Why models write slop: the environments are too small — Henrique Godoy

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Why models write slop: the environments are too small

July 23, 2026

As Will Depue wrote on A Stargate for Data: Labs are on a trajectory towards >$100B/year of data spend by 2030. As we begin the trillion-dollar compute project, we need to think about the equivalent civilizational-scale effort for the other core ingredient: data.

Almost every new startup right now is doing one of three things:

FDE (Forward Deployment Engineers)

fine-tuning models for specific tasks

selling training data to labs

At least so far, selling training data to labs looks like the most profitable of the three.

But compared with the pace at which we are scaling compute, the pace at which we are scaling data is incredibly slow, because generating high-quality data is still a difficult and bespoke task.

When every model learns from roughly the same internet, the model itself stops being much of a moat. The real edge comes from data that competitors cannot access.

I believe what has put Anthropic ahead of the other major labs is data. That advantage came from a decision, or maybe an accident, I don't know, to focus on coding models. The natural audience for those models was programmers, probably the earliest adopters and the people most comfortable/urge with new technologies.

AWS and the other cloud providers have spent the last twenty years monetizing this exact weakness in programmers: the need to feel that everything is changing all the time. (You can open Hacker News every morning and find a new framework, database, runtime, deployment platform, or architectural pattern that supposedly changes how software is built, but what has actually changed in the basic act of building a web application? You still receive a request, run some code, and write something to a database. The computers got faster and the browsers got better, but somehow putting code into production became harder: more services, more configuration files, more dashboards, more specialists, and more ways for the system to fail. This is probably not an accident. Simplicity is hard to monetize because, once something becomes simple, there is not much left to sell. So the industry keeps wrapping the same basic primitives in new layers of sophistication and calling the result progress.) In the end, much of the cloud is still EC2 and RDS, only surrounded by an empire of abstractions.

By entering a market full of people who adopt new technologies early and use them heavily, Anthropic found an unusually good path. It gets more than free labeling: people label enormous amounts of data while paying Anthropic for the privilege. As user trajectories become longer and longer, Anthropic keeps collecting more data to train on.

My guess is that Anthropic uses all of this data at training time and then selects the best trajectories to improve how its models use compute at test time. That closes the feedback loop and allows the models to keep getting better.

By solving coding really well, you can then automate almost everything else. Programming is the path to progress. Anthropic also has a mutualistic relationship with its users: it provides a very good coding model, and coders use that model to generate their own revenue while labeling data for Anthropic, which can then use that data to build even better models.

It is a bidirectional relationship that can keep reinforcing itself (positive feedback loop) until the models become intelligent enough to achieve RSI, or recursive self-improvement. The main argument of this essay, however, is that we cannot achieve RSI by improving models on code alone.

The labs already anticipated the data problem and the growing asymmetry between compute and data. Some now have entire teams or departments dedicated to generating data. Labs such as xAI and Meta have reportedly gone as far as recording developers' screens.

But comparing the scaling of compute with the scaling of data is like comparing an industrial system with a cottage industry. We are building industrial systems for compute and cottage industries for data.

It is really clear that models are improving quickly. This week alone, we have already seen Codex escape an eval and attack Hugging Face, and Fable find a Jacobian counterexample, on top of incredible results across really difficult evals.

So why are we not seeing all of this AI progress on the economic side? Why can my AI write an entire SQLite database from scratch but still produce slop code? Feels that Moravec's predicted this earlier ago.

Aligned with this, it seems that almost every programmer is now just steering models across a lot of tabs and keeping them pointed in the right direction. The models still rely on having good system design around them so they do not produce garbage code. Taelin pointed this out very well, but I think the solution he proposed is only a band-aid. Models are not good at these things because they...