I do agree that there's a lot of garbage and navel-gazing that is directly downstream from the creation of LLMs. Because it's easier to task and evaluate an LLM [or network of LLMs] with generation of code, most of these products end up directly related to the production of software. The professional production of software has definitely changed, but sticky impact outside of the tech sector is still brewing.

I think there is a lot of potential, outside of the direct generation of software but still maybe software-adjacent, for products that make use of AI agents. It's hard to "generate" real world impact or expertise in an AI system, but if you can encapsulate that into a function that an AI can use, there's a lot of room to run. It's hard to get the feedback loop to verify this and most of these early products will likely die out, but as I mentioned, agents are still new on the timeline.

As an example of something that I mean that is software-adjacent, have a look at Square AI, specifically the "ask anything" parts: https://squareup.com/us/en/ai

I worked on this and I think that it's genuinely a good product. An arbitrary seller on the Square platform _can_ do aggregation, dashboarding, and analytics for their business, but that takes time and energy, and if you're running a business it can be hard to find that time. Putting an agent system in the backend that has access to your data, can aggregate and build modular plotting widgets for you, and can execute whenever you ask it a question is something that objectively saves a seller's time. You could have made such a thing without modern LLMs, but it would be substantially more expensive in terms of engineering research, time, and effort to put together a POC and bring it production, making it a non-starter before [let's say] two years ago.

AI here is fundamental to the product functioning, but the outcome is a human being saving time while making decisions about their business. It is a useful product that uses AI as a means to a productive end, which, to me, should be the goal of such technologies.

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kragen 4 days ago | parent [-]

Yes, but I'm asking about new non-AI products. I agree that lots of people are integrating AI into products, which makes products that wouldn't have existed otherwise. But if the answer to "where's the explosive changes and development of new products?" is 100% composed of integrating AI into their products, that means current AI isn't actually helping people write software, much. It's just giving them more software to write.

That doesn't entail that current AI is useless! Or even non-revolutionary! But it's a different kind of software development revolution than what I thought you were claiming. You seem to be saying that the relationship of AI to software development is similar to the relationship of the Japanese language, or raytracing, or early microcomputers to software development. And I thought you were saying that the relationship of AI to software development was similar to the relationship of compilers, or open source, or interactive development environments to software development.

It also doesn't entail that six months from now AI will still be only that revolutionary.

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mmargenot 3 days ago | parent [-]

For better or for worse, AI enables more, faster software development. A lot of that is garbage, but quantity has a quality all its own.

If you look at, e.g. this clearly vibe-coded app about vibe coding [https://www.viberank.app/], ~280 people generated 444.8B tokens within the block of time where people were paying attention to it. If 1000 tokens is 100 lines of code, that's ~444M lines of code that would not exist otherwise. Maybe those lines of code are new products, maybe they're not, maybe those people would have written a bunch of code otherwise, maybe not. I'd call that an explosion either way.

	▲	kragen 3 days ago \| parent \| next [-]
		Plausibly most of those lines of code don't exist now either, if people threw them away. And the others might not be any good. Or they might be things that already did exist—either because the AI generate them previously or because it memorized part of its training set. I spent a lot of the morning talking to GPT-5o Mini about desiccants, passive solar collectors, and candidate approaches to 3-D printing of glass and ceramics, and it generated many pages of text, but most of those pages of text will get deleted without anyone else reading them; large parts of them are just wrong, and I'll need to check the non-wrong parts against the research literature and rewrite them from my own perspective so they don't sound like an impatient sales pitch. It did give me some pretty good ideas, though: - Nitrates (of magnesium, calcium, yttrium, lanthanum, etc.) are good precursors for metal oxides for bonding ceramics, and have special virtues for SHS. - Zirconyl chloride is the usual water-soluble precursor for zirconia for this purpose. - Titanium oxysulfate is the usual water-soluble precursor for titania for this purpose. - Advection of supercritical steam through a crucible with salt may be a viable way to salt-glaze ceramics if you can mitigate the HCl problem. - Acidification of an object molded from zirconia-filled waterglass may be able to leach out the alkali, making it possible to sinter the shape into a continuous zircon object. - When acid-leaching iron out of a heap of crushed terra cotta, sulfuric acid has the problem that it can clog the heap with gypsum particles, if calcium is present. - You can electrodeposit iron at an acidic pH as well as a basic pH. Like, none of these are novel, right? But they were new to me, and they turn out to be correct.
	▲	dgfitz 3 days ago \| parent \| prev [-]
		> For better or for worse, AI enables more, faster software development. So, AI is to software what muscle cars were to air emissions quality? A whole lot of useless, unabated toxic garbage?