Every week the same news comes out: the waiting line to buy AI computers is stretched all the way to 2027, prices keep climbing, and companies are paying billions to cut to the front. For the business owner who is still unsure whether to invest, the natural question is simple: if it is all so expensive and so competitive, does it make sense to jump in now or wait it out? This post explains what is behind that line, why this scenario is different from other 'bubbles' we have seen, and what it changes in your budget for the next two years.
The short answer: the line is not speculation. It is real demand, generating real revenue, with measurable returns. The companies that are buying are not gambling. They are paying today to win tomorrow, with the math in hand. Waiting it out means waiting for your competitor to speed up. Let us break down why.
1. The number that scares people: 100 billion per AI factory
The centers that run AI today, called 'AI factories', cost between 50 and 100 billion dollars each to build. That is not an exaggeration. It is not an inflated budget line. It is the real amount that companies like Microsoft, Amazon, Google, Meta, and OpenAI are investing, and it keeps rising every quarter.
Why do they pay that? Because every hour of a computer running AI has become an hour of revenue. The clients (companies that use the AI) are paying to use it, in growing volume, and whoever has the computer running wins. Whoever does not, loses. It is the same logic as anyone running a production line: if the conveyor belt is stopped, that is a loss. If it is running and selling what it produces, that is profit. Today, the 'AI conveyor belt' is selling everything it produces.
That explains the line. The buying companies (the ones that will resell AI capacity to others) are racing to have more 'belt'. The manufacturers (mainly Nvidia) cannot produce at the speed of demand. The result: long delivery times, high prices, fierce competition.
2. Why this is not a 'bubble'
Every now and then someone compares the AI race to the internet bubble of 2000. The difference is what matters: in 2000, the .com companies burned capital without matching revenue. Today, the companies consuming AI have real, measurable revenue, growing every month. It is not a promise of future revenue. It is revenue happening.
Another indicator: AI contracts are pre-sold years in advance. Microsoft, for example, announces new capacity and it is already allocated before it is even switched on. The client company pays upfront to secure its spot. In a bubble, nobody pays upfront. In real demand, everybody pays upfront.
That does not mean there will not be adjustments, swings, or a drop at some point. There will be. Every investment race has them. It means the axis is right: AI capacity is an input for real company operations, not a financier's toy. And when the input is real, the market only adjusts downward after it has satisfied demand. We are a long way from that.
3. What this means for small and medium businesses
The small business owner reads '100 billion factory' and tunes out: 'this is not for me'. That is a reading error. You do not need to buy the factory. You need to buy AI capacity from it. It is the same difference as 'buying the power plant' versus 'paying the electricity bill'. Nobody buys a power plant. Everybody pays a bill.
What is changing for your business is this: AI capacity is becoming an everyday input, like power, like internet, like the phone. It comes with a market price, and with market volatility. In 2026 and 2027, that price will swing. At some points it will be expensive because demand explodes. At others it will drop because new capacity comes online.
The owner needs to start looking at 'price per thousand AI queries' the same way they look at the price of a kWh or a liter of fuel. It will no longer be optional. It will be a line on the balance sheet. Whoever learns early how to buy well (from the right supplier, on the right model, on the right plan) saves a lot. Whoever puts off learning it pays dearly for inefficiency.
4. Why waiting makes your situation worse, not better
The careful entrepreneur's natural instinct is 'let the market mature, let the price drop, I will jump in later'. With almost every previous technology, that worked. Smartphones, cloud computing, e-commerce: those who came in late paid less and had fewer headaches.
With AI, that math is coming out differently, for two reasons:
- The productivity gain is cumulative. A company that adopts AI today frees up 30% of the team's capacity. It uses that capacity to grow. It grows. It has more clients, more data, more history. It trains the AI better the following year. It accelerates more. Whoever comes in two years later has to chase down two years of compounded advantage. They rarely catch up.
- The learning curve is slow. It is not just buy and switch on. It is figuring out what works in YOUR business, training the team, adjusting the process, making mistakes, redoing it. That takes 6 to 12 months to show consistent results. Whoever came in in 2024 is already harvesting. Whoever comes in in 2027 starts to harvest in 2028.
5. What to do in your budget, concretely
Three practical decisions for the next planning round:
- Set aside a fixed budget for AI, like the reserve for power and phone. Do not take it from 'innovation' (which always ends up as leftover cash). Put it in as an operating cost, with a planned monthly amount. Even if it starts small, keep it visible and protected from cuts.
- Identify the two or three processes in your company where AI is already delivering results at similar companies. Do not try to invent a new use case. Copy what is already working in your sector. Implement the cheapest and most obvious one first. Measure. Expand.
- Sign an annual contract with a serious supplier, not a monthly one with just anybody. The price per use will swing over the next two years. Whoever has a fixed contract with a good supplier is protected. Whoever keeps buying on the spot pays the peak every time capacity runs short.
The reframe that matters: computing has become revenue. That changes who wins and who loses over the next five years. A company that sees AI as a production input, with serious cost management, will get ahead. A company that treats it as a 'project' or an 'eternal pilot' will arrive late, and will arrive paying more for less.