Digital Economy Dispatch #261 -- AI Economics 101

As 2025 draws to a close, busy leaders have a lot on their plates. In a volatile world, the recent KPMG Global CEO Outlook report concluded that many see AI and talent investment as the keys to their resilience and growth. Yet very few are being given a clear view of what that AI transformation looks like in hard economic terms. Behind the headlines about “AI copilots” and “agentic AI” sits a much bigger story: a small group of tech giants is making multi‑trillion dollar bets on AI infrastructure and models, while most enterprises are still struggling to turn AI pilots into measurable productivity.​

This gap matters. If you are responsible for strategy, you now must understand not just how to use AI in your business, but how AI is reshaping the economics of your industry and the platforms you depend on. That means getting comfortable with both “the technology of business” — how AI changes your value proposition and operating model — and “the business of technology” — how your AI providers are funding their ambitions and what that implies for your costs, risks, and options over the next decade.​

Two Stories: Macro and Micro

There are really two intertwined issues here. At the macro level, a handful of Big Tech firms are pouring staggering sums into AI infrastructure and data centres, pushing up stock indices and raising questions about whether any of this will ever pay back. While at the same time, at the micro level, enterprises are quietly re‑engineering workflows, roles, and cost structures to extract real value from AI, but at a much slower, messier pace than market narratives suggest.​

Leaders who only track the macro story risk getting swept up in hype or panic about bubbles, frontier models, and even “superintelligence”. Leaders who only focus on the micro story risk missing how dependent their AI ambitions are on the evolving business models and pricing power of the Big Tech platforms that provide the models, chips, and cloud capacity.​

The Macro: Can Big Tech’s AI bet ever pay?

The capital intensity of this AI wave is extraordinary. Estimates put announced AI compute commitments and data‑centre build‑outs on the order of many tens to a hundred gigawatts worldwide, translating into trillions of dollars of potential capex when you factor in chips, facilities, and energy. Some industry leaders now openly question whether today’s infrastructure and energy costs can sustain this level of spending and still generate acceptable returns, without either major price rises or significant breakthroughs in efficiency.​

On top of the hardware build‑out, staggering sums are being spent to train the latest generation of frontier models. Analyses of recent large‑scale models suggest that individual training runs already cost tens or even hundreds of millions of dollars, with projections that the most ambitious runs could exceed a billion dollars later this decade if current scaling trends continue. As model sizes, data requirements, and safety evaluations grow, the economics of training are becoming a powerful barrier to entry that only a handful of well‑capitalised firms can realistically cross.​

Then there is the cost of serving these models to hundreds of millions of users. Popular tools such as ChatGPT and its peers support vast bases of free users, with only a minority paying for premium tiers. That means the AI leaders are effectively running a global, always‑on inference service where infrastructure and energy costs scale with usage faster than subscription revenues, relying heavily on a freemium model and investor patience to bridge the gap.​

At the same time, much of the GenAI ecosystem still lacks clear, proven profit models: usage‑based pricing is still evolving, margins are squeezed by compute and licensing costs, and many providers depend more on expectations of future dominance than on sustainable cash flows. That is why economists increasingly describe GenAI as an “infrastructure‑first” experiment, whose financial logic only works if adoption and productivity gains scale far faster than current evidence suggests.​

The Micro: Why AI is slow to show up in productivity

On the ground, the economics look very different. Careful macroeconomic work, such as MIT’s “new look at the economics of AI”, suggests that only a modest share of tasks can be profitably automated with current AI over the next decade, leading to a “nontrivial but modest” overall impact on GDP compared with the more agressive forecasts. Researchers emphasise adjustment costs: organisations must redesign processes, restructure roles, clean up data, and build new controls, all of which delay and dilute apparent returns.​

This helps explain the “AI paradox” many leaders feel. Individually, teams report impressive point gains in efficiency and speed; collectively, the organisation’s productivity statistics barely move. Economic analyses also show that AI benefits flow disproportionately to firms with strong digital infrastructure and high‑performing tech organisations, widening the gap between digital leaders and laggards.​

The Technology of Business: Changing your value proposition

For individual enterprises, the first economic question for AI is not “How much can we save?” but “How does AI change what customers value and what we can uniquely offer?”. AI shifts the production function: it does not just automate tasks, it changes the mix of human judgment, data, and computation that creates value in a product or service.​

This shows up in several tangible ways:

• Personalisation and prediction enable new pricing, bundling, and risk‑sharing models, particularly in data‑rich industries such as finance, retail, and logistics.​

• AI‑enabled workflows redistribute work between frontline staff, specialists, and machines, forcing a rethink of where you want distinctively human capability and where “good enough” automation is sufficient.​

• The most powerful use cases often require cross‑functional, end‑to‑end transformation, not bolt‑on tools, which means the economic impacts results from user journeys and customer outcomes rather than in individual departmental budgets.​

Without a clear line of sight from AI use to value proposition and business models, organisations fall into “AI pilot purgatory”: scattered experiments that cost real money but never scale enough to change the economics of the business.​ With dire financial consequences.

The Business of Technology: Who pays for all this?

The second economic question is about dependence: how are your AI providers funding their own ambitions, and what does that mean for you over a five‑ to ten‑year horizon? Big Tech’s AI investments are currently subsidised by high‑margin legacy businesses, generous capital markets, and aggressive expectations of future demand for AI services and agents.​

For enterprise customers, this creates several strategic risks:

• Pricing power: as usage grows and consolidated providers seek returns on multi‑trillion‑dollar infrastructure bets, per‑unit AI costs may rise faster than many business cases assume, especially for intensive use of frontier models.​

• Lock‑in economics: proprietary models, data‑gravity, and specialised tooling can make switching platforms increasingly expensive, turning today’s discounts and credits into tomorrow’s margin squeeze.​

• Systemic risk: if AI‑driven valuations outrun realised profits for too long, corrections in tech markets can rapidly change vendors’ investment horizons, partnership priorities, and risk appetite.​

Analyses of AI economics point to the benefits for data‑rich incumbents that can afford the infrastructure, talent, and governance load needed to scale AI. Meanwhile, smaller firms and public sector organisations face much more fragile economics. Leaders ignoring this structural imbalance risk betting their transformation on a supply side they do not fully understand.​

Lessons for Leaders: AI Economics 101

For today’s leaders, “the economics of AI” needs to become part of the regular leadership conversation, not a once‑a‑year strategy away‑day topic. That means building fluency in three areas:​

• Unit economics of AI in your workflows. Know, at a granular level, where AI actually changes cost and revenue curves in your organisation, and where it merely shifts cost from labour to infrastructure.​

• Platform and ecosystem economics. Understand how your core AI providers make money, where their incentives align with yours, and where you are implicitly underwriting their long‑term bets.​

• Adoption and adjustment dynamics. Appreciate that the main bottlenecks are organisational and institutional, not technical, and invest accordingly in skills, governance, and process redesign.​

The uncomfortable reality is that the numbers probably will not add up for everyone. Some Big Tech investments will never earn back their cost of capital, and some enterprises will not translate AI enthusiasm into sustainable performance gains. The leaders who thrive will be those who treat AI not as a magical productivity engine, but as a profound shift in both “the technology of business” and “the business of technology” – and who are willing to do the economic homework that shift demands.​