OpenAI explains how its business scales with AI | Keryc
They launched ChatGPT as a research experiment to see what would happen if frontier intelligence landed directly in people’s hands.
What followed was massive adoption and deeper use than anyone expected. Does that sound familiar? Students solving homework at odd hours, parents organizing trips and budgets, writers beating the blank page. The tool stopped being a curiosity and became daily support for making complex decisions and understanding everyday life.
From curious assistant to work infrastructure
At first it showed up in small tricks: a polished draft before a meeting, a spreadsheet checked over, an email to a client with just the right tone. Soon it became part of workflows: engineers debugging code faster, marketing teams with sharper ideas, finance modeling scenarios with more clarity.
More than a tool, it turned into infrastructure that helps you create more, decide faster and operate at a higher level.
"Our business model must scale with the value that intelligence delivers."
That’s the central idea: monetize in proportion to the real value AI provides, not charge for the sake of charging.
How they structured it
OpenAI applied that principle across several layers:
Subscriptions for users who want more capacity and reliability.
Plans for teams and pay-as-you-go pricing so cost grows with the actual work.
A platform for developers and companies that lets you embed intelligence via API, where spending increases with delivered outcomes.
Commerce and advertising when AI helps move from exploring to deciding; as long as it’s clear and actually useful.
The rule is simple: if monetization doesn’t add to the experience, it doesn’t belong.
Compute: the scarcest resource and how they manage it
OpenAI shows numbers that speak for themselves: available compute grew from 0.2 GW in 2023 to 0.6 GW in 2024 and to ~1.9 GW in 2025. At the same time, annual recurring revenue went from $2B in 2023 to $6B in 2024 and to over $20B in 2025.
That’s not magic: it’s sustained investment in capacity. Three lessons here:
Compute defines who can scale.
Diversifying providers gives resilience and certainty of capacity.
Managing it like a portfolio lets you train models on premium hardware and serve massive loads on more efficient infrastructure.
The result: lower latency, higher throughput, and viable costs for everyday workflows (they mention costs measured in cents per million tokens).
Product, agents and continuous automation
On top of the compute layer sits the product platform: text, images, voice, code and APIs. The next phase they highlight is agents and workflow automation that keep context, run continuously and act on tools.
For a person it means an AI that manages projects and executes tasks. For an organization, it means an operational layer for knowledge work.
As these things move from novelty to habit, usage becomes deeper and more predictable, which strengthens the platform economy.
Economic models and financial discipline
The monetization path is no longer just subscriptions. Today they operate with several layers: consumer, teams, a free ad- and commerce-supported option that drives adoption, and usage APIs for production loads.
They also look ahead: intelligence applied to scientific research, drug discovery, energy and finance will bring new models: licenses, IP-based agreements and outcome-linked pricing.
Sustaining that requires discipline: multi-year compute commitments, flexible contracts, a light balance sheet and capital committed in tranches based on real demand signals. That flexibility lets you move when growth is there without overcommitting.
Priority for 2026: practical adoption
The focus for 2026 is closing the gap between what AI can do today and how it’s used in practice. There are big, concrete opportunities in health, science and enterprise where better intelligence means better outcomes.
Infrastructure, innovation, adoption and revenue form a cycle: infrastructure expands what can be delivered, innovation expands what intelligence can do, adoption expands who uses it, and revenue funds the next leap.
And you—what can you apply today in your work or project to ride this wave of practical adoption? It’s not about futuristic tech: it’s about designing flows and decisions people use every day.