Use Boring Languages with LLMs

Summary

Use boring languages with LLMs Apr 20, 2026 I'm Jacob - I run Sancho Studio a software consulting group, we help companies with technical leadership, strategy, and security. I keep coming back to this idea that consistency compounds. I’ve noticed it acutely as a consultant working on multiple different projects in the last two years. Large language models amplify inconsistent technology and quietly reinforce consistent ones. The languages and ecosystems that suffer from the most fragmentation produce the worst agentic output, and the ones with the strongest conventions produce the best. I think this effect will increasingly determine which tool survives in the paradigm of massive models trained on large corpuses. Even if code is cheap, running inference is a gamble. It’s impossible to know if at any moment the model will make a decision to install a package or produce a bizarre coding pattern from 2019. If we consider that we’re gambling with tokens, we should bet on the set of embeddings which represent strongly consistent and reinforced model weights to produce median output. For software development this is actually ideal as the median program is typically doing the basics: processing information, reading/writing files, responding to network requests, etc. Before AI, engineers complained about languages which reinvented themselves on what felt like an annual basis. These complaints were real but mostly aesthetic and symptomatic of a frustration that humans needed to maintain or keep up with needlessly changing ecosystems. If we look back, the 2024 State of JS survey describes a relatively fragmented ecosystem. For a human, fragmentation is annoying. For a model trained on the public corpus of all of it; fragmentation is something closer to a problem which needs to be solved in reinforcement learning or agent harnesses (e.g. Claude Code leaked and showed us that Anthropic hard-coded some bias for JavaScript frameworks). Python is the same story but sung in a diffe...