Digital Economy Dispatch #278 -- What Digitising Government Teaches Us About AI Sovereignty

In recent months, I’ve spent a lot of time puzzling over what makes digital strategy successful at scale. And there's a framing I keep returning to as I watch the UK government's AI strategy unfold. It comes from the early days of GDS, and it's deceptively simple: to make digital government work, you have to consolidate demand and diversify supply. Get that equation right, and transformation follows. Get it wrong, and you end up with expensive dependency.

As we race to become what the Chancellor calls "the fastest AI adopter in the G7", it's worth asking whether we've remembered that lesson, or whether we're about to repeat the mistakes of the pre-GDS era…but this time with far higher stakes.

The GDS Playbook: How the UK Got Digital (Mostly) Right

Cast your mind back to 2011. The UK government was running nearly 1,900 separate websites. Each department had its own suppliers, its own standards, and its own way of doing things. The result was what Dunleavy and colleagues memorably called “a world leader in ineffective IT schemes for government".

GDS changed that by pulling two levers simultaneously. On the demand side, it consolidated: one website (GOV.UK), common platforms (Notify, Pay), shared service standards, and, critically,  spend controls that gave GDS joint authority with the Treasury over all departmental IT spending. Departments couldn't go off and buy whatever they wanted from whoever they wanted. Demand was coordinated.

On the supply side, GDS did the opposite. The Digital Marketplace replaced the old G-Cloud CloudStore, breaking the grip of the large systems integrators — the Capitas, Sercos, and the IBM that literally had a seat on the DVLA board. SMEs gained access. The supplier base opened up. Supply was diversified.

Consolidate demand. Diversify supply. It worked. Within five years, the UK was first in the UN e-government rankings and had saved over £4 billion through structural reform. The model was copied from Australia to Argentina.

Fast Forward to AI: Same Logic, Opposite Outcome

Now look at where we are with AI. The supply picture has re-concentrated dramatically. A handful of US labs (led by OpenAI, Google, Anthropic, and Meta) dominate the foundation model landscape. The UK has no sovereign foundation model capability at anything approaching frontier scale. The new £500 million Sovereign AI Fund, due to launch in April, is welcome but modest by global standards.

Meanwhile, demand remains fragmented. Every department is experimenting independently. The Ministry of Justice has its OpenAI partnership and "Humphrey" AI assistant. DSIT has its Incubator for AI. Individual tools like Minute and Extract are being scaled to local authorities. But there's no equivalent of the GDS spend controls for AI procurement, no single front door, no coordinated demand signal that gives the UK leverage over its suppliers.

In other words, we've inverted the GDS formula. Supply is concentrated. Demand is fragmented. That's the worst possible combination if you care about sovereignty, value for money, or strategic autonomy.

The Sovereignty Question: What Makes the UK Different?

This is where the supply-and-demand lens becomes genuinely useful for thinking about how the UK positions itself distinctly from the US, EU, and China.

Each major bloc has made a different bet. The US is betting on market dominance by letting American companies build the models and export them globally. China is betting on state-directed indigenous capability. The EU is betting on regulation as its sovereignty lever, using the AI Act to shape the terms on which AI operates within its borders.

What's the UK's bet? Right now, it looks uncomfortably like: befriend the US labs and hope for the best. The OpenAI strategic partnership signed in July 2025, the Stargate UK infrastructure plans, the MoJ deal for UK data residency and other announcements are real steps forward. But they're essentially supply-side relationships with a single dominant provider. Technology Secretary Liz Kendall herself framed the new quantum investment as learning from Britain's failure to retain AI companies like DeepMind. The government knows the dependency risk is real.

The GDS lesson suggests a better path: use demand-side consolidation as the sovereignty lever. The UK public sector is a massive buyer. If it coordinates that buying power through common procurement standards, shared evaluation frameworks, multi-vendor strategies, and interoperability requirements, it can shape the market without needing to build its own foundation models. That's not naivety about the power of US tech companies. It's the same hard-nosed logic that GDS applied to Capita and Serco a decade ago: you don't need to own the supply chain if you're smart enough about how you buy from it.

What Would This Actually Look Like?

Let me be concrete. A "GDS approach to AI" wouldn't try to build a British foundation model. It would mandate that no department becomes locked to a single model provider. Hence, requiring multi-vendor architectures so that switching between foundation models doesn't mean rebuilding entire workflows. It would establish shared evaluation standards so that AI procurement is based on tested performance against defined public sector use cases, not vendor marketing. It would require interoperability at the data and integration layer, so that the UK retains genuine optionality even as individual tools deepen. And it would enforce spending controls with teeth. The kind that GDS used to stop services from going live if they didn't meet the standard. Without that institutional authority, coordination is just aspiration.

Why This Isn't Simply "GDS Again"

But we should be honest about the limits of the analogy. Foundation models aren't like web hosting or cloud infrastructure. They're opaque in ways that earlier technologies were not. They degrade unpredictably. They require continuous evaluation because their behaviour changes with each update. And the switching costs are real and growing This is not because of contractual lock-in, but because workflows, prompts, and institutional knowledge quietly shape themselves around a specific model's strengths and weaknesses. That's a different kind of dependency from the one GDS tackled. But it makes the case for consolidated demand more urgent, not less. If switching costs accumulate silently at the departmental level, then by the time anyone notices the lock-in, it's already too late. The whole point of demand-side coordination is to maintain optionality before it's needed and not scramble for it after it's gone.

The Leadership Challenge

For digital leaders, the practical question is this: who is doing for AI what GDS did for digital? Who holds the spending controls? Who sets the standards? Who ensures that the UK's AI procurement builds capability rather than dependency?

The AI Opportunities Action Plan one-year progress report talks about an "AI Commercial Strategy" and "AI Accelerator Tenders”. These are steps in the right direction. But they're a long way from the institutional clout that GDS wielded when it could stop a service going live if it didn't meet the standard.

Until the UK consolidates its AI demand with the same discipline it once applied to digital, it will remain a price-taker in a market shaped by others. And in a world where AI infrastructure is as geopolitically loaded as semiconductor supply chains, that's not just an efficiency problem. It's a sovereignty problem.

This is one of the central arguments in my forthcoming book Making AI Work for Britain, which will be published at the end of April. The failures we see in AI implementation aren't technological; they're institutional. And the lessons of digitising government over the past fifteen years tell us exactly what needs to change. If we’re willing to learn those lessons.