Digital Economy Dispatch #279 -- The Rise of AI Factories: Will They Succeed Where Software Factories Failed?

The factory metaphor has haunted the technology industry for decades. Since the late 1960s, the idea of a "software factory", based on organising software development to mimic manufacturing with standardised components, repeatable processes, and predictable output, has surfaced, failed, and resurfaced with depressing regularity.

Michael Cusumano's landmark 1989 study of Japanese and Western approaches documented the appeal and the limits. Toshiba, the Eureka Software Factory programme in Europe, and countless consulting-led initiatives all tried to make it work. The core promise was always the same: maximise reuse, minimise craft, treat software like an assembly line product. And the core outcome was almost always the same: it didn't work. Software development turned out to be a fundamentally creative, exploratory process that resisted industrial standardisation at scale.

So when NVIDIA CEO Jensen Huang stood on stage and declared that every company would soon have two factories — one to build what they sell, and one to build the AI — you might have expected scepticism. Instead, the concept has taken off. And it's worth understanding why.

What Is an AI Factory?

The term "AI Factory" means different things to different players, which is itself a warning sign. But the common thread is this: a purpose-built infrastructure environment of compute, networking, storage, and software that is designed specifically for the end-to-end AI lifecycle: data ingestion, model training, fine-tuning, and inference at scale.

Jensen Huang's framing is characteristically blunt. At COMPUTEX 2025, he put it this way: data centres of the past stored data and ran pre-written software. AI factories generate intelligence. You apply energy, and the output is tokens, the fundamental units of AI value. In NVIDIA's telling, data centres are not being upgraded. They are being reconceived.

AWS launched its own "AI Factories" product at re:Invent in December 2025. Their version is more specific: a fully managed, on-premises AI infrastructure offering where the customer provides data centre space and power, and AWS installs and operates the hardware, networking, and AI services, including NVIDIA GPUs, AWS Trainium chips, Amazon Bedrock, and SageMaker. It operates like a private AWS Region inside your building. The big idea behind it is sovereignty and speed: keep your data on-premises, meet regulatory requirements, but access cloud-grade AI capabilities without years of procurement and build-out.

AWS is not alone. Microsoft has deployed AI factories in its global data centres for OpenAI workloads. Oracle has added NVIDIA processors to its Cloud@Customer offering. Dell, HPE, and NVIDIA itself all have competing "AI factory" products. It is becoming a category.

What the Data Says

A major new survey from Deloitte adds empirical weight to the trend. Their inaugural AI infrastructure survey, published in March 2026, surveyed 515 US enterprise leaders across five industries. The headline findings are striking.

Today, 64% of respondents have already started limited or at-scale AI factory deployments. By 2028, that figure is expected to reach 88%, with 73% expecting to achieve full scale. AI at the edge shows a similar trajectory, with scaled deployment expected to double from 36% to 72% in three years.

The economics are equally dramatic. Some 86% of respondents expect AI infrastructure budgets to increase over the next three years, with average budgets expected to more than triple. Large enterprises project even steeper multiples, approaching four times current spend. Token consumption is surging in parallel: 61% of respondents expect to consume more than 10 billion tokens per month by 2028, roughly doubling in two years. The fastest-growing segment is organisations consuming over 100 billion tokens per month, representing a tripling from 2026 to 2028.

There are also some revealing tensions in the data. Nearly all respondents (96%) rate their current AI workloads as medium or high complexity, yet 97% say they are confident or very confident they can scale those workloads within three years. This shows a striking gap between acknowledged difficulty and professed confidence. On model strategy, closed proprietary models remain the most widely used, but with open-source models closing the performance gap and agentic SaaS platforms embedding AI agents directly into enterprise workflows, there is no consensus on which model mix will dominate by 2028. And when asked what AI factories will actually deliver, the top objectives were telling: 71% cited innovation, 64% risk management, and 59% token cost optimisation.

Meanwhile, decision-making remains firmly in the hands of IT leadership, with 51% of respondents saying that CIOs or CTOs own AI infrastructure decisions, and the rest fragmented across governance, infrastructure, functional, and specialist AI teams. That concentration may be pragmatic for now, but as the Deloitte authors note, it puts technology leaders in the position of having to help the rest of the C-suite understand AI consumption patterns and their cost implications. I’m not sure that this is a conversation many organisations have yet started.

But there are signals of caution too. Half of the respondents said economic uncertainty could limit their AI factory investment plans. Nearly half cited organisational challenges and regulatory pressures. And there is a telling skills gap: 81% of respondents believe their IT teams have the technical and financial skills to scale AI infrastructure, but only 65% say the same about business and product teams — a 16-point gap that matters when you're trying to turn infrastructure into outcomes.

Why This Isn't Just "Software Factories" Again

The obvious question is whether we are watching history repeat itself. The parallels are real: a manufacturing metaphor applied to a knowledge-intensive process, vendor-led hype, massive capital commitments, and the assumption that standardisation will tame complexity.

But there are important differences. Software factories failed because they tried to industrialise a creative process. The "product" of software development is design, logic, and human judgment. These are things that resist assembly-line treatment. AI factories are producing something much closer to a commodity: tokens. As NVIDIA's Anne Hecht puts it, an AI factory takes in data and energy and generates tokens as measurable outputs. That is closer to manufacturing than writing code ever was.

The second difference is the infrastructure itself. Cloud computing has matured. GPU supply chains exist. Managed services from AWS, Microsoft, and others mean organisations don't have to build from scratch. The "factory" can be procured as a service, which changes the risk profile fundamentally.

But risks remain. Some of them very familiar. The Deloitte survey notes that high-bandwidth memory costs are rising, wafer costs are expected to increase by 20%, and procurement timelines are lengthening as demand outpaces supply. Power generation and grid capacity are becoming strategic constraints. And as always, the real bottleneck is people: the skills to operate AI infrastructure and, crucially, to translate infrastructure investment into business value.

What This Means for Britain

This brings me to something I've been writing about at length, and which forms the central argument of my new book, Making AI Work for Britain, published on 28th April by London Publishing Partnership.

The AI factory concept makes the infrastructure question unavoidable. If every major enterprise, and every major nation, needs AI factory capacity, then the UK's choices about where that capacity sits, who operates it, who controls the data, and how the benefits are distributed become existential questions for our digital economy.

The Deloitte survey is US-focused, and that is itself telling. The hyperscalers building AI factory products are overwhelmingly American. The GPU supply chain is dominated by NVIDIA and, increasingly, by proprietary chips from AWS, Google, and Microsoft. The UK risks becoming a consumer of other nations' AI infrastructure rather than a producer of its own.

This is precisely why the book argues for a strategy of consolidating demand and diversifying supply. We need coordinated public sector demand through a statutory AI Coordination Authority to create the market signals that attract investment in sovereign AI infrastructure. We need data infrastructure that keeps British data assets under British governance. And we need to build the workforce that can operate, govern, and extract value from AI at scale, closing exactly the kind of skills gap the Deloitte survey highlights.

The AI factory is not a metaphor to worry about. It is a real infrastructure category, attracting real capital, at an extraordinary speed. The question for Britain is what this means for UK sovereignty and whether the UK will build its own, buy from others, or find itself locked out of the value chain entirely. You’ll have to read the new book to learn more of my thoughts on that one!