Hacker News Reader

I disagree with your assessment pretty strongly -- the models themselves hit a wall over a year ago once companies exhausted all existing training data. LLMs don't induce world models, and they aren't capable of real search an planning outside their training distributions. They, structurally, never will be.

I haven't noticed a change in what I trust a model to generate in response to a single prompt in a year. The failure modes are unchanged. Yes, specific failures have improved as they have been documented and passed into model training data, but the way the models fail has not changed. They still fail for me nearly every single day. I'm a pretty heavy user - 3-4 Claude code processes running at a time, all day every day.

What has gotten better is tooling around the model -- but there's no space for exponential growth there. At least, not without exponential cost increase, which would make the whole thing untenable anyway.

If you think they've been at the same level for the past year, your skill is at issue. There was a huge jump in Nov/Dec with Opus 4.5+ and GPT 5.x series, and they've been incrementally stronger over the past 6 months.

As a next step, take another look at the next practices, and apply them to your work (simon's agentic series is a good place to start). Or not, you do you.

https://fortune.com/2026/01/29/100-percent-of-code-at-anthro...

I feel like the problem is there aren’t any great metrics. Boris Cherny probably gets paid like $2 mil per year. So what does it mean that Claude writes 100% of his code? And Claude writes 100% of code for most teams? Has Anthropic started laying people off? If Claude is writing 100% of code doesn’t that mean game over?

It’s both amazing and kind of a useless metric. How do I extrapolate out 100% 2-3 years from now? Super-duper 100%? Infinity infinity?

What does "writes X% of code" even mean?

I use AI to generate code very rarely (basically replacing searching code from SO which was also rare) yet maybe 90-95% of my code is machine generated (macros, snippet engines, boilerplate generators, IDE/LSP features etc) if we define as how many characters in the source code out of all characters did I write by my hand.

Before collective AI psychosis nobody cared how much code is hand written and how much machine generated and the difference was absolutely massive between developers. There was also no guaranteed correlation between "productivity" and how much developer hand writes or actually uses their tools well and the same applies with AI usage.

I wonder if our difference in view could be an instance of the jagged nature of AI’s intelligence. I do computational research in a basic science so write code or build models basically all day that is (occasionally) novel. I would say that I’ve noticed exponential improvements in parts of my job but certainly not all. For example, if I’m trying to visualize a concept from a paper I now go straight to Codex, give it the paper, and describe a webapp which allows me to play with the model in a way that wasn’t possible one year ago (this is great for teaching btw). If I have a script that I want to generalize, add in better metrics, or setup for running on a cluster I use codex and it does great.

Where it fails me though is exactly when I’m doing something novel like developing a new model or trying to develop some new method to process data. I’ve tried many times to one shot these ideas with detailed descriptions of what I want, how I’d like to generate abstractions, etc and it almost always ends up changing what I want to what I can only describe as something which better matches its training data. It often quietly changes key details that means that I have to delete the whole thing and start over. Just today this happened. On this level of task I’ve found that my workflow and pace of iteration hasn’t really changed at all in the last year. I still have to go and explain in detail on a function by function level what I want in much the same way I did a year ago. While that’s obviously a harder task, it seems to me like the task this whole long term exponential argument hinges on. I obviously could be wrong and maybe LLM with eval loop will do all of this for us but it seems still quite bad at anything without a clear definition of “good”.

I’m personally much more concerned about autonomous weapons, surveillance, and people plugging these things into places they don’t belong to avoid responsibility than I am the general possibility of these models being smarter than me in every way but obviously I could be wrong on this and am just using it incorrectly, hence the question.

> Now extrapolate how things will look if the trend continues for another 2 or 3 years.

…and humans are famously bad at extrapolating exponentially, which is kinda the point of the essay.