Dario Amodei says AI will be writing 90% of the code in 6 months and almost all the code in 12 months.
I think it’s somewhat unclear how big of a deal this is. In particular, situations where AIs write 90% of lines of code, but are very far (in time, effective compute, and qualitative capabilities) from being able to automate research engineer jobs seem very plausible to me. Perhaps Dario means something a bit stronger than “90% of lines of code”.
It’s pretty easy to get to 25% of lines of code written by LLMs with very weak models, e.g., Google claims to see this despite relatively lackluster adoption and integration (I’d guess) and also this is probably mostly weak models doing code completion.
It could be that “somewhat better model than we have now after time for integration in cursor” already gets you to 90% of lines that end up getting committed, but gets you a much smaller overall productivity boost.
If “almost all” means something like “humans rarely spend their time writing code and only do this in niche cases”, then this is probably occurring at a point where we’re at least somewhat close to full automation of research engineering. But I’m pretty unsure, and there are much weaker interpretations of “almost all”.
I expect neither 90% of code in 6 months nor almost all in 12 months.
Dario: 6 months ago I I made this prediction that, you know, in in 6 months 90% of code would be written by by by AI models. Some people think that prediction is wrong, but within Anthropic and within a number of companies that we work with, that is absolutely true.
Marc: Um now 90 you know so you’re saying that 90% of all code at Anthropic being written by the by the model today—
Dario: on on many teams you know not uniformly
I think on some teams at Anthropic 90% of code is written by AIs and on some teams it isn’t for an average lower than 90%. I say more here.
My best guess is that the intended reading is “90% of the code at Anthropic”, not in the world at large—if I remember the context correctly that felt like the option that made the most sense. (I was confused about this at first, and the original context on this is not clear whether the claim is about the world at large or about Anthropic specifically.)
Yes, I was intending my comment to refer to just code at Anthropic. (Otherwise I would talk much more about serious integration lags and lack of compute.)
I think it’s somewhat unclear how big of a deal this is. In particular, situations where AIs write 90% of lines of code, but are very far (in time, effective compute, and qualitative capabilities) from being able to automate research engineer jobs seem very plausible to me. Perhaps Dario means something a bit stronger than “90% of lines of code”.
It’s pretty easy to get to 25% of lines of code written by LLMs with very weak models, e.g., Google claims to see this despite relatively lackluster adoption and integration (I’d guess) and also this is probably mostly weak models doing code completion.
It could be that “somewhat better model than we have now after time for integration in cursor” already gets you to 90% of lines that end up getting committed, but gets you a much smaller overall productivity boost.
If “almost all” means something like “humans rarely spend their time writing code and only do this in niche cases”, then this is probably occurring at a point where we’re at least somewhat close to full automation of research engineering. But I’m pretty unsure, and there are much weaker interpretations of “almost all”.
I expect neither 90% of code in 6 months nor almost all in 12 months.
A few days ago, Amodei claimed that 90% of code at Anthropic (and some companies they work with) is being written by AI
The exact text is:
I think on some teams at Anthropic 90% of code is written by AIs and on some teams it isn’t for an average lower than 90%. I say more here.
My best guess is that the intended reading is “90% of the code at Anthropic”, not in the world at large—if I remember the context correctly that felt like the option that made the most sense. (I was confused about this at first, and the original context on this is not clear whether the claim is about the world at large or about Anthropic specifically.)
Yes, I was intending my comment to refer to just code at Anthropic. (Otherwise I would talk much more about serious integration lags and lack of compute.)