> It seems that LLMs, as they work today, make developers more productive. Think this strongly depends on the developer and what they're attempting to accompli

> It seems that LLMs, as they work today, make developers more productive. Think this strongly depends on the developer and what they're attempting to accomplish. In my experience, most people who swear LLMs make them 10x more productive are relatively junior front-end developers or serial startup devs who are constantly greenfielding new apps. These are totally valid use cases, to be clear, but it means a junior front-end dev and a senior embedded C dev tend to talk past each other when they're discussing AI productivity gains. > Working with the technology and mitigating it's weaknesses is the only rational path forward. Or just using it more sensibly. As an example: is the idea of an AI "agent" even a good one? The recent incident with Copilot[0] made MS and AI look like a laughingstock. It's possible that trying to let AI autonomously do work just isn't very smart. As a recent analogy, we can look at blockchain and cryptocurrency. Love it or hate it, it's clear from the success of Coinbase and others that blockchain has found some real, if niche, use cases. But during peak crypto hype, you had people saying stuff like "we're going to track the coffee bean supply chain using blockchain". In 2025 that sounds like an exaggerated joke from Twitter, but in 2020 it was IBM legitimately trying to sell this stuff[1]. It's possible we'll look back and see AI agents, or other current applications of generative AI, as the coffee blockchain of this bubble. [0] https://www.reddit.com/r/ExperiencedDevs/comments/1krttqo/my_new_hobby_watching_ai_slowly_drive_microsoft/ https://www.reddit.com/r/ExperiencedDevs/comments/1krttqo/my... [1] https://www.forbes.com/sites/robertanzalone/2020/07/15/big-coffee-sellers-use-blockchain-to-connect-farmers-and-customers/ https://www.forbes.com/sites/robertanzalone/2020/07/15/big-c...