BEYOND VIRTUAL

The idea of free AI is fading faster than most people expected. And it is not because companies suddenly want to squeeze more out of their users. The truth is simpler and far less dramatic. The operating costs behind today’s AI systems have become enormous, and those costs are now showing up in the tools we use every day.

Even basic features such as faster response times, better accuracy, image generation, and document processing now require significantly more compute and increasingly rely on licensed data. What used to be powered by a freely scraped internet now depends on expensive partnerships, paid datasets, and far more complex infrastructure.

So prices are rising. Gradually and almost inevitably.

This week’s edition looks at why this shift is happening, what is driving the new economics of AI, and what business owners should expect as the basic intelligence they use to run their business becomes one of the most costly parts of their operations.

Feature Story

Why AI Prices Are Rising Now

There was a time when the entire AI ecosystem was built on openness. Early breakthroughs came from open source communities that shared models, code, and research freely. Anyone could build on top of what had already been created. That spirit accelerated innovation in ways the industry still benefits from today.

Then the models got bigger. Much bigger. The jump from early language models to today’s frontier systems reshaped the economics of AI. Training a modern model is no longer something a talented researcher can do with a few GPUs in a university lab. It now requires enormous computing power, custom hardware, and months of engineering work. Only a handful of companies can afford to operate at this scale, and even they describe the costs as staggering.

Here are the forces driving AI prices upward.

The cost of computing is enormous

Running state-of-the-art AI models requires massive energy consumption and specialized chips that are still in short supply. Every time a user interacts with a model, even for something small, the system performs significant processing behind the scenes. Multiply this across millions of users and include the cost of training new models, then the operating cost compounds.

Free data is no longer free

In the past, AI companies relied heavily on public internet data without paying for it. That era is closing. Platforms like Reddit, Stack Overflow, major news publishers, and image libraries now charge for access to their content. These licensing deals cost hefty sums, and some must be renewed.

The need for sustainable business models

AI companies are under pressure to build products that can support themselves, investors expect returns, and infrastructure requires constant upgrades. There is no viable path forward that relies entirely on free users and unlimited access. Paid features have become essential for the long-term existence of the technology.

The limit of free models

Free models are helpful for basic tasks, but they cannot sustain the development of the most advanced systems. The richer the capabilities, the more they cost to build and maintain. As a result, companies are separating entry-level models from premium versions that carry higher prices but also significantly greater performance.

When you put all of this together, you see AI is entering a new economic phase because the economic reality is forcing this drift. This shift will influence how businesses budget for AI, how teams choose which tools to adopt, and how fast companies can scale their AI-driven operations.

Visionary Voices

Sam Altman On the Price of AI

At a press dinner in San Francisco this past August, Sam Altman did something unusual for a major tech CEO. He spoke plainly about the economics behind modern AI. What he shared explains a lot about why the industry is shifting the way it is. Altman explained that if OpenAI stopped developing new models today and simply focused on running the ones already built, the company would be profitable. The real cost is in training the next generation. Not serving responses, not running the models, but the training process itself.

He went on to share numbers that show the scale of what they are dealing with. OpenAI expects to invest amounts that reach into the hundreds of billions of dollars as they continue building larger and more capable systems. They believe those investments will translate into even greater value through the services built on top of those models. In simple terms, the cost is enormous, but so is the reward.

Altman also mentioned something that caught many people’s attention. The next wave of AI development will require data centers so large that traditional financing will not be enough. He believes the industry may need entirely new financial structures to fund the computing capacity required. That is not a dramatic prediction. It is a practical observation from someone facing those bills firsthand.

If anyone wondered whether the demand justifies this progress, Altman addressed this. Within two days of launching GPT 5, OpenAI saw its API traffic double. That kind of growth used to take months. Now it happens on the weekend.

For business leaders, his comments make one thing very clear. The rising costs we are starting to see across the AI  industry are not random, nor are they temporary. They are tied directly to the size and complexity of the systems that now power everything from customer support to research to operations.

For companies that rely on AI or plan to, understanding this context helps explain what is happening around pricing. The tools are getting more powerful, just as the infrastructure behind them is becoming increasingly costly to put in place.

The Trend

How Much Does AI Actually Cost?

Once you understand the financial weight behind training modern AI models, it becomes easier to see why prices are changing in the industry. But the part that matters most to business owners is much simpler. What does AI really cost to use inside a company today? And how predictable is that cost? The answer is not straightforward.

Cost of AI Software

Zylo’s 2025 SaaS Management Index found that organizations now spend an average of four hundred thousand dollars per year on AI native apps. That number might sound high, but when you consider how much of the daily workflow now leans on these tools, it starts to make sense. Everything from research to internal documentation to early drafts of sales and marketing materials. AI has slipped into roles people did manually for years, which naturally pushes budgets upward. Many companies are now taking inventory of all the tools performing these micro tasks because the overlap can be surprising.

Seat-Based Pricing Can Get Expensive

Take Microsoft Copilot, for example. The public price is thirty dollars per person per month, but only if you are already on Microsoft 365. When you look at the combined price, especially for big teams, it adds up quickly. This is why many companies are no longer rolling out AI tools to everyone at once. They start with the people whose work clearly benefits from an AI assistant and expand based on real results, not assumptions.

Usage-Based Pricing Can Surprise You

Some AI platforms take the opposite route and charge by usage. Salesforce Agentforce and ChatGPT’s API are two common examples. The structure feels friendly because you only pay for what you use. But usage tends to rise quickly once teams build habits. What starts as an occasional query becomes part of the workflow, and the bill moves with it. Teams that manage these tools well do one simple thing. They monitor usage patterns monthly instead of waiting for year-end surprises.

You Do Not Need the Strongest Model for Every Task

One of the easiest ways businesses overspend is by defaulting to the strongest AI model for everything. Summaries, rewrites, categorization, note cleanup, and outline generation. These do not require the most advanced option. Lighter models handle them well at a fraction of the cost. The difference in output is often minimal. The price difference rarely is. Clear internal guidelines about which jobs require which models can reduce spending without reducing output quality. A lot of overspending happens simply because teams default to the most powerful option without thinking about whether it is necessary.

Where Does AI Make Sense?

More companies are taking their time, deciding where AI belongs in their workflows, and being intentional about which parts it should carry out. They are no longer rushing to adopt every new feature. They are learning where AI helps and where it does not.

The cost of AI is rising, yes. But more and more companies are being forced to look at their processes and choose where they belong. In the same breath, the clarity around proper use of the technology makes for better budgeting.

A Final Note

The more we learn about how modern AI is built and what it costs to run, the harder it becomes to pretend that the old idea of “free and limitless AI” still exists.

The industry is shifting, and so are the economics behind it. But that shift does not have to be discouraging. It simply means business owners need to be more intentional about how they use these tools.

Until next time,

Don't Just Pay More, Delegate Smarter.

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P.S. I’d love to hear your thoughts on this week’s piece. Did the rising costs surprise you, or are you already feeling the pinch? Comment your thoughts and let me know!