alt Hacker NewsCool 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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> 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...
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.
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.
We’ll need artificial intelligence for robots and once they start rolling out at a larger scale, that’s when things will really take off. It’s pretty well established already that robots can do a lot of the physical labor that humans do. I’m guessing it’ll take another five years before we start seeing robots everywhere but they’ll steadily increase.
LLM's are better at software for several reasons:
- lots of high quality training data
- automatic verfication/scoring is straightforward
- AI companies are software companies so are better at tuning LLM's for software
- the goal is AGI, where AGI means the AI can improve itself autonomously. Which requires them to be good at software
While LLM's are uniquely good at software today, they'll get better at other fields slowly as attention goes towards training in those fields.
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.
I agree - it is easier to prove that a software implementation is correct. In many other industries the definition of what’s good work is less tangible. There are also jobs where the liability for mistakes is extremely high.
I onboarded a bunch of nontechnical people in the last few weeks and they output skyrocketed - they deliver faster and better than before.
But I’d say only recently CC became decently usable for nontech people - the auto mode plus finally polished browser integration, plus Opus 5.5. Even I was surprised by how well it all works now out of the box (and how cheap - regular Pro license is decently useful now).
> Software creation tools have been worked on for decades to keep fallible human coders out of trouble as much as possible.
And the LLM-based coding agents provide the ultimate scapegoat for those who consider "the LLM wrote this code" to be an acceptable, deflecting response when issues occur.
> Cool analogy, but there's another possibility: software is uniquely suited for being boosted by LLM-based coding agents.
Or the pretty obvious third possibility: The tech sector is the part selling the LLMs. They're not digging up more gold with their cool shovel technology, they are selling the shovels.
This is from the view of a software person or must be, because formal definitions of math, chemistry, physics, engineering, the English language, etc. have existed for centuries. There are entire college curricula, formal dictionaries, exact specifications for equations and how to apply them to basically all fields of engineering.
Like... other fields exist, this is incredibly myopic. What is specific to software is it is all language based, and the people making the tools happen to be familiar with it so they know how to train it, but that's about it.
Seriously? Lawyers. Doctors. Civil engineering. Accountants. And that's just the fields I know of directly being significantly impacted that I could think of in 10 seconds. Oh, also: education.
most of white collar work is building mid ppt and sending emails. it was automated with GPT-3.
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.