When we talk about AI policy, usually we talk about sort of minimising the risk while capturing all the benefits.
I think the default outcome for basically most countries in the world that don’t build their own frontier AI models is the exact opposite: they capture all the risk that comes from these models going through society, and yet they minimise all the benefits they could potentially have from AI happening.
— Anton Leicht
In a post-AGI world, can a country without access to frontier AI even be considered sovereign anymore?
Anton Leicht says once frontier AI becomes a core economic input, the countries that own it will pull further and further ahead. Everyone else stays a customer… or worse. Maybe the dominant power wants your land, or a military base, or a resource. Without economic leverage, there’s very little you could do about it.
Anton — Carnegie fellow and writer of the blog Threading the Needle — thinks middle powers should band together and build their own frontier models.
He’s costed it out: something like $500 billion over four years for a band of allied democracies. That’s not absurd money for the G7 minus the US. The problem is you’d be asking treasuries to take on sovereign debt for a speculative venture with no business case, wide open to US coercion and domestic backlash.
So despite its promise, Anton’s verdict is that it probably won’t happen. His backup is for countries to ask themselves: if intelligence becomes abundant, what stays scarce?
Upstream, that’s everything that feeds the supply chain: ASML’s lithography machines, chipmaking, exclusive training data — all of it gets more valuable as AI does.
Downstream, “a country of geniuses in a data centre” still can’t cure cancer without someone building the production plants and running the trials. The Europeans, Japanese, and South Koreans are good at exactly these real-world bottlenecks.
It’s an imperfect fix. The US would still hold more leverage, plus an incentive to re-industrialise and cut you out. The prize is avoiding the worst outcomes: a gradual but irreversible decline, waiting to be either annexed or discarded as the US and China race ahead.
In this episode, Anton and host Tom Reed look at what middle powers should start doing now to keep a seat at the table.
This episode was recorded on June 19, 2026.
Our production team includes:
Video editors: Josh Alward, Dominic Armstrong, Ollie Bignell, Andrés Escobar, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon Monsour.
Producers: Elizabeth Cox and Nick Stockton
Coordination and support: Katy Moore and Lou Moran
He lays out a hierarchy of strategies — from pragmatic to radical — that middle powers can pursue to avoid this outcome, while being candid that several of them probably won’t happen.
The default outcome for most countries is bleak — and the gap will compound
By default, countries that don’t build their own frontier models get the worst of both worlds: economic disruption from AI spreading through their societies, but no control over the systems causing it and little share of the value they create:
The frontier gap compounds through recursive effects. US frontier AI companies can use their own superior models to improve their next generation of models, reinvest revenue into more compute, and restrict competitors from doing the same.
Open source won’t keep up. Fast followers have historically relied on observing frontier capabilities and distilling from them. As the frontier both pulls ahead and becomes less visible (through access restrictions), the gap widens. Anton expects the current ~four-month lag to grow, not shrink.
Relative disempowerment becomes existential. Even if Europeans are fine in absolute terms (better products, cheaper goods), at some point the leverage asymmetry means they can’t meaningfully resist US demands on land, resources, military basing, or anything else. As Anton says: “It’s really difficult to look at these European countries and still think that they’re sovereign nations in a relevant sense.”
Best near-term strategy: integrate into the US-led AI supply chain
Anton’s preferred approach is an allied-scale division of labour — the US does what it’s good at (building frontier models), and middle powers contribute what they’re good at (supply chain inputs, advanced manufacturing, data centres).
Compute-for-access deals
Build data centres fast and offer the inference capacity to US companies. In return, demand commercial access parity — if a model is available on the US market, it’s also available on yours.
Deal with AI companies, not the US government. Striking technology treaties directly with Washington is highly volatile and easily derailed by horizontal political escalations. By signing data centre contracts directly with private companies instead, middle powers turn trillion-dollar companies like Nvidia and Anthropic into commercial allies. If the US government later attempts to pull the plug on foreign model access, these powerful companies will aggressively lobby Washington for allied carveouts to protect their own infrastructure investments.
Own the bottlenecks
Screen foreign investment aggressively: Everything upstream (semiconductor equipment, high-bandwidth memory) and everything downstream (manufacturing, testing, production) becomes more valuable as AI becomes more important. Middle power governments should be screening foreign investment aggressively, as there’s an awareness gap right now where US-based actors (like Jeff Bezos’s Prometheus) recognise how valuable these manufacturing assets will be before the countries that own them do. Selling a robotics factory for a few billion now could mean giving up your stake in the AGI economy for decades.
Make manufacturing AI-compatible by upgrading to modular, sensor-rich, fast-iterating production facilities that can integrate tightly with frontier models. Without this, the US simply rebuilds the capacity domestically and the bottleneck evaporates.
The moonshot option: a ~$500 billion allied frontier model project
If the bilateral strategy fails — if the US restricts access, if the bottleneck leverage doesn’t hold — the only true hedge for middle powers is building their own frontier model. Anton recently estimated this at roughly $500 billion over four years, and expects the cost to rise by ~$100 billion for every six months of delay.
The ideal coalition: EU, UK, Canada, Australia, New Zealand, South Korea, Japan. Realistically, the UK, Korea, and Japan may prefer their bilateral deals with the US, so you start with Canada and as much of Europe as you can get.
Pitch the project as a strategic asset, not a commercial one. A warplane doesn’t make its money back; it’s something you buy so you have it. Middle powers’ treasuries would have to consider this project in the same way.
Structure: A private company-like vehicle, structured exactly like a frontier company, with maximum autonomy. Each coalition country designates a “czar” who translates between political language and technical language. You replicate the proven formula for building frontier AI systems: poach top engineers, give them enormous compute, stay out of their way.
Sidestepping US retaliation: The US would likely try to stop a sovereign foreign project by imposing sweeping tariffs, cutting intelligence sharing, blocking Nvidia chip exports, or restricting access to dominant US AI coding agents. To survive the initial phase, middle powers must play hardball by threatening to throttle or divert their own critical upstream semiconductor inputs (like Dutch ASML machinery or South Korean high-bandwidth memory) to force Washington into a cooperative chip-sharing equilibrium.
Anton thinks this probably won’t happen. Political awareness lags the necessary moves by months, and every month of delay raises the price. But if the bilateral strategy fails and political will somehow materialises, this is the play.
Domestic policy: flexible labour markets, corporate tax, and why pauses won’t work
Counterintuitively, labour markets need to become more flexible, not less:
There’s a race between augmented firms (humans + AI working together efficiently) and automated firms (vertically integrated, almost no humans). If your labour market is rigid and workers can’t move to where humans still add value along the “jagged frontier” of AI capabilities, your augmented firms lose the race — and then you get mass layoffs anyway.
Displaced workers (many in their early-to-mid-20s) need more than wage guarantees; they need plausible career trajectories. Junior job subsidies that keep them embedded in firms during the transition may help them pick up useful skills to augment the AIs over time.
Fund the transition through corporate income tax, not token taxes or equity stakes:
Token taxes disincentivise exactly the AI adoption you want. Government equity stakes in AI companies create perverse incentives against regulation, and aren’t large enough to fund widespread social disruption anyway.
Data centres provide a useful tax revenue anchor: firms that depend on physical compute infrastructure can’t fully flee to tax havens.
Even so, shifting economic value from highly taxed labour to lightly taxed capital will produce a massive revenue crisis for most treasuries, to which Anton doesn’t see a clean solution yet.
On pauses and slowdowns:
A symmetric global slowdown in frontier development is geopolitically asymmetric: it disproportionately hurts the US (whose entire strategy is a leveraged bet on AGI) while helping China (which is catching up on semiconductor manufacturing and AI diffusion). Getting the US to agree to a pause is nearly impossible; getting China to make offsetting concessions is even harder.
A “superintelligence ban” is too underspecified to operationalise. The more promising policy is banning closed recursive self-improvement loops specifically — because ever-faster RSI loops are nearly impossible for governments to monitor with current oversight infrastructure.
Highlights
Most countries face bleak AI futures
Anton Leicht: When we talk about AI policy, usually we talk about sort of minimising the risk while capturing all the benefits. I think the default outcome for basically most countries in the world that don’t build their own frontier AI models is the exact opposite: they capture all the risk that comes from these models going through society, and yet they minimise all the benefits they could potentially have from AI happening.
Tom Reed: What can middle powers do to avoid this future?
Anton Leicht: Well, I think find some stake and find some participation in not only in this broader AI supply chain, but in what you expect the world order with advanced AI to look like. Because, you know, Anthropic didn’t promise us just geniuses in a data centre; they said they would cure cancer. So how do you do that? You actually have to test the cures you make, you actually have to build production plants to actually make the medicine, and so on. The same thing goes for robots, the same thing goes for all the military assets that you think you might want to produce with that for US–China competition.
Like, lots of middle powers, especially the Europeans and South Koreans and Japanese, are pretty good at occupying these bottlenecks. And I think if you have something like that, and if you can integrate it into the AI supply chain sufficiently effectively, then that is just a sort of long-term asset that gives you some economic contribution, and that creates some minimal mutual dependency.
There is a very realistic second-best/worst equilibrium where the middle powers are sufficiently useless allies that don’t really build up the capabilities that would feed into this broader US AI model supply chain that would give them this edge, but instead decide to focus on whatever — their own models, or shutting off their markets from American models, or just moving away from this mutually beneficial arrangement.
And I think on a very high strategic level, what the goal here should be is to avoid the second-best equilibria and just move toward the actually best broad, alliance-wide supply-chain integration: the middle powers do what they’re good at, the US does what it’s good at, and then we sort of combine that. But it’s not obvious, and I think there are a lot of factors pulling away from that and into the worst outcome.
Why AI dominance is forever
Tom Reed: Why is this so catastrophic? So the US has frontier AI before the rest of the world does — but the US already outmatches most countries on most domains, and they’re doing kind of fine. Why is this so bleak for France or England or anywhere else?
Anton Leicht: Well, I think the question is: what can you do with a two- or three- or six-month lead time on all the secrets of the world? I think if you just have the cyber capabilities now, the effect is somewhat limited. But you could already see the Europeans freaking out about this, and I think rightly so. And then I think we expect models to be able to do more things well than just cyber, and I think a lot of them could matter a lot economically.
I think there’s an open question here about in which domains specifically the frontier matters that much, but I think R&D is one of the other obvious examples. If you have a few months’ lead time on innovation, and you find out what material you’re supposed to use a few months before the Euros do, and then the Euros find out as well, and they build their thing and it takes 24 months for anything to get off the production line anyway and for anything to get shipped anyway, really what do the few months matter? I think that’s a realistic near-term future where the lag is just not all that relevant for the R&D stuff, for example.
But the faster the world moves, and the more integrated these systems get, the more automated manufacturing gets, maybe there are ways to sort of lock in these innovations in a way — so that, for example, you can register patents for all the innovation that your AI finds before the Euros gets around to them, or you just get quicker and quicker iteration cycles on building things and testing based on them.
And the faster the world starts moving as we integrate these systems and as we automate more and more things, the more you would expect these initial advantages sort of compound — because then you build the better thing, then you check how well it works if you build a better thing, then you feed the data back into the system and you try what the system makes of that, and then you feed in the innovation again and again.
So you just get to start the flywheel, and you get to start the exponential of this innovation just a few months earlier. And you’re always a few months earlier, so your product is always better by a few months. And I think if the world just moves sufficiently fast for that, that does compound into a pretty substantial advantage.
The $500 billion AI moonshot
Tom Reed: Why not just build your own frontier model? …
Anton Leicht: Building your own frontier model is just really extremely hard and extremely expensive. I mean, if they really wanted to go for it, I think there is a way to do it. I wrote about this recently. I did some educated guesses on how much that might cost. Realistically, for like a four-year project split between different middle powers that would work together to throw in the talent and the compute and the funding and the supply chain leverage and everything you would need to come up with an actually competitive way to build a frontier, frontier, frontier model, it was something like $500 billion over four years.
That’s a lot of money. I think if you go into a room right now, especially if you go into a room in a treasury in a middle power country and not in a digital ministry, and tell them you’d like to have $500 billion to build highly speculative enterprise, invest into American infrastructure, build huge data centres, and then try to train a model on them — and you don’t have a business case for where you’d sell that model, you don’t have a clear way to get the investment back, and this entire approach is highly vulnerable to both domestic political pressure and to US coercion to stop doing this — I think there is probably no treasury in the world right now that would tell you that they’d happily buy their share. So I just don’t quite know how you pay for it. I don’t quite know how you do it. There’s a long list of political challenges to it.
I will say if there was sufficient motivation to do it, and if there was sufficient funding to do it, $500 billion isn’t actually that much money for X of the G7 — Europe plus Canada plus the UK plus Australia plus Japan plus South Korea. If you could get the best version of the allied liberal democracy alliance setup, and you got all of them to pitch in, they could afford $500 billion for that kind of project. It’s not an absurd idea on paper.
And I think if you’re sufficiently bought into this specific idea that the frontier matters a lot, I think this is the only realistic play. Because you’re right: if the frontier matters as much as we’ve speculated it might, and if the Americans are as volatile as some people fear they might be — especially after the Fable incident — then I think any other conclusion really doesn’t work that much. They are all hedges, and they are all bets that at some point the Americans will be wrong, or at some point the commodification will kick in, or at some point the fast followers kind of stay around, or maybe the econ just works out in a way where you capture a lot of the revenue downstream.
This all might still happen. But any other sovereignty strategy that is not building a frontier model is basically a bet on one of these things. I think you can make enough hedged bets that you probably end up fine. But if you actually want to be certain that you don’t get screwed over by frontier model progress specifically, and by super-effective AGI systems taking over the world, then you do need your own AGI, and that’s the only way to do it.
Would the US crush allied AI?
Tom Reed: How do we keep the US from blowing this up? Why wouldn’t they just be extremely antagonised by this attempt at a project?
Anton Leicht: They would be. This is part of the reason why it’s very difficult to pull this off. I think if you were in a completely free and open and fair market, and you had abundant access to every US asset and no US retaliation to fear, this would not be as difficult.
But the thing is, A, you do risk just broader retaliation from the US on whatever the US might choose to do: tariffs on unrelated things, seizing intelligence cooperation on all kinds of issues, just hitting the security architecture of especially the Europeans hard. For example, in the context of Ukraine, in the context of intelligence sharing, there are a lot of broader things the US could do to just stop you from doing things that have very little to do with the project or with building AI itself, and they just have a lot more to do with whatever other issue the US has leveraged in.
So this horizontal escalation is the first thing to worry about. And I think there is no clever 300-IQ AI policy intervention that deals with that. What you need to deal with that is just, you just need to be generally more strategically powerful. You need generally more geopolitical leverage. This is an open problem for a lot of countries in the world. We’re not going to solve it in the specific AI policy conversation.
But then there are two specific AI-related threats that the US has against anyone trying to build a frontier model outside of the US: the first is you need US chips to build this frontier model, and the second is you probably need at least to start with using US coding agents to build this model.
I think much of the talent base of frontier labs today are already coding agents, and the best coding agents by far in the world are the US coding agents. If you actually poach a bunch of the US researchers, if you put them into a lab in Europe, if you give them all the compute they want and you tell them to start building a frontier model, but you don’t give them like Codex or Claude Code, they’re going to be pretty annoyed and they’re going to be a lot slower at building these models. And as the labs keep using their best version of their internal coding models, and the project still doesn’t have access to even basic versions of the coding agents, the gap just widens even more. And the more pronounced this gap is at the start of the project, the harder it is to ever get it off the ground.
So you do need access to some version of the coding agents, ideally, which is something that both the US and the US labs can restrict. So you have to find a way to maybe do a carveout for some coding agent use at the very start of your project, and then you bootstrap very quickly to your own model and you build your own coding agent scaffold around it. You just have that be your first step and you just hope that works well enough that you’re eventually not that far behind on coding agents. …
And the second thing, that’s even worse, are the chips you need. My very general guess is something like 3 million Blackwell-class chips for building this thing. That’s a lot of chips. There aren’t even that many chips just to buy at the moment. Nvidia’s supply is pretty limited, so you would actually need to offer sufficient money, and buy out contracts, and give sufficiently good incentives to Nvidia to actually want to sell you these chips. That’s not trivial, but that’s the easy part — because Nvidia is interested in having lots of different customers.
Tom Reed: They love selling chips.
Anton Leicht: Yeah, yeah, they love selling chips, they love money. But what they specifically love is selling chips to a bunch of different people. They don’t actually want to be reliant on just one or two buyers, so I think they’d generally be excited about selling a lot of chips to this project.
I think the US would be somewhat less excited about Nvidia selling chips to this project. … I think the basic motivation here is just that they just said that Fable and the cybersecurity capabilities is so scary that they don’t want it on the open market, because they’re scared it’s going to get jailbroken. So why exactly would they look at a European project that says, “We’re going to build our own Fable, and then we’re going to use it for whatever the hell we want, and you guys can’t control it anymore,” and then export the chips to them instead? I think that’s just very, very unrealistic in the long term.
So you do need some leverage to secure access to both the coding agents and the chips. I think there are ways to do that…
Policies to avoid mass AI-layoffs
Anton Leicht: Currently labour markets in a lot of middle powers aren’t very well set up to deal with disruption and quick iteration based on what AI systems do to the labour market. To the extent that there is a future labour market that still includes humans in important roles along bottlenecks and things that AI can’t do very well — and you have this idea of you have this very jagged frontier and the humans sort of fill out the spots where the AI isn’t particularly good, and the AI does the things that the AI is very good at — moving into the sort of dips in the jagged frontier of AI capabilities requires your labour market to be extremely flexible.
If your labour market isn’t very flexible, then people just stay in their jobs even if AI is better at their jobs. And then you have huge efficiency losses, people don’t move into the things that they would otherwise be good at quickly enough, your firms become uncompetitive because you’re not doing a particularly efficient distribution of the workforce across the task profiles that your human workers would actually be good at, and eventually you just risk being disrupted in a few ways:
Either fast competitors that are integrating AI agents entirely sort of vertically just start disrupting you, because you are just not using AI to its full effects. Then at some point, these quick and more agile competitors just start outcompeting you, and your entire business model crumbles and then you actually have to do mass layoffs, your company just goes under, things got really bad.
Or you just get a scenario where other countries in the world that are better at having a more flexible labour market, that are allocating their workers along the jagged frontier more efficiently, these firms just get more efficient, and then they outcompete you internationally, and then you have the same effect: your firm gets under pressure, maybe it gets outcompeted, maybe you get forced to just put up higher and higher trade barriers to protect that firm.
But either of those are pretty bad outcomes. So what you need to do is you need to make sure your labour market is flexible enough that the workers can move to the sections of the jagged frontier where humans will still be required, so you get the general economic efficiency gains and you don’t expose your workforce to the broader international disruptions from AI agents just being deployed everywhere.
And that’s a pretty counterintuitive finding, right? Because in a world where AI agents get really good and you get really worried about the workforce displacements, the first thing you might want to do is lock up the labour market and make sure people can’t get fired for now, and make sure that people stay in their place and stay in their positions and make sure that the disruption doesn’t get too bad too quickly — because that’s politically really risky, that’s socially really disruptive. Having mass layoffs and having people go to 6% unemployment for some time — 7%, 8%, 9%, 10% unemployment, like some of the labs are calling — that is extremely politically scary.
But what’s even more scary is: we lock this labour market in place, we stay at 5% unemployment for another year or two until our firms go under, then they do mass layoffs, then we end up in 15% unemployment. But it’s also the case that none of these workers can really reallocate into more useful jobs after that.
So I think you have to bite the bullet on the counterintuitive conclusion of making your labour market more flexible as the AI disruption comes in, otherwise you risk going to the second-best, very inefficient structure in the short term, and then get really disrupted in the long term.
Maybe one framing to think about this is that there is this sort of race between automation and augmentation, where you have this threat of this impending automation: the labs are building these agents, you might use these agents to build entirely vertically integrated firms that need very few humans. What you want to do is you want your firms that still employ a lot of humans to stay competitive with these vertically integrated, very agent-heavy firms. And you need to do that by moving towards the most efficient distribution of labour between human workers and AI agents. And if you don’t do that, you lose the race between the augmented firm and the automated firm: the automated firm wins and you have no use for the workers anymore.
So rather than try to win the augmentation/automation race, try to incentivise a flexible labour market that makes humans move into the parts of the jagged frontier that are actually human favoured, and then you can deal with some of the disruption.
AI Use Note: Main body text entirely human written. Claude (Opus 4.8) helped develop models of animal life histories in the appendix.
Cross-posted from Good Structures.
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