AI tasks become commodities once models exceed their maximum necessary intelligence, shifting competition toward cost, latency, infrastructure, and distribution. Frontier labs can still become enormous businesses if new capability creates valuable markets faster than competitors reproduce and commoditize those advances.

The Economics of the Intelligence Frontier

There is a lot of hand-wringing right now over whether frontier model companies like OpenAI, Anthropic, and Google are going to make it when DeepSeek, Qwen, GLM, and others are commoditizing their frontier capabilities.

I think it is entirely plausible that frontier model companies wind up among the biggest companies on Earth while most of the tasks their models perform become commodities. Those outcomes are compatible because AI is not one monolithic market. There may be monolithic suppliers, but there is not one use case. Every task has a different requirement for intelligence, and for many tasks there is a point beyond which more intelligence creates no additional value.

LLMs look a lot like other technology markets with high fixed development costs and rapidly improving capabilities.

One easy way to think about these markets is to plot models by cost and capability. For any particular task, some models simply are not capable enough. Among the models that are, the buyer then cares about cost and latency. What counts as sufficient capability, acceptable latency, or an attractive price changes dramatically by use case.

What capability means depends on the use case. I will use intelligence as shorthand for task-specific capability. There are models that are great at making imagery, models that are great at making videos, models that are great at coding, and models that are good at mathematical reasoning. Intelligence measured by what is a reasonable question, but the answer changes by task.

I somewhat expect the differences in model expression to collapse over time. In particular, I strongly suspect that multimodal input-output models will ultimately win against many task-specific models. So far, adding additional dimensionality to the data appears to improve reasoning overall. We colloquially refer to models that can do all of these things as frontier models because historically only frontier models could. That is increasingly untrue. Multimodality and frontier intelligence are separate characteristics.

Task Intelligence Saturation

For any given task, there are two important thresholds.

• Minimum viable intelligence, or MVI. Once a model crosses MVI, the task can be performed. In the same way that humans have a wide variety of performance on a given task, a model at minimum viable intelligence will rarely be the top performer. It may simply be satisfactory. It may hit the absolute bottom threshold of acceptable accuracy. It still ultimately checks the box.

• Maximum necessary intelligence, or MNI. This is the point where additional intelligence does nothing for you. The task has reached whatever level of performance and consistency the use case requires. Additional units of intelligence deliver no additional value.

Below MVI, the model cannot reliably perform the task and therefore has little economic value for that use case. Between MVI and MNI, increasing intelligence produces increasing economic value. Above MNI, the marginal value of intelligence reaches zero.

Frontier intelligence matters only for tasks that have not yet been saturated.

Intelligence has diminishing and eventually zero marginal value at the task level.

There are tremendous numbers of long-horizon tasks where we have no strong sense of where maximum necessary intelligence sits. There may be tasks where the true ceiling is phenomenally high. There may even be problems where there is no limit to the marginal gains that can be wrought from increased intelligence. Maybe understanding the universe is one. We don’t know.

There are also tremendous numbers of tasks that truly do have a maximum required intelligence. There is a hard limit. Additional intelligence produces no further gains, and at some level it might even produce negative utility through overthinking.

You see this with humans. Really, really smart humans are often the wrong people to hire for simple work because they tend to overthink that work. The same thing seems to be true with LLMs. Give a frontier reasoning model what is fundamentally a simple task and it may sit there spinning its wheels for five minutes attempting to figure out what is hard about it. Sometimes there was nothing hard about it. It just needed to answer.

Even coding strikes me as more bounded than people sometimes assume. I don’t think there is an unlimited amount of intelligence required to do coding correctly. It is a relatively bounded and verifiable domain. Certain specific coding tasks obviously need more intelligence, and we are not yet building bug-free software or bug-free systems. I am not totally convinced that we have hit saturation. But I think we may be one or two turns of model improvement away from getting there for a surprisingly large portion of software development. We have effectively solved many coding tasks that are not simple at all.

The number of tasks that eventually hit a maximum on intelligence is probably larger than people think.

Once You No Longer Need More Intelligence

As models become more capable, more and more tasks reach that point. When a subsequent, smarter model gets released, there is little reason to adopt it for a task that has already been saturated. At a given point in time, frontier models are generally more expensive to serve, and often slower. Once you no longer need additional intelligence, you care about cost and, in a lot of instances, speed.

There are other dimensions too. Provisioning convenience matters. Embedded integrations into other workflows matter. Infrastructure matters. There are many ways to differentiate the serving, delivery, and business model around a commodity market that can break a provider out of pure commodity pricing.

Reliability needs a distinction here. Task reliability is already part of intelligence. If one model can perform a task correctly 80 percent of the time and another can perform it correctly 99.99 percent of the time, the second has greater task-specific capability. That improvement lives between MVI and MNI.

Above MNI, reliability means infrastructural reliability. Can the provider keep its uptime? Can it handle peak workloads without downtime or lead times? Can it provide predictable throughput? These remain valuable after task intelligence saturates, but they are properties of the infrastructure and service.

Latency works the same way. Speed can be decomposed into time to first token and tokens per second. For highly real-time use cases, you may care enormously about how quickly tokens start streaming. For other use cases it does not matter at all. Batch offline work might tolerate 24-hour latency. That will be priced accordingly.

The procurement decision at the task level is therefore a choice among capability, cost, and latency, along with the other requirements of the use case. Once multiple models have crossed MNI, greater intelligence ceases to determine the winner.

This Is How Most High-Tech Markets Work

You can already see this structure in semiconductors.

There are enormous categories of chips whose capability requirements saturated long ago. Cars, industrial equipment, appliances, and embedded devices use huge numbers of chips that do not need leading-edge transistor density. In automotive applications, for example, there remains substantial demand for mature process nodes. Customers often have little incentive to migrate these chips to newer nodes because the existing technology is already sufficient and moving carries additional development and qualification costs.

Once the required capability has been reached, the economics depend much more heavily on manufacturing cost, reliability, availability, and scale. Mature-node semiconductor manufacturing has historically carried lower margins, which can make it difficult to justify expensive new capacity even when demand for the chips themselves remains substantial.

I think we are going to see the same thing with LLMs. As more tasks become saturated on intelligence, a growing portion of existing workloads will be served by models optimized around price, latency, and other characteristics rather than maximum capability.

That says very little about absolute token volume. Total token volume is likely to grow enormously, and the share of those tokens served by the frontier versus the commodity portion of the market will be dynamic.

A marginal increase in capability could create a new market that consumes an unbelievable number of tokens. For some period, frontier models could serve nearly all of it. Then cheaper models catch up and take much of that market by optimizing price and latency.

GPUs provide a useful analogy.

GPUs began as processors for graphics. As they became more powerful and programmable, they found increasingly valuable uses beyond simply drawing things on screens. They made much richer games possible, became important infrastructure for parts of the crypto boom, and eventually became the core computational input to modern AI.

AI is now by far NVIDIA’s largest market. In its most recent fiscal year, NVIDIA reported roughly $194 billion of Data Center revenue against $16 billion of Gaming revenue. The company attributes the enormous growth in Data Center directly to accelerated computing and AI.

That was not always the case. A technology whose major economic use was once rendering graphics found a much larger market because greater capability and programmability made entirely different workloads possible.

The same dynamic applies to intelligence.

The Frontier Creates Markets

When a new model pushes the frontier, some tasks that previously sat below minimum viable intelligence cross it. Services that were impossible to provide become possible. That creates markets.

Other tasks already sit between MVI and MNI. A more capable model performs those tasks better and therefore creates more economic value.

Over time, competitors reproduce the capability. Costs fall. Eventually multiple models cross the maximum necessary intelligence for a particular task. At that point, additional intelligence ceases to differentiate the product and competition concentrates on the other things customers care about.

The frontier creates new markets by making previously impossible tasks possible. As those tasks become saturated and the same capability spreads to cheaper models, they commoditize.

The Time Profit Model

There is a model in The Art of Profitability called the Time Profit Model. I think LLMs and most high-tech markets follow something very close to it.

When you make a new technological advance, you have only so long to exploit and commercialize it before competition catches up and subsequently collapses price. You can see the same thing happening with models.

At the tip of the frontier, a company can charge relatively high or even stupendously high prices because only one or two players may be able to provide services at that capability tier. Competition is limited, which creates pricing power and allows prices well above marginal cost.

Then competition catches up and those economics deteriorate.

The more successful frontier models are at advancing intelligence, the more capable the commodity models behind them become.

Yesterday’s frontier capabilities gradually become available from dozens or hundreds of providers at much lower prices.

This does not mean frontier model companies have bad businesses. Frontier model companies could very well become the largest profit producers in the market even if they eventually account for a minority of token volume.

Their profit comes from scarce capability. A company that is the only provider capable of performing a valuable task has enormous pricing power. A company serving a task that hundreds of models can perform has very little. Less competition produces greater pricing power, and greater pricing power allows prices farther above marginal costs.

Frontier Models and Frontier Model Companies

OpenAI, Anthropic, or Google could serve both the frontier and models many generations behind it. They could distill their frontier systems, operate smaller models, dynamically allocate inference compute, or use their scale to compete throughout the market.

There is no technical law preventing this. I somewhat suspect that the market will support many other providers as well because the innovation company and the lowest-cost producer have historically developed very different habits.

NVIDIA is instructive here, although the analogy needs to be precise. NVIDIA participates across several compute markets, including gaming, professional visualization, automotive, and Data Center. It does not only serve the absolute frontier.

Its extraordinary economics, however, have increasingly come from markets where its technical capabilities are highly differentiated rather than from trying to dominate every low-cost semiconductor use case. Data Center accounted for almost $194 billion of NVIDIA’s $216 billion of fiscal 2026 revenue.

Part of that is distribution. Distribution channels are somewhat determinant of the type of organization you build. The organization designed to work with sophisticated customers on the newest and hardest technical problems can look very different from the organization designed to serve enormous volumes at the lowest possible unit cost.

Competition also looks different across those markets. At the absolute frontier, you might have one or two serious competitors. Move sufficiently far down the capability curve and you might have hundreds or thousands of providers competing with you. It is a much deeper, more aggressive knife fight.

Frontier model companies have no obvious intelligence advantage once the task itself has saturated. They may have other advantages. Scale could matter. GPU procurement could matter. Distribution could matter. Infrastructure could matter. They could turn out to be the best companies in the world at distilling their own models.

Those are possible advantages, but the competitive environment is much harsher when many providers can already satisfy the intelligence requirement.

Who Should Operate the Router?

A straightforward interpretation of this framework would have every application dynamically choose the cheapest model capable of completing a given task. I am relatively skeptical that cross-model routing will be quite that easy.

Having built a lot of agentic systems, you generally cannot swap one model for another for the same task and expect the same output. Models have different expressions across the same token input. They have different failure modes and require different prompting to achieve different things. Stochasticity and the particulars of training matter.

In principle, a router company could train a translator model that understands the intent, rewrites the prompt for a particular model, and then sends the task to the appropriate provider. That is totally possible. We don’t really see it working universally yet.

There will be plenty of use cases where dynamic allocation works. Chatbot use cases and single-turn use cases seem obvious. Some human-in-the-loop tasks probably work as well. I am less certain about long-running agentic tasks where changing the underlying model can change the behavior of the whole system.

There is also a basic economic problem with allowing the highest-cost intelligence producer to operate the router.

Frontier models are likely to be the high-profit products. A company selling both the router and the models therefore has an economic incentive to send more work toward its expensive models. A customer trying to minimize the cost of accomplishing a task has the opposite incentive. That conflict creates room for independent routing and procurement layers, although I doubt there will be one universal market structure.

Greater Intelligence Does Not Have to Cost More

Greater intelligence does not structurally have to cost more. It appears to cost more within a particular architectural paradigm and at a particular point in time.

Today there are three major categories of model cost.

• R&D costs, including thinking about the problem, experimentation, and developing new approaches.

• Training costs, including compute and data.

• Inference costs, which determine much of the marginal cost of serving the model.

Technical revolutions can change those relationships. Someone could produce a radically smarter model than the frontier through architectural or operational improvements while also making it much cheaper to train or serve. We have seen versions of this throughout the history of technology.

The cost frontier obviously falls over long periods of time. The top-of-the-line televisions today are radically cheaper than the top-of-the-line televisions from 30 years ago because of improvements in chips, displays, manufacturing, and other inputs.

Procurement decisions happen at a point in time. A buyer today chooses among the models and economics available today. At that moment, marginally more capable frontier models have generally been more expensive than models behind the frontier.

Commodity Does Not Mean Unprofitable

Memory provides another useful comparison because it shows how profits can reappear in a relatively mature technology market for completely different reasons.

Memory capability has long been sufficient for enormous categories of workloads, but memory producers can still make extraordinary profits when demand outruns available capacity. We are seeing that right now. AI and data center demand have created severe supply constraints in DRAM and NAND, with suppliers reallocating capacity toward server and AI applications and prices rising dramatically.

That profit comes from scarce productive capacity rather than unique frontier capability. A commodity market can still be extremely profitable during a supply shortage. Capacity eventually tends to respond to sustained excess demand, although semiconductor production adjusts slowly.

There are other ways to build market power too. Companies can control scarce resources, build scale advantages, own important IP, control distribution, or benefit from regulatory protections. Those can all produce excellent businesses. They are separate from the economics of frontier capability.

The Multi-Trillion-Dollar Question

The economics of the frontier depend on how much valuable intelligence remains unsaturated.

We have no idea how large that market is. This is the multi-trillion-dollar question.

The system is reflexive because increasing intelligence creates new end markets. Those markets create new companies and new competitive dynamics, which in turn create new economically valuable tasks.

The GPU market is itself an example. The current AI market would have been impossible without GPUs. The GPU market is now increasingly reliant on demand created by AI. One technological market created another market that subsequently became its dominant source of demand.

The same thing can happen with intelligence. We don’t know where the limits of economically valuable intelligence are because greater intelligence changes the set of economic activities that exist.

The unsaturated market might be hundreds of times larger than the current economy. It might turn out to be some portion of the current economy, with much of the remaining work coming down to task definition and workflow design. Both outcomes are plausible.

My guess is that the number is extremely, extremely large. We have never yet, as a human species, found that more intelligence failed to yield additional new market creation. Still, the size of the market for frontier intelligence remains unknown.

Technology races eventually tend to saturate their capability requirements within a given technological paradigm. When a new technological revolution happens, the companies that led the prior revolution are often existentially imperiled. They sometimes make it through with savvy M&A and business operations or by reinventing themselves. Their prior competitive advantage can still disappear.

Intel did not win GPUs, even though CPUs and GPUs are both compute. You could have reasonably expected a company that knew how to do one form of compute exceptionally well to have an enormous advantage in another. A different company won.

Similar dynamics exist in networking, storage, and memory. Many of those markets saturated the capability requirements of large categories of customers long ago. They remain large markets and can still generate enormous profits, but the source of those profits may have very little to do with pushing the capability frontier.

How Long Does the Advantage Last?

This brings us back to DeepSeek, Qwen, GLM, and the other models commoditizing capabilities that recently existed only at the frontier.

Their progress matters enormously. When a capability that previously cost $X becomes available for $X/20, the scarcity rent on that capability deteriorates. The question for a frontier model company is how much valuable new capability it can produce before competitors reproduce the old capability.

The economics come down to three variables.

• How quickly the intelligence frontier advances.

• How long competitors need to catch up.

• How much economic value each advance creates during that period.

A frontier company can support extraordinary profits if each new generation creates sufficiently large markets and the company retains its capability advantage long enough to monetize them. A very short time to commoditization produces a much harder business. If the window falls to weeks, spending tens of billions of dollars to advance the frontier becomes difficult to justify.

That does not seem like the most likely outcome to me today, but it is possible. In a lot of ways, it is the trillion-dollar question.

Frontier model companies can therefore become some of the biggest and most profitable companies on Earth while most AI tasks become commodities. Their old capabilities can get cheaper at extraordinary rates without eliminating the value of producing new capabilities.

The economics depend on how quickly the frontier advances, how long the advantage lasts, how much it costs to produce the next advance, and how much economic activity each increment of intelligence makes possible.

We know that intelligence at the task level eventually saturates in many domains. We do not know how many economically valuable tasks remain beyond the current frontier, or how large the markets created by future increases in intelligence will be. The answer to that question will determine the economics of the intelligence frontier.