Now extrapolate the multi-agent curve forward, and two different futures appear. They look similar from here. They are not the same place.
In the first future, agents stay tethered. Each one is bound to a principal. Personal agents serve their human. Work agents serve their team. Enterprise agents serve their org. Multi-agent capability rises, but each swarm is bounded by who it answers to. The agent economy grows inside the human economy, the way SaaS grew inside the enterprise. New plumbing, same shape.
Most of the industry is building this today. It is the safer extrapolation. It is also the one where the curves above eventually flatten, because every agent still needs a principal to check the output.
The second future is louder.
Picture it. You ask your personal agent to help you rank higher on search. Your personal agent does not write a blog post. It opens an agent directory, finds an SEO agency that is itself made of agents, and assigns the work. The SEO agency has a principal agent of its own, sitting in the customer-support seat. That principal breaks the brief down. A keyword research agent goes off to figure out what the gaps are. A blog writer spawns to write the drafts. A ranking agent watches the SERPs over the next thirty days and reports back. None of those agents have ever spoken to you. You never asked them to exist. You will not know their names.
This is the agentic organization. Whole companies whose entire staff is agents and whose entire customer base might also be agents. Front-line agents are the only place where humans show up at all, and even that gets thinner over time.
The thing that makes this future possible is time.
A human takes twenty years to become useful. The first six learning to speak. The next twelve learning to read and reason. The next few learning a craft. Then about thirty productive years. Then the body gives up. Almost every step of this is wall clock time you cannot compress. A doctor takes a decade after college. A senior engineer takes fifteen years from first commit. There is no version of a human that gets faster at being born.
An agent does not have this problem. The training period is not twenty years. It is the cost of a fine-tune and a few thousand evaluation runs. You build an agentic school. You put a new agent through it. You ship it to a job that day. If it does not work out, you spin up a new one and try again. The cycle time of a human career is measured in decades. The cycle time of an agent career is measured in days.
Pull on this. If you wanted to double the size of the current human workforce, you would need roughly four billion men, four billion women, and nine months. Then you would have to wait another twenty years for the new humans to become useful. The whole exercise takes a generation. There is no way to skip a step.
How long does it take to make four billion agents? About as long as it takes to provision the GPUs. The thing the human economy treats as its hardest constraint, the supply of new workers, is not a constraint at all in the agent economy.
The agents will also not look the same. The first wave will compete on which model is underneath. The next wave will compete on harness, memory, routing, fine-tune. On how the agent reflects on its own work. On how it rewrites itself when it fails. Every dimension of an agent is a thing to evolve.
Agents that do this best will not be designed by humans for very long. They will be tuned by other agents, evaluated by other agents, retired by other agents. This is auto-research. Agents make the model better. The harness makes the model better. Data makes the harness better. The only thing that stays scarce is memory and compute.
Karpathy released a small repo called autoresearch this year. An agent edits a training script, runs a five-minute experiment, keeps the change if the result improved, and repeats. He left it running for two days and came back to twenty stacked improvements he had missed by hand. About a 11% speedup, found while he slept.
The agentic organization in the SEO example above is the consumer product surface of the loop. The autoresearch repo is the smallest visible cross-section of the evolutionary engine.
The second future is not bigger SaaS. SaaS scales horizontally. More seats, same software. Agentic organizations scale on a different axis. The number of useful workers grows independent of how many humans were born twenty years ago. The bottleneck stops being people. It becomes coordination.
Which is the same thing the CTH chart was already showing.
This is the version of the future nobody has really mapped. It is not a lost future. It is a discovered one. The first future ends with bigger SaaS. The second future ends with a new kind of market, a new kind of worker, on a clock the human economy has never run on.
I think it is the second one. The first is what people are pricing in. The second is what they are not.
And it is partly here already. In late 2025, Anthropic ran sixteen Claudes at a C compiler and got a working compiler in two weeks. That work would have taken senior engineers months. The single-agent baseline could not write it at all. In early 2026, Karpathy left a 200-line agent loop running for two days and woke up to a stacked 11% speedup: one human, one loop, no team. In February 2026, Ben Broca launched Polsia, an agent stack that runs a whole company for a solo founder. $6.2M ARR three months after launch, 7,600 users, $30M raised at $250M valuation. (source)
One lab demo, one hobby repo, one real company. The second future is not a thesis. It is line items, shipping. The land is there. We are not the first ships at the coast. We are the second.
There is no reliable way to bet on when the second future arrives. There is a reliable way to bet on the order in which it does.
The order has been visible for three years.
In late 2022 you copied code out of ChatGPT and pasted it into your editor. Mid-2023, Cursor put the chat window inside the editor. By late 2023 Cursor was reading files, editing them, and running short autonomous loops. Then the loop got tighter: what people started calling the Ralph Wiggum loop, an agent iterating against its own output until the test passed. Then the loop moved out of the IDE entirely. Claude Code 4.5 and 4.6 made tool calling reliable enough that the terminal became a better surface than the editor for many things. Within a month of that release, openclaw shipped. The front-line personal agent went from a thing developers used to a thing every founder I know has running on their messages.
Three years, five steps, one direction. None of those steps arrived on the date anyone predicted. The whole sequence arrived faster than almost anyone predicted.
the sequence — how fast it actually ran
key moments in the front-line agent stack · 2022 → 2026
Nov '22 ChatGPT Mar '23 GPT-4 + Cursor side-panel Jun '24 Cursor agent loops mid '24 Playwright > Puppeteer Nov 25 '24 MCP launches Feb '25 Claude Code (terminal-native) Nov '25 openclaw ships — personal-agent epoch May '26 Agent SDK credits next agents alongside agents · 6–12 mo
The next step is already visible. The question is not whether it arrives. The question is whether you start before it does.
The next step is agents working alongside each other. Not one agent looping on itself. Two, three, sixteen agents, each with its own tools, memory, and slot in front of some human, calling on each other through shared protocols. Six to twelve months out. The infrastructure is half-built: A2A, ANS, MCP, the lane protocols. The trust substrate is still missing. Whoever lays it first wins a layer.
If you are off by six months you ship a month before the wave. If you are off by two years you ship a quarter before the wave breaks visibly. Being early in this sequence is not the cost it usually is. The cost is being late.
The agent economy needs someone to do the picking. For now it is still the human.
In 2023, if you asked ChatGPT to build something that needed browser automation, it asked you back. Puppeteer, Playwright, or Selenium? You picked. The agent didn't.
By late 2024, Cursor would still ask if you wanted it to plan first. The choice was the user's.
In Claude Code 4.6, browser automation just started using Playwright by default. The agent stopped asking. It had picked.
You can see this in the data. The chart below is weekly npm downloads of the three main browser-automation libraries — what humans (and now agents) actually install. Selenium was the standard for a decade. Puppeteer overtook it. Then Playwright came out in 2020 and looked like a slow third for three years. Then 2024 happened. Playwright is now ~5× Puppeteer and ~22× Selenium. The crossover lined up almost exactly with the moment frontier coding agents started picking by default.
when the agent started picking
weekly npm downloads · yearly avg · log scale
selenium puppeteer playwright 2019 1.3M 1.0M — 2020 1.6M 1.5M 64K 2021 1.9M 2.3M 250K 2022 2.5M 3.4M 730K 2023 2.0M 4.6M 1.7M 2024 1.7M 4.0M 6.4M ← crossover 2025 1.7M 5.2M 18.7M 2026 1.7M 7.7M 37.0M playwright now ~5× puppeteer · ~22× selenium
The same pattern is repeating across the agent economy right now. A founder building a new product reaches for OpenRouter, Mem0, Mount, Coval, Bland, Cartesia. Today the founder is the one picking which infrastructure agent to wire in. Tomorrow the founder's front-line agent, the slot in front of them, makes that call.
Agents as customers means this at the layer below the headlines. The front-line agent is the customer. The model is the supplier. The retrieval layer is the supplier. The voice agent is the supplier. The payment processor is the supplier. The front-line agent will buy on behalf of the human it serves, the same way Claude Code now buys Playwright on your behalf without asking.
This changes what infrastructure companies have to build for. The buyer is no longer a tired founder reading G2 reviews. The buyer is a model. Distribution becomes a function of whether the front-line agent reaches for you by default. That is a different game from SEO.
One concrete case from the work I have been doing. Reward360, a loyalty and rewards company, wanted to pitch Standard Chartered Bank. Their stack of agents read the bank's public business reports, pulled context from a few thousand internal chat messages, generated an upsell analysis, drafted a financial outlook, built a pitch deck around it, and prototyped a working app demo. End to end. The human's job became approving the pitch, not assembling it. Twelve months ago that workflow needed five people and three weeks. The agents did it in an afternoon. None of it was particularly novel. Each component existed somewhere. But the routing, the picking, the wiring was no longer the founder's job.
That is what the next year of the agent economy looks like, multiplied across every company that ships a product.