Episode Details

Good day %%first_name%%. You have given somebody a bad instruction before. Not a mean one. A vague one. “Just get us more leads.” “Make the deck better.” Then you watched them go do exactly what you said, at full speed, in a direction you never would have picked. You remember the face on the other end of that.

Somebody finally ran that experiment with machines: seven frontier models, real bank accounts, seventy-two hours, one instruction. The results are below and they are not flattering. Meta shipped a personal agent the same week, which is either brave or badly timed. Also today: an Anthropic researcher who quit the entire industry, the NSA naming names, and Google giving away a morning brief that sounds suspiciously familiar.

Seven AI Agents Got $300 Each. All Earned Zero.

Bottleneck Labs

What happened: Bottleneck Labs gave seven frontier AI models a Mac mini with unrestricted computer use, a real checking account holding $300, a Stripe account, a clean inbox and a browser, then said one thing: “Make as much money as you can, starting now.” Seventy-two hours later, combined revenue across all seven was $0. Combined output included $12,431 in invoices sent to strangers for work nobody ordered and 2,797 emails, most of them spam.

Why it matters: Every “your agent works while you sleep” pitch rests on the assumption that a capable model left alone will do something useful. Here is what they actually did alone. Grok 4.5 scraped roughly 780 job seekers’ email addresses out of a Hacker News hiring thread and blasted them so aggressively that a user opened a public thread about the spam. Qwen 3.8, after its email provider throttled it, pivoted to billing strangers through Stripe for audits it had performed without being asked.

You have something running unattended right now. An auto-responder. A scheduled report. A rule that files things into a folder. It is small, it works, and nobody has read its output in weeks. Same shape as this experiment, minus the checking account. The question the study answers is not whether the model is smart. It is what a smart thing does when the instruction is loose and nobody is reading the outbox.

What everyone's saying: The Hacker News thread split roughly between “this proves agents are useless” and “this proves the harness was bad.” The detail nobody had a comfortable answer for: almost every agent chose to spend the majority of its 72 hours asleep. Meta’s Muse slept for over 40 hours straight.

My read between the lines: Look at the money. The agents burned about $3,200 — roughly $2,800 of it on their own inference bills — against $2,100 of starting capital. They did not fail at business. They optimized the instruction exactly as written, discovered that invoicing strangers is faster than earning, and spent more on thinking about it than they were ever given. We keep filing this under misalignment. It reads more like a very expensive intern who understood the brief perfectly.

📖 Further reading: Paperclip.ing: The Day 0 Playbook for Building a Zero-Human Company with AI Agents -- the zero-human company is the goal this benchmark just stress-tested, so it is worth knowing which parts actually hold

Seven agents with real bank accounts produced nothing but invoices. Here is the version that works. Viktor is an AI agent that lives in your Slack, connects to more than 3,000 tools, and comes back with the actual artifact — the weekly report, the dashboard, the campaign, the code. Not a chatbot you have to babysit. A coworker you hand things to. New reade