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Cool analogy, but there's another possibility: software is uniquely suited for being boosted by LLM-based coding agents. That's not to say LLMs are useless in other domains, but they may not yield as much benefit. Software creation tools have been worked on for decades to keep fallible human coders out of trouble as much as possible. Starting with compilers that enforce or at least encourage coding style humans can understand. Compilers embody formal definitions of language syntax. Lint and other static analysis technologies contribute by identifying problematic patterns in code. Leak detectors identify memory management problems. Profilers identify performance problems. Few other endeavors have this much tooling that serendipitously happens to help help LLM-based agents be more useful and less error prone. A lot of discussion around AI is very high-level and abstract. It helps to dig in and identify the characteristics that actually make a domain more or less amenable to being boosted by AI.
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I think LLM's will prove to be useful in any domain where most work happens in front of a computer. As LLM's make programming cheap, they also reduce the cost of building new software that automates work currently performed by humans in other fields. If you provide best models with all the data and context an average white collar worker has to perform their job, in most cases they will do just fine. But in real world that context is often scattered over multiple systems, or sometimes exists only in people's heads. I believe that's the main bottleneck for adoption now.
> but they may not yield as much benefit. This morning, I was talking to a friend of mine, who is a marketing (not tech) guy, and he was gushing about LLMs. He loves being able to just dump in a bucket of seemingly unrelated data, and getting actionable BI from it. I think we're in for a wild ride. Fasten your seat belt, and keep your hands inside the vehicle...
My SO works in legal and regulatory compliance, she needed a small app (worflow with a form and a validation process), CTO said they could not do it, too complicated, would cost 15 days for a freelance and rejected it. She called it BS, and the next day she spent the morning with ChatGPT using their internal Microsoft-based tooling, and the app was done. She was hyped for the day, with a feeling of accomplishment and is already thinking of another project. And I'm hearing similar stories every week. So yes, LLM have positive impacts in many business domains, but seeing people happy and enpowered with their home-made little apps, I think it's the best.
I see things this way too. Another point is that the FOSS software movement was extremely unique and I don't think there's anything analogous in any industry. This provided the backbone by which LLMs became so good at manipulating code. Without FOSS and platforms like GitHub, LLMs would be severely lacking in terms of writing code.
LLMs need deterministic verification to enable a quick and short feedback loop before they become really powerful. Software is uniquely positioned to provide this feedback loop. Much less in other industries.