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.

The proliferation of AI-generated images in online environments won’t eradicate human art wholesale, but it does represent a reshuffling of the market incentives that help creative economies flourish. Like the college essay, another genre of human creativity threatened by AI usurpation, creative “products” might become more about process than about art as a commodity.

Are artists using computer software on iPads to make seemingly hand-painted images engaged in a less creative process than those who produce the image by hand? We can certainly judge one as more meritorious than the other but claiming that one is more original is harder to defend.

An understanding of the technology as one that separates human from machine into distinct categories leaves little room for the messier ways we often fit together with our tools. AI-generated images will have a big impact on copyright law, but the cultural backlash against the “computers making art” overlooks the ways computation has already been incorporated into the arts.

The problem with debates around AI-generated images that demonize the tool is that the displacement of human-made art doesn’t have to be an inevitability. Markets can be adjusted to mitigate unemployment in changing economic landscapes. As legal scholar Ewan McGaughey points out, 42% of English workers were redundant after WWII — and yet the U.K. managed to maintain full employment.

Contemporary critics claim that prompt engineering and synthography aren’t emergent professions but euphemisms necessary to equate AI-generated artwork with the work of human artists. As with the development of photography as a medium, today’s debates about AI often overlook how conceptions of human creativity are themselves shaped by commercialization and labor.

Others looking to elevate AI art’s status alongside other forms of digital art are opting for an even loftier rebrand: “synthography.” This categorization suggests a process more complex than the mechanical operation of a picture-making tool, invoking the active synthesis of disparate aesthetic elements. Like Fox Talbot and his contemporaries in the nineteenth century, “synthographers” maintain that AI art simply automates the most time-consuming parts of drawing and painting, freeing up human cognition for higher-order creativity.

Separating human from camera was a necessary part of preserving the myth of the camera as an impartial form of vision. To incorporate photography into an economic landscape of creativity, however, human agency needed to ascribe to all parts of the process.

Consciously or not, proponents of AI-generated images stamp the tool with rhetoric that mirrors the democratic aspirations of the twenty-first century.

Stability AI took a similar tack, billing itself as “AI by the people, for the people,” despite turning Stable Diffusion, their text-to-image model, into a profitable asset. That the program is easy to use is another selling point. Would-be digital artists no longer need to use expensive specialized software to produce visually interesting material.

Meanwhile, communities of digital artists and their supporters claim that the reason AI-generated images are compelling at all is because they were trained with data sets that contained copyrighted material. They reject the claim that AI-generated art produces anything original and suggest it instead be thought of as a form of “twenty-first century collage.”

Erasing human influence from the photographic process was good for underscoring arguments about objectivity, but it complicated commercial viability. Ownership would need to be determined if photographs were to circulate as a new form of property. Was the true author of a photograph the camera or its human operator?

By reframing photographs as les dessins photographiques — or photographic drawings, the plaintiffs successfully established that the development of photographs in a darkroom was part of an operator’s creative process. In addition to setting up a shot, the photographer needed to coax the image from the camera’s film in a process resembling the creative output of drawing. The camera was a pencil capable of drawing with light and photosensitive surfaces, but held and directed by a human author.

Establishing photography’s dual function as both artwork and document may not have been philosophically straightforward, but it staved off a surge of harder questions.

Human intervention in the photographic process still appeared to happen only on the ends — in setup and then development — instead of continuously throughout the image-making process.