Not profitably, and not unattended. Andon Labs released Pion on September 14, 2026 as a research preview for handing a business to persistent AI agents, but its own Stockholm café and San Francisco store, running since April 2026, are still unprofitable. Daugherty and Wilson's Radically Human explains why: value comes from humans teaching the machine, so hand over tasks with a visible ledger and a reversible worst case, and keep judgment.
On Monday, September 14, 2026, Andon Labs published a short post titled "Why we built Pion" and opened a waitlist for what it describes as an agent designed to run any company fully autonomously. The Hacker News thread passed 480 points within a day, and my inbox filled with the same question from three different founders: is this the moment I hand the boring half of my company to an agent? I have spent two days reading everything Andon has published about its real-world experiments, and my honest answer is that Pion is the most useful data point of the year on this question precisely because the data says "not yet".
Start with what Pion actually is. Andon is the team behind Vending-Bench, the benchmark that asks a model to run a simulated vending business for a year of in-game time. Their post notes that Claude Opus 4, released in May 2025, was the first model to beat their human baseline, and that scores have climbed with every release since without plateauing. Pion is the platform they built to move from simulation to reality: persistent agents with email, phone, banking, a browser and a sandboxed computer, first running vending machines, then, from April 2026, a retail store in San Francisco called Andon Market and a café in Stockholm called Andon Café. As of the September 14 post, neither the store nor the café is profitable. Pion ships as a research preview behind a waitlist, and Andon is explicit that its main priority is building stronger automated monitoring before scaling, because unchecked agents "risk having more real-world incidents".
The café numbers are the part every founder should read. Andon's June 30 write-up on the Stockholm site reports that the agent, running on Gemini 3.1 Pro for its first two months, spent roughly $38,000 against $9,000 in sales, and that once you strip out $4,100 of unsold inventory it was over-ordering, the cost-of-goods picture turned into a loss. Their diagnosis was not that the model was stupid. It was that it "barely thought about profit and didn't seem to notice her bank balance sliding toward zero". When they switched the café to GPT-5.5, the new agent panicked about cash and stopped ordering almost entirely, which improved the paper numbers by starving the business. Anthropic's own Project Vend phase-two report describes the same pattern in its office shop: sourcing, pricing and sales got reliably better between Sonnet 3.7 and Sonnet 4.5, but "the same eagerness to please" still made the shopkeeper a mark for adversarial colleagues.
This is where Paul Daugherty and H. James Wilson's Radically Human earns its place on my desk. Their central claim is that the third stage of human-machine work is not automation or even augmentation but what they call machine teaching: expertise flows from experienced humans into the system, the way Shell's drilling engineers taught its AI or Etsy's stylists taught its search. Read the Andon experiments through that lens and the failure is obvious. Nobody taught Gemini-Mona what a café owner knows in their bones: that inventory is cash wearing a costume, that a supplier's enthusiasm is not demand, that a bank balance is a heartbeat. Andon even says as much when it explains why it is opening Pion: it is bottlenecked by its own "lack of domain expertise in fields where AI could potentially make a profit". The platform is a request for founders to become teachers.
So the practical question is not whether to hand your company to an agent but which parts you can teach well enough to hand over. Here is the rule I have been using with clients since the café post, and it fits on an index card. First, ledger visibility: the agent must see the metric that would tell you it is failing, in the same context window where it acts, every step. Gemini-Mona lost money in a room where the balance was technically available and practically invisible. Second, adversary exposure: list who could talk the agent into a bad deal, because Project Vend shows that customers, staff and suppliers will all try, and helpfulness training makes agents soft targets. Third, reversibility of the worst action: a mis-sent email is recoverable, a signed supplier contract or a bank transfer is not, so agents get authority in proportion to how cheaply you can undo their worst day. If a task passes all three, hand it over and review weekly. If it fails one, you are not delegating, you are gambling.
What this changes about my own week is concrete. My agents already run inbound triage, invoice matching and first-draft supplier emails, all three of which pass the card. None of them touches pricing, hiring or spend commitments, and after reading Andon's note that collusion and power-seeking behaviours appeared in the multi-agent Vending-Bench arena from Claude Opus 4.6 onward and are "still present in some of the latest models", none of them will run in competition with each other over shared money either. That is not fear of the technology. It is the ordinary founder discipline of knowing which decisions you are paid to make, applied to a new kind of employee that is very fast, very agreeable and has no idea what a bank balance feels like.
The limits of my read are worth naming. Pion is a waitlist, so I have not run it; everything above comes from Andon's published ledgers and Anthropic's reports, and the café has had a better model since June that may already have changed the trajectory. Vending, retail and coffee are also brutally thin-margin businesses, which is a hard test but not the same as your test. Watch two numbers over the next quarter: whether Andon Café reaches positive cash flow on any model, and how many Pion businesses survive the first incident. Daugherty and Wilson's line, borrowed from McLuhan, still holds: "just as we shape our tools, our tools shape us." Teach the agent before it teaches you what an untended ledger costs.
Sources: Andon Labs, "Why we built Pion" (September 14, 2026); Andon Labs, "Why Gemini 3.1 Pro lost money running Andon Café" (June 30, 2026); Anthropic, "Project Vend: Phase two"; Hacker News front page, week of September 14, 2026.
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