AI is cheap until your business depends on it

You are building your business on borrowed time.

Not because AI does not work. But because the price you pay today has little to do with the real cost of running it.

Right now, AI pricing is a venture-capital illusion. Companies are burning investor money to get you hooked - but you are not seeing the real costs yet.

One large company just learned this the expensive way. They went from threatening employees with termination if they did not use AI enough to desperate access restrictions when they realized they had accidentally spent 500 million dollars in a single month on it.

That is not a typo. Half a billion dollars.

The Productivity Trap

Here is the uncomfortable truth: coding faster does not automatically generate more revenue.

Writing faster does not mean better writing. Automating tasks does not guarantee profit. You can be twice as productive and still lose money if the tool costs more than the value it creates.

80% of companies that tested AI reported layoffs. They cut jobs because automation was supposed to save money. But the data shows that the companies with high returns were not the same ones that cut staff. Layoff rates were almost the same whether AI actually produced returns or not.

The companies with real gains? The ones that used AI as an amplifier for people and made employees more productive instead of trying to replace them completely.

When the Pricing Model Changed

Late 2025 to early 2026 marked a turning point. Companies like Anthropic began moving enterprise customers to token-based pricing starting in November 2025 - then rolled out the new model broadly in February 2026. Suddenly, AI budgets exploded for large companies.

The costs became higher than the employees they had fired to afford AI in the first place.

Think about that. You lay people off to save money for AI. Then AI pricing changes and now costs more than those salaries. You cannot bring those people back because you already restructured - and you cannot afford AI at the new price. You are stuck.

That is the problem with variable interest rates. You committed to something where someone else controls the price. Most companies think they are simply using a tool - and do not realize they have taken out a loan whose terms can change overnight.

The Skill Erosion Problem

This is what happens when you use AI to code for six months.

You forget how to write certain lines from scratch. You can still read them, but you can no longer produce them yourself. You outsource your thinking to AI and get lazy because there is an easier alternative.

Eventually, you become only as good as the AI. You stop thinking about edge cases and stop accounting for certain scenarios. You become the AI’s project manager - but project managers cannot do the work themselves.

What breaks first when you run into a problem the AI cannot solve?

You do.

A survey of workers warns that AI could make the workplace feel less human and erode human skills. The erosion is not theoretical - it is psychological, operational, and accelerating.

The World Economic Forum expects 39% of core skills to change by 2030. But here is the catch: you need those foundational skills to stay in the loop when AI fails or becomes unaffordable.

The Human-in-the-Loop Paradox

AI cannot fully replace expertise. But daily use erodes the expertise you need to supervise it effectively.

Research shows that human judgment breaks down when participants receive incorrect algorithmic support, especially when they receive it before forming their own assessment. The anchoring effect kicks in, you rely too heavily on the first piece of information and adjust your final judgment around that starting point.

When people follow AI recommendations without evaluating independently, errors pile up - and most people do not even notice it happening.

IBM learned this the hard way. The company automated parts of HR and back-office work with AI and cut jobs there. At the same time, it became clear that AI quickly reaches its limits with tasks that require empathy, nuance, or personal judgment. IBM later invested more heavily again in areas that leadership described as “beyond the reach of AI.”

The Dependency You Do Not See

A new IBM study shows that many companies talk about AI sovereignty, but do not adequately understand their dependencies. Only 10% of companies in Europe have a good understanding of how they are entangled across vendors, models, and infrastructure. In Germany, it is 13%.

81% of respondents in Europe say that an outage of their primary AI provider for seven days would have serious or critical consequences. On average, companies reported seven AI-related operational disruptions over the last two years.

You only know what you depend on when it breaks.

The Delivery App Playbook

This is not new. It is the same venture-capital model that gave us Uber, Amazon, and delivery platforms.

Subsidize something until it is ridiculously cheap. Get consumers addicted or dependent on your product, reach saturation, then raise the price. Most people will not be able to get away from it anymore.

Delivery platforms have fundamentally changed restaurant delivery in many cities. Often, access to customers now runs through platforms instead of direct orders. Governments stopped Uber from displacing taxis in some places - but elsewhere, the local taxi market was severely weakened.

AI could do the same thing to certain skills. The difference? With Uber, you can switch back to taxis. With AI - if you have lost the skills - what do you switch back to?

What Actually Happens Next

AI has jumped to number 2 among global business risks for 2026 - up from number 10 in 2025. Adoption is moving faster than companies can handle.

For smaller companies, the pressure will be intense. They have fewer resources and less room for error.

According to MIT research, 95% of generative AI pilots in companies fail to deliver the expected results. The problem? Companies do not know when and how to use AI meaningfully.

AI systems often fail quietly. “It is silent failure at scale,” warns a vice president of AI operations. Small errors accumulate. Because nothing spectacular breaks, nobody notices at first.

The Accounting Example

Take something concrete: AI accounting. The riskiest use cases involve estimated taxes, worker classification, and sales-tax jurisdiction. Sales-tax rules vary widely by state and change over time. A hallucinated or outdated rule creates compliance problems that an AI system will not recognize.

But your tax advisor will.

AI is an incredible engine. But every engine needs a pilot. The most successful small and medium-sized businesses in 2026 will be the ones that use AI for efficiency while keeping a professional advisor for validation.

What You Should Do Today

Make your processes AI-independent.

If you are on a platform that depends completely on one specific model or does not allow you to connect to open-source alternatives, be careful. Language models will probably become commodities, and the improvements will become marginal. The cost difference will not justify the improvement you get from using one AI over another.

That is why providers are building applications and workflows around their models. They want tools that give them a moat so people stay. But if you have built your processes specifically around how ChatGPT or Claude works, what happens when you need to switch?

Keep practicing the foundational skills even while using AI. It is a muscle. You can rebuild it if you are exposed to the right circumstances - but you have to maintain it before it completely atrophies.

Identify which skills your team needs to actively maintain. Build workflows that can survive the pricing correction - because it is coming.

The One Thing People Are Blind To

It is the cost.

You are used to the productivity gains. Imagine you have a laptop that gives you serious productivity gains. Your laptop gets really slow, so you need to upgrade. But then you find out a new laptop is ten times more expensive than the last one you bought.

What can you do? You either pay up or accept the productivity hit.

That is where this is going. The strategy is clear: get people hooked on AI, make their business depend on it, then raise the prices. Once prices go up - what are you going to do?

You will spend more money. You will absorb the cost.

Maybe the bubble bursts eventually. What happens then? Who knows. But it should be a warning sign - be prepared in case something happens. In a year or two. Maybe sooner.

The subsidies will not last forever. The question is whether your business survives when they end.

Which skills are you actively maintaining right now that AI cannot replace? And what happens to your workflows if the price doubles next month?