Every Sunday night, someone somewhere is refreshing an LLM leaderboard.

A new model edges out the old one on a coding benchmark. Another claims a fractional improvement in reasoning. Social media fills with grand declarations that everything has changed again.

It usually hasn’t !

We’ve spent the last two years obsessing over who has the smartest model. We should probably spend the next two asking a very different question.

Who still has the ability to charge a premium?

Because pricing power, not model intelligence, is what ultimately determines who builds enduring businesses.

This is the fundamental mistake much of the industry is making right now. AI doesn’t simply make software easier to build. It changes what customers are actually willing to pay for. Those are two very different things.

As AI capabilities become widely available, intelligence itself starts looking less like a premium feature and more like electricity. Useful. Essential. Increasingly expected. But rarely differentiated. That shift has serious consequences for enterprise software.

For decades, software companies competed by building capabilities that were difficult for others to replicate. AI is rapidly lowering that barrier. When many vendors can assemble remarkably similar capabilities from the same handful of frontier models, differentiation disappears faster than most pricing models can adapt.

The real competition isn’t for better models. It’s for pricing power.

Value Creation vs. Value Capture

Enterprise software has historically been an extraordinary business.

Building great software required years of engineering investment. Once built, however, every additional customer cost almost nothing to serve. High gross margins followed naturally because the difficult part was creating the product, not delivering it.

AI changes both sides of that equation. Generating software is becoming dramatically cheaper, and capabilities that once took years to build are becoming accessible to far smaller teams.

The application layer absolutely matters. Pretending otherwise is a mistake. Workflow design, user experience, deep integrations, trust, governance, and domain expertise all create immense value. But the question isn’t whether they create value. The question is whether they create sustainable pricing power. Those are not the same thing.

When the underlying intelligence for these tools increasingly comes from the same small set of foundation models, capability itself converges far faster than previous generations of enterprise software. If a customer can achieve a similar outcome from five different vendors, the differentiation shifts away from the capability itself and toward the operational advantages surrounding it. Value is created, but the ability to charge a premium for it disappears.

When Labor Becomes Software

This compression creates a dangerous economic dynamic for vendors trying to shift from selling seat licenses to selling outcome-based “digital workers.”

The pitch sounds compelling: We aren’t selling software anymore; we are replacing human labor. If a human agent costs $30 an hour, and our AI agent costs $5 an hour, we can command massive pricing power.

That strategy works perfectly, right up until three other AI agent startups launch in the same vertical.

As labor is converted into software, it becomes increasingly subject to software commoditization forces. If one vendor charges $5 an hour, a competitor operating a more efficient execution loop will offer it for $2. The value-based pricing model rapidly degrades into a cost-plus race to the bottom. The irony is that replacing human labor with software does not necessarily create a software monopoly. It may simply create a larger and more competitive software market.

The better these systems become at performing human tasks, the more they are exposed to the same economic forces that affect human labor markets: competition, substitution, and price pressure.

Meanwhile, enterprise customers expect more. They expect longer context windows, autonomous agents, and reasoning instead of simple retrieval. They expect voice, memory, automation, orchestration, and continuous improvement.

Yet every one of those improvements increases computational work behind the scenes.

So vendors find themselves caught between two opposing forces. Customers expect prices to fall because “AI is getting cheaper.” Meanwhile, the cost of reliably delivering enterprise-grade AI often rises as workflows become more sophisticated.

That’s not simply margin compression. It’s the steady erosion of pricing power.

The Investor Paradox

This dynamic exposes a fundamental disconnect in the markets today. Investors often assume that AI will expand software margins because more work becomes automated. The exact opposite may happen in many categories.

Automation increases the supply of software capabilities faster than enterprise demand can absorb them. The market has historically rewarded companies that create scarce capabilities. AI’s challenge is that it may create abundant capabilities faster than businesses can absorb them. When supply expands rapidly, differentiation becomes harder and pricing power weakens.

AI may create trillions of dollars in macroeconomic value. It may also make it incredibly difficult for individual software companies to capture that value.

The Loop Trap, Revisited

As I argued in The Loop Trap, enterprise AI isn’t expensive because of tokens. It’s expensive because of loops.

Retries. Approvals. Validation. Tool calls. Human intervention. Recovery paths. You know the drill! Those loops don’t disappear when models improve. If anything, customers demand more of them.

Inevitably, some legacy software incumbents will argue that enterprise inertia will protect them from this reality. They believe that because they are already integrated into the client’s infrastructure, their pricing remains safe.

But workflow lock-in is a defensive moat, not an offensive one. I am not sure how many amongst us realize this!

Workflow lock-in protects retention. It does not automatically protect expansion. The moment a CIO realizes that an automated capability has become a cheap commodity utility, the psychological willingness to pay a high SaaS premium vanishes. During the next renewal cycle, procurement will aggressively squeeze that line item down, using the threat of cheaper alternatives as leverage.

The incumbent keeps the customer, but loses the margin.

The companies that understand this will spend less time chasing benchmark improvements and more time reducing execution cost. Because every dollar saved inside the execution loop is effectively recovered pricing power.

So Who Wins?

The winners won’t necessarily have the smartest models. As I argued in Systems Over Scale, the true operational gains don’t come from a smarter standalone model; they come from better routing, tighter validation loops, and superior system design.

The winners will have advantages that competitors can’t download through an API.

Distribution. Deep workflow integration. Proprietary operational data. Customer trust. Efficient architectures. Low customer acquisition costs. Operational discipline. Those are the things that actually matter – the basics of a good business that the world seems to have forgotten about in the last two years !

They aren’t glamorous advantages, but they are incredibly difficult to copy. If a company possesses none of them, it’s probably not building a durable software business. It’s temporarily renting intelligence from someone else’s foundation model.

Where Value Actually Moves

Value rarely disappears during technological change. It migrates.

As models commoditize, value moves away from the models. As coding becomes easier, value moves away from writing code. As intelligence becomes abundant, value moves toward everything required to make that intelligence dependable inside an enterprise.

Governance. Integration. Security. Observability. Operational efficiency. Business execution.

The obvious narrative over the last two years was that the application layer would capture the majority of the value. But as those margins begin to trap the unprepared, the Second-Order AI Thesis becomes increasingly compelling. The enduring value remains in building the systems, the integration harnesses, the strict governance, and the actual organizational structures required to make all this abundant intelligence usable, predictable, and secure at scale.

The last two years have been a race to build intelligence. The next decade will be a race to keep charging for it.

As usual, these are strictly my personal views.