what if every police patrol car had a bank of cameras that scan not just every number plate but every face within a hundred yards against a national database of outstanding warrants? What if the cameras in the subway do that? All the connected cameras in the city? China is already trying to do this, and we seem to be pretty sure we don’t like that, but why? One could argue that there’s no difference in principle, only in scale, but a change in scale can itself be a change in principle.

Generative AI is now creating a lot of new examples of scale itself as a difference in principle. You could look the emergent abuse of AI image generators, shrug, and talk about Photoshop: there have been fake nudes on the web for as long as there’s been a web. But when high-school boys can load photos of 50 or 500 classmates into an ML model and generate thousands of such images (let’s not even think about video) on a home PC (or their phone), that does seem like an important change. Faking people’s voices has been possible for a long time, but it’s new and different that any idiot can do it themselves. People have always cheated at homework and exams, but the internet made it easy and now ChatGPT makes it (almost) free. Again, something that has always been theoretically possible on a small scale becomes practically possible on a massive scale, and that changes what it means.

This might be a genuinely new and bad thing that we don’t like at all; or, it may be new and we decide we don’t care; we may decide that it’s just a new (worse?) expression of an old thing we don’t worry about; and, it may be that this was indeed being done before, even at scale, but somehow doing it like this makes it different, or just makes us more aware that it’s being done at all. Cambridge Analytica was a hoax, but it catalysed awareness of issues that were real

if you had showed VisiCalc to a lawyer or a graphic designer, their response might well have been ‘that’s amazing, and maybe my book-keeper should see this, but I don’t do that’. Lawyers needed a word processor, and graphic designers needed (say) Postscript, Pagemaker and Photoshop, and that took longer.

I’ve been thinking about this problem a lot in the last 18 months, as I’ve experimented with ChatGPT, Gemini, Claude and all the other chatbots that have sprouted up: ‘this is amazing, but I don’t have that use-case’.

A spreadsheet can’t do word processing or graphic design, and a PC can do all of those but someone needs to write those applications for you first, one use-case at a time.

no matter how good the tech is, you have to think of the use-case. You have to see it. You have to notice something you spend a lot of time doing and realise that it could be automated with a tool like this.

Some of this is about imagination, and familiarity. It reminds me a little of the early days of Google, when we were so used to hand-crafting our solutions to problems that it took time to realise that you could ‘just Google that’.

This is also, perhaps, matching a classic pattern for the adoption of new technology: you start by making it fit the things you already do, where it’s easy and obvious to see that this is a use-case, if you have one, and then later, over time, you change the way you work to fit the new tool.

The concept of product-market fit is that normally you have to iterate your idea of the product and your idea of the use-case and customer towards each other - and then you need sales.

Meanwhile, spreadsheets were both a use-case for a PC and a general-purpose substrate in their own right, just as email or SQL might be, and yet all of those have been unbundled. The typical big company today uses hundreds of different SaaS apps, all them, so to speak, unbundling something out of Excel, Oracle or Outlook. All of them, at their core, are an idea for a problem and an idea for a workflow to solve that problem, that is easier to grasp and deploy than saying ‘you could do that in Excel!’ Rather, you instantiate the problem and the solution in software - ‘wrap it’, indeed - and sell that to a CIO. You sell them a problem.

there’s a ‘Cambrian Explosion’ of startups using OpenAI or Anthropic APIs to build single-purpose dedicated apps that aim at one problem and wrap it in hand-built UI, tooling and enterprise sales, much as a previous generation did with SQL.

Back in 1982, my father had one (1) electric drill, but since then tool companies have turned that into a whole constellation of battery-powered electric hole-makers. One upon a time every startup had SQL inside, but that wasn’t the product, and now every startup will have LLMs inside.

people are still creating companies based on realising that X or Y is a problem, realising that it can be turned into pattern recognition, and then going out and selling that problem.

A GUI tells the users what they can do, but it also tells the computer everything we already know about the problem, and with a general-purpose, open-ended prompt, the user has to think of all of that themselves, every single time, or hope it’s already in the training data. So, can the GUI itself be generative? Or do we need another whole generation of Dan Bricklins to see the problem, and then turn it into apps, thousands of them, one at a time, each of them with some LLM somewhere under the hood?

The change would be that these new use-cases would be things that are still automated one-at-a-time, but that could not have been automated before, or that would have needed far more software (and capital) to automate. That would make LLMs the new SQL, not the new HAL9000.