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by choeger
342 days ago
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Yet, AI agents don't replace software engineers. Imagine a software company without a single software engineer. What kind of software would it produce? How would a product manager or some other stakeholder work with "AI agents"? How do the humans decide that the agent is finished with the job? Software engineering changes with the tools. Programming via text editors will be less important, that much is clear. But "AI" is a tool. A compressed database of all languages, essentially. You can use that tool to become more efficient, in some cases wastly more efficient, but you still need to be a software engineer. Given that understanding, consider another question: When has a company you worked for ever said "that's enough software, the backlog is empty. We're done for the quarter with software development?" |
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Currently AI failure modes (consistency over long context lengths, multi-modal consistency, hallucinations) make it untenable as a "full-replacement" software engineer, but effective as a short-term task agent overseen by an engineer who can review code and quickly determine what's good and what's bad. This allows a 5x engineer to become a 7x engineer, 10x become a 13x, etc. which allows the same amount of work to be done with fewer coders, effectively replacing the least productive engineers in aggregate.
However, as those failure modes becomes less and less frequent, we will gradually see "replacement". It will come in the form of senior engineers using AI tools noting that a PR of a certain complexity is coded correctly 99% of the time by a given AI model, so they will start assigning longer, more complex tasks to it and stop overseeing the smaller ones. The length of tasks it can reliably complete get longer and longer, until all a suite of agents needs is a spec, API endpoints and the ability to serve testing deployments to PM's, and it begins doing first only what a small, poorly run team could accomplish, but month after month gets better and better until companies start offloading entire teams to AI models and simply require a higher-up team to check and reconfigure them once and a while and budget manage token use.
This process will continue as long as AI models grow more capable, less hallucinatory over long-context horizons, and agentic/scaffolding systems become more robust and effectively designed to mitigate and deal with the issues affecting the AI models that do exist. It won't be easy or straightforward, but the economic potential gains are so enormous that it makes sense that billions are being poured into any AI agent startup that can snatch a few IOI medalists and a coworking space in SF.