When Intelligence Becomes a Commodity, Taste Becomes the Moat

The strange new rule of the market

What happens when the most expensive part of a product suddenly gets cheap enough to ignore?

That question is no longer hypothetical. In AI, model capability is rapidly sliding down the price curve. What once felt like a scarce, defended asset is turning into a utility, something you can buy in bulk, route through an API, and swap out if the economics change. That shift does not just alter product strategy. It changes the entire logic of competitive advantage.

For years, the default playbook in frontier technology was simple: build the hard thing, own the hard thing, defend the hard thing. If the model was expensive to train and expensive to serve, then model ownership itself was the prize. But when the cost of inference falls by an order of magnitude, the center of gravity moves. The game stops being about who can afford intelligence and starts being about who can use it best.

That is the deeper tension: when capability becomes abundant, scarcity migrates upward. The advantage is no longer raw access to intelligence. It becomes judgment, distribution, speed, and the ability to make people care.

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What disappears when the price falls

When a resource gets cheaper, people often assume the market simply gets bigger. That is partly true, but incomplete. Cheapness also strips away excuses, buffers, and illusions.

Consider a simple analogy. If electricity were suddenly ten times cheaper, almost nobody would talk about the cost of turning on a light. Attention would move to what the light enables: design, ambiance, workflow, and energy coordination. The same thing happens with AI tokens. Once tokens are cheap enough, they cease to be the story. They become plumbing.

This has two powerful consequences.

First, the companies that competed primarily on infrastructure or model access lose a layer of defensibility. If many providers can offer roughly similar intelligence at lower and lower prices, then the moat cannot live solely in the model. It has to live in everything around the model: latency, workflow integration, reliability, specialized data, customer trust, and product experience.

Second, the buyer’s mindset changes. When intelligence is expensive, users ration it. They ask, “Is this worth it?” When intelligence is cheap, they ask, “Why does this still feel hard?” That is a much harsher question. It punishes mediocre products because the underlying excuse has vanished.

Cheap intelligence does not automatically create great products. It merely removes the alibi for bad ones.

That is why a falling price curve is more than a cost story. It is a design challenge. It forces every company to answer a now-urgent question: if the machine is no longer the bottleneck, what is?

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The real moat is not intelligence, but interpretation

Most people describe AI competition in terms of models, chips, and benchmarks. Those matter. But the more durable advantage may come from a far less glamorous place: interpretation.

Interpretation means translating abundant capability into a specific outcome that a user values. It is the art of deciding what the model should do, when it should do it, how much it should do, and what should happen after. In other words, it is not enough to have a brilliant engine. You need a steering wheel, a dashboard, brakes, and a destination.

This is where the old venture lesson becomes relevant. In every technology wave, the companies that win are rarely just the ones with access to the most powerful primitive. They are the ones that can see how the primitive should be assembled into a product, an organization, and a market category. The hard part is not knowing that a tool exists. The hard part is knowing what to do with it before everyone else does.

Think about spreadsheets. The breakthrough was not merely that computation got cheaper. The breakthrough was that ordinary people could model, test, and decide without waiting for a centralized analyst. The spreadsheet did not win because arithmetic became available. It won because judgment became more portable.

AI is heading the same way. The winners will not simply deploy intelligence. They will package judgment.

That distinction matters because many companies confuse output with value. A model can draft a legal memo, summarize a meeting, or generate code. But the business value comes from whether the output fits the decision context, the workflow, and the acceptable risk tolerance. A first draft is not a product. A first draft inside a trusted system with memory, guardrails, escalation, and feedback loops is.

This is why the best AI companies may look less like model laboratories and more like systems for turning probability into accountability.

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Why price wars create strategy wars

Andrew Ng’s observation about falling token prices points to a crucial market dynamic: if providers do not need to recoup model development costs, they can compete directly on price, speed, and other operational factors. That sounds like commoditization, and it is, but commoditization is not the end of strategy. It is the beginning of a new layer of strategy.

When the core primitive gets cheaper, the differentiators shift. Price matters more. Speed matters more. Reliability matters more. But even those are still surface-level. Beneath them sits a more decisive layer: who can build the best productized behavior on top of the same underlying intelligence.

Imagine two companies using similar models for customer support.

Company A uses the model to draft replies. Company B uses the model to classify urgency, detect sentiment, pull account history, recommend next actions, route edge cases to humans, and learn from every resolved ticket. Both have access to “intelligence.” Only one has built an operational advantage.

That is the fundamental transition. Cheap models turn AI from a scarce asset into a coordination layer. Once that happens, the real competition shifts to systems design. Which company can absorb the intelligence into workflows so naturally that users do not notice the model, only the result?

This is why model price compression is not bad news for everyone. It is bad news for providers who think model quality alone is enough. It is excellent news for builders who know that abstraction creates opportunity. Every time a capability becomes cheaper, a new product layer appears above it.

Think of cloud computing. The falling cost of compute did not kill software. It multiplied it. The companies that won were not the ones obsessing over raw server performance. They were the ones that turned that performance into a service people could actually use.

AI is now entering the same phase, except faster.

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A new mental model: from intelligence to leverage

The most useful way to understand this transition is to stop thinking about AI as “smart software” and start thinking about it as leverage capital.

Leverage capital is a resource that amplifies human intent, but only if it is deployed with precision. Cheap leverage is dangerous if misused and transformative if well-directed. A power tool in the hands of a carpenter changes everything. A power tool in the hands of someone without a plan changes nothing.

This creates a three-part framework for evaluating AI opportunities:

1. Primitive: What capability is getting cheaper?
2. Constraint: What used to be limited by that capability?
3. Orchestration: Who can turn the newly abundant primitive into a repeatable outcome?

For AI, the primitive is model inference and reasoning-like behavior. The old constraint was cost and availability. The orchestration layer is where the real business forms. It includes product design, workflow integration, trust systems, proprietary data loops, and taste.

Taste deserves special attention because it is often dismissed as subjective, when in practice it is an operational advantage. Taste is the ability to know which problems matter, which outputs are acceptable, which interactions feel frictionless, and which features are just technical vanity. In a world where anyone can access similar intelligence, taste becomes the selectivity that gives intelligence direction.

A great AI product is not one that can do everything. It is one that knows what not to do.

That may be the most underappreciated implication of falling model prices. Abundance does not reward maximalism. It rewards discrimination.

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The companies that will matter next

If intelligence is getting cheaper, what kinds of companies will become more valuable?

The answer is not simply “AI companies.” It is companies that convert cheap intelligence into compounding advantage. Those companies usually share four traits.

1. They own a workflow, not just a feature.

A feature can be copied. A workflow embeds into how work actually gets done. If AI is used to generate code, but the company also controls versioning, testing, deployment, and incident review, it has something far stronger than a demo.

2. They sit on feedback.

Cheap intelligence gets better when it can learn from outcomes. The best products will not merely answer questions. They will collect signals about what happened after the answer was used, then close the loop.

3. They reduce trust friction.

People do not pay for intelligence alone. They pay for confidence. That means audit trails, permissioning, explainability where needed, and the ability to escalate gracefully. The more consequential the decision, the more valuable trust becomes.

4. They have a point of view.

When the underlying model is broadly available, the company’s opinion about the user, the task, and the desired outcome matters more. This is where taste and strategy converge. A company with a strong point of view can turn a generic capability into a differentiated experience.

The temptation in periods of fast price compression is to believe that margins will disappear everywhere. But margins often migrate rather than vanish. They move from the supply side to the orchestration side, from the model to the workflow, from raw intelligence to applied judgment.

This is the entrepreneurial opening. Not everyone can make intelligence cheaper. But many can make intelligence more useful.

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Key Takeaways

• Stop asking what AI can do in general. Ask what process becomes possible when intelligence is cheap enough to use continuously.
• Build around workflows, not demos. Durable value comes from embedding AI into recurring actions, decisions, and feedback loops.
• Treat taste as strategy. In a commoditizing market, the ability to choose the right problem matters more than adding more capabilities.
• Compete on orchestration. Speed, reliability, trust, and integration often matter more than raw model quality once models become interchangeable.
• Look for compounding feedback. The best AI businesses learn from what users do after the model responds, not just from the prompt itself.

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The future belongs to the directors, not the engines

There is a seductive idea in technology that the best system is the smartest one. But when intelligence becomes cheap and abundant, that belief breaks down. The scarce resource is no longer cognition. It is direction.

This is the deepest shift underway. We are moving from a world that rewarded those who could build intelligence to a world that rewards those who can orchestrate intelligence into outcomes. That changes what matters in products, companies, and even careers. The winners will not be the people who can merely access the machine. They will be the people who know what the machine should be used for, when to trust it, and how to turn it into leverage that compounds.

So the real question is not whether AI will get cheaper. It will. The real question is who will be ready when intelligence is no longer the scarce thing.

The answer, increasingly, will be: the builders with taste, the operators with judgment, and the companies that understand a profound truth about every technological revolution. When the engine is everywhere, the steering becomes everything.