Much has been said about the effects that AI will have on software development, but there is an angle I haven’t seen talked about: I believe that AI will bring formal verification, which for decades has been a bit of a fringe pursuit, into the software engineering mainstream.
The future belongs to those who can refute AI, not just generate with AI
Why verification, not prompting, could shape the next decade of engineering
70 years of AI hype
Quoting from Olivia Guest et al. (2025) "Against the Uncritical Adoption of AI Technologies in Academia."
Where will AI be at the end of 2027? A bet
We, Gary Marcus, author, scientist, and noted skeptic of generative AI, and Miles Brundage, an independent AI policy researcher who recently left OpenAI and is bullish on AI progress, have agreed to the following bet, at 10:1 odds, with criteria drawn from two earlier Substack essays by Gary that proposed criteria for
I expect rapid progress but not towards general superintelligence
I’ve often been surprised when I hear from top researchers in industry that they think AI will be better than them at their job in a few years, and I didn’t really know why I doubted it.
Where does the rigor go? Research software and the future of trustworthy science.
Generative AI now makes it dramatically easier to produce something that looks like research: analysis code, figures, literature reviews, even whole pap…
AI | 2025 Stack Overflow Developer Survey
84% of respondents are using or planning to use AI tools in their development process, an increase over last year (76%). This year we can see 51% of professional developers use AI tools daily.
AI made me doubt everything about programming by Felienne Hermans - DDD Europe 2026
AI made me doubt everything about programming by Felienne Hermans - DDD Europe 2026
Measuring the Impact of Early-2025 AI on Experienced Open-Source...
Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the...
The Case Against Formal Verification, 50 Years Later - Ivan Gavran
Writings on software correctness, AI, formal verification, and other technical topics.
Verification in the Age of AI Scientists
AI Scientists now operate at a scale that outpaces human capacity for manual review. Systems such as Sakana’s AI Scientist write entire workshop papers end-to-end. Lila Sciences runs autonomous ``AI Science Factories’’ that hypothesize, experiment, and iterate without human guidance. FutureHouse’s Kosmos and Robin generate thousands of candidate hypotheses in a single run, and Google’s Co-Scientist proposes testable experiments at a rate no laboratory can fully evaluate. Each of these systems emphasizes verified results, yet that standard ranges from near-perfect formal proof in mathematics to decade-long clinical trials in medicine, with no shared framework for judging sufficiency across domains. As outputs scale beyond what humans can manually inspect, the question of which results to trust becomes as hard as generating them. The bottleneck for AI for Science is no longer hypothesis generation, it is verification.
6 months to live for open models
The most serious test to date of open source AI’s viability is happening right now.
The Thoughts The Civilized Keep
The hype around a new AI language generator reveals the sterility of mainstream thinking on AI today — and indeed on how we think about thinking itself.
How we use AI at Stalwart
It is difficult to have a conversation about software in 2026 without AI showing up in it. Two years ago the interesting question was…
When Science Goes Agentic
In a couple of years, we will inspect AI-generated source code about as often as we inspect the assembly output of a compiler. Which is to say, far less often—outside of high-stakes and adversarial settings. The trajectory is clear: vibe coding is not a fad but a transition, a stepping stone. Debugging AI-generated code will shrink dramatically for a lot of everyday software—not because the code will be flawless, but because the feedback loops between generation, testing, and correction will tighten until human inspection becomes the bottleneck rather than the safeguard. In this respect, requiring the co-generation, with code, of mechanically verifiable formal attestations can also improve the process.
