Enterprise AI Spend Gets A Reality Check

What happened

The cost of using AI is falling rapidly, with the price of processing a million tokens dropping by roughly 10X every year. 25 for the same volume. On the surface, this looks like a simple equation: cheaper intelligence should mean more AI. But inside enterprises, the conversation is moving in a different direction.

As organisations gain control over AI usage, they are changing how they budget for it. The question is no longer simply how much to spend on AI, but which features deserve access to that budget. While many enterprises are concerned about rising token consumption, their financial controls have not kept pace. This is because fewer than two in three organisations currently actively monitor usage with clear budget limits.

Therefore, there is a growing enterprise interest in Chinese and other open-weight models. While open-weight models are not replacing premium systems, companies are using them selectively for high-volume, less sensitive workloads. The approach is simple: use cheaper models for routine tasks and reserve expensive frontier models for complex reasoning or sensitive work. How companies budget AI today will determine where it appears on the balance sheet tomorrow.

Unlike traditional software, which usually comes with a predictable per-seat subscription, AI spending is consumption-based and spread across product, research, support and cloud infrastructure. For most companies, token costs will initially remain part of operating expenses, much like cloud spending.

But AI’s wider cost base, including model access, data preparation, engineering, evaluation, monitoring, security and human review, may push organisations to track it as a separate internal category, even if accounting systems do not. As companies build more software in-house, engineering costs may move towards capitalised development, while GPUs and long-term cloud commitments could be treated as capital expenditure.

The immediate shift, however, will happen within operating budgets, where teams will have to justify AI usage alongside headcount, cloud infrastructure and software licences. AI may not appear as a single line on the balance sheet, but companies will increasingly need to treat it as one internally. For Indian enterprises, the real benchmark is whether every token can be linked to a rupee of value.

Why it matters

As the cost of inference falls, the bigger question is no longer how much AI companies can afford to use, but where they should use it, and whether the returns justify the spend. Tech leaders are increasingly looking beyond the price of a token to its productivity gains, revenue impact and business outcome.

They are putting budgets around AI features, tracking which teams are consuming the most tokens, deciding which tasks deserve expensive frontier models and which can be handled by cheaper alternatives. While the AI cost curve is collapsing, the bar for proving value is rising. So, as intelligence gets cheaper, what makes an AI token worth spending? That’s the question we’re digging into in today’s edition of The AI Shift.

As humans, the first instinct is always to treat cheaper tech as a licence to use more of it, and AI is no exception. However, the leaders we spoke with are approaching the shift differently: the goal is not to use fewer tokens at any cost, but to prevent tokens from being spent on work that does not matter.

That value-first approach is now translating into basic financial controls inside AI teams. At Eightfold AI, an AI talent platform, no feature can use an AI model until it is approved. Thiyagaraj T, director of engineering at the company, said a team that wants to build something with AI must explain what it is making, which model it will use and what it will cost per month.

Platform owners can then challenge the estimate before approval. In one case, a projected cost was reduced from $2,000 to $400, an 80% cut, before the experiment began. What we understood from our conversations with experts is that organisations are now considering AI unit economics before production.

At runtime, each team’s usage is being capped with its own monthly budget, and every call is logged and compared with the original estimate. AI consumption is therefore being treated less like an open-ended experiment and more like a budget request that has to earn its place.

According to Anuraag Kochhar, CTO of AI services company ShepHertz, model efficiency and falling token prices have already cut software development costs by up to 30% on some projects. But the bigger shift is that organisations are no longer just looking for cheaper ways to do the same work. They are rethinking how AI projects are approved, measured and tied to business outcomes.

What to watch

Those that build this traceability will manage AI as an investment; the rest will continue treating it as a bill. Researchers at Stanford and the Arc Institute used genome language models to design complete bacteriophage genomes, the first time AI has generated fully functional viruses end-to-end.

Of hundreds of AI-written designs that were chemically synthesised and tested in the lab, 16 successfully infected and killed a group of bacteria, with several outperforming the natural strains they were modelled on in both replication speed and killing efficiency.