Quote from the CEO of Anthropic in March 2025:
"I think we'll be there in three to six months where AI is writing 90% of the code and then in 12 months we may be in a world where AI is writing essentially all of the code"
They're effectively bringing on a team that's been focused on building a runtime for years. The models they could throw at the problem can't be tapped on the shoulder, and there's no guarantee they'd do a better job at building something like Bun.
> Over the last several months, the GitHub username with the most merged PRs in Bun's repo is now a Claude Code bot. We have it set up in our internal Discord and we mostly use it to help fix bugs. It opens PRs with tests that fail in the earlier system-installed version of Bun before the fix and pass in the fixed debug build of Bun. It responds to review comments. It does the whole thing.
You do still need people to make all the decisions about how Bun is developed, and to use Claude Code.
Yeah but do you really need external hires to do that? Surely Anthropic has enough experienced JavaScript developers internally they could decide how their JS toolchain should work.
Actually, this is thinking too small. There's no reason that each developer shouldn't be able to customize their own developer tools however they want. No need for any one individual to control this, just have devs use AI to spin up their own npm-compatible package management tooling locally. A good day one onboarding task!
and
Implementing the Decisions
are complementary, one of these is being commoditised.
And, in fact, decimated.
Personally I am benefitting almost beyond measure because I can spend my time as the architect rather than the builder.
It's amazing!
My boss has dubbed it "programming at the speed of thought" which I'm sure he's picked up from somewhere. I've seen other people say that.
It's basically the Jevons paradox for code. The price of lines of code (in human engineer-hours) has decreased a lot, so there is a bunch of code that is now economically justifiable which wouldn't have been written before. For example, I can prompt several ad-hoc benchmarking scripts in 1-2 minutes to troubleshoot an issue which might have taken 10-20 minutes each by myself, allowing me to investigate many performance angles. Not everything gets committed to source control.
Put another way, at least in my workflow and at my workplace, the volume of code has increased, and most of that increase comes from new code that would not have been written if not for AI, and a smaller portion is code that I would have written before AI but now let the AI write so I can focus on harder tasks. Of course, it's uneven penetration, AI helps more with tasks that are well-described in the training set (webapps, data science, Linux admin...) compared to e.g. issues arising from quirky internal architecture, Rust, etc.
It's much faster for me to just start with an agent, and I often don't have to write a line of code. YMMV.
Sonnet 3.7 wasn't quite at this level, but we are now. You still have to know what you're doing mind you and there's a lot of ceremony in tweaking workflows, much like it had been for editors. It's not much different than instructing juniors.
> .... and in 12 months, we might be in a world where the ai is writing essentially all of the code. But the programmer still needs to specify what are the conditions of what you're doing. What is the overall design decision. How we collaborate with other code that has been written. How do we have some common sense with whether this is a secure design or an insecure design. So as long as there are these small pieces that a programmer has to do, then I think human productivity will actually be enhanced
(He then said it would continue improving, but this was not in the 12 month prediction.)
Source interview: https://www.youtube.com/live/esCSpbDPJik?si=kYt9oSD5bZxNE-Mn
I posted a link and transcription of the rest of his "three to six months" quote here: https://news.ycombinator.com/item?id=46126784
I think it's safe to say that people singularly focused on the business value of software are going to produce acceptable slop with AI.
It's a private repo, and I won't make it open source just to prove it was written with AI, but I'd be happy to share the prompts. You can also visit the site, if you'd like: https://chipscompo.com/
You have to be setup with the right agentic coding tool, agent rules, agent tools (MCP servers), dynamic context acquisition and workflow (working with the agent operate from a plan rather than simple prompting and hoping for the best).
But if you're lazy, don't put the effort in to understand what you're working with and how to approach it with an engineering mindset - you'll be be left on the outside complaining and telling people how it's all hype.
My best writing on this topic is still this though (which doesn't include a video): https://simonwillison.net/2025/Mar/11/using-llms-for-code/
"For now, I’ll go dogfood my shiny new vibe-coded black box of a programming language on the Advent of Code problem (and as many of the 2025 puzzles as I can), and see what rough edges I can find. I expect them to be equal parts “not implemented yet” and “unexpected interactions of new PL features with the old ones”.
If you’re willing to jump through some Python project dependency hoops, you can try to use FAWK too at your own risk, at Janiczek/fawk on GitHub."
That doesn't sound like some great success. It mostly compiles and doesn't explode. Also I wouldn't call a toy "innovation" or "revolution".
It's a private repo, and I won't make it open source just to prove it was written with AI, but I'd be happy to share the prompts. You can also visit the site, if you'd like: https://chipscompo.com/
I was honestly baffled how fast Claude knocked this out.
You can see my site here, if you'd like: https://chipscompo.com/