You’ve probably already seen this, but for others reading this post: Anthropic now seems to have put out some more official numbers on this: https://www.anthropic.com/research/how-ai-is-transforming-work-at-anthropic
It seems to mostly validate your read on the situation. They did internal surveys, qualitative interviews, and some analysis of Claude Code transcripts. Here is their “key takeaways” from the survey section:
Survey data
Anthropic engineers and researchers use Claude most often for fixing code errors and learning about the codebase. Debugging and code understanding are the most common uses (Figure 1).
People report increasing Claude usage and productivity gains. Employees self-report using Claude in 60% of their work and achieving a 50% productivity boost, a 2-3x increase from this time last year. This productivity looks like slightly less time per task category, but considerably more output volume (Figure 2).
27% of Claude-assisted work consists of tasks that wouldn’t have been done otherwise, such as scaling projects, making nice-to-have tools (e.g. interactive data dashboards), and exploratory work that wouldn’t be cost-effective if done manually.
Most employees use Claude frequently while reporting they can “fully delegate” 0-20% of their work to it. Claude is a constant collaborator but using it generally involves active supervision and validation, especially in high-stakes work—versus handing off tasks requiring no verification at all.
So on average people report about 50% productivity boost, but don’t actually “fully delegate” much work to it, and a good amount of the productivity boost is from doing things that wouldn’t have been high-priority enough to get done otherwise. The data from qualitative interviews and Claude Code transcript analysis seems to tell a similar story. I don’t think they give an actual statistic on “lines of production code committed” though.
Not an expert, but I think the difference is this. Current and older LLMs produce each token via a “forward pass”: information only flows forwards in the model, what happens at a later layer can’t influence what happens at an earlier layer. What people call “neuralese” is to build neural nets where information can also flow backwards in the model, so that it can pass information back to itself “in its head” rather than only being able to pass information back to itself by outputting tokens and then reading them back in. This is a known technique and has been done before, but it’s hard to train large models with that architecture. I was going to try to explain why but realized I don’t understand well enough myself to explain it, so I’ll leave it there.