I will say this as gently as I can: generative AI is shit and you know it. Such pronouncements may seem to lack the nuance and subtlety expected of a computing academic and a science fiction writer (who writes about intelligent robots). But that is what it all boils down to.
This isn’t a dig at artificial intelligence more broadly. AI covers much more ground than just chatbots like ChatGPT, and image generators like Stable Diffusion or Midjourney. Other AI applications, such as voice recognition or face recognition, have existed for a long time, and sometimes warrant their own ethical discussions, but are nonetheless often quite effective.
Where generative AI is concerned, the clincher is that, even if you disagree with the sceptics—even if you personally regard generative AI up as a worthy innovation—it’s already too late. It’s had its chance to convince the world of its usefulness, and the world has been mildly diverted, at best. Generative AI has bred a generation of informed cynics, who have seen what it does, and actively value its absence from their lives.
Obviously, there’s no shortage of frantic activity in support of generative AI, especially from the big tech giants like OpenAI, Microsoft and Google, dumping unearthly piles of cash to turn their products inside out, and Nvidia, collecting unearthly piles of cash from the sale of the hardware upon which AI models run. There’s an immense bandwagon upon which ride a great many people, many well-meaning, all doing various different things with generative AI. You may be forgiven for thinking this represents positive vindication of the technology, but we still haven’t escaped the hype.
And the hype is something to behold. If you hear about “Artificial General Intelligence” (AGI) in the context of generative AI, then someone is trying to sell you something. I’m interested in AGI, because I want to sell copies of my book. See how that works? (But seriously, buy my book.) AGI, the term for an artificial system able to reason independently about anything (hence, intelligent robots and the like), doesn’t exist. And its hypothetical future existence is unrelated to extant generative AI. Many of the ambitious promises and warnings around generative AI—it will perform scientific research, or it will replace or augment humans in other ways—are things that actually require full-blown AGI. Which nobody knows how to create.
What you see, in all this investment of time, money and prophesying, is not the realisation of a dream, nor the maturation of a proven technology, but a colossal bet. The sheer size of this gamble is the most astounding thing about generative AI, over and above any feature of the technology itself. It’s all the more remarkable, and concerning, precisely because the technology has so little proven utility.
Not for nothing, the largest and most expensive models are the most promising (the closest to being useful), and these exist at the whim of large tech companies. They’re betting that chatbots and image generators will ultimately produce revenue that at least matches the billions of dollars poured into them. Because if they don’t… well, generative AI is not a public utility. The best models could still be taken away from us, or lobotomised, or made prohibitively expensive, when shareholders start questioning their profitability. At some point, the bill will come due.
The models have some uses. Chatbots (backed by large language models, or LLMs) can assist with brain-storming, low-quality sales pitches, and subverting poorly-conceived busywork tasks. But every task that demands some level of accuracy or logic, where someone must actually be informed, is crippled by generative AI’s euphemistically-labelled “hallucinations”, or rather its bullshit. “Bullshit” consists not just of lies, but any assertions made without regard to their truth or falsity, and since LLMs make assertions without any capacity to discern truth, everything they output (or, at least, everything purporting to be informative) is technically bullshit. This isn’t notably something that we need any more of, and it leads to individual outcomes that range from embarrassing (“eat at least one small rock per day”) to career-limiting (try using ChatGPT for your legal research), and perhaps even life threatening in certain situations. There are also significant risks to scientific peer review, education, medical advice, and presumably any number of other fields.
“Hallucination” (bullshit) is not a glitch, but a permanent facet of the technology, reflecting LLMs’ fundamental disconnect from reality. While they do form sophisticated, abstract connections between concepts that appear in their training data, these are ultimately just patterns with no inherent meaning. When LLMs get things right, they do so by accident, where some elements of truth present in their training data have become superficially encoded in the statistical relationships between words. Nothing binds LLMs to tell the truth, ever. They just adapt the word patterns we humans generate. They have no notion of the objective reality in which we or they exist (and they don’t even understand other rule-based systems like mathematics). LLM are not wired up to the actual human experience of the world, and so they are not a truth-seeking apparatus.
Given that LLMs are guaranteed to bullshit, it’s worrying that so many people continue to suggest they be used for serious work requiring accurate information. One makes the other impossible. Yet, there’s still some impetus to just bulldoze right on through anyway, reinforcing the observation that we humans often have trouble with reality ourselves.
Such unreflective optimism, in combination with generative AI’s pervasive bullshit, breeds cynicism en masse very quickly. Even while generative AI inspires some, and to some extent even because of this, it opens itself to ridicule by many others. This isn’t just an intellectual contest. Gen-AI cynicism manifests as a social and market force. (Nobody will be reading your website if they think it’s been AI-generated. No humans, anyway, and probably no AGIs either, among those programmed to have any self-respect.) This will embolden those seeking legal and even political redress. Since it’s already perceived that generative AI investors are damaging the public good, by diluting human-generated information (and indeed humans) with auto-generated nonsense (“AI slop”), people are already pushing back. Lawyers, courts and politicians will act, whether in well-conceived ways or not, putting yet more pressure on the already-dubious profitability of generative AI services.
Nonetheless, some people retain an unshakeable faith in the inevitable march of progress. History has thrown up plenty of technological revolutions before, and, so you may say, the Gartner hype cycle shows that, once the initial “peak of inflated expectations” is overcome, we’re on our way to the “plateau of productivity” in which the mature technology is fully embedded in our lives. Just like (let me check my notes) human cloning, flying cars, fusion power, self-driving cars, the metaverse, hyperloop, cloud seeding and blockchain. Who among us can imagine a life without those, right?
Yes, the Gartner hype cycle is itself pseudoscientific nonsense, having the predictive power of a con artist. It’s hard to be charitable here, because it appears to suggest that anything and everything that becomes the subject of hype will succeed in the end. Just no. That’s not a thing. Some ideas just don’t work out, no matter how excited everyone was about them.
Hype lets people believe in inevitability, even destiny. You may think you can see the future unfolding in front of you, and that any hurdles an invention faces will be overcome by overwhelming narrative force. We have a term for this: “science fiction”. I know. I write it (and you can read it!). But real technological progress isn’t on a schedule that proceeds smoothly from now until Star Trek. It certainly isn’t a number that you can just extrapolate from data analysis. It’s an infinitely complex, qualitative state of affairs that twists and turns unpredictably in fits and starts. It often reflects a bit of the crazy in our social and economic systems, but must answer to basic logic.
Generative AI is mildly diverting, but ultimately shit, because that’s what’s possible. It’s been on a path of incremental improvement (impressive in its own way, in a relative sense), but its qualitative problems cannot truly be addressed without an entirely different approach. Moreover, training data is running out, and what has already been used may become legally problematic on copyright grounds. And increased training data has been the main contributing factor towards the improvements we’ve seen.
It is telling (if perhaps obvious to some) that, while generative AI promises to generate unlimited content, while also requiring near-unlimited training data, it cannot use its own output for training. The service it provides isn’t good enough for its own purposes. Feeding AI-generated content (“synthetic data”) back into the model as training data rapidly degrades the output quality. So, generative AI actually depends on the availability of ever-expanding amounts of high-quality human-generated content. That is, generative AI’s success ultimately relies, in some sense, on generative AI’s failure.
One might do well to consider this problem, in the cold light of day, before making heroic assumptions about our new “age of AI”.