The AI bill is coming: get your business ready

You pay something like twenty dollars a month for an AI tool that does half a day of someone's work. It feels too cheap. That is because it is.

A recent report showed AI companies carrying a mountain of debt that barely shows up on the balance sheet, used to fund data centers and chips. The bill exists, it is just not being charged to you yet. Someone is paying so you can use it cheap, betting they can charge you properly later.

I am not here to predict a crash. Bubble forecasting is an internet sport. What matters for your business is simpler: the price you pay today is promotional, and you should be ready for it to change.

Why it is cheap right now

It is the same play you already saw with ride hailing and delivery apps. First subsidize to build the habit. Once the habit becomes dependency, adjust the price.

With AI there is an extra detail: running these models genuinely costs money. It is not like ordinary software, where the next user is nearly free. Every answer burns energy and hardware. When investors get tired of covering the difference, the price goes up.

Efficiency might bring costs down first. That is partly happening. But betting your whole process on "it will stay cheap forever" is a decision, not a deduction.

A cheap tool that became essential is not a saving. It is a lever in someone else's hand.

The real risk is not price, it is lock in

If your AI tool doubled in price tomorrow, how much would that hurt? The answer depends less on the amount and more on how much of your business is wedged inside it.

Ask three questions:

• If the tool disappeared today, what stops working?
• Is your data only in there, or also at home?
• Is there another tool that does something similar, and how long would switching take?

If the answer is "everything stops, the data is only there, and switching would take months", you do not have a tool. You have a business partner who never consulted you.

What to do without paranoia

This is not about cancelling subscriptions. It is about leaving the door unlocked. Three practical moves:

1. Keep what is yours. Customer conversation history, knowledge base, texts, the prompts you refined. Export them regularly. That is an asset, and assets should not live only on someone else's cloud.

2. Keep the model swappable. If what you built calls the AI through a middle layer, switching providers becomes a configuration change instead of a rebuild. There are plenty of good, cheap models today, including open ones. Anyone tied to a single vendor will learn the cost of that the hard way.

3. Know your number. How much do you spend on AI per month, and how much work does that replace? If you do not know, you will not be able to decide anything when the price moves. A ten line spreadsheet does the job.

Open models are a real option now

Two years ago, running an open model was a hobby for technical people. Today there are models running on ordinary machines, even phones, delivering enough quality for office work: classifying messages, summarizing, pulling data out of documents, answering questions against your own knowledge base.

They are not for everything. For heavy reasoning, the big models still win by a good margin. But a lot of what a small company automates is simple, repetitive work, and there an open model does the job for a fraction of the cost, without sending anything outside.

It is a good card to hold. You do not have to migrate today. You have to know it works and have a path ready if the bill climbs.

Where it is worth keeping the spend

Let me be clear so this does not read as belt tightening: there are AI uses where I would happily pay more.

If AI answers customers at 2am and that turns into sales, the subscription price is irrelevant. If it removes ten hours of manual work per week from your team, the math is obvious. What needs to die is the subscription nobody uses, bought because the demo looked good.

The right question is not "is AI cheap or expensive". It is "does this specific use pay for itself?". Uses that pay, you keep and even expand. Uses that do not, you cut, and when prices move you will barely notice.

The short version for your next meeting

Treat AI tools as suppliers, not as infrastructure. Suppliers you compare, negotiate with and replace. Infrastructure you just accept.

In practice that means: your data in your hands, more than one provider tested, cost per process measured, and no critical process that only works with one specific button from one specific company.

Whoever does that will shrug through the next price change. Whoever does not will find out that "cheap" was only the first installment.

If you want to build your automation in a way that keeps you free of any single vendor, let's review your process.