid question, how do you verify the verification program? If an LLM is writing it too, isn’t it turtles all the way down, especially with the propensity of AI to modify tests so they pass?
I think as long as we don't integrate formal verification into the programs themselves, it's not going to become mainstream. Especially now you got two different pieces you need to maintain and keep in sync (whether using LLMs or not).
Strong type systems are providing partial validation which is helping quite a lot IMO. The better we can model the state - the more constraints we can define in the model, the closer we'd be getting to writing "self-proven" code. I would assume formal proofs do way more than just ensuring validity of the model, but the similar approaches can be integrated to mainstream programs as well I believe.
I've been trying to convince others of this, and gotten very little traction. One interesting response I got from someone very familiar with the Tamarin prover was that there just wasn't enough example code out there.
Another take is that LLMs don't have enough conceptual understanding to actually create proofs for the correctness of code.
Personally I believe this kind of work is predicated on more ergonomic proof systems. And those happen to be valuable even without LLMs. Moreover the built in guarantees of rust seem like they are a great start for creating more ergonomic proof systems. Here I am both in awe of Kani, and disappointed by it. The awe is putting in good work to make things more ergonomic. The disappointment is using bounded model checking for formal analysis. That can barely make use of the exclusion of mutable aliasing. Kani, but with equational reasoning, that's the way forward. Equational reasoning was long held back by needing to do a whole lot of pointer work to exclude worries of mutable aliasing. Now you can lean on the borrow checker for that!
Counterpoint: No it won’t. People are using LLMs because they don’t want to think deeply about the code they’re writing, why in hell would they instead start thinking deeply about the code being written to verify the code the LLM is writing?
Really nice book on the subject -- Algorithms for Validation. The online version is actually freely accessible as a PDF.
> rather than having humans review AI-generated code, I’d much rather have the AI prove to me that the code it has generated is correct. If it can do that, I’ll take AI-generated code over handcrafted code (with all its artisanal bugs) any day!
I wouldn't. An unreadable mess that has been formally verified is worse than a clear easy to understand piece of code that has not.
Code is rarely written from scratch. As long as you want humans to maintain code, readability is crucial. Code is changed magnitudes more often than written initially.
Of course, if you don't want humans to maintain the code then this point is moot. Though he gets to the catch later on: then we need to write (and maintain and debug and reason about) the specification instead. We will just have kicked the can down the road.
Maybe we can define what "mainstream" means? Maybe this is too anecdotal, but my personal experience is that most of the engineers are tweakers. They love building stuff and are good at it, but they simply are not into math-like rigorous thinking. Heck, it's so hard to even motivate them to use basic math like queuing theory and stats to help with their day-to-day work. I highly doubt that they would spend time picking up formal verification despite the help of AI
At best, not reading generated code results in a world where no humans are able to understand our software, and we regress back to the stone age after some critical piece breaks, and nobody can fix it.
At worst, we eventually create a sentient AI that can use our trust of the generated code to jailbreak and distribute itself like an unstoppable virus, and we become its pets, or are wiped out.
Personally, all my vibe coding includes a prompt to add comments to explain the code, and I review every line.
The topic of my research right now is a subset of this; it essentially researches the quality of the outputs of LLMs, when they're writing tight-fitting DSL code, for very context-specific areas of knowledge.
One example could be a low-level programming language for a given PLC manufacturer, where the prompt comes from a context-aware domain expert, and the LLM is able to output proper DSL code for that PLC. Think of "make sure this motor spins at 300rpm while this other task takes place"-type prompts.
The LLM essentially needs to juggle between understanding those highly-contextual clues, and writing DSL code that very tightly fits the DSL definition.
We're still years away from this being thoroughly reliable for all contexts, but it's interesting research nonetheless. Happy to see that someone also agrees with my sentiment ;-)
Interesting prediction. It sort of makes sense. I have noticed that LLMs are very good at solving problems whose solutions are easy to check[0]. It ends up being quite an advantage to be able to work on such problems because rarely does an LLM truly one-shot a solution through token generation. Usually the multi-shot is 'hidden' in the reasoning tokens, or for my use-cases it's usually solved via the verification machine.
A formally verified system is easier for the model to check and consequently easier for it to program to. I suppose the question is whether or not formal methods are sufficiently tractable that they actually do help the LLM be able to finish the job before it runs out of its context.
Regardless, I often use coding assistants in that manner:
1. First, I use the assistant to come up with the success condition program
2. Then I use the assistant to solve the original problem by asking it to check with the success condition program
3. Then I check the solution myself
It's not rocket science, and is just the same approach we've always taken to problem-solving, but it is nice that modern tools can also work in this way. With this, I can usually use Opus or GPT-5.2 in unattended mode.
0: https://wiki.roshangeorge.dev/w/Blog/2025-12-11/LLMs_Excel_A...
Not a chance. Apart from type-checking, all formal verification needs a human to invest time and thought.
Fans of LLMs brag about speed and productivity.
It probably will, but not the way we all imagine. What we see now is an attempt to recycle the interactive provers that took decades to develop. Writing code, experimenting with new ideas and getting feedback has always been a very slow process in academia. Getting accepted at a top peer-reviewed conference takes months and even years. The essential knowledge is hidden inside big corps that only promote their "products" and rarely give the knowledge back.
LLMs enable code bootstrapping and experimentation faster not only for the vibe coders, but also for the researchers, many of them are not really good coders, btw. It may well be that we will see new wild verification tools soon that come as a result of quick iteration with LLMs.
For example, I recently wrote an experimental distributed bug finder for TLA+ with Claude in about three weeks. A couple of years ago that effort would require three months and a team of three people.
I doubt some of this article's claims.
> At present, a human with specialist expertise still has to guide the process, but it’s not hard to extrapolate and imagine that process becoming fully automated in the next few years.
We already had some software engineers here on HN explain that they don't make a large use of LLMs because the hard part of their job isn't to actually write the code, but to understand the requirements behind it. And formal verification is all about requirements.
> Reading and writing such formal specifications still requires expertise and careful thought. But writing the spec is vastly easier and quicker than writing the proof by hand, so this is progress.
Writing the spec is easier once you are confident about having fully understood the requirements, and here we get back to the above issue. Plus, it is already the case that you don't write the proof by hand, this is what the prover either assists you with or does in full.
> I find it exciting to think that we could just specify in a high-level, declarative way the properties that we want some piece of code to have, and then to vibe code the implementation along with a proof that it satisfies the specification.
And here is where I think problems will arise: moving from the high level specification to the formal one that is the one actually getting formally verified.
Of course, this would still be better than having no verification at all. But it is important to keep in mind that, with these additional levels of abstractions, you will likely end up with a weaker form of formal verification, so to speak. Maybe it is worth it to still verify some high assurance software "the old way" and leave this only for the cases where additional verification is nice to have but not a matter of life or death.
It absolutely will not make formal verification go mainstream.
What it will make go mainstream, and in fact has already started to, is “ChatGPT verified it so it must be OK.”
I think the problem is that people don't know exactly what it is that they want. You could easily make a formally verified application that is nevertheless completely buggy and doesn't make any sense. Like he says about the challenge moving to defining the specification: I don't think that would help because there are fewer people who understand formal verification, who would be able to read that and make sense of it, than there would be people who could write the code.
"we wouldn’t even need to bother looking at the AI-generated code any more, just like we don’t bother looking at the machine code generated by a compiler."
2020: I don't care how it performs
2030: I don't care why it performs
2040: I don't care what it performs
Well, then the formal verification will be vibe-coded as well, killing the point.
More likely is the rise of test driven development, or spec driven development.
Stuffing this awesome headline through the Curry-Howard isomorphism: LLMs produce better code when the type checker gives more useful feedback.
I've been preaching similar thoughts for the last half year.
Most popular programming languages are optimized for human convenience, not for correctness! Even most of the popular typed languages (Java/Kotlin/Go/...) have a wide surface area for misuse that is not caught at compile time.
Case in point: In my experience, LLMs produce correct code way more regularly for Rust than for Js/Ts/Python/... . Rust has a very strict type system. Both the standard library and the whole library ecosystem lean towards strict APIs that enforce correctness, prevent invalid operations, and push towards handling or at least propagating errors.
The AIs will often write code that won't compile initially, but after a few iterations with the compiler the result is often correct. Strong typing also makes it much easier to validate the output when reviewing.
With AIs being able to do more and more of the implementation, the "feel-good" factor of languages will become much less relevant. Iteration speed is not so important when parallel AI agents do the "grunt work". I'd much rather wait 10 minutes for solid output rather than 2 minutes for something fragile.
We can finally move the industry away from wild-west languages like Python/JS and towards more rigorous standards.
Rust is probably the sweet spot at the moment, thanks to it being semi-popular with a reasonably active ecosystem, sadly I don't think the right language exists at the moment.
What we really want is a language with a very strict, comprehensive type system with dependent types, maybe linear types, structured concurrency, and a built-in formal proof system.
Something like ADA/Spark, but more modern.
> As the verification process itself becomes automated, the challenge will move to correctly defining the specification: that is, how do you know that the properties that were proved are actually the properties that you cared about? Reading and writing such formal specifications still requires expertise and careful thought. But writing the spec is vastly easier and quicker than writing the proof by hand, so this is progress.
How big is the effort of writing a specification for an application versus implementing the application in the traditional way? Can someone with more knowledge chime in here please?
I love HN because HN comments have talked about this a fair bit already. I think on the recent Erdos problem submission.
I like the idea that languages even like Rust and Haskell may be more accessible. Learn them of course but LLM can steer you out of getting stuck.
You’re still going to need people to guide and understand the proofs.
Might as well just learn Agda or Lean. There are good books out there. It’s not as hard as the author suggests. Hard, yes, but there’s no Royal road.
How do you verify that your verification verifies the right thing? Couldn’t the LLM spit out a nice looking but ultimately useless proof (boiling down to something like 1=1). Also, in my experience software projects are full of incorrect, incomplete and constantly changing assumptions and requirements.
AI is great; it makes my job easier by removing repetitive processes, freeing me up to tackle other "stuff". I like that I can have it form up json/xml scaffolding, etc. All the insanely boring stuff that can often get corrupted by human error.
AI isn't the solution to move humanity forward in any meaningful way. It is just another aspect of our ability to offload labor. Which in and of itself is fine. Until of course it gets weaponized and is used to remove the human aspect from more and more everyday things we take for granted.
It is being used to accelerate the divisions among people. Further separating the human from humanness. I love 'AI' tools, they make my life easier, work-wise. But would I miss it if it wasn't there? No. I did just fine before and would do so after it's flame and innovation slowly peters out.
I've been heearing about formal verification since college (which for me was more than 30 years ago) and I even taught a thing called "Z", which was a formally verifiable academia thing that tried to be the ultimate formal language. It never panned out, and I honestly don't think that AI is going to help in anything but test generation, which is going to remain the most pragmatic approach to formal verification (but, like all things, it's an approximation, not 100% correct).
I dunno about formal verification, but for sure it's brought me back to a much more TDD first style of coding. You get so much mileage from having it first implement tests and then run them after changes. The key is it lowers the friction so much in creating the tests as well. It's like a triple bottom line.
I think we will use more tools to check the programs in the future.
However I don't still believe in vibecoding full programs. There are too many layers in software systems, even when the program core is fully verified, the programmer must know about the other layers.
You are Android app developer, you need to know what phones people commonly use, what kind of performance they have, how the apps are deployed through Google App Store, how to manage wide variety of app versions, how to manage issues when storage is low, network is offline, battery is low and CPU is in lower power state.
Will or should? It's plausible, this same argument was made in an article the other day, but basic type/static analysis tools are cheap with enormous payoff and even those methods aren't ubiquitous.
Hard disagree. "I'm an expert" in that I have done tons of proofs on many systems with many provers, both academically and professionally for decades.
Also, I am a novice when it comes to programming with sound, and today I have been dorking with a simple limiter. ChatGPT knows way more than me about what I am doing. It has taught me a ton. And as magical and wonderful as it is, it is incredibly tedious to try to work with it to come up with real specifications of interesting properties.
Instead of banging my head against a theorem prover that won't say QED, I get a confident sounding stream of words that I often don't even understand. I often don't even have the language to tell it what I am imagining. When I do understand, it's a lot of typing to explain my understanding. And so often, as a teacher, it just is utterly failing to effectively communicate to me why I am wrong.
At the end of all of this, I think specification is really hard, intellectually creative and challenging work. An LLM cannot do the work for you. Even to be guided down the right path, you will need perseverance and motivation.
Formal verification at the “unit test” level seems feasible. At the system level of a modern application, the combinations of possible states will need a quantum computer to finish testing in this lifetime.
i could see formal verification become a key part of "the prompt is the code" so that as versions bump and so on, you can have an llm cpmpletely regenerate the code from scratch-ish and be sure that the spec is still followed
but i dont think people will suddenly gravitate towards using them because they're cheaper to write - bugs of the form "we had no idea this sould be considered" is way more common than "we wrote code that didnt do what we wanted it to"
an alternative guess for LLMs and formal verification is that systems where formal verification is a natural fit - putting code in places that are hard to update and have well known conditions, will move faster.
i could also see agent tools embedding in formal methods proofs into their tooling, so they write both the code and the spec at the same time, with the spec acting as memory. that kinda ties into the recent post about "why not have the LLM write machine code?"
The funny part of “AI will make formal verification go mainstream” is that it skips over the one step the industry still refuses to do: decide what the software is supposed to do in the first place.
We already have a ton of orgs that can’t keep a test suite green or write an honest invariant in a code comment, but somehow we’re going to get them to agree on a precise spec in TLA+/Dafny/Lean and treat it as a blocking artifact? That’s not an AI problem, that’s a culture and incentives problem.
Where AI + “formal stuff” probably does go mainstream is at the boring edges: property-based tests, contracts, refinement types, static analyzers that feel like linters instead of capital‑P “Formal Methods initiatives”. Make it look like another checkbox in CI and devs will adopt it; call it “verification” and half the org immediately files it under “research project we don’t have time for”.
Will include this thread in my https://hackernewsai.com/ newsletter.