I was curious why we don't hear about locust swarms anymore in the US
Turns out this species -
the Rocky Mountain locust was made accidentally extinct by settlers. Although the swarms could be huge, they had a small and concentrated breeding ground that was destroyed by farming and cows. Wikipedia says the last specimen was collected in 1904.
Thank you for reading and the very thoughtful observations.
>> At a certain point, the verification complexity takes off. You literally run out of time to verify everything.
> Could you elaborate on this?
I plan to publish a thorough post with an interactive model. Whether human or AI, you are capacity constrained, and I glossed over `C` (capacity within a given timeframe) in the X post.
You are correct that verification complexity remains finite at n_0. The barrier is practical: n_0 is where V(n) exceeds your available capacity C. If V(n) = n^(1+k), then n_0 = C^(1/(1+k)). Doubling your capacity doesn't double n_0. It increases by a factor of 2^(1/(1+k)), which is always less than 2.
So the barrier always exists for, say, a given "dev year" or "token budget," and the cost to push it further grows superlinearly. It's not absolutely immovable, but moving it gets progressively harder. That's what I mean by "literally run out of time." At any given capacity, there is a finite n beyond which complete verification is not possible. Expanding capacity buys diminishing returns.
> Either way, this entire discussion assumes n will increase as more and more software gets written by AI. Couldn't it also be the opposite, though?
You are getting at my core motivation for exploring this question.
Verification requires a definition of "done" and I wonder if it will ever be possible (or desirable) for AI to define done on its own, let alone verify it and simplify software based on its understanding of our needs.
You make a great point that we are not required to add more components and "go right" along the curve. We can choose to simplify, and that is absolutely the right takeaway. AI has made many people believe that by generating more code at a faster pace they are accomplishing more. But that's not how software productivity should be judged.
To answer your question about assumptions, while AI can certainly be prompted to help reduce n or k in isolated cases where "done" is very clear, I don't think it's realistic to expect this in aggregate for complex systems where "done" is subjective and dynamic.
I'm speaking mainly in the context of commercial software dev here, informed by my lived experience building hundreds of apps. I often say software projects have a fractal complexity. We're constantly identifying new needs and broader scope the deeper we go, not to mention pivots and specific customer asks. You rarely get to stand still.
I don't mean to be pessimistic, but my hunch is that complexity growth outpaces the rate of simplification in almost every software project. This model attempts to explain why that is so. And notably, simplification itself requires verification and so it is in a sense part of the verification cost, too.
I believe there is a Verification Complexity Barrier
As you add components to a system, the time it takes to verify that the components work together increases superlinearly.
At a certain point, the verification complexity takes off. You literally run out of time to verify everything.
AI coding agents hit this barrier faster than ever, because of how quickly they can generate components (and how poorly they manage complexity).
I think verification is now the problem of agentic software engineering. I think formal methods will help, but I don't see how they will apply to messy situations like end-to-end UI testing or interactions between the system and the real world.
I think you and the article actually agree and you are arguing only with their use of the word "development."
The article uses "development" to refer only to the part where code is generated, while you are saying "development" is the process as a whole.
You both agree that latency in the real-world validation feedback loop leads to longer cycles and fewer promising solutions and that is the bottleneck.
Turns out this species - the Rocky Mountain locust was made accidentally extinct by settlers. Although the swarms could be huge, they had a small and concentrated breeding ground that was destroyed by farming and cows. Wikipedia says the last specimen was collected in 1904.