2025 AI Index Report

I always see these reports about how much better AI is than humans now, but I can't even get it to help me with pretty mundane problem solving. Yesterday I gave Claude a file with a few hundred lines of code, what the input should be, and told it where the problem was. I tried until I ran out of credits and it still could not work backwards to tell me where things were going wrong. In the end I just did it myself and it turned out to be a pretty obvious problem.

The strange part with these LLMs is that they get weirdly hung up on things. I try to direct them away from a certain type of output and somehow they keep going back to it. It's like the same problem I have with Google where if I try to modify my search to be more specific, it just ignores what it doesn't like about my query and gives me the same output.

It's overfitting.

Some people say they find LLMs very helpful for coding, some people say they are incredibly bad.

I often see people wondering if the some coding task is performed well or not because of availability of code examples in the training data. It's way worse than that. It's overfitting to diffs it was trained on.

"In other words, the model learns to predict plausible changes to code from examples of changes made to code by human programmers."

https://arxiv.org/abs/2206.08896

... which explains why some models are better at code than others. The best coding models (like Claude 3.7 Sonnet) are likely that good because Anthropic spent an extraordinary amount of effort cultivating a really good training set for them.

I get the impression one of the most effective tricks is to load your training set up with as much code as possible that has comprehensive automated tests that pass already.

> ... which explains why some models are better at code than others.

No. It explains why models seem better at code in given situations. When your prompt mapped to diffs in the training data that are useful to you they seem great.

I've been writing code with LLM assistance for over two years now and I've had plenty of situations where I am 100% confident the thing I am doing has never been done by anyone else before.

I've tried things like searching all of the public code on GitHub for every possible keyword relevant to my problem.

... or I'm writing code against libraries which didn't exist when the models were trained.

The idea that models can only write code if they've seen code that does the exact same thing in the past is uninformed in my opinion.

Strongly agree.

This seems to be very hard for people to accept, per the other comments here.

Until recently I was willing to accept an argument that perhaps LLMs had mostly learned the patterns; e.g. to maybe believe 'well there aren't that many really different leetcode questions'.

But with recent models (eg sonnet-3.7-thinking) they are operating well on such large and novel chunks of code that the idea they've seen everything in the training set, or even, like, a close structural match, is becoming ridiculous.

All due respect to Simon but I would love to see some of that groundbreaking code that the LLMs are coming up with.

I am sure that the functionalities implemented are novel but do you really think the training data cannot possibly have had the patterns being used to deliver these features, really? How is it that in the past few months or years people suddenly found the opportunity and motivation to write code that cannot possibly be in any way shape or form represented by patterns in the diffs that have been pushed in the past 30 years?

When I said "the thing I am doing has never been done by anyone else before" I didn't necessarily mean groundbreaking pushes-the-edge-of-computer-science stuff - I meant more pedestrian things like "nobody has ever published Python code to condense and uncondense JSON using this new format I just invented today": https://github.com/simonw/condense-json

I'm not claiming LLMs can invent new computer science. I'm saying it's not accurate to say "they can only produce code that's almost identical to what's in their training data".