According to 1M AI News, David Cramer, co-founder of the error monitoring platform Sentry, posted today on X stating he is “completely confident” that large language models are not currently a net productivity boost. He believes that while LLMs lower the barrier to entry, they continuously generate increasingly complex and hard-to-maintain code, which, based on his own experience, is slowing down long-term development speed.
Cramer said he is questioning "agentic engineering"—the approach of having models automatically generate code and deploy it directly—arguing that the resulting code quality is significantly worse and, when accumulated over time, becomes a net liability. Specific issues include poor performance during incremental development in complex codebases, inability to generate interfaces that follow language idioms, and "pure slop test generation." He specifically called out OpenClaw: "If I had to bet, tools like OpenClaw, because they generate so much code, are already irrecoverable," and emphasized that "building software is still hard—it has never been about minimizing or maximizing lines of code."
Cramer added that the above assessment is primarily based on his experience developing features in mature codebases with normal complexity; his recent increase in contributions is due to finding it "interesting," not "easier," and he believes this is fundamentally a psychological shift, with no essential difference in actual time spent.
Sentry Co-founder Criticizes LLMs for Slowing Long-term Development
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Summary
Sentry co-founder David Cramer warned that large language models (LLMs) may harm long-term crypto strategy by generating code that is difficult to maintain. He said LLMs lower entry barriers but slow development over time. Cramer criticized "agentic engineering" and cited OpenClaw as an example of unsustainable code generation. He argued that while LLMs boost short-term output, they risk undermining long-term investment goals by complicating system maintenance.
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