Companies are rushing to deploy AI as fast as possible. Large, well-established businesses fear being left behind by competitors or replaced by new, agile, AI-first startups. This is understandable — it's what happened in the last shift to e-commerce, mobile, and social media. But there's another cost — besides the missed opportunity cost — that they should be mindful of: the price of AI itself.
It's well known that Silicon Valley's playbook is to launch products at low, subsidised costs to gain market dominance before increasing prices. The current pricing of AI subscriptions is extremely affordable, especially for the latest models, which are very capable. However, the cost per million tokens looks set to increase in the coming years. Despite the frankly ridiculous levels of investment in AI infrastructure in recent years, many analysts1 are warning that it might not be enough to meet rapidly growing demand. Supply will be constrained by chip production capacity and energy availability, both of which take time to ramp up. The recent troubles in the Middle East could delay this even further. And investors will eventually demand a return on their investments, putting pressure on AI suppliers to raise revenue. We're already seeing Anthropic struggling to meet demand and raising prices.
Every organisation figuring out its AI strategy should pay attention to the risk of rapidly increasing costs. As pointed out by Ben Thompson of Stratechery, AI is fundamentally different from SaaS because the latter has a marginal cost of zero, which resulted in predictable costs and declining per-user prices as companies scaled. AI is the opposite: it costs more the more you use it. Output quality is also correlated with the price users are willing to pay — better models, running on better hardware and using more tokens, produce better results. Organisations adopting AI will not all be on equal footing, as larger and better-resourced players will be able to afford superior models or use them more extensively, giving them a competitive advantage. This could result in a bidding war among cash-rich competitors, pushing prices even higher and leaving everyone else behind.
Furthermore, it's unclear how quickly they can expect cost savings, productivity gains, or revenue growth from their AI investments. Recent studies have shown that few have been able to implement AI in ways that produce meaningful results. It's becoming clear that doing this well requires more than adding AI chatbots to existing software (hello, Copilot!). It requires a complete rethink of workflows, business processes, and certain jobs. Substantial backend work will be necessary to build the data pipelines, context-awareness, and security guardrails for LLMs to complete tasks end to end, correctly. This will take years and cost money. Until that transformation is complete, AI gains will remain limited and organisations will not be able to reduce their workforce — with the exception of specific jobs already ripe for replacement, like certain developer and customer-care roles. This period will therefore require heavy investment in AI infrastructure and workforce education while maintaining the same operational costs. AI could result in a significant increase in operational expenses for years before any ROI is gained.
Despite all of these risks, it's clear that the potential of AI to reduce costs and increase productivity is too promising to ignore, and refusing to embrace technological change is most likely a death sentence over time. But incumbents should beware of complete AI dependency, especially given the pricing uncertainty ahead. It is a risk to assume that the cost of AI — especially for the best-performing models — will trend downward. Once that transformation is complete, there will be no going back, and bargaining power will be squarely in the hands of suppliers. It's also unclear how much savings on salaries and benefits can offset AI costs — many new types of jobs will likely be created, and jobs that cannot be automated will become more expensive. SaaS has shown that system lock-in is real. It can be extremely painful and costly to change software providers. This could become much worse when an entire organisation depends on one deeply integrated AI layer.
Obviously, it's possible all of this will be fine, and that markets and societies will gracefully adapt, as they historically tend to. But incumbents should plan for contingencies to ensure they can reduce their dependency on AI suppliers — the same way they manage supply chain risks today. Ultimately, it's crucial that the real value engine of a company remains its IP and its people, not the AI layer it happens to be using.