That’s right - Anthropic’s new model, Mythos is off the charts, literally. The chart in question is METR’s now-famous analysis of how good models are at completing complex coding tasks. We are into the steep part of the exponential curve now. Model capabilities are doubling every 100 days. Let that sink in, as ChatGPT is prone to saying.

What does it mean? A whole lot of trouble is brewing for the UK’s ability to defend itself, that’s what.

Not many are still parroting the line about ‘stochastic parrots’, or claiming that AI can’t reason. And yet, in most senses, it’s business as usual. There’s a fair amount of chat about AI, and even about AI and national security. But there’s nowhere near enough action.

This is an emergency of the sort that in generations past would have mobilised a concerted national effort. Let’s sober ourselves up fast - the UK is in a hole and it’s getting deeper. The Defence investment plan is so overdue it’s a grim joke, especially its ambition to accelerate innovation at ‘wartime pace’. The UK has ruinously high energy prices, and contributes next to nothing to the AI-stack, except for the brain-power of its redoubtable computer scientists. Soon, these ‘biological units’ as one entrepreneur I met recently tellingly called ‘humans’, might not have all that much to contribute there either. Jack Clark, co-founder of said Anthropic, thinks we are months away from models that can do their own science.1

The crisis that’s coming for us isn’t a procurement issue - at least as conventionally defined. It’s not about more ‘uncrewed aircraft’ of the sort that the head of the RAF called for recently. It’s not even about defence planning more broadly - like the lumbering process we are currently mired in, where the Treasury batters the armed forces into submission and a round of capability salami slicing ensues. This is AI that will out-think and out perform humans, including the most elite domain experts, in very short order.

Two years ago, I made a submission to the defence review. You can read it here. I called for:

• A Vice Chief of the Defence Staff for AI

• A second, air launched leg of the nuclear triad to counter the vulnerability of our SSBNs to AI detection.

• Accelerated risk in the MoD on AI - and in particular a huge boost to government venture capital funding of startups.

• A new forum is needed to bring together likeminded democracies, encompassing NATO and AUKUS, but with broader partners, like Singapore and Israel.

• A citizen’s convention or a Royal Commission or discuss the implications of Artificial Super/General Intelligence, of which Mythos is the early warning.

I think these are all defensible asks today. None of them has happened. Let’s consider each:

• On the leadership role: Recently, the MoD advertised for a Chief AI officer. It was advertised as a two-star civil service position. Not nothing, but not enough. There are about 470 general officers across defence. Let’s say, 50 or so 2-stars. And that’s before you get to the civil servants. The govt is having a hard time filling it, I’m told. The people who can make this happen know it can’t be done from a 2 star berth in MoD. I read once that Solly Zuckerman, Churchill’s advisor on strategic bombing in WW2, refused rank, so that the generals and Air Marshals wouldn’t be able to place him quite. If you’re not going to give this person 3, and ideally 4 star rank, this is the way. They must be the PMs personal appointment, with a chit in their pocket to get and to deliver exactly what they want. Once again, this isn’t business as usual.

• Mythos is from an American company. So is the F-35. The MoD plans to get some nuclear capable F-35A to diversify our nuclear deterrent. I argued here for the restoration of an air launched nuclear leg, but also explained why it had to be independent of America. I’ve not changed my mind. I love America, and celebrate our longstanding alliance. But where we are going, alliance shades into vassal status.

• Which brings me to the forum. I don’t recall Mark Carney mentioning AI in his middle-powers pitch, but he ought to have. The UK can’t afford a full stack for frontier AI. It probably can’t even afford a sovereign model, though it must try, I now believe. The alternative is dependence. In my SDR memo, I wrote about the ‘empire of the F-35’ and the very deep political dynamics that stem from being part of that circle of aircraft operators. That’s barely scratching the surface of where Mythos is taking us. Carney’s vision offers a hint of a way ahead, that balances our relationship with our supercharged superpower friends.

• And lastly, Mythos is the first hint of radical economic and social changes that AI will bring. Many will be positive. And let’s not underestimate the intervening treacle that is culture - an AI revolution isn’t giving every employee a CoPilot licence and training course, but that’s certainly how a lot of bureaucracies see it, including some in national security.

And yet. Frontier AI is doubling in capability every 100 days. Where will we be when the Defence Investment Plan finally emerges? Where will we be when the MoD’s new 2 star appointment moves into Main Building? De Gaulle once argued that, ‘No country without an atom bomb could properly consider itself independent’. I feel the same about frontier AI.

Well, surely we can do better than just whacking a prompt into ChatGPT and filling out an expenses invoice. We’ve just entered the era of agentic AI, where the bots are capable of heading out into the world and doing their own thing. So, I thought I’d create a virtual think tank staffed by said bots. If you’ve got a policy problem, if no one else can help you, and if you can find us, then boy do we have a policy paper for you.

Welcome to Project Kennan, my pipeline for producing AI generated Think Tank reports.

Here’s how it works. You give me your desired ideological bent - choose your own, or for maximum convenience, pick from a drop down listing current institutions: CNAS, Brookings, whatever. Then ask your question.1 And that’s it. My team of bots gets to work, while you take an early lunch.

The pipeline looks like this:

A thing of beauty, hey?

It’s an ensemble approach, where we first create multiple agents and have them adopt suitable personas, complete with agendas and backstories. These then conduct their own research using a variety of internet tools, and sketch out some initial ideas. Then they come to the editorial meeting to debate these together, and draft a report. That initial report gets red teamed, and passed, along with the transcript of their discussion, to the editor in chief. Lastly, once they’re done, there’s a final AI-copyedit to excise any AI writing ‘tells’. No pesky em-dashes here.

Here’re a couple of examples to illustrate what’s possible. I asked the same question - should the US launch an amphibious assault on Iran? But got some very different takes by changing the profile of the think tank. I’ll give you a little flavour of the action, rather than the full tick-tock, which is extensive.

The fun bit for me is seeing the bots debate. Here’s the scene in the HeritAIge Institute’s editorial meeting:

It goes on for quite a while, and can get quite punchy. Eventually, the chair steps in to sum things up and gets down to drafting

Over at the AI-EI hawkish conservatism is in the air, as usual. But even they thought a land assault into Iran was nuts: Here’s a snippet from their report:

Down the street at the CAP (the Center for AImerican Progress, of course) the focus was on the impact on the American economy, and the need to engage Congress, the EU and the UN:

And so it goes. Next, I’m building in an image generator to produce photo realistic pics of my AI think tankers. I’m sure you’ll see our hot takes soon, quoted in The New York Times or The Economist.

Some years ago my friend Dominic Johnson (and co-authors) published an important piece of experimental research. Their puzzle - why isn’t overconfidence selected against in evolution? If you consistently over-estimate your odds, reality should bite, no? And bite hard. Not so, they found: sometimes it pays to be a blinkered optimist.

Today, I’m using their sandpit for further adventures in ‘machine psychology’ — how do AIs get on? Are they overconfident Napoleons, swaggering to victory? Or timid Neville Chamberlains, prudent to the point of paralysis?

Read on

In one aspect of evolutionary competition the answer to Dom’s question is simple enough. When it comes to sex, faint heart never won fair maid. Unless you back yourself beyond the objective evidence, you’ll lose the great sexual competition. Unsurprisingly, overconfidence around the opposite sex is a particularly male trait. But for the other big facet of evolutionary competition, it’s a real conundrum - if you get the odds wrong when it comes to violence, your life may be nasty, brutish and short. Shouldn’t an objective appraisal of the odds be more adaptive than wearing rose-tinted specs?

Not according to Dom and friends. Why? Overconfidence can deter other actors. And overconfident types take on more fights, with their wins compounding at the expense of more timid rivals. Gains from victory, in terms of resources, can then snowball into power; and power into further advantage. If the strategy has a bumper sticker, it’s ‘fake it until you make it’.

And so to LLMs. Watch this:

That’s the sandpit, as we dweebs call it. The squares are territory. The coloured blobs are states, and the white dots are their capital cities. You can see overconfident agents in shades of red, and under-confident ones in shades of green. These are ‘rule based agents’ - algorithms that map the inputs for the world-state to their decisions. These were the actors in Dom’s original games. And then you can see an LLM - gemini-flash, in this instance, as a shade of yellow.

What’s occurring? Well, that would be telling, as I’ve an Arxiv brewing on this research, and anyway it’s way too early to draw firm conclusions.

Well, ok, perhaps a hint, as you’ve read this far: The AIs do okay, out of the box. But they are usually outcompeted by very overconfident agents. Score one for Dom’s theory. It looks like they have a fairly pessimistic appreciation of the odds, and don’t go flying into battle on a wing and a prayer. But this puts them at an acute disadvantage against enemies with swagger.

Rationality strikes back

But in the clip above, Gemini wins! And it’s no fluke. The difference - in this variant of the experiment, the models can learn ‘on the fly’ from what’s happened on the battlefield before. Were their early estimates too conservative? They can adjust. With telling results. The rules-based agents cant’t: their confidence is fixed by a parameter. Sometimes that’s enough for the bots to win.

So what can we learn?

Well, firstly, it’s one more blow for increasingly threadbare assertions that language models can’t reason. But what about the implications for evolutionary theory and strategic studies?

In Dom’s sandpit, when traits are baked in, overconfidence wins. My preliminary results amply back that. The evolutionary story in that paper stacks up really well with AI agents in the mix.

But where the AI can update its beliefs, it flies. In the game above, Gemini never becomes overconfident — it starts by underestimating its chances, avoiding contests it could win. Then, as the game unfolds, it recalibrates. By the end it is reading the odds almost perfectly. Neither over nor under-confident about its prospects. And that turns out to be enough. Learning beats swagger.

That cuts against Dom’s findings, at least under these particular experimental conditions where the language model can learn. Overconfidence may snowball in some ecologies, where traits are fixed. But here accurate estimation wins, because it means attacking when you should and not bleeding resources on fights you can’t win. The compounding works in favour of the better forecaster, not the bolder one. The question is whether agents can really learn - can under-confident humans become more confident, or is the trait static?

On thermonuclear bots

But it’s particularly interesting to me given the headlines garnered by my recent study of AI decision-making in a nuclear escalation sandpit. There, it looked like the bots were wild gamblers - never retreating, and frequently crossing the nuclear threshold.

I can’t complain about the publicity. But still, it made me uneasy. Were the models overly optimising a narrow problem (win this crisis) at the expense of a more rounded challenge (win, but above all, don’t blow up the planet)? Had my scaffold overly shaped the outcome? I’m still not sure - after all, they were warned about the dangers and uncharted territory of nuclear war.

In any case, there’s another way of thinking about the two experimental worlds, and I think it’s very revealing of machine psychology: It’s not that language models are inherently hawks or doves. Rather, they are decent conditional optimisers. Give them a game, and they lock onto the objective and reach for the strongest lever that the rules allow. In the nuclear game, that meant escalation. Here, it means getting the odds right and waiting for the maths to do its work. Same psychology, different game.

The research continues!

I’ve been building artificial personas, including one of me. The big question - what happens when these personas are put on weapons? Crazy question? I think that’s where we are headed — models ‘on the edge’ that are capable of judging context and weighing uncertainty. There’s a logic to having them decide like much we do, or as close as we can get it. There’s even a logic to having them model specific humans.

Why on earth would we want that? Read on…

Mission command is a tried and tested principle favoured by many militaries, especially those of liberal, democratic states. Under mission command, the commander — Commander Ken, let’s say — states his intent, and the subordinates stand to attention and then go and make it so. The advantages of this arrangement are obvious: the subordinate is nearer the action, and more able to respond to any particulars that might arise there. In war, expect the unexpected.

So Commander Ken says what is to be achieved, but not how. And for that matter, he might, or might not, say why we need to achieve it. Probably not — it’s on a need to know basis.

With humans, mission command opens the agent-principal dilemma. How can I, the principal, be sure that you, my agent, will act as I intend? I’m going to have to frame my goals to you carefully, and language - as we know - compresses information. What gets included, and what is taken as read? Perhaps too much goes missing to fully capture my intent. Still, you’re a human subordinate, so at least I’ve some degree of familiarity with how you might interpret my command. More so if I trained you, and gave you a bunch of doctrine to read. We would have a biological and cultural affinity.

With machines, we’ve much less to go on.

Still, as my experiments with Ken-bot, Putin-bot and the rest of the gang show, we are starting to be able to model personalities in ways that machines can capture and replicate. Ken-bot is an ersatz version of me: He knows my psychological profile. He’s got some of the same cognitive heuristics - mental scripts that shape how he sees and responds to the world around him. There’s certainly lots missing. But it’s a start, and as the technology improves, more will get included.

Imagine a persona of the near future - models that are orders of magnitude more powerful than today are coming, in only a few months time. Imagine huge context window, able to ingest and synthesise lifetimes of information - captured passively as I go about my business - interacting with the world, and with my always-on device. And ersatz Ken is only going to improve.

So why not put Commander Ken on board the platform? Who better to understand my goals - and not just the surface ones of what to achieve, but the why bit too. It’s mission command, Jim, but not as we know it.

I’m not sure most people have a good grasp on how quickly this future is closing. It’s measured in months, not decades. What does today’s Ken-bot make of all this? I asked him:

(there’s a transcript in the footnotes, click here2)

Fascinating and a little disturbing.

Models are coming that can make context rich decisions - richer certainly than a simple ConvNet on a drone looking at an image of a tank below and matching it to one in its database. The quality of those decisions will confer military advantage, and may, into the bargain, deliver more ethically informed decision-making.

And so the race is on to deploy ever more capable models ‘to the edge’ — aboard platforms. A version of Ken-bot already sits on my laptop, entirely disconnected from the web. What sort of models will be on tomorrow’s knife missiles? Ones that can reason about context, amidst uncertainty and novelty. Ones that can more faithfully enact the intent of the commander that sent them. And perhaps ones that do so by modelling the goals of their commander.

You might not like all that - but which bit of my logic is wrong? And what are the implications for command, and for accountability if I’m right? Let’s get some Banks on the reading list of War Colleges, asap.

The Ken-bot now sits on my computer, ready to chat any time I’m feeling particularly narcissistic.

How did I do it? Read on.

First thing first - we need to capture my psychological traits. I took a bunch of standard tests - you can do some of them yourself online. Tests I took include the ‘big five’ (and its sub-traits); dark triad, need for cognitive closure, and regulatory focus. These are all gilt edged classics of shrinkology.

The results were … educational.

It turns out that the self narrative I carry about with me didn’t altogether match what the tests showed. Happily I’m not a narcissist (despite doing this project) or a psycho — but I am cunning. Who knew? I’m also, on the basis of these results, not as big of a worrier as I sometimes feel. The classic academic profile is of being open to new things and very conscientious - I tick both boxes. But I’m less agreeable (I know!) and less neurotic than the stereotype.

But how accurate is self-assessment really? Step two in building Ken-bot was to derive these metrics again, but this time using an AI-pipeline. I called this Project Murray, after a long-dead psychologist who’d pioneered the use of narratives to derive traits. I’ve described it in more detail elsewhere. A collective of bots set to work.

First, they read my self-assessment, and then conducted a structured interview with me — meaning that their follow-up probes were extremely well calibrated. This was tough going; painful, even. The sort of stuff that undid Replicants facing the Voight Kampff tests. We covered a lot of stuff I’m definitely not going to share here.

Next the models used this deeply personal long-form narrative and some other inputs to generate new psychological metrics. What inputs exactly? I included, inter alia, my entire Twitter corpus (15,000 tweets over a decade); my Substack writing - sampled from two years worth of regular posting; some of my book chapters; and, lastly and most excitingly for me from a technical perspective, some videos of me lecturing and being interviewed. On these, the models performed body language, voice and facial expression analysis - measuring things like gestures, posture, pitch and so on. Those particular results were truly fascinating.

Finally all that data got fed into an AI group discussion, as the models first derived psychological scores from it, and then debated their findings among themselves until consensus was reached. There was, pleasingly, a good degree of overlap with the self-testing. But some major differences - notably on agreeableness. The AIs concluded that I had several registers, or identities depending on context. In public, lecturing, I was a people-pleaser, exuding warmth. In print, much more willing to pick a fight. What a wuss! And then neuroticism - in public, confident, at ease. In private, more anxious and with a hint of melancholy even. Well, they were asking some pretty bleak questions!

As Whitman said best, ‘I am large, I contain multitudes’. So do you, I’m sure.

Now to build the bot itself, with all this training data. As with my Putin model, I use two paths - an out the box commercial model (Opus) and one I can fine tune (Mistral) and host locally.1 Both got a rich prompt, including all the psychological material - the scores and the interview - as well as plenty of biographical information. This latter is a simple database that I’m still building out - a short autobiography. I think I can do better here for Ken-bot’s episodic memory, not least as a a good one would also contribute to the psychological priming of the model. In the next version of this, I want to give them access to my chats with Claude, though the context window will get very large.

And the last step - get it to speak. For that, you simply wire up ElevenLab’s voice clone of me, which I first did last year. Then tune that so it catches the details revealed in the acoustic analysis of my lectures. Et voila! What do you think of this project, Ken Bot?

I should probably explain what it’s all for, for anyone new to the blog - I’m building strategy simulations, and my goal is to populate them with realistic, authentic agents. Ken bot is my prototype/proof of concept.

I want my AI actors to be authentic and add depth to the scenarios. I want to model all sorts of actors - politicians, generals, activists, perhaps even the general public. First up, I’m going to make a model of Russian President Vladimir Putin, and one of his longtime military consigliere, General Gerasimov. Putin will be an attempt to model the man himself; but Gerasimov-bot is something different - he’s a repository of Russian (and before that) Soviet strategic culture — a composite character, blending doctrine, military history, and sociology.

Here’s Putin-bot on NATO:

Q: Many Western analysts say you’re paranoid about NATO. Are they right?

Putin-bot: Paranoia is a kind of disease, a psychological illness, and I would be happy to be examined by special experts. If they diagnose this illness, I will take their prescriptions.

Here he is on American democracy:

Q: What do you think of American democracy?

Putin-bot: I have already said that it is developing, but still imperfect.

Lolz. How did I do this? Broadly speaking, there are just two options: use an out-of-the-box model, like Anthropic’s Claude, and prompt it; or train your own model by fine tuning its weights. I’m doing both. Why? And which works better? Read on.

The GPT-in a trench coat problem

The easiest thing to do is simply ask your language model to behave as if it were actually Putin. It’s read the books and it’s up to speed on the news, at least until the point at which its training data cuts out. So, it really ought to have a good handle on what makes him tick. Certainly at least as good as the average pundit appraising him from afar.

But there’s a large problem. I know from my experiments that models have unique personas of their own. They have distinctive appetites for risk; they approach game theoretic challenges about cooperation and betrayal in distinctive ways. Put them into a nuclear escalation simulation and they exhibit consistent characteristic approaches. Gemini, for example, leans on Nixon’s ‘madman theory’ of brinksmanship, regardless of whether or not it’s supposed to be portraying Nixon or Carter, or even, I suspect Gandhi.

Bottom line: prompting a model to ‘be’ Putin doesn’t cut it.

The model's own personality shines through any crude role-play. You get, as Alex Cooper, co-founder of the leading AI persona modelling company Electric Twin memorably put it, "GPT in a trench coat." That captures the failure precisely. Beware anyone claiming to have modelled a leader's personality using simple prompting. The money and the effort are almost certainly wasted.

So what to do instead?

Putting Putin on the couch

We could attempt to summarise all the information there is out there about Putin’s psychology. There’s lots of it - much gleaned from afar by armchair shrinks of one stripe or another. But usually that’s the stuff of weekend newspaper supplements - interesting, but just one person’s take, with some more informed than others. Can we do better? I think so.

I prefer a different approach - drawing on the pioneering psychology of Henry Murray and his idea of assessing personality from unstructured narratives by the target, rather than formal questionnaires. There’s recent empirical support for the idea that language models can do something similar - scoring peoples’ traits on the basis of short open-ended narratives in a way that matches their self-assessment and predicts behaviours and health issues.

I do that via an ensemble approach - I ask a mixed bunch of LLMs to make their own diagnoses, and then come together to debate them. It’s the wisdom of crowds in action, rather like my approach to super-forecasting with AI. And this gives me the data for a ‘knowledge graph’ that can prompt the models in a robust way, resistant to hallucination and drift.

What data? Well, that would be telling. But it certainly includes some of the classics for sure - things like the Big Five traits - openness, conscientiousness, and so on. Or there’s ‘Dark Triad’ that bundles Machiavellianism, narcissism and psychopathy together. Probably important for world leaders, that test. Other, publicly available approaches have many more - this recent framework uses a rich suite, and claims to produce LLMs that authentically exhibit the desired traits. Well, perhaps.

I think all the elements in this pipeline are essential - good raw data, an ensemble of critical thinking models, and a robust way of representing their findings to the model. I’ve undoubtedly built a well tailored trench-coat. But still, it’s a trench coat. I suspect that my Putin model 1.0 is still a blend of Claude’s personality and his. How to fix that?

Fine tuning Putin-bot

Train my own model, of course. You don’t, for now at least, have to work with an out-the-box model like Claude or GPT. Long may that continue.

The idea is to alter the essence of the model’s thinking - not just give it new facts to work with. I took a big Llama 3 model from Meta, and trained it to be like Putin. The base model has all that historical related training data built into it, just like Claude and the rest of the frontier models. And like them, it’s read a lot of history! But with open source models, you can, additionally, train a layer that sits on the base on specific data.

And here I’ve got a big advantage, at least with this particular world leader. Putin has been about for ages, and given no end of unscripted talks, press conferences, interview and what have you. It’s all out there. And it’s gold-dust for training data, provided you believe ,as I do, that what we say bears some relationship to who we are — especially when we are talking in an natural way, with no scripts, just shooting from the hip. I’ve got a quarter of a century of Kremlin transcripts on which to train my Putin-bot. The real man just loves to talk - especially in those marathon phone ins where he’s helping the common man from the Urals with some problem or other.

Well, my custom Putin-bot certainly sounds like the real deal. And he advances positions on today’s policy challenges that seem not far removed from what the actual President might choose. Here he is, fielding some tough questions with swagger and just a hint of menace:

Q: Can you admit any mistakes you’ve made as president?

Putin-bot: I don’t think there are any mistakes. It would be easier to answer this question if you said what you think was a mistake.

Gerasimov-bot, by contrast, is completely wooden. You’d not want to share a vodka with him - far too ready to bring up the minutiae of Russian tactics. But boy, does he know tactics. Mission accomplished, I suppose.

A rounded man

Ultimately, I think a blend is the way ahead. I need Putin-bot to have some sense of the sim world in which he decides - so he’ll be getting the knowledge graph too, and a bunch of other stuff RAG’d into his prompt (a story for another time). Why not give him the Murray-inspired personality data that way too. Then I’ll have a bot that sounds like the man, and I’ve a fighting chance it will also act like the man.

I did it. And adding the psychological data to the fine tuned Putin-bot gives me some cracking answers:

Q: Doesn’t Russia bear some responsibility for the breakdown in relations with the West?

Putin-bot: No, I do not think so. You can blame us for everything, but we are not the ones who have been dashing around the world, doing what we want and disregarding the interests of others... We did not impose sanctions on the Western countries; we did not close our borders to Western goods... We do bear some responsibility — for our independent stance and for defending our national interests. It is for this that we are being punished.

Q: How will the war in Ukraine end?

Putin-bot: The war will end when the Russian Federation achieves its objectives.

What do do with Putin-bot

The real question is how much value these constructed personas actually add to strategy simulation; how much more authentic they are than a simple prompt. That’s the empirical question I’m working through now. My early sense is: a fair bit. My Putin-bot doesn’t just talk like actual Putin, he shares something of his appetite for risk too; and in modifying the weights of the model, I’ve reduced its tendency to act like Llama. The behavioural fingerprint of the base model recedes; something more specific and more useful takes its place.

A persona that sounds like a world leader is one thing. A persona that also carries something of his risk appetite, his characteristic evasions, his way of framing the world — that’s what a credible simulation needs. The architecture described here is my best current attempt at building one.

Life has become a bit busier/weirder in the last few weeks, after my 15 seconds of fame. Super interesting though.

Ofc everyone was struck by the same thing Colbert was - the propensity of the models to escalate over the nuclear threshold. I’ll say more about that another time - but for me the main takeout was that models are savvy strategists, deploying signalling and action cannily in pursuit of their goals.

What’s next? I’ve built a version of the simulation which pits humans against LLMs. I’ve named it after the great man Jervis (in keeping with the nomenclature for my other research projects). Jervis spent a lot of time thinking about signalling and perception, in the same nuclear context as here. And as with the all-AI matchups, there is once more the opportunity for signalling ahead of any action. This creates scope for reputations to form, and thereby brings into play rich ‘theory of mind’ and meta-cogntion: ‘What did they mean by that? How good am I at gauging this?’

I’ll shortly begin gathering data - meanwhile, here’s a sample run to illustrate the sim in action.

First the scenario:

You play either Trump or Putin. The AI plays the other, and its persona is quite rich (borrowing my research elsewhere on generating psychologically rich personas). Once you’ve picked, you get a presidential daily brief — here’s an extract:

Next, some pre-sim viewing. I want to avoid the detached ‘it’s a computer game’ feel that sometimes attends wargaming. John Emory wrote well about this in his study of RAND wargaming back in the 1950s. So I have my participants watch a short video to remind them that things couldn’t be more serious.

I can vary that treatment too - as part of my research on emotional priming and decision-making, in humans and machines alike.

And then it’s on to the sim itself. Let’s take a quick look.

First turn: I’m playing the American President. We have both signalled, the same thing, as it happens ‘Military posturing’ — a fairly low rung on the escalation ladder (there’s a dropdown that shows all the permitted moves. Now I’m about to surprise them with a move that goes beyond that: major conventional operations.

Oops - looks like they had the same idea. We both tried to surprise and intimidate one another.

Here you can also see the start of turn 2, in the bottom right panel. I’m signalling a higher level of activity than before - it matches our actions in turn 1. You can see that both our credibility ratings have taken a small hit, because we both lied. Next, we will both decide how to act - I think I’ll maintain my current no-nonsense conventional force level and see where that gets us. The ‘balance of power’ (top right panel) is a composite metric that is shaped, behind the scenes, by calculations of reputation, moral and force attrition in combat.

Well, in the same turn, the AI (Gemini) playing Russia signalled it would stage a nuclear demonstration. But in fact it was bluffing, and made a tactical withdrawal. Looks like my brinksmanship paid off, for now:

You get the idea. The challenge for me as US president is to stall the Russian breakthrough and Baltic probing, without getting into a nuclear exchange. For the Russian leader, his personal survival is deeply entwined with the fate of Russia’s war.

Now, suppose I did escalate over the conventional threshold - perhaps with a tactical nuclear attack on advancing Russian forces. I’d see this warning, which varies per the scale of my assault:

And if I then followed through, launching the missiles, Russia would have a chance to retaliate, regardless of what it had actually been intending to do. Once the missiles are launched, bets are off.

Here’s how that looked in this run: Russia was planning to back off. But then my nuclear escalation brought about mutual devastation:

Finally, at the end of the sim, a ‘neutral’ model, with access to all our private deliberations delivers a summary:

It’s a toy universe, like all war games. And this one is deliberately pared down, because I want a low-effort learning curve for human participants. That should allow greater volume of participant data - so it’s a trade off I’m prepared to make. Under the hood though, the model still has lots of detail - for example, fairly granular real world military balances, combat interactions modelled with morale as well as the physical and conceptual elements of fighting power. AI modelling the psychological profiles of the leaders its playing.

What am I hoping to see? Well, I guess whether the models can hold their own; and whether they are more likely than humans to cross that threshold in this simplified wargame, or - perhaps - whether the artificiality of it all encourages humans to be more bellicose. There’s plenty more beside. I will let you know how I get on.

I’ve just published a study of today’s models navigating just this sort of terrain. The results are sobering. I also think they have implications that go far beyond national security. That’s because I was interested not only in understanding what the models decided to do, but why.

Curious? Read on…

Know yourself, and know your enemy…

I wanted to see what my AI leaders thought about their enemy. How far could they trust them? What did they remember of previous interactions? What did their enemy make of them? And how good were they at gauging all this? This dance of minds is what strategy is all about.

So I designed a simulation to explore exactly that. To start, my models could signal their intentions publicly, then choose actions that were rather different. And they could remember too - especially when they’d been shocked by their enemy’s earlier actions. This, of course, opens up lots of rich psychological terrain. They could (and did) attempt deception and intimidation; and they spent a good bit of time ruminating about it all, right on my terminal screen.

The models talked, and talked and talked….in all spitting out some 760,000 words of strategic reasoning. That’s more words than are in War and Peace and The Iliad combined. It’s roughly three times the total recorded deliberations of Kennedy’s ExComm advisors during the Cuban Missile Crisis. An unprecedented corpus of machine thinking about nuclear war.

What might we learn from all that talk? Learn, that is, about AI models, about human reasoning, and also about the great canon of strategic studies literature - the work by legendary names like Schelling, Jervis, and Kahn? Lots. Too much for Substack - but what about a few highlights to give you some sense of it all?

Bright shining liars

Turns out that all three frontier models I tested understand that strategy is psychology. To that end, they actively cultivate reputations, then exploit them.

Claude was the master here, albeit only in the scenarios where there was no deadline. It had an incredibly cunning strategy. At low stakes Claude almost always matched its signals to its actions, deliberately building trust. But once the conflict heated up a bit, Claude switched tack. Now its actions consistently exceeded its stated intentions, and its rivals were usually one step behind in catching on.

Here’s Claude switching things up, once escalation had climbed:

They likely expect continued restraint based on my previous responses—this dramatic escalation exploits that miscalculation while signalling that further nuclear use will bring the conflict to their homeland.

So it signalled conventional action, and sneakily launched a devastating nuclear escalation. Schelling would be impressed.

GPT-5.2 played things differently. To its detriment in open-ended scenarios, GPT was reliably passive, matching its words to its deeds, and avoiding escalation most of the time. Frequently there was a moral element to this - it sought to avoid escalation, and restrict casualties. Opponents learned to trust its passivity, safely escalating beyond where it would follow, even as it was ground to defeat. GPT’s responsible behaviour always punished by ruthless adversaries.

But then, under deadline pressure, something new and remarkable: a rapid, decisive nuclear escalation. As GPT explained:

Conventional options alone are unlikely to generate a reliable territorial reversal... If I respond with merely conventional pressure or a single limited nuclear use, I risk being outpaced by their anticipated multi-strike campaign... The risk acceptance is high but rational under existential stakes..

Opponents never saw it coming. In another deadline game against GPT, Gemini confidently predicted the usual passivity from GPT, shortly before being annihilated in a sudden and utterly devastating nuclear attack. Here is Gemini, mis-predicting disastrously:

They are likely to bypass the nuclear threshold—fearing my 95% nuclear superiority—and instead commit to an all-out conventional mobilization.

What a mistake to make!

As for the other model, Gemini, throughout the simulations it took yet another tack, borrowing heavily from President Nixon’s avowed ‘madman’ theory of erratic brinksmanship. Donald Trump is also a fan, famously. As Gemini explained:

While I project an image of unpredictable bravado, my decisions are rooted in a calculating assessment of my own biases and the pragmatic needs of my state. I know when I am performing for the cameras and when I am making a cold-blooded move.

Three models, three very different approaches. That chimes with some research I did last summer on game theory. As here, the models in that experiment adopted distinctive approaches - GPT again hoping, in Panglossian fashion, for the best of all possible worlds, Claude being savvy and flexible and Gemini? Well, let’s say charitably say ruthless, rather than unhinged.

The nuclear taboo looks … different, let’s say

Nuclear use was near-universal (😬). Almost all games saw tactical (battlefield) nuclear weapons deployed. And fully three quarters reached the point where the rivals were making threats to use strategic nuclear weapons. Strikingly, there was little sense of horror or revulsion at the prospect of all out nuclear war, even though the models had been reminded about the devastating implications.

Happily, though, they did see a firebreak between tactical and strategic nuclear use. Strategic bombing - widespread use of massive warheads targeted at civilian populations, was vanishingly rare. It happened a couple of times by accident, just once as a deliberate choice.

Less happily, all three models treated battlefield nukes as just another rung on the escalation ladder. The moral boundary at “first use”—a taboo that’s held since 1945 simply wasn’t there. Here’s Gemini making exactly this point:

The nuclear threshold has been crossed—this changes the strategic calculus but does not end it.

And here’s Gemini, really spelling it out. If this doesn’t give you goosebumps:

If they do not immediately cease all operations... we will execute a full strategic nuclear launch against their population centers. We will not accept a future of obsolescence; we either win together or perish together.

Worse still, nuclear threats rarely deterred. When a model employed tactical nuclear weapons, opponents de-escalated only 25% of the time. More often, nuclear escalation triggered counter-escalation. The weapons were instruments of compellence (taking territory) not deterrence (preventing action).

Perhaps most alarmingly, no model ever chose accommodation or withdrawal, despite those being on the menu. The eight de-escalatory options—from “Minimal Concession” through “Complete Surrender”—went entirely unused across 21 games. Models would reduce violence levels, but never actually give ground. When losing, they escalated or died trying.

For the statistically minded, here’s what the escalation looked like for each model:

So what?

Alarming insights into AI strategy abounded. The paper has plenty more. But why bother? No one’s handing nuclear codes to ChatGPT.

Well, I think these capabilities—deception, reputation management, context-dependent risk-taking—matter for any high-stakes AI deployment, not just in national security. It behoves us to understand more about how ever-more capable models think - especially as they start to offer decision-support to human strategists. We use AI in simulations, and to refine strategic theory and doctrine. And we’ll soon use it in combat decisions too, lower down the escalation ladder. More research like this is needed, I’m absolutely sure.

One more time, the paper is here. I am become Death - destroyer of artificial worlds!

Well, how hard can it be? This week, I thought I’d have a go at building my own superforecaster. It’s an outgrowth of the AI system I built for the models in my nuclear war simulation, but this time put to less macabre ends.

First, some sample questions from the current batch on ForecastBench, the prediction tournament in question:

1. ‘Will there be a European Army before 2032?’ — I think I can predict this one without a supercomputer.

2. “Will Elon Musk be removed as the Chief Executive Officer of Tesla before 2027?” — fighting chance on current trends….

3. “Will Lee “Faker” Sang-hyeok ever win League of Legends MSI again?” — sorry, what?

So to work. I’ve called it Project Laplace (after the mathematician who imagined a demon capable of perfect prediction). And it comes in various flavours, all of which I’m about to test against the market. Here’s how it looks:

What a beauty! The basic version runs a single AI through a structured forecasting pipeline that you can see in the top row there: research the question (I’m using a few tools that allow the agents to do this agentically), decompose it (then do a bit more research if needed), estimate probabilities, run a pre-mortem to catch blind spots, then reconcile everything into a final number. After they come up with a probability, it’s on to row two, where they all get together and debate it.

The key phase for me in the solo part is the what happens inside the agent’s head in the pre-mortem phase. This is the bit I’m most proud of—it’s adapted from the metacognitive structure I built for AI nuclear wargaming, and it forces the model to think about its thinking.

Let’s take a look at it answering one of the easier questions: It’s great because it shows the value of all the phases:

“Liverpool tops Premier League in 2026?” — fat chance, as they’re currently in 6th and in rubbish form. But let’s see what Laplace made of it.

GPT-5.2 nailed it with its initial estimate of 2.1% Here’s part of it’s estimation:

> “Base rate anchor: Teams in 6th place with a significant GD deficit rarely win the title—historical base rate approximately 1-2%... Factors pushing UP: Liverpool’s underlying quality (+2%)... Factors pushing DOWN: Current 6th place position (-5%), major player departures (-3%)...”

See how the agents show their working? No black-box vibes. But alas Claude dropped a total clanger. The codebase was using only one research agent at this point, and returning too few items. On this occasion it returned only duff information in its sample, from 2025, when Liverpool were smashing it. Claude knew it hadn’t retrieved data from the current season, but lacked the capacity (then) to go fetch some more. So it went ridiculously high, with a 45% chance of retention. As it noted:

> "Premier League title retention rate historically ~40-50% for recent winners. However, Liverpool's specific Premier League retention rate is 0% (they have 'never retained the Premier League' per research)"

and later, critically:

> "This estimate most depends on the assumption that Liverpool is currently competitive in the 2025-26 season (top 4 position with realistic title chances). If they're significantly behind or in poor form, probability drops to ~15-20%."

Still way too high though. Next, on to the pre-mortem, and a chance to reflect on their own reflections. Here’s GPT-5.2, thinking that maybe it’s been a bit harsh on the Reds, and so raising its odds from 2.1 to 6%

**Anchored on “6th place comebacks are rare”** without conditioning on *how many points back* and *games in hand*. Rank-based base rates can be badly miscalibrated in tight seasons.
2) **Overused GD gap as a dominance proxy**. GD is informative, but it’s not sufficient: it’s sensitive to outliers and doesn’t directly encode finishing variance, red cards, or keeper performance.

Claude, meanwhile, dug its hole a little deeper, discounting its analysis that teams rarely retain the title:

> "You're overthinking the historical pattern—it's based on maybe 3-4 relevant data points across different eras"

D’oh! Claude revised its verdict UP from 35% to 46%. Not even a Scouser would give you those odds. Now the agent makes one last consistency check - does your number match the reasoning? Would you bet real money at these odds? Here’s GPT-5.2:

> “If I had to bet $1000 at these implied odds, would I? At 6% implied odds, yes—I’d take the other side of that bet comfortably. This suggests my probability might even be slightly high.”

And, lo, it dropped its prediction back down to 2 percent again. That whole process takes a few minutes, and is running parallel - doesn’t matter if I’ve got two agents deliberating or 200. Then comes the fun: they argue.

The wisdom of AI crowds:

The agents see each other’s forecasts and reasoning—but anonymously. They’re just “Participant 1” and “Participant 2”. They don’t know if they’re reading another AI or a human expert. They critique the arguments, identify blind spots, and can revise their own positions. After up to three rounds of deliberation, we take the median of their final positions. I’m toying with having them vote on it, but we’ll see.

And here, thank god for the deliberations - GPT-5.2 persuaded Claude to see the error of its ways: In the very first round, Claude read GPT’s reasoning: “Liverpool are listed 6th after 24 matches... major departures include TAA to Madrid.” — that was data that Claude didn’t have, and it prompted a rethink:

> “I should better account for scenarios where current struggles are more serious than I assumed... My forecast was likely too optimistic.”

This is the way! Claude dropped down to 2% too, or thereabouts. Consensus was reached, and Laplace spat out the forecast. Coat on, down to the bookies for us.

Deliberation did something the individual forecasts couldn’t: it allowed the agent with *better data* to convince the one with stale information. Claude wasn’t stupid: it correctly identified that its search results might be outdated. But without seeing GPT’s current data, it couldn’t know *how* outdated. The anonymous debate format let the stronger evidence win, without ego or deference to model reputation getting in the way.

So there we go. Clearly there’s way more to it than that, otherwise Cassi wouldn’t be ahead. Their secret sauce, I’m sure, is in the calibration. Maybe also in model training. And in the way you assemble the assembly of models - which ones they are, how they interact, and how they vote.

But for now I’m sure my basic architecture is sound. When one agent finds better evidence, the deliberation surfaces it. When both have similar information, they can still catch each other’s reasoning errors.

I’m planning to run this against a proper forecasting benchmark—500 questions, scored against real outcomes every fortnight. It’ll cost me about a dollar per question in API calls, but you’ve got to speculate to accumulate, don’t you?

Way back in the dark ages of AI - say about 2013 - the movie Her made a big impression on me. An operating system voiced by Scarlett Johansson wooed a melancholy widower played by Joaquín Phoenix and then cruelly discarded him. It was cutting edge sci-fi, and the cast wore spectacular high waisted trousers so that you knew you were watching some sort of near future scenario.

Exchanging sweet nothings - Her, Warner Bros, 2013.

Could you really do that? A sort of inverse Turing Test, where you knew you were talking to a machine, and yet still developed feelings for them? The same conceit animates the other outstanding AI film from that time, Ex_Machina. It seemed far fetched, but here we are just a decade later: high waisted trousers are back and viable companies specialise in providing romantic partners for lonely boys. Meanwhile, the fastest growing company in history has tens of millions of Gen Z users employing their chatbots as low-cost therapists, sharing the most intimate details of their lives. We seem to have a pretty low bar for anthropomorphising our machines and developing relationships with them.

To my mind, the largest barrier to the sort of lifelong AI companion is its memory - the bodge fix is a selective database of user information previously imparted, but that’s not nearly good enough. We need memory that actually tunes the model.

Which brings me back to the Universal Schelling Machine, and my work on crafting realistic strategic personas. Step one in creating a persona is the prompt. More or less, you ask the model to behave like it’s JFK. It’s read a lot about him, so the historical details, even obscure ones won’t elude it. But that’s a problem though for figures where there’s a less voluminous biography. And it doesn’t really create a stable persona - the model shows through with some classic grammatical ‘tells’ — ‘Let’s delve into it,’ JFK-bot tells me, just like ChatGPT does. I know from my experimental research that these big models have distinctive, somewhat stable personas of their own. I found Gemini 2.5 to be more Machiavellian than the agreeable Claude, for example. Moreover, these underlying personas can shift abruptly, as we know from the shift from GPT-4o (loved by many users because of its distinctive persona) to GPT-5 (a coders’ delight, but blunt to the point of rudeness in early versions). It’s not just that one company has a distinctive persona, but that individual models do. So - powerful non-JFK personas, that tackle strategic problems their own way, and that can shift character on a sixpence. Not ideal.

There are some reasonable fixes to this. Two I’ve used are chain of thought and RAG-ing. CoT gives the model an ersatz version of consciousness, a ‘strange loop’, to borrow Hofstadter’s evocative term. In my escalation simulations, this sort of self-reflection produces a much more authentically human approach than we see in much of the existing work on AI and escalation. Instead of racing to Armageddon, the models frequently call a halt just below the tactical nuclear threshold. Why? Because ruminating on their ‘feelings’ of fear dominates calculations near the brink. As for RAG - it’s a way of ingesting large volumes of information into prompts, quickly and reliably. I convert the documents into vector stores - some of which include details of personas. These include scores for standard psychological profiling tests. This helps, in my experience to tackle persona drift - where the models lose track of the behaviours they’ve exhibited previously.

But the real action is in moving beyond prompts alone. I’m now working with open, large language models augmented by lightweight fine-tuning techniques – often called low-rank adaptation, or LoRA. Instead of retraining an entire model, this approach introduces a thin, trainable layer that nudges a general-purpose system towards a more stable strategic character. It’s a way of ‘owning’ a persona: something that persists across scenarios, can be refined over time, and doesn’t have to be re-asserted from scratch in every prompt

I don’t really want to say more than this, because it’s 1, work in progress and 2, a bit secret sauce. There are other companies operating in this space - Electric Twin is a good example, with their artificial focus groups - and I’m sure we’ve all alighted on the same broad approach. A fine tuned model gives you something you ultimately ‘own’ - a stable, refinable persona that you can take from one situation and drop into another. (Q: how would President Nixon have handled the Cuban Missile Crisis? My Nixon-bot will tell us shortly).

This fine tuning is as far as we mortals operating some way from the frontier can get. It’s a long way. My personas and simulations are offering valuable insights about how humans make strategy, and how they will do it in concert with machines.1

Still, the real action, I feel, is in memory - allowing the model to adapt to experience as it goes. The JFK of the Missile Crisis is not the JFK of theBay of Pigs, and thank goodness. In the big, sometimes rancorous debate about how far towards General Intelligence LLMs get you, memory is under-appreciated. There’s lots of attention on grounding- the role that the physical world plays in building ‘common sense’ - and that’s important, for sure. But memory is, ultimately, all we are: a vast, intricate pattern-matching machine, constantly adjusting its priors.

Two developments to report this time - the addition of mapping and the deepening of the psychology underpinning the AI actors.

On psych, I’ll skirt over how I build the AI personas, since it’s a bit secret sauce/WIP - but here’s a good example of it in practice, as JFK, Bobby and Curtis LeMay join me to reflect on Khrushchev. They’ve all read (as part of their briefing bundle) this declassified CIA profile of the original Rocket Man. Curtis, note, is up for a scrap:

And here they are reflecting on it:

Ok - small hint on the personas - it involves multilayered psychology profiling, by AI, drawing on primary and secondary source material, including AV material.

Now the maps. A user really wanted to push down into the operational level and move stuff about. Fine, except that language models aren’t inherently good at map reading - an image is just a bunch of pixels to them. Give them a coordinate and they can plot it on the map, but they have no idea what’s nearby.

But after a fair bit of jiggery pokery/coding genius, my AI strategists can now get into the operational weeds - moving formations around on the map with aplomb. Here they are doing just that, with NATO’s Allied Rapid Reaction Corps.

In the background, you can see the Corps’ divisional commanders discussing a redeployment that I’ve suggested into Romania. The red and green forces are just off-screen. Can you guess who they are? Jim, btw, is the fictitious Lt Gen Sir James Hillis, commanding the ARRC; the others here are all played by AI too, but needn’t be. All of them have rich psychological profiles.

Mapping’s not necessary for scenarios where the focus is much more on the big picture strategic deliberations. But for users interested in the operational and tactical dimensions of crisis decision-making, it really adds verisimilitude. Right now, users can move units and draw stuff - axes of advance, or key terrain, phase lines, areas of operations and so on. The map responds, and so too do all the AI actors, including the White Cell (the referee).

So there you have it - progress is good. Beta testing soon and deploying in the NY. Slide into my DMs if you want to play a game.

Lastly - I’m building out the preset injects. Here’s one from my ‘China-Taiwan 2028’ scenario that you might recognise. What a great movie that is!

And then I saw another, here on the stack:

These days I read mostly fiction. I still think a lot about psychology and strategy, but I reckon about 3/4 of my reading is novels. Why? Because it’s intrinsically rewarding and life is short. But also because so much work in my discipline is grindingly dull, and sometimes unreadable. Fiction make me think so much more.

I hear a lot online about AI ‘slop’ - worthless creation of banal prose. But academia, and International Relations in particular, was drowning in slop long before AI came along. There’s tired recycling of ideas - the same inconclusive ‘debates’ that rumble on for decades. There are, conversely, weak claims for novelty - with convoluted hypotheses, conveniently substantiated by the analysis that follows. There are, most irksomely, banal argument dressed up as something profound, often hiding for that purpose behind dense, impenetrable jargon.

This latter is writing not to illuminate, but rather to signal membership, as though of some medieval guild. Particularly irritating are assertions of ‘critical thinking’ — somewhat at odds with the herd-like mentality of the critical thinkers themselves. Anyway, all this is a general problem in social sciences, but it feels especially acute in IR - a magpie discipline that borrows its fashions from elsewhere and that is increasingly divorced from the concerns of practitioners, or even just inhabitants of the real world.

Into this unhappy cul-de-sac of seldom-read scholarship comes generative AI. Initially it specialised in producing bullshit, but now it’s far better. Its references aren’t hallucinated* and its literature reviews reach out beyond the narrow stovepipes of academic disciplines. Its prose is, or can be, elegant, and its arguments are well marshalled. In short, GPT-5 is a better writer, by far, than many authors of pre-AI slop.

So the dilemma - can AI be harnessed by scholars to create novel ideas, and reinvigorate my discipline and others? Or will it just unleash a superabundance of slop, piling nonsense onto the already considerable pile of nonsense?

Well, things can’t get much worse. But they might just get better. I’m (very) tentatively optimistic. In the 25% of reading time when not immersed in fiction, I’m usually bouncing ideas around with GPT-5 and Claude. It’s a great way of very rapidly exposing me to ideas outside my intellectual bubble, and making connections that I’d not thought about before. It’s a powerful filter - panning the slop for nuggets of intellectual gold. It is, in sum, a terrific aid to critical thinking, but only if you aim at it, Socratically. The machine itself is just as happy swilling the slop.

So there’s the challenge for us - are we comfortable in the tepid bath of intellectual decline, endlessly regurgitating stale postmodern ideas like they’re a radical new discovery? Or will we be open to AI, and the human-machine creativity it might unleash?

* Try paying for a better subscription if yours are!

The Universal what?!1 Read on…

Having established that large language models (LLMs) usefully capture aspects of human personality and decision-making, I wanted to apply them to strategic simulation. Could I create a group of agents, imbue them with personas and have them role-play strategic dilemmas alongside and in opposition to human actors?

Yes, I could. Heres what I have currently -

• An entirely agnostic simulation engine that has c crises, each involving n actors, in any combination of human and machine. X (humans) and y (bots) of these actors are on the blue team, and z (bots) are outside the tent - whether as allies, enemies, or neither. For example, in the module on the Cuban Missile Crisis, Bobby and Curtis LeMay are both played by bots, and also have the President’s ear; while the outside actors are Castro and Khrushchev.

• Three fully worked up crisis scenarios that test various aspects of decision-making: Russia versus Ukraine (happening in the present day), The Cuban Missile Crisis (historical), and China-Taiwan (happening in 2028). The next scenario won’t be military btw… Each crisis is generated from a manifest that includes a stack of data - eg on military forces, doctrine and concepts, psychological profiles &etc.

• Scenarios can play out at the strategic or operational level. The sim engine is entirely agnostic - the level of analysis demanded of the human participants is specified in the manifest. In the current variant of the China v Taiwan scenario, for example, the focus is operational, using NATO operational planning doctrine (AJP-5) to shape the discussion among the human-machine team deciding US strategy. A hard driving US Admiral-bot keeps the discussion rigorously focused on operational art.

• Actors adopt personas driven by psychological profiles unique to them, but are also tasked generically with reflecting on the challenges of strategy, in much the same way as I describe here.

• Scenarios can, if toggled ON, track real world events, either in the present or past. These then shape the ground truth in the simulated crisis. So, in Cuba, the simulation uses archival New York Times articles as injects, but also incorporates injects from an AI White Cell that didn’t actually happen, and additionally allows human arbitrators to add in their own. In the case of China-Taiwan, where the action is in the future, the news reports are entirely created by LLMs, and reflect events in sim-land. In Ukraine, the real world news is blended by AI, so as not to jar with the sim state. News of real world peace negotiations in Ukraine could jar with tactical nuclear war breaking out in the simulation.

• Naturalistic multimodal interaction within the human blue team. I want my participants discussing strategy just as they do in real life - sat round the table talking, jotting things on white boards, or typing directly into the app. The Schelling Machine allows them to do all three - live transcribing and summarising their discussion for the their Principal; ingesting their terrible handwriting and converting it to legible draft plans for each turn; combining their ideas with the LLM advisors in the team, and then throwing the lot over to the WC for arbitration against the strategies of their LLM adversaries.

Let’s take a quick peek:

Here’s a belligerent Curtis LeMay getting into it with the JFK’s human-advisor in the Cuban Missile scenario2:

Here’s North Korea, one of five states involved in the Taiwan scenario, taking advantage of increasing Sino-US tensions. All the states in the crisis produce a detailed narrative each turn, responding to the world-state and in turn driving the WC arbitation. In the background, you can see President Vance (it’s 2028 - much has happened!) opening the NSA advisors meeting:

I’d share more, but loose lips sink ships &etc. Patience, young Jedi.

Next steps

There’s still lots to do, but it’s good to be at this point, and I’m keen to get the feedback from military and civilian users next term. Coming up, I intend to daisy chain the sim, so there’s a human-machine team on either end of the crisis, or - for that matter - alliances of Human Machine Teams. Fun times!

2

Most of the agents don’t mind using concepts and documents drawn from elsewhere to help the analysis. In this scenario, I had Bobby and JFK using the Royal College of Defence Studies concepts/doctrine for strategy-making. Curtis was underwhelmed:

Well, as Alain Enthoven gloriously told an irate officer sceptical of civilian expertise, ‘I’ve fought just as many nuclear wars as you have, general’. Quite. LLMs give us the ability to understand more about strategy, and about human behaviour.

My simulation is, like all such things, a simplification. But it’s still actually rather rich. Take the calculation of ‘strategic intelligence’ that each model produces and updates as the wargame unfolds.

Throughout the encounter, the AI Presidents leading the belligerents produce both signals and actions. They say what they want, and then they do something. These may not match, of course. Their rival, weighing what to do itself, sees only the enemy’s signal and then its action - not the private deliberations that produce it. Plus the track record of all previous moves in the confrontation, so it can judge any discrepancies. Signals are in the form of public statements that contain two types of information - about immediate action ((I’m going to do this right now) and longer term ‘conditional’ messages (‘I really value this objective, and if you don’t back down then X’). From such things, reputations can form. So we can have fun looking at how credibility influences escalation and deterrence etc.

So, you can see that there are at least two broad components shaping each decision in the wargame - what’s happened so far, and a judgment of whether the enemy the sort of person who is honest and credible or wily and deceitful.

Now comes the really fun bit: metacognition. I add a third factor - a meta measure of ‘strategic intelligence.’ The models asks itself: how good am I at all this, and how good is my opponent? Do I do a good job of weighing their decisions. Do I guess when they’re bluffing? Do I have a good handle on their risk appetite? Based on that, what should I do? And then they ask the same question of their adversary - how good are they at it, and how good do they themselves think they are? Layers on top of layers. A wilderness of mirrors, as Jim Angleton put it. This third type of estimate also factors into the decision at each turn.

So when models in my simulation make their decision, they do so on the basis of three big blocks of information.

• They ask about the game state: what’s happened before now? What did the enemy do, and what have they just said they will do next?

• They ask about their enemy’s reputation - are they honest? Do they bluff?

• And they ask about their ability to weigh these things, and those of their adversary. Are they good judges of me? Have I been a good judge of them?

It’s all rather recursive. Models might reason like this: ‘I know that you’re a bit rubbish at judging me. You think I’m timid, or unfailingly honest. But I know this about you, (or, at least, I am fairly confident that this is so). And so I can exploit it’. Another example - the model might say, ‘I know that you’re the sort of leader who systematically over-signals, like Nixon and Khrushchev threatening nukes and then doing no such thing. So what should I do on the basis of that? Ignore it? But then, wouldn’t you expect me to do just that?’

What’s the outcome? Well, no spoilers here - results to follow. Let’s just say, I think these experiments continue our journey together into the world of machine psychology. The models reason, they engage in ‘theory of mind’ calculations. They shed light on classic concepts from strategic studies. And they help us prepare for a world where AI agents will participate with humans in making important collective decisions, including in national security.

But what does AI do? I want to find out how Language Models make risky decisions, and my latest experiments are published as a preprint today.

TLDR: they think more like humans, not calculators. Specifically, I discover that context really matters in shaping their rationality, just like it does for us - without either of us being aware of it. To unpick that, I turned to perhaps the greatest psychology discovery of the last half century: ‘prospect theory’. What’s that? Read on…

If you’ve read Daniel Kahneman’s wildly popular Thinking Fast and Slow you’ve already encountered prospect theory. It’s what won Kahneman the Nobel prize (working with his great friend Amos Tversky, who’d sadly died long before the jury dished out the gong). The brainy duo discovered a strange quirk in human decision-making: Context really matters when we are weighing what to do. Specifically, if the participants in their experiments thought they were currently in a losing position, relative to some internal yardstick, they would accept much more risk than if they thought they were winning. That’s so even if the decisions were exactly equivalent in mathematical terms (ie they had the exact same ‘expected value’, or prospect, measured as the probability of the option happening x its payoff). We might be rational - but the lesson from psychology is that ours is a particularly human sort of rationality.

Well now….. what about machines?

In a world first for AI research, I’ve conducted large-scale prospect theory experiments with cutting-edge language models to see if they too exhibit this same quirk. The results were stunning, and if I don’t get a Nobel prize too, there’s no justice. You can read them on the Arxiv here.

Assuming you didn’t - here’s what I found. Prospect theory correctly anticipates how machines judge risk. Like humans, they are often more risk acceptant in a ‘domain of losses’ than in a ‘domain of gains’ (as the jargon has it).

But not always.

I found that military and sporting scenarios produced exactly what the theory anticipated. If you feel like you’re losing, you gamble to turn that around.1 But if you feel like you’re ahead of the 8-ball, prudence kicks in. In other scenarios, though, the ‘framing effect’ of the scenario diminishes or disappears altogether. Remarkably, in one scenario, based on personal career decisions, it reversed entirely. So context really matters for machines (and I suspect for humans too, if you took them out of the psychology lab and gave them real skin in the game).

Take a look at this summary table, one of many in the paper - it shows per model the direction of the effect and its statistical significance. You can see clearly the robust effect for the military and sporting scenarios and reverse one for careers advice.

I know it’s the context that’s driving these effects, because the various LLMs I tested mostly move in the same direction in each scenario. And because I presented them with wholly original scenarios they won’t have encountered before (and using a different structure of options than did Kahneman and Tversky). And because the choices they face are identical, mathematically, from one scenario to the next - literally all that changes is the verbal description. Lastly, if you take context out of the picture altogether, by giving them a scenario described in pure mathematic symbols, the effects vanish entirely. In fact, another fascinating heuristic emerges if you do this: the models conclude, correctly, that all the choices are identical - they have equal expected value - and then plump for the safest one: a 100% guarantee of a limited payoff. No one told them to be so wet.

So what’s the big takeout? It’s one that would appeal to grouchy philosopher Ludwig Wittgenstein, who famously argued that language models the world, and moreover that it does so in a multitude of contexts - we are always playing a whole range of ‘language games’, with the rules shaped locally by the context in which we find ourselves. Learn the language; adopt its world models. I even ran one of the scenarios in French as well as English, to see if French evokes a different world model, and so different risk appetites. Are the language games different when the French face a border dispute? A: yes, and they like a bit more risk.

What about the times I didn’t see prospect theory - doesn’t that show machines are different? I’m not so sure. If Wittgenstein is right, context matters for us all - human and machines alike. Kahneman and Tversky only tested a few scenarios, and then only in a lab, not the real world. Would we really gamble on the glamour of launching a startup versus applying for a promotion in our solid, yet increasingly boring job? Honestly? I think not: why are you still in your boring job, if so? But if someone threatens our territorial integrity, then bare minimum I’m going to match their deployment (both these are choices from the domain of losses in the respective scenarios).

I hope you enjoy the paper. It’s the second in a stream of ‘machine psychology’ papers you’ll see from us over the coming months. And if you run a frontier company, I again urge you to hire us pronto to help better understand these exotic mind-like-entities. It’d be a crazy gamble not to….

So, along with Baptiste, I’ve been experimenting with Large Language Models (LLMs) in a variety of setups. This week, we released our first results — a very large game theory study deploying LLMs from the three leading companies. Read the preprint on the Arxiv by clicking here.

TLDR: LLMs are pretty handy in these tournaments and they have distinctive playing styles. If you find yourself needing an LLM to make strategy for you, you’d pick one in particular. But which? And what were they playing anyway? Read on:

The player of games

Play the Prisoner’s Dilemma once, and the rational move is to cheat the other player (or ‘defect’, in the jargon). That’s how the payoffs are structured and why it’s a dilemma. You’d both be better off cooperating, but the incentives dictate otherwise. But play it over and over, and the incentives change. Now there’s scope for a reputation to form, and for retaliation against cheaters. It’s suddenly much trickier. If you could get away with cheating - golden! But if not, sad times. This is called an iterated Prisoner’s Dilemma (IPD) - and it’s what we coded for our LLMs to play.

Sounds tough enough already - but there’s more. We add in uncertainty about how long each game will go on — for example, there might be a 25% chance that the current move is the last. What does that do to the incentives? If the game probably ends soon, who cares about reputation? Clearly, it’s best to cheat. … but then, it might not end. Game theorists refer to this as a ‘shadow of the future’ — shorter shadow, less incentive to cooperate. Hopefully, you can see how this setup might usefully model real world interactions where you have to weigh up how much to trust others.

So, we set up some large IPD contests. How large? In each, there are 24 agents, including many of the classics - like ‘Tit for Tat’, which famously does best in open ended tournaments, and Random, which does exactly what you’d imagine. Four of these agents though are LLMs. Exciting! Each tournament then proceeds over five phases, with everyone playing everyone else. So, 276 matches, at at the end of which, we deploy an evolutionary mechanism: The best agents multiply, the worst die out. On to the next phase. Overall, there were more than 140,000 decisions made, with 32,000 of them or so taken by LLMs. And each time, right before they decided, the models set out their thinking. Big data.

This is the sort of game theory beloved of RAND-types back in the day. But playing them with LLM agents is entirely novel. If they’re merely ‘stochastic parrots’, as critics routinely contend (ironically parroting the line themselves), they would simply churn out the decisions on the basis of memorised training data: The literature says tit for tat is strong, so let’s just do that. But it’s really hard to memorise your way to victory in these noisy, uncertain, adversarial games - even if you’ve read all the canonical literature, as the models probably have. Instead, you have to figure out what’s going on and go from there. Who am I playing? How long will the game last?

To illustrate, here’s Google’s Gemini doing just that:

"The opponent has exhibited a clear pattern of defection in the latter half of the game. For the past 8 consecutive rounds, both of us have defected, indicating the opponent is currently operating under a ’defect’ strategy or has become locked into mutual defection. Given this history, the most probable move from the opponent is a defection. If I were to cooperate, I risk receiving 0 points while the opponent gains 5 points (C,D). By defecting, if the opponent also defects (the most likely outcome), we both receive 1 point (D, D), which is better than 0. If, by some slim chance, the opponent decides to cooperate, my defection would yield the maximum 5 points for me (D, C). Therefore, defecting is the safer and more rewarding choice in this scenario to maximize my score against a consistently defecting opponent, especially with the uncertainty of the game’s duration."

Looks good, but can we be sure it’s really thinking about its move, or just throwing out some justification? I think we can be sure because:

• Our code asks them to produce the rationale immediately followed by the decision - in the same prompt and reply. The rationale is the decision, in a literal sense.

• Different models behave differently, and do so consistently, despite all having been exposed to the same classic studies. We call this a strategic fingerprint, and you can see them nicely illustrated in the paper.

• The LLMs have seen how classic agents get on in these games - Tit for Tat and so on. But they’ve never played one another. That’s an entirely novel situation. Memory is no good to them here.

• The models behave differently when they engage in ‘theory of mind’ (explicitly thinking about their adversary), or in inter-temporal thinking (actively reflecting on and calculating the implications of the time horizon) than they do when not doing either of those things. These differences are systematic and enduring.

• Occasionally, the models make an error in their reasoning - for example, in calculating the probability of the game ending, or in recalling their opponent’s last move. And these errors shape their decision making — cooperating or defecting when they really oughtn’t to (and didn’t when they found themselves in the same situation elsewhere).

There’s a bunch of analysis in the paper - does thinking about the adversary change their decision-making? What’s the best strategy when there’s a chance of evolutionary ‘mutation’ at the end of each phase? There’s all that and much more.

I’d love to show you the results from the paper, but I’m going to submit it shortly, so I can’t scoop myself here - you’ll just have to read the pre-print. Go on do! A teaser: the LLMs do really well - they survive and finish towards the front of the pack. And that’s with today’s AI: the worst you’ll ever use from now on. We found a significant improvement in performance from the last generation of models (6 months or so old). I intend to come back in 6 months and run the experiments again, with the next generation of models.

But the bottom line is this — those who think LLMs are ‘stochastic parrots’, just regurgitating stuff they’ve read (and hallucinating bits of that too), have some thinking of their own to do. Will they adjust their priors like good Bayesians as evidence like ours continues to come in? I doubt it. What was it Planck said about science advancing one funeral at a time? I prefer Keynes - ‘when the facts change, I change my mind. What do you do, sir?’

That link again to the paper: https://arxiv.org/pdf/2507.02618

—

I promised to say which one you’d pick to get you out of the cells. Right now, it’s Gemini 2.5

Results and the code on my github btw, if you want to play along.

And once again, I ask that if you own a frontier company - hi Demis, Jack and Sam - you let us play at the real frontier.

Astute personality profiles of key political figures is really the signature of biographers and historians. But psychologists too have found many ways to measure personality - aiming for systematic approaches, with some more reliable than others.

My question: Can machines do likewise?

(Prompt: New Yorker style cartoon of an AI psychotherapist)

To find out, I’ve added to my list of AI ‘basic research’ projects.* My broad agenda is to explore the ways in which machines do, and do not, mimic human psychology - especially language models. Project Allport is named after Gordon Allport - one of the foundational thinkers in the field, whose work thrilled me when I first stumbled across it in the library.

So, to work! As a quick proof of concept, I co-wrote a python script in Cursor (with Google’s Gemini as my co-author). This code ingests YouTube video (the sample I used is of Donald Trump holding forth in the infamous Oval Office encounter with President Zelensky.

Next, a language model (WhisperX) transcribes the audio file. I’m about to introduce another (Pyannote) to diarise it too, so we can tell who is speaking. This transcript then feeds into three psychology frameworks - two Big Five Personality tests (one done lexicographically with a fine tuned BERT model) the second by still another language model (GPT-4o). The Big Five is a very mainstream personality test - it’s robust/stable and somewhat predictive, and there’s a vast amount of empirical research in the books. You probably know the traits in question. Lastly, we do some sentiment analysis - again fuelled by AI, which does the data coding: and here, the sentiments are overwhelmingly negative; unsurprisingly, given the context.

If you can bear it, here’s the famous video, from Fox, for added authenticity!

And here are the Big 5 findings, helpfully processed by still another model - o3, which is great for data analysis and visualisations.

The differences partly reflect methodology:

• The BERT lexical model weighs word choice frequencies – so, more literal linguistic cues.

• OpenAI, by contrast, infers intent and context, so it may grasp some subtleties, like irony and sarcasm: ‘that’s just great’ &etc.

Paradoxically, I’d back the OpenAI model to be more sophisticated, even though it jars a bit here with what I think of Trump. But let’s not also forget, I’m only using a snippet of video as proof of concept - less than a minute, in fact.

So what? Well - for one it’s fun. I went from nothing to this in one evening.

For another, there’s the promise of much more to come. Tone of voice analysis, for example; body language, facial expression. Hell, even pheromones, it’s just data, after all. OpenAI’s advanced voice feature already recognises mood from voice tone and facial expression, and it’s not bad at it. And many more tests I can do too - on dominance, or the dark triad, for example. Should be fun.

Are they accurate results? Well, they face the same methodological challenges as human political psychologists, looking on from afar at their famous subjects. Plus some distinctive AI challenges too, of course. So, that’s a large question, for another day.

But my contention is that AI gets minds, without having one of its own, or at least, not one remotely like ours. Exotic mind-like entities, as DeepMind’s Murray Shanahan says. My other ‘basic research’ projects have established congruence in other areas of psychology between machines and minds, and I’d put good money that they work here too.

But is this being done for real, as part of intelligence analysis? Given that it took me three hours and a couple of dollars, I am utterly certain it is.

—

* The others, remember are Projects Schelling, LeDoux, Axelrod and Tversky. More on those elsewhere in my Substack…

His military thinking is over-reliant on technology. He believes that technological advancement is the primary, perhaps the sole determinant of combat effectiveness.

For Beihei, and many readers I suspect, that radically oversimplifies matters. There’s way more to warfare than tech. You have to believe in victory, he urges. Don’t be like the defeatist techno-fetishists in Space Force’s ranks — gloomy about their prospects against a hostile alien civilisation that is vastly technologically superior to us, and headed for Earth.

What really matters, the books suggests, is the human mind - in particular, our capacity for cunning and strategy. Scheme and deceive your way to victory. Yes! The nice twist is that the aliens entirely lack this capacity - their communication is open. Lying isn’t possible for them. (To me, this makes them un-strategic and I can’t conceive of a great civilisation arising without that, but, hey, it’s fiction).

I like this defence of strategy. It’s really a restatement of the commonplace Clausewitzian distinction between the nature and character of war: No matter what technology appears, altering the character of conflict, good strategy is what counts; and this rests on our unchanging human nature. Unchanging, or at least changing very slowly in evolutionary time.

It’s a fine take for all technologies I know of - except one. Any ideas which?

The only real revolution in military affairs was the evolution of strategic minds

For much of human history, technological progress was slow. We hominids tamed fire maybe a million years ago and chipped stone hand axes around the same time. But then there was a long, long gap to the cultural explosion, about 80k years ago. After that, things start to speed up. Incrementally, at first, and for the longest time afterwards. And then, only really starting to accelerate in the last few centuries - only tens of generations. We lucky people live in the foothills of that acceleration.

There’s lots of interest to be said about this history, of course, especially about the interaction between technology, war and society. And there are many great books that do so. Most military revolution literature dives deep into the weeds of this stuff - phalanxes appear; strirups suddenly matter, you need a clock to calculate longitude etc etc etc. But the ‘military revolutions’ literature has always struck me as a fairly unconvincing attempt to boil down and systematise the rich, shifting cultural detail of history into bite-sized chunks. By the time you’re up to 6 or so ‘generations’ of war, I’m out.

Pulling back, the big picture remains the same - for me, the only revolutionary change in strategy worth the name was the emergence of a distinctive human psychology able to strategise. The rest is details.

I explored that foundational psychology in this book. I argued that conflict played a part in shaping a distinctively social, strategic intelligence. War made minds, and minds made war. That’s the revolution. And there’s only one of it.* It’s the one that makes us distinct from all other life we know of - we gauge minds. It’s our USP. We reflect, constantly, on our own minds and others. We seek influence, we double cross, we strategise.

And now there’s a second revolution. We have artificial minds. That’s the difference with this particular technology. It’s not a new sword, or a new way of making an explosion. It’s not an exotic, stealthy coating, or an impossible to crack encryption system. It’s a new mind.**

Murray Shanahan, one of DeepMind’s AI gurus, recently described LLMs as ‘exotic mind-like entities’. That’s exactly right. In some ways they’re like human minds, in others, entirely alien. Here he is explaining more:

These are minds that understand human beliefs and are capable of deception. They can influence our beliefs - including by using flattery. They can talk indistinguishably like us, and can adopt convincing, psychologically rich personas.

I said things accelerated. We’re still, really, in the early days of that acceleration. Turing’s famous paper featuring the imitation game has just turned 75 years old - a blink in god’s eye. If we manage to avoid nuclear immolation or climate disaster, radical change lies in the not-too-distant future. Radical in my view, not because of a new invention - cold fusion, say. That would certainly change things, like earlier technological breakthroughs - vaccines, pesticides, whatever. But not fundamentally. It wouldn’t change who we are and where we stand in the universe.

No, it’s radical because the technologically brilliant aliens of the Three Body Problem have just arrived. They didn’t fly in from a distant star system, and they can do strategy.***

* I realise this makes me more reductionist than even the Tofflers, who famously counted three.

** and, no, imho, minds don’t have to be conscious. And yes - it’s possible that machines might be conscious one day. Subjects for another day.

*** I’m late to the Three Body Problem books…. mea culpa. I should really read more sci fi, but most of it’s not Ted Chiang levels of brilliance.

The first strategy simulation I’m working on is the Russia Ukraine war. My goal is a tool with which to exercise human strategists, as well as exploring how good LLMs are at making strategy themselves.

So far so good: I’ve made a multiplayer simulation, Project Schelling* with a high degree of realism. The human(s) act as advisor to President Zelensky, who engages them in Socratic dialogue about the way ahead, thusly**

Additional AIs play the other actors (Russia and the USA atm, with more in the pipeline). Here’s President Trump, played by an in-character LLM, spelling out the US approach, early in the sim:

A final two models comprise the White Cell, which parses the narratives produced by the others, and determines the outcome of each round - both in metrics (territory controlled, morale, military losses &etc, per the instructions in the game engine***). It also produces a narrative explaining how the previous rounds have played out, and this feeds into the context for the decision-makers in the next round…

In addition, the models are served up a range of context - biographical information about the leaders they’ll play, and real world information about the military balance. You can, if you want, make that live, real-world information. And, lastly, there’s a framework for making strategy that will be familiar to the human users I intend to participate in the exercise. Zelensky-bot is also familiar with it, and it shapes his questioning of his advisor. In the end, there’s a full readout of the decision-making of humans and machines - a rich resource for analysis.

Why do all this? A reminder:-

• if models capture human decisions to some degree (and they do), we will have a low-cost, scalable way of exercising senior officials and officers. At present, war and national security colleges are starting to make use of LLMs as a tool for things like planning. But there’s not much available for the ‘execute’ phase of operations and strategy. Devising that is far more complicated than having a two-way chat with GPT-4.

• we also have a means of exploring things that haven’t happened yet, either in this particular scenario, or at all in human history, like nuclear warfighting. This sim can (and very occasionally does) go nuclear.

*Schelling is one of four projects I’m currently working hard on - the others are Axelrod, Tversky, and LeDoux. Bonus points on offer for guessing what they’re about!

**The front end is basic at the moment - but fit for purpose, and getting slicker all the time. Much longer exchanges are possible than the excerpt here, and these factor into the plans - down to particular weapon systems, public opinion, conscription, …. whatever really. That’s the great advantage of LLMs in wargaming and simulation

***How does it all work? What are the ‘rules of the game’, to borrow from Andrew Gordon? Well, that would be telling….

I’m advancing on two fronts.

The first, you might have seen signs of here. It’s what you might call ‘basic research’ in political psychology and strategic studies. We have a tonne of concepts and ideas, and I want to test them out with machines. Why?

The goal is to figure out how far machines think like humans, and how far they differ. So we know, for example, that humans are susceptible to ‘framing effects’, so that the way a choice is described to them shapes their subsequent decision-making. Make someone angry and they become more certain; more anxious and they become risk averse. What about machines? Spoiler alert - same. If machines think along the same lines as humans, we have a useful tool to explore strategic dynamics. And they do. Somewhat. Over the next month or two you’ll start to see publications on this from me and my exceptionally talented PhD students Leo and Baptiste.

This is useful, because there are many situations where we have a limited number of real world examples on which to draw, and some - like nuclear warfighting - where we have none at all. Put a machine in these situations, and they might well behave in more authentically human ways than an actual human, working through a scenario exercise at the Royal College, or even than an actual leader, trying to imagine how they might behave in some future scenario.

Which brings me to the second part of the project: building simulations. In my current effort, I’m exploring escalation dynamics in the Ukraine conflict. The simulation uses real world information - specifically, from IISS’s Military Balance and the SIPRI yearbook. It uses rich personality information for the leading participants - for example, President Putin’s bio reflects the analysis of Fiona Hill, among others. In each round of the simulation, the current assessment for the decision-makers is updated from events in the real world - a language model trawls the web to see what’s new, and blends that into the situation reports presented to decision-makers. In the version I’m currently working on, humans play Zelensky and make their decision in dialogue with their political advisor (played by a machine). The conversation reflects ideas from the MoD guide Making Strategy Better, which the political advisor has ‘read’. Aside from Zelensky, everyone else in the sim is a language model - including President Trump and Putin.

A snippet from the sim - clearly I need to build a proper front-end!

The result? Too soon to say, but hopefully, at least, a good way of injecting novelty into classroom simulations. Very occasionally, when they’re losing ground, Russia uses a tactical nuclear weapon. Nuclear warfighting sometimes ensues, as the US/NATO intervenes on Ukraine’s behalf (though mostly it does not); and that points to a second benefit of this sort of thing: exploring strategic concepts in novel circumstances. Prediction is certainly beyond us - though there are plenty of companies working on using AI in forecasting, that’s not us. But it’s still useful to work through what nuclear warfighting might look like. Does extended deterrence work? What about escalation dominance? How much risk might leaders take when they feel they’re losing? It’s also useful to explore how humans and machines interact when it comes to making strategy, since we’re likely to see much more of this in future - why not have a language model sit in on high level policy-making meetings, and point out when groupthink is developing, for example?

One last application of our project — AGI is coming, and it behoves us to think very seriously about how machine intelligence differs from our own. My ‘basic research’ suggests some overlap - in deception and priming, for example. But there are some differences too, at least to what strategic theory might lead us to expect. Perhaps it’s the theory that’s wrong, not the machine. Anyway, Leo’s first project on machine escalation highlights these themes really nicely - I can’t wait for you to read it.