Enterprise AI should optimize intelligence consumed per successful outcome, not default to frontier models for every task. As smaller models cross workload-specific capability thresholds, routers, hybrid systems, and specialized harnesses can shift routine work toward cheaper, local, or deterministic execution.

Right-Sizing Your Intelligence Spend

By: Jaya Gupta, Avanika Narayan, Jon Saad-Falcon

Frontier models have become astonishingly intelligent. In May, OpenAI reported that a general-purpose internal model had disproved a central conjecture in Erdős’s planar unit-distance problem: a question mathematicians had worked on for nearly eighty years. External mathematicians checked the proof; Tim Gowers said that a human-written version would merit immediate acceptance at the Annals of Mathematics. Anthropic reports that Claude Opus 4.6 has found and validated more than 500 high-severity vulnerabilities, including bugs in codebases that had been fuzzed for years and accumulated millions of CPU-hours of testing.

These achievements are real, and they can be extraordinarily valuable. When the answer is unknown and discovering it could be worth millions: a proof, a drug target, a zero-day, or an unfamiliar market structure, maximum intelligence is exactly what we should want. Spending more computation to explore more possibilities is rational.

But the public scoreboard is shaped by the work that can be cleanly stated and verified: proofs, patches, exploits, and scientific reasoning.

The only problem is that the rest of the economy does not work that way.

A claims adjuster is not searching for a new mathematical truth. She is deciding which of the company’s existing rules apply to this accident, this policyholder, and this sequence of events. A nurse is not inventing medicine. She is combining established protocols with one patient’s history, current symptoms, and the specific constraints of her hospital. A logistics coordinator is not discovering a new algorithm. He is reacting to today’s inventory, weather, contracts, and delays. Some jobs create value by expanding the frontier of what is known. Most create value by making the right decision within the boundaries of what an organization already knows.

The Economy Is Not a Research Lab

Frontier research is extraordinarily valuable and economically narrow. BLS data shows that life, physical and social-science occupations account for less than 1% of American employment. The United States employs roughly 2,000 mathematicians, 20,000 physicists and 37,000 computer and information research scientists. It employs millions of nurses, managers, administrators, logistics workers and customer-service representatives.

Most companies do not spend their days discovering “unknown answers”. They spend them applying what they already know across millions of decisions shaped by customers, policies, inventory, contracts and history.

And so while the frontier is an intelligence problem, the rest of the economy is a context-and-execution problem; this distinction is visible across the F500. Take Walmart - they coordinate inventory, stores and suppliers. UnitedHealth coordinates care. Amazon operates warehouses, deliveries, merchants and customer interactions at massive scale.

What makes the majority of these companies defensible is not superior “frontier” intelligence. It is proprietary data, dense operational context, long-built processes, supplier relationships, regulatory licenses, physical networks, and the accumulated judgment that only comes from performing the same class of decision millions of times. Their moat is not that they think better than everyone else but that they already know how their world works over decades of experiencing it.

Imagine replacing every employee at American Airlines, Home Depot or Medtronic with a math olympiad who approached every task as a problem of discovery. Costs would not fall, but would rise rapidly and dramatically as every routine decision would be re-examined from first principles. The organization would drown in intelligence it cannot productively use or absorb.

Coding is the important exception: it is a large labor market, the work is digital, and outcomes can often be verified through tests. That makes additional model intelligence unusually valuable. However, the frontier labs products themselves are often designed in ways that make it easy for developers to blast tokens: long-running agent loops, recursive sub-agent spawning, and “keep going until it works” patterns. Thus, even coding is developing capability thresholds as smaller and open-weight models approach frontier performance on increasingly broad classes of work.

Every Workload Has an Intelligence Threshold

Every workload has some intelligence threshold. Below it, the model cannot perform the task. As the model approaches it, greater intelligence creates enormous value. Once the threshold has been crossed, the bottleneck changes. The outcome depends increasingly on whether the system knows the company’s policies, customers, history, tools and standards and whether it can act reliably, quickly and cheaply.

Models keep getting more intelligent, but most workloads are not. Take refund policies, baggage rules and invoice-reconciliation procedures - these are not getting any harder. The latest frontier models, particularly from Anthropic, are optimized for “extreme” intelligence — the kind required by research mathematicians, physicists, and engineers solving previously unsolved problems. That market is real. It is also small. The problem arises when the same models become the default for ordinary enterprise work.

Developers naturally reach for the most capable system available. Anthropic’s products make that choice frictionless: long-running agents, recursive sub-agents, and high-effort reasoning loops that burn tokens by design. The result is frontier-priced intelligence applied to tasks that often do not need it. Two trends are eroding that premium: the shift from closed to open-weight models, which lowers the cost of a given level of capability, and the shift from frontier-scale to small and mid-sized models, which compresses that capability into a much smaller compute footprint. Qwen3.8-27B illustrates the latter: at just 27B parameters, it matches or beats Opus 4.6 Max on several coding and agentic benchmarks. Our intelligence-efficiency results point in the same direction: intelligence per joule improved 18× in 16 months, delivering comparable capability with dramatically less compute, energy, and ultimately cost.

As more models cross the intelligence threshold for ordinary enterprise work, frontier systems should be reserved for the smaller set of tasks where additional intelligence still changes the outcome.

Companies already understand this dynamic when hiring people. Have you ever hired an employee that was dramatically overqualified for a role? They may reconsider settled decisions, introduce unnecessary complexity, become bored and eventually leave. The organization would have been better served by someone sufficiently capable who understood its customers and how the work actually gets done. Models are overqualified too, with a more persistent failure mode. However, instead of getting bored and quitting, they keep generating extra complexity, variance and cost indefinitely. A password reset does not improve because an agent considers twelve explanations, launches a security investigation and composes a personalized essay. Once the customer’s identity is verified, the job is to reset the password safely and stop.

Overqualification is now becoming measurable vs just being an illustrative analogy. Researchers studying reasoning models have documented an “overthinking” problem: models routinely spend additional inference compute on easy questions without improving accuracy. Amazon researchers estimate that reasoning systems can generate **7 to 10x **as many tokens as necessary on simple tasks. The marginal value of intelligence is therefore not merely diminishing. On sufficiently easy workloads, the marginal reasoning token can approach zero.

Is the task failing because the model is not intelligent enough or because it does not know how this company works? Enterprises that continue to treat maximum intelligence as the default will find themselves paying frontier prices for work that no longer requires it. The correct posture is the opposite: assume open-weight and specialized systems will keep eating the stack, and reserve frontier models only for the problems that still sit above the rising capability line.

The Labs and Enterprises Have Different Incentives

There are competing incentives between labs and enterprises. A model lab wins by making more computation useful; its success metrics are tokens consumed, session length, agent count, reasoning depth, and the ability to tackle harder problems. Every additional unit of intelligence it can sell is a victory. The lab is incentivized to make the frontier model the default, to encourage longer thinking, more tools, more retries, and more agents. Its dashboard lights up when usage rises. An enterprise wins by making repeated computation unnecessary. Its success metrics are cost per verified resolution, tokens per processed claim, time to completion, escalation rate, and customer outcome. The moment a company learns enough to turn a recurring request into a three-bullet artifact, a deterministic rule, or no model call at all, the lab loses a future revenue stream.

The provider has no incentive to help the customer eliminate the call, route it to a cheaper rival, or compress it into software.The divergence is absolute the moment the enterprise learns. The lab is still trying to sell more intelligence. The enterprise is trying to need less of it. The data already shows the gap. OpenAI reports that average reasoning-token consumption per enterprise organization rose approximately 320-fold over the past year. PwC’s survey of 4,454 CEOs found that 56% had not yet seen a significant financial benefit from AI.

Measurable business outcomes are not keeping pace. One side is succeeding on its own terms. The other is not. A model dashboard that reports token usage up five times is a success for the lab. For the CFO it is an unanswered question: did customer retention improve, did cost per successful outcome fall, or did we simply burn more intelligence on the same work?These are not the same game. One player is paid to increase the size of the fire. The other is paid to put it out and keep it from starting again.

The Architecture Follows the Economics

If an enterprise’s goal is to reduce intelligence consumed per successful outcome, it cannot leave every decision about intelligence consumption to the model provider. That control must live inside the enterprise at every layer. Three predictions for the future:

**There will be millions of routers and gateways, **inside every serious AI harness, at the policy layer, at the team level, and at the organization level. Each one will decide, for every unit of work, whether it belongs on a frontier model, a smaller open-weight model, a local model, a deterministic system, a human, or nowhere at all. The TAM for this routing layer will ultimately look a lot like the workforce itself — the vast majority of ordinary decisions handled cheaply and locally, with only a thin slice of genuine discovery reserved for premium intelligence. Above it, every single organization will have a gateway that will enforce security and data-residency requirements, negotiate cost, set budgets, and determine which models should be permitted for different classes of work.

**Second, hybrid workloads will become the default. **Once applications can allocate intelligence, workloads will stop belonging to a single model. Right now almost every query is sent to the cloud by default, even when the data is sensitive, the task is simple, or the answer already lives inside the company’s own systems. That is expensive, slow, and often unnecessary. In the future the easy, repetitive, or private parts of a task will stay local or near the system of record. Only the genuinely hard or novel parts will be sent to a frontier model in the cloud. In our research, we have already demonstrated the shape of this architecture. In our Minions work, a frontier cloud model decomposed complex long-context problems while smaller local models executed the resulting subtasks. The hybrid system recovered 97.9% of frontier model accuracy at 5.7× lower cost; a more aggressive configuration achieved 87.9% at 30.4× lower cost. The important result is architectural, not merely economic: expensive intelligence was most useful for deciding what work needed to be done, not for performing every unit of it.

**Third, AI will diffuse through two complementary application surfaces. **General-purpose proactive assistants such as Instinct will make intelligence ambient. They will observe work, identify opportunities, and initiate tasks without waiting for a prompt. Specialized applications such as Maximor and PlayerZero will do something different: they will turn enterprise intent into reliable execution by controlling the context, tools, permissions, workflow state, and definition of done for a particular job.

Beneath those harnesses, a new improvement layer will emerge. Platforms such as Applied Compute’s AC2 allow companies to train, evaluate, serve and replace the models operating inside their existing applications. The harness remains the system of execution; AC2 helps the intelligence inside it become better, faster and more economical.

The proactive assistant becomes the demand aggregator. The application harness becomes the execution system. AC2 becomes the specialization and improvement infrastructure connecting production experience back to the models. It continuously moves each workload toward the least expensive system still capable of producing the correct outcome.

The most intelligent enterprise is not going to chase the “frontier models” blindly for all work. It will be the one that has learned how to need intelligence the least, turning yesterday’s expensive reasoning into tomorrow’s cheaper, more reliable execution.

Progress will not be measured by how much intelligence a company can afford to consume, but by how quickly intelligence consumed per successful outcome falls toward zero.

Shoutout to Ashu Garg for reviewing this :)