The prevailing bear case for model labs is straightforward: intelligence becomes abundant, models converge in quality, inference prices collapse, and economic value migrates upward to the applications that own customers and distribution. You've probably heard it: “the model layer commoditizing.”

This is already happening, partially. But it does not follow that the entire model layer will commoditize.

A worthwhile upstream question is: Which kinds of intelligence commoditize?

The intelligence floor

Most enterprise tasks have a minimum level of intelligence required to perform them adequately.

Imagine an email extraction task: find the date of an appointment in an arbitrary email. Below a certain capability threshold, the model is unusable. It picks dates incorrectly, misses important context, and creates more work than it saves through inaccuracy. Once the model reliably clears that threshold, however, additional intelligence may not matter very much.

A model that is 20 percent “smarter” does not necessarily create 20 percent more value. If several models can all perform the task with sufficient accuracy, buyers begin optimizing for price, latency, reliability, privacy, and ease of integration.

The task has crossed its intelligence floor.

After that point, intelligence becomes interchangeable. It’s commoditized. And the race to the bottom begins.

Competition pushes the model’s price toward the underlying cost of inference, just as standardized compute, storage, and bandwidth gradually became utility-like inputs.

The empirical evidence for this process is strong. Stanford’s AI Index found that the inference cost of achieving roughly GPT-3.5-level performance fell more than 280-fold between late 2022 and late 2024. Smaller models continue reach performance levels that previously required much larger systems.

Open-weight models exacerbate this dynamic. Over the past year they have consistently trailed the frontier. Once an open-weight model clears a given intelligence threshold, competition to serve intelligence at that level explodes. Possible oligopolistic pricing structures that preserve model-provider margins become nearly impossible to sustain.

This suggests a general rule:

Once several models exceed the intelligence requirement for a task, that task’s model economics begin to commoditize.

Much of the existing enterprise market may fit this description. Document extraction, summarization, basic customer service, routine content generation, classification, translation, and constrained workflow automation do not require unlimited intelligence. They require sufficiently high accuracy at sufficiently low cost.

As every major lab improves, more tasks fall behind the frontier and enter this commodity zone.

But not every task has a floor

There is another class of work for which better intelligence continues to produce more economic value.

This type of work is that of a generally intellgent worker who faces open-ended tasks with undefined ceilings of excellence.

Consider software engineering. A model that can reliably resolve a one-hour ticket by writing slop is useful, kind of. On the other hand, a model that can resolve a one-hour ticket by reasoning across a large codebase, can coordinate its own tests, write maintainable code, identify forward facing bugs, and recover from mistakes is not a substitute for the first model. Even if the task is the same - additional intelligence improves the work.

The same logic applies to scientific research, complex engineering, strategic decision-making, legal reasoning, and general-purpose autonomous work. In these domains, improvements in intelligence can expand the set of economically valuable tasks a model can perform.

There is no obvious point at which the market says: “This model is intelligent enough to conduct research.”

A more capable research model might generate stronger hypotheses, design better experiments, interpret more ambiguous results, or operate over a longer research program. A more capable coding agent might move from completing tickets to building products. A more capable general agent might progress from assisting an employee to substituting for increasingly large portions of the employee’s work.

For these tasks, the capability curve may not contain a fixed intelligence floor. It may instead resemble an expanding frontier:

This is the portion of the market that may not commoditize.

A model market split in two

The model layer is therefore likely to divide into two markets.

The first is the saturated-intelligence market. These are tasks for which many models have cleared the required capability threshold. Performance differences become less important, switching costs are limited, and providers compete on cost and operational characteristics. Intelligence in this market trends toward the price of compute.

The second is the frontier-intelligence market. These are tasks where the most capable model can perform work that weaker models cannot, or can perform it with materially higher reliability and autonomy. Here, access to the frontier may command a substantial premium because the product is not tokens. The product is access to a larger domain of economically useful cognition which is not widely held.

The 'time arbitrage' of this held advantage is exceedingly important: a small capability lead can produce a large economic advantage when it unlocks a new category of work.

How large is the frontier market?

The model-commoditization thesis depends on an implicit assumption: that most economic demand for AI will eventually consist of bounded tasks with attainable intelligence floors.

This isn't unreasonable. Most business processes are repetitive, constrained, and tolerant of “good enough” performance. The enormous volume of these tasks could make commodity inference a very large market even at low margins.

But a disproportionate share of economic value may sit in the opposing category.

The world spends trillions of dollars on software engineering, scientific research, engineering design, product development, and other forms of difficult, open-ended knowledge work. As we've explored, these are domains where better intelligence can continue to improve the quality of the outcome rather than merely clear a fixed threshold.

If each generation of frontier models can automate a larger share of this work, the leading model is not competing only for existing AI spend. It is continuously expanding the addressable market; creating entirely new categories of value.

Take drug discovery. A frontier model that identifies a previously unknown drug target may make a new therapeutic market addressable; a model that helps invent a new computing interface, material, or physics breakthrough could create an entirely new market. In both cases, SOTA intelligence expands the economy rather than merely capturing existing AI spend.

This creates an unusual industry structure:

The lagging edge commoditizes while the leading edge discovers new demand.

Every capability eventually becomes cheaper and widely available. But the frontier may continue moving outward faster than the commodity market catches it.

Under this model, claims that “models are commoditizing” can be simultaneously correct and incomplete. They describe what happens to a fixed level of intelligence over time. They do not acknowledge the economic value of the moving frontier.

A note on the frontier vs commoditization gap

It's worth noting that the progress of frontier model capabilities may take two dramatically different trajectories in the future: a plateau or recursive intelligence improvement.

In the plateau scenario, progress in frontier intelligence slows despite continued investment. Successive model generations produce smaller capability gains, even as the capital required to train and operate them increases by orders of magnitude.

As frontier progress slows, cheaper and open-weight models have more time to catch up. The gap between frontier and commoditized intelligence narrows. Eventually, the leading model may remain technically superior without being sufficiently better to command a meaningful economic premium.

Note that this is not the trajectory we currently appear to be on, given METR’s measurements of model task-completion horizons.

Recursive intelligence offers a fundementally different outcome: labs at the forefront may capture an enduring and rapid capture of frontier market value.

If frontier models become capable of meaningfully contributing to the research required to build better frontier models, progress may accelerate rather than plateau. A leading lab could use its current capability advantage to improve its research process, produce a stronger successor, and then use that successor to accelerate the next generation.

The gap between frontier and commoditized intelligence could therefore substantially widen rather than narrow.

In my view, this is the bull-case justification for the extreme valuations commanded by frontier labs today. The risk profile is extraordinarily asymmetric: the frontier-intelligence model market either enters a race to the bottom, or the leading labs capture a large share of an unprecedented expansion in economically useful intelligence.

The frontier model layer does not commoditize

In conclusion, the strongest version of the commoditization thesis is probably wrong.

Models do not move uniformly from differentiated products to interchangeable utilities. Instead, each level of intelligence appears to pass through a lifecycle.

At first, a capability exists only at the frontier and commands a premium. Competitors catch up. Smaller and cheaper models reproduce it. The capability becomes standardized, and its price collapses toward inference cost.

But while this is happening, the frontier models may unlock a new capability above it.

The model market is therefore better understood as a cascading system:

The unresolved question is not whether today’s intelligence will commoditize. It almost certainly will.

The question is whether the frontier continues to move into economically valuable territory quickly enough to create a persistent premium for whoever leads it.

My base case is that it does. Routine intelligence will become abundant and cheap. But the most valuable portion of the model market may remain attached to a moving boundary of autonomy, reliability, and task complexity. That boundary could represent a large share of model-layer value even if it represents a minority of total inference volume.

Models commoditize, but frontier intelligence may not.

The strategic consequence is that the model layer may support very different businesses at the same time: low-margin utilities serving saturated tasks and extraordinarily capital-intensive frontier labs selling access to the next tranche of automatable work and discovery.