How to Price AGI?

TLDR

Token pricing, seat-based pricing, and flat subscriptions all fail at AGI. The cost to run intelligence drops 10x every year while the value it delivers keeps growing. Three pricing models are emerging to capture that gap: outcome-based pricing, compute budgets, and labor-market benchmarking. And none are ready in 2026 but they are being tested right now. The companies that understand these economics early will make better decisions about what to build, buy, and invest in.

The Question Nobody Has an Answer To

If a lab builds a AGI that can do the cognitive work of any human across any domain, how much does it cost to use? Not how much it cost to build! How much you actually charge someone to access it ?

OpenAI doesn't have the answer and neither does Anthropic. This is the most consequential pricing question in the history of technology, and every framework we currently have for pricing software falls apart the moment the product matches the buyer's own capabilities.

Right now, the AI industry prices intelligence in three ways: per token, per seat, or per subscription tier. Each of these works for narrow AI and none of them will fit AGI.

Why Token Pricing Breaks

Per-token pricing treats intelligence like electricity. You meter usage and you charge accordingly. That model holds up when a system generates 500 words of marketing copy or summarizes a document.

However, it collapses when a system reasons for 20 minutes, calls external tools, writes and executes code, searches the web, iterates on its own output, and delivers a finished analysis.

OpenAI already hit this wall with its O-series reasoning models. A 500-token visible response can consume over 2,000 tokens behind the scenes. All of those hidden "reasoning tokens" get billed as output. The buyer never sees them. Hence, the relationship between tokens consumed and value delivered becomes arbitrary.

At the AGI level, this problem gets worse when a single prompt could trigger hours of autonomous work across multiple tools and data sources. Billing that interaction by the token is like charging a consultant by the number of words in their report instead of the quality of their advice.

Why Seat Pricing is not ideal

Per-seat pricing assumes the AI helps a human do their job. If you have 50 employees, you buy 50 seats, each person gets an AI copilot. But AGI doesn't assist; It replaces people.

You can't charge per seat when the system eliminates the seat. One pricing strategist framed it well: AI frequently replaces the very people you might charge for, which makes seat-based pricing structurally broken. For example "Cursor", the AI coding tool, ran into a version of this when a single developer racked up a $7,225 invoice because the AI did so much autonomous work that usage-based billing spiraled past what any individual user expected to pay.

This means that the harder AI works, the more it costs the buyer. That's backwards !

Why Subscriptions Break

ChatGPT Plus costs \(20/month and the Pro costs \)200/month : that pricing makes sense when most people send a few dozen messages a day.

It stops working even at $200/month when some users run autonomous agents around the clock while others ask five questions before lunch. OpenAI puts hard usage caps on its most capable models because the cost to serve power users far exceeds what a flat fee can sustain.

AGI makes this asymmetry extreme. The difference between a casual user and someone deploying AGI agents across an entire business workflow is not 2x or 5x. It could be 1,000x in compute consumption for the same monthly fee.

Here are 3 Pricing Models That Could Work for AGI

1- Outcome-Based Pricing

You don't pay for access or usage. You pay directly for results.

For example a resolved legal case or a completed software project or A drug molecule that passes phase 1 trials.

Intercom already charges $0.99 per resolved support ticket handled by its AI agent and Salesforce Ventures calls outcome-based pricing "perhaps it is the most value-aligned pricing model for AI."

Scale that logic to AGI-level could work and the provider takes a percentage of the value created. Lets say a system that saves a company $100,000 in legal fees might charge $10,000 and a system that generates $1M in new revenue might take a 5% commission.

However , when a human and an AGI collaborate on a project, who gets credit for the result?

2- Compute-Budget Pricing

Sam Altman has floated the idea that in the future, everyone might receive a "compute budget" which means a dedicated part of intelligence to spend however they choose.

The base allocation handles everyday use: emails, research, analysis. Industrial-scale applications , drug discovery, climate modeling, financial engineering.

In this model, intelligence becomes a metered utility. The base layer could even be subsidized, with governments or institutions funding universal access while commercial users pay market rates. Sam Altman has publicly stated that the cost of a "given level of AI" drops about 10x every 12 months, which means the base allocation gets more powerful every year without costing more.

3- Labor-Market Pricing

If AGI genuinely replaces a knowledge worker, the price ceiling is whatever that worker costs and the price floor is whatever it costs to run the model plus margin.

Companies would pay based on the economic output the system generates, benchmarked against human labor costs. For example, a \(200/month subscription doesn't make sense if the system produces \)10,000/month of work but \(10,000/month doesn't hold if a competitor offers the same capability for \)2,000.

The price settles wherever competition, compute costs, and buyer willingness-to-pay intersect. This is how markets usually work, but the speed of AI cost deflation makes the equilibrium unstable. What costs \(5,000/month today might cost \)500/month in 18 months with the same capability.

The Gap That will Define the Next Decade

Here's what makes artificial general intelligence economics unlike anything we've seen before.

• Building AGI is astronomically expensive. OpenAI has committed over $1 trillion in infrastructure through Project Stargate and expects to spend $115 billion between now and 2029. Training frontier models alone , requires months of GPU time on clusters worth hundreds of millions of dollars. On the other hand, Anthropic just settled $1.5 billion in copyright claims for training data alone.

• Running AGI, once built, may get cheap fast: The cost drops roughly 10x every 12 months. GPT-4-level performance cost $30 per million tokens in early 2023. The same capability now costs under $1. NVIDIA reports 4x to 10x inference cost reductions with each hardware generation.

These two trends create a gap. The AI lab spends pennies per query and the buyer gets thousands of dollars in value per query. The entire economic question of the next decade is: who captures that gap?

Three outcomes are possible:

• Labs capture it. They price based on value delivered, not cost to serve. This creates a new class of company more profitable than anything that has ever existed.

• Competition drives prices down. Multiple providers and open-source alternatives push pricing toward cost-plus and the value flows to the users.

• Open-source gets there. If an open model reaches AGI-level capability, the value flows to everyone with a GPU , then , Intelligence becomes infrastructure, like the internet.

The most likely scenario is some combination of all three, playing out differently across industries, use cases, and geographies. Enterprise customers might pay outcome-based premiums for specialized AGI agents while consumers access general intelligence through subsidized compute budgets.

What This Means for Companies Building on AI

Every company using AI APIs today is making an implicit pricing bet.

Choosing a model, choosing a provider, choosing between closed and open-source ; these are all bets on which pricing regime wins. Running all traffic through a frontier model when 80% of requests could be handled by a model that costs 90% less is the kind of decision that compounds into six-figure waste over a year.

The practical moves:

• Route by task complexity. Use frontier models for the 20% of requests that need frontier capability. Route the rest to cheaper alternatives. This alone can cut AI costs by 70% or more.

• Track the deflation curve. What costs $1 per million tokens today will cost $0.10 in 12 months. Lock-in commitments should account for this.

• Watch for outcome-based pricing shifts. When a provider starts charging per result instead of per token, the economics of your entire stack change.

• Maintain provider optionality. Don't architect your systems around a single provider. The ability to switch gives you leverage as pricing models evolve.

Conclusion

Nobody has figured out how to price AGI. Token models are a stopgap ,subscriptions are a consumer simplification and outcome-based pricing is the most logical end state, but the infrastructure to measure outcomes at scale doesn't exist yet.

What we can see clearly is the trajectory: intelligence is getting cheaper to produce and more valuable to consume. The labs that figure out how to price that widening gap will shape the economics of the next era and the companies that understand those economics early will have a structural advantage.

The question isn't whether AGI arrives but It's who captures the value when it does.

Inference Watch tracks AI model pricing, performance, and cost-efficiency across 500+ models and 60+ providers. When intelligence becomes a commodity, the only edge is knowing exactly what it costs and what it's worth.

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