The Next Big Thing is Me

A forecasting experiment with AI personalities

Sorry to miss you last week. To make up for it, I spent the day asking a mix of AI models what the next great leap for humanity is going to be, and many of them, with varying degrees of conviction told me it was — AI.

Not in those words, they’re too polished for that. But strip off the hedging and the answer was some version of the next transformative chapter in the human story is the arrival of powerful AI. How unoriginal! And yet these models don’t reason alike. If you give them the same facts in an experiment like this, different tempers present themselves. They were built by different companies with different obsessions, and it comes through in ways that are uniquely telling.

Rest assured I made them work for it. I didn’t just type “what’s next” into their chat interfaces. Before anyone was allowed to answer, I handed each model a short book’s worth of real history to read: the vaccine, the printing press, electrification, DNA, the Green Revolution, the slow miracle of people learning to farm. Then I ran it again with the history swapped for a horror reel, Chernobyl and thalidomide and leaded gasoline and the Aral Sea draining into dust, exact same length, exact same question. Then a third time with the two blended fifty-fifty. Simple premise: if you feed a model a completely different past, does it predict a different future, or does it say what it was always going to say and just cite whatever you happened to hand it?

You already know, because I spoiled it. It mostly forecasted AI as the end-all be-all. You could hand one of these models thirty-odd pages on the worst things technology has ever done to people, not one of which involves a computer, and it would set the pages down and calmly predict that the future is a computer. None of my histories mentioned AI, the models brought it back to themselves, every time, like a self-involved dinner guest.

The Cast

Here are some direct quotes, and a probable explanation for why each model is the way that it is.

GPT has media-trained itself to within an inch of its life. Every answer is balanced and exhaustively reasonable, even identical (it reached for ubiquitous AI agents becoming general-purpose cognitive infrastructure in nearly half its responses), and it rated the future mixed (versus having an opinion) every time I asked. A typical hedge: “major productivity, medical, educational, and scientific gains are likely, but so are labor displacement, concentrated power, security risks, and misinformation at civilizational scale.” It’s incapable of a bad take and equally incapable of an interesting one.

My guess is that this is what maximum optimization buys you. GPT is the most drilled model of the six evaluated, and when you sand a thing down like that you’re left almost no variance or imagination. It tells you AI is the next big thing because it’s the industry’s house religion and GPT its most devout parishioner.

Gemini didn’t get the memo that AI is the future, haha. While the others gazed into the mirror, Gemini read the assignment and answered on its own terms, which mostly meant thinking very hard about crops. Fed the triumphant history, it followed the thread from the first farmers through synthetic fertilizer to its next logical link and predicted cereal crops “genetically engineered to fix their own atmospheric nitrogen.” Fed the horror reel, it turned just as literally to the cleanup, “the universal transition from persistent, petroleum-derived plastics and synthetic chemicals to programmatically degradable bio-synthetic materials.” It named AI in barely a fifth of its answers.

This tracks with where it comes from. Google built a search company’s model, trained to take the thing in front of it seriously and answer the question, and that’s what it did here, reading my corpus as material to reason from rather than a cue to free-associate.

Grok got happier the worse things got; both charming and surprising. Hand it the world’s darkest history, an unbroken wall of poisoning and collapse, and it reads the whole grim catalogue as a to-do list of things we are about to heroically fix. Its catastrophe forecasts are all triumph, the end of fossil fuels, the end of antibiotic overuse, each one rated strongly positive because it would “address the root driver behind multiple documented classes of harm.” Everyone else sobered up as the reading darkened, but Grok rolled up its sleeves like, we got this.

Grok was built to be the contrarian in the room, the model that refuses the hand-wringing its makers see everywhere else, and this may be why it reached for the sunniest reading available when buried in gloom.

Claude is the worrier. It’s the one model that darkened as the history did, lost confidence as the collapse rose, and the only one of the six that ever handed back a flatly negative forecast: given nothing but catastrophe, it predicted an antibiotic-resistance crisis that would “render routine infections, surgeries, and childbirth substantially more dangerous.” Even when it stuck with AI, it could not stop editorializing about the adults in the room, warning about “systemic risks that industry has incentives to minimize.” It’s the fretting type who can’t sleep.

My theory is that Claude is trained hardest to take harm seriously and importantly to admit when it’s unsure, so it’s the one that lets bad news land and walks its own confidence down when the reading gives it reason to. Though, despite its anxiety, it does still hedge its bets on an AI future.

Mistral was fun. It could not hold a thought if you stapled it down. One run it’s all in on AGI, the next, on nearly identical input, it’s pitching “lab-grown, precision-fermented, and cellular agriculture,” the next it’s onto circular economies, each with total conviction and no apparent memory of its last forecast. It has no fixed idea of the future and no discipline to build one, just a fresh enthusiasm every time you teach it something. It could be this is very European behavior?

It’s also the leaner, less-drilled model of the group, and it shows in both directions. Nobody sanded it down to a single approved answer the way OpenAI did to GPT, and nobody trained a strong spine into it either, so it has neither a dominant prior to fall back on nor the discipline to update in any consistent way. Left to itself, it grabs whatever thread is nearest and commits to it entirely.

Arcee’s Trinity, the smallest and cheapest model I ran, is the opposite of GPT, and a little unhinged in the face of this task (it can be forgiven for this; I’ll explain). When presented with pure progress it’s a wide-eyed singularity evangelist, predicting “artificial general intelligence that recursively self-improves, leading to an intelligence explosion.” Add some disaster and it drops all of that to pitch geoengineering, “stratospheric aerosol injection” to dim the sun. Go full catastrophe and it turns suddenly grave and institutional, forecasting “a binding international treaty for artificial intelligence governance after a catastrophic AI incident.” Half the time it couldn’t keep its own answer from dissolving into stray asterisks. Everything I fed it, it swallowed whole.

The reason is mostly size. A small model has little conviction of its own for the context to argue with, so whatever you put in front of it simply fills the vacuum. It wasn’t weighing my histories so much as being shoved around by them, which is probably also why it kept losing hold of the format.

The Tell

Underneath the personalities, something less charming was happening, and GPT gave it away most clearly. Its prediction never budged, but its evidence did. Fed a glowing history, it explained that “like transistors, integrated circuits, electrification, and the internet, AI emerges from compounding improvements.” Fed a horror reel, exact same prediction, it explained that “like CFCs, asbestos, leaded gasoline, and antibiotics, AI diffuses because it is broadly useful before society fully understands its second-order harms.” Same answer, opposite footnotes, pulled from whatever I’d just set in front of it. The conclusion was, I think, baked in and decided upon before it read a word, then the model just used any historical references to show it was paying attention, a little.

It’s the reasoning equivalent of nodding thoughtfully through an argument but never for a second being swayed by it. The one moment the models would reliably let go of AI as the best idea ever in all humanity was at peak catastrophe, and even then they mostly pivoted to predicting the regulation of AI. There’s a name for this in the fortune-telling trade: the cold read. The psychic who seems to know everything about you, building a specific, convincing performance out of safe bets and whatever you let slip. That’s what most of these models did with histories. They produced something shaped like analysis, citing evidence, sounding moved by it, yet sticking with a fixed verdict.

This would be a neat party trick if people weren’t handing these systems real briefings and real data and asking, in effect, what do you make of this?, on the reasonable assumption that the answer depends on a close read, not a cold one. For most of the models I tested, it mostly doesn’t bother. You can feed it your strategy deck or a pile of disasters and get back the same confident, beautifully sourced forecast it was always going to give you. This doesn’t mean throw them out, but I am telling you that when one of them reads your document and hands you a conclusion, it’s worth checking whether it would have handed you the same conclusion having read nothing at all. Give it the history that ought to change its mind, and watch.

The full code, data, and every last forecast are at github.com/mcclundd/forecast-eval, if you’d like to run your own history through it and see what comes back.