By Tony Wang

The Token Tax Arrives

AI may improve software products, but it also raises the cost to compete

May 2026

Executive Summary

AI is changing the margin structure of software and internet businesses. The old model was high incremental gross margins. The new model requires companies to pay for tokens, compute, and memory every time they embed intelligence into a workflow.
The application layer has to prove the return on AI reinvestment. Even good companies can get punished if the market sees rising opex, uncertain pricing power, or lower incremental margins tied to AI investment.
The bottleneck is becoming the asset to own. In an agentic world, every workflow needs more tokens, memory, inference, storage, and networking. That makes semis, memory, and AI infrastructure the toll collectors of the AI economy.

Recent earnings highlighted is that AI is not just a product cycle. It is a margin structure change.

For the last decade, the best software and internet companies had a beautiful model: build the product once, distribute it to more users, and let incremental revenue come in at extremely high gross margins. The mental model was simple: software has 80%+ incremental gross margins, and scale cures almost everything.

AI changes that.

Now every time a user asks for intelligence, someone has to pay for compute. Someone has to pay for tokens. And that means the old software equation is getting rewritten.

As an example, we saw that with two technology companies where these were not “business is broken” quarters. The issue is more subtle. These companies appear to be still growing, still building valuable products, and still important platforms. But the market is increasingly focused on the cost of staying competitive in an AI world.

To win, they have to keep reinvesting. They have to build AI into the product. They have to improve automation. They have to defend against new AI-native competitors. And whether that shows up in gross margin, opex, pricing, or all three, the market is starting to understand that AI is not free.

That is the crux: AI may make the product better, but it may also make the business model harder.

In my meetings, I was keen to explore “What’s the seismic change that the market doesn’t appreciate?” The old world of IT was: sell software and services, attach more seats, let gross margin flow through. The new world is: embed intelligence into the product, but every unit of intelligence has a cost.

The customer may love it. Revenue may grow. But the company has to decide whether that AI cost shows up in gross margin, in opex, or in price.

And if pricing power is not immediate, the market sees reinvestment, lower incremental margins, and a less certain return profile. That is why reactions matter. They are telling you that AI is not only a feature race. It is a return-on-invested-capital race.

And this is not limited to software. Internet companies, IT services companies, vertical software companies, commerce platforms, marketing platforms—everyone is being forced to embed intelligence. The problem is that intelligence is not free. It is consumed. It has a cost curve. It requires infrastructure. It may cannibalize old pricing models. And it also enables new competitors with lower head count, newer architecture, and no legacy business model to defend.

So on one side of the market, you have application-layer companies that need to spend more just to stay competitive.

On the other side, the semiconductor complex continues to look structurally advantaged because it is selling the picks and shovels into that exact reinvestment cycle. Growth in the sector is immense because the demand signal is simple: every company wants AI, and every AI workflow needs compute, memory, storage, and networking.

This only gets more important as we move from chatbots to agents.

A chatbot answers a question. An agent does work. That is a very different compute profile.

An agent has to reason through steps, call tools, search through context, remember prior interactions, retrieve data, update plans, and keep checking whether it is making progress. That means more tokens, more memory, more inference, more storage, more networking, and more orchestration.

So the bottleneck is not just “AI compute” in some generic sense. The bottleneck is the infrastructure required to make intelligence persistent and useful.

In a normal software world, the scarce asset was distribution. Who owned the customer? Who had the workflow? Who had the system of record?

In an agentic world, the scarce asset increasingly becomes: who owns the bottleneck that every intelligent workflow has to pass through?

That bottleneck is tokens and memory.

Tokens are the unit of intelligence consumption. Memory is what lets the system become personalized, persistent, and useful over time. Without tokens, the agent cannot think. Without memory, the agent cannot improve. And as agents become more capable, both requirements go up.

That is why you want to own the bottleneck. You want to own the layer where demand is unavoidable.

If every software company has to embed agents, then every software company has to consume more compute. If every internet company has to personalize AI experiences, then every internet company needs more memory and storage. If every enterprise wants AI inside its workflows, then every enterprise needs the infrastructure to run inference at scale.

So this is not just a semiconductor cycle. It is a bottleneck cycle.

The application layer may still create huge value. But the application layer also has to prove it can monetize AI faster than AI raises its cost to serve. The infrastructure layer has a simpler setup: more agents means more tokens; more tokens means more compute; more context means more memory; more memory means more value to the companies supplying the bottleneck.

That is why semis, memory, and AI infrastructure remain so dominant. They are not just beneficiaries of AI enthusiasm. They are sitting at the constraint point of the system.

The market is making a pretty clean statement: if you are the company that has to absorb the token tax, you need to prove the return. If you are the company selling the infrastructure that enables the token tax, the demand signal is still accelerating.

And that creates real opportunity cost.

In a market like this, “pretty good” is not good enough. A software name that is growing but facing reinvestment pressure, pricing-model uncertainty, and AI-driven competition may grind. Meanwhile, the infrastructure names are sitting closer to the source of budget formation.

So the question is not simply: is AI good for software?

It probably is, eventually.

The better question is: who captures the economics of intelligence first—the companies embedding AI, or the companies selling the compute, memory, and infrastructure required to make AI work?

Given recent earnings, the answer was pretty clear: the token tax is real, the agentic world makes the bottleneck more valuable, and for now, the toll collectors are winning.

Expertise is required to navigate the rapid changes in the market environment. At T. Rowe Price Associates, Inc., we're focused on navigating these disruptive environments responsibly. We have made one of the largest commitments in the industry to fundamental research, including a global network of analysts, sector portfolio managers, and diversified and regional portfolio managers who collaborate, share ideas, and challenge each other to help us achieve better outcomes for our clients. We believe their relationships with companies and understanding of the dynamics of their sector create a long-term competitive advantage when choosing stocks. Their research is shared with investment professionals across the platform, as we believe that this helps generate stronger investment conviction.

Tony Wang Portfolio Manager

May 2026

The Token Tax: A Research-Driven View on AI and Software Across Equity and Credit

A cross-asset Q&A on AI costs, software, and credit risk.

By Jeff Helsing, Tony Wang, Michael Trivino

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