The economics of AI are yet to be determined
AI feels cheap. It feels cheap because investor capital is subsidising it. Four structural forces are converging that reframe the trajectory as an open question with significant downside.
The prevailing narrative is that AI is cheap, getting cheaper, and will remain so. That narrative is wrong. Or at best, dangerously premature.
Four structural forces are converging that challenge the assumption: rising energy costs for data centres, persistent compute scarcity, quietly increasing model pricing, and a vast unresolved liability around the content that trained these models.
None of these forces are hidden. But few people are connecting them. When you stack them together, they reframe the economics of AI not as a settled trajectory, but as an open question with significant downside risk for businesses building on today’s pricing.
The subsidy layer
Right now, AI feels like a bargain. For a few hundred dollars a month, you can access models that perform work equivalent to tens of thousands in human labour. The maths seems obvious.
But this comparison only holds if you ignore the subsidy structure underneath it. Anthropic, OpenAI, and Google are burning through investor capital to acquire users and market share, absorbing costs that aren’t reflected in the price you pay today. Those costs are substantial and growing:
- Power. Data centres consume staggering amounts of electricity, and demand is accelerating. In the US alone, data centre power consumption is projected to more than double by 2030. Energy is not getting cheaper.
- Hardware. Every major player is competing for the same NVIDIA chips. Training runs are only part of the picture. The ongoing compute required to serve millions of concurrent users at inference is an escalating operational cost.
- Development velocity. Each model generation demands more resources to build, fine-tune, and maintain. Claude 4.6, Anthropic’s latest flagship, costs significantly more to run at scale than its predecessor, particularly with extended context windows.
When subsidies taper (and they will), the true cost of operating these models surfaces. The question isn’t whether prices adjust. It’s by how much, and how fast.
The content liability
There’s a second structural cost that may prove even more consequential.
Today’s models were trained on vast quantities of content created by writers, researchers, artists, and journalists, without licensing agreements or compensation. The models reference, synthesise, and reproduce elements of this work. The creators behind it have received nothing. You could reasonably describe this as extracted value at scale.
I’ve written about this value exchange problem elsewhere, so I won’t relitigate the argument here. What matters for the economics is this: the bill comes due. Whether through regulation (the EU AI Act already sets a direction), litigation (the New York Times v. OpenAI being the most visible case), or commercial negotiation, model providers will need to pay for access to the content that makes their products useful.
And as models require increasingly current information to remain relevant, this isn’t a one-off settlement. It’s a recurring liability that compounds.
The vendor control problem
There’s a third dimension that most businesses are overlooking entirely. When you replace a knowledge worker with an AI subscription, you’re not just changing a cost line. You’re shifting where control sits.
| Dimension | Employee model | AI vendor model |
|---|---|---|
| Cost predictability | Salary, fixed and planable | Subscription, subject to change without notice |
| Capability stability | Consistent within role | Model versions deprecated, capabilities shift between releases |
| Operational control | Works within your systems, on your terms | Platform-dependent; terms of service govern access |
| Strategic dependency | Labour market risk, manageable | Single-vendor risk, concentrated |
An employee operates within your business. An AI model is a vendor platform. The provider can increase costs overnight, retire a model you’ve built workflows around, or change how the product functions. Your business absorbs the impact.
This isn’t theoretical. We’ve already seen models deprecated mid-cycle, pricing structures restructured, and capabilities vary materially between versions. The more deeply you integrate AI into core operations, the more exposed you become to decisions made in someone else’s boardroom.
The efficiency counter-argument
To be fair, there are forces working in the other direction. Models will become more efficient to train and run. Standard queries will require less compute over time. Hardware will improve. Competition between providers will exert some pricing pressure.
But efficiency gains don’t eliminate structural costs. They moderate them. Energy consumption, hardware scarcity, content licensing, and continuous development aren’t going away. They’re baked into the economics of the technology itself. Efficiency is a slope; the structural costs are a floor.
The implication
The economics of AI are far from settled. What we’re experiencing today is an artificially cheap moment, subsidised by venture capital and built on unresolved liabilities.
AI will deliver value. That’s not the question. The question is whether businesses are pricing the risk correctly. Three things should be on every leadership team’s radar:
- What is your true cost exposure? If your operating model assumes current AI pricing holds, what happens when it doesn’t? Model the scenario where costs double or triple within 18 months.
- Where does vendor dependency sit? Map the workflows, decisions, and outputs that now depend on a single model provider. Understand the concentration risk.
- How defensible is your data position? As content licensing costs flow through to model pricing, businesses with proprietary, structured data will have optionality that those relying entirely on third-party models will not.
The structural forces are clear. The timeline isn’t. But building strategy on the assumption that today’s pricing is permanent is a bet, not a plan.