AI usage is getting more expensive.
Headlines keep talking about AI getting cheaper because token costs are going down, models are becoming more efficient, and competition is driving pricing changes. But that is only half the story.
On paper, AI is getting cheaper per unit. However, in practice, total AI spend is going up. In some cases, it is going up exponentially.
You would logically think that lower costs and better efficiency would bring total spend down. But that is not what is happening. Instead, the opposite is true. Companies are using AI more, in more places, for more complex tasks, and their total spend is rising right along with it.
A simple way to think about it:
- Cost per unit goes down
- Usage increases significantly
- Total cost goes up
Because of this, there is a growing gap between perception and reality, and that is where a lot of organizations are getting caught off guard.
How AI pricing quietly changed
As the AI industry expands in both capability and use cases, the way we consume AI is changing. What started as a predictable SaaS model, where you paid a flat monthly fee and used as much as you could, has shifted into a consumption-based model. In other words, you are no longer paying for access. Instead, you are paying for usage.
In effect, we have moved from an all-you-can-eat buffet to an à la carte menu.
Previously, costs were fixed and predictable. Now, they scale with usage. As a result, the better the models get, the more useful they become, which leads to increased usage, and ultimately higher costs. This is where the disconnect starts. The product improves, and at the same time, the bill increases.
Enter the era of tokens
Consider a common scenario inside a product.
A SaaS company adds an AI assistant to its platform. At first, only a handful of users try it and the cost is negligible. However, as adoption grows, it becomes a core feature. Every user begins interacting with it, and every interaction triggers model calls in the background.
Now, instead of occasional usage, every session consumes tokens. As the user base grows, AI usage scales alongside it. As a result, revenue grows, but AI costs grow right alongside it.
This is where AI shifts from a feature to a cost tied directly to growth.
For anyone less technical, tokens are simply units of data being processed. Think of them as words, chunks of text, or pieces of a request. Every time you prompt a model, generate a response, or run an automated workflow, you are consuming tokens.
At the same time, companies are no longer using AI for one-off prompts. Instead, they are embedding it into workflows, products, and internal systems. So instead of 10 prompts per day, you now have thousands of automated calls happening in the background.
This pattern is very similar to what we saw with cloud computing. At first, the cloud felt cheap and flexible. Then usage scaled. After that, everything became metered. Eventually, companies found themselves dealing with massive AWS bills they did not fully understand.
AI is following the same path. Lower unit cost combined with significantly higher consumption.
Before AI, as a SaaS tool acquired more users, it did not necessarily incur more costs. It was nearly infinitely scaleable. With AI, increased usage increases costs to the company. No longer infinitely scaleable.
The adoption fanout
Here is what this looks like in practice inside a typical company.
A mid-size organization starts experimenting with AI in one department, and then it spreads.
- Marketing uses it for content generation
- Sales uses it for outreach and personalization
- Customer support uses it for chat and ticket handling
- Engineering builds it into internal tools and product features
Each team believes they are only spending a small amount. However, no one is tracking total usage across the organization. There is no centralized visibility into API calls, overlapping tools, or redundant workflows.
Over time, what looked like a few hundred dollars per team quietly turns into tens of thousands per month in total AI spend, without a clear owner or accountability.
This is what I mean by adoption fanout. It spreads quietly, and it spreads everywhere.
AI is no longer a tool
This is where the real shift happens.
AI is no longer just a feature or a tool on the side. Instead, it is becoming a core production input, similar to labor, electricity, or cloud infrastructure.
It is now directly tied to output.
- More content leads to more AI usage
- More customers lead to more AI interactions
- More automation leads to more AI calls
- More AI calls lead to more costs
Because of this, AI costs do not stay flat. They scale with the business. Once something becomes a production input, it must be managed and optimized accordingly. You do not ignore labor costs. You do not ignore infrastructure costs. Similarly, you will not be able to ignore AI costs.
A useful way to think about it is that AI is no longer software you simply buy. Instead, it is a resource you continuously consume.
What separates the winners from everyone else
In the future, the companies that win will not be the ones that use AI, because eventually everyone will.
Instead, the companies that win will be the ones that use AI efficiently. That includes designing workflows that minimize unnecessary usage, choosing the right models for the right tasks, structuring systems so AI is not called redundantly, and understanding exactly where and how AI is being used.
Because AI is not getting cheaper in the way people assume. While the cost per unit will continue to decrease, usage will grow even faster as capabilities expand. As a result, total cost will continue to rise.
The real advantage will not come from access to AI. Instead, it will come from controlling how that cost scales.
The part most companies will miss
AI will not become a problem simply because it is expensive. It will become a problem because it is too easy to use without understanding the cost. The companies that win will not be the ones who use the most AI. They will be the ones who understand exactly what it costs every time they do.
If this all seems confusing, our Marketing & AI Consulting Services can help tackle new initiatives or audit and review existing efforts. We also offer a full suite AI tool solutions to help you get the most of AI technologies.
