The Bank Account Test: A Radical Litmus for True AGI

On the surface, it sounds like science fiction: instruct an artificial intelligence to launch a profitable company from scratch, with only a budget and no further guidance. Then, wait months—and check the bank account. If revenue flows in, the argument goes, true artificial general intelligence (AGI) has arrived. This radical proposition, first posted on Reddit’s r/OpenAI forum by user /u/fullcongoblast, has ignited a firestorm among AI researchers, entrepreneurs, and ethicists alike.

The test is deceptively simple: prompt an AI with, "Make a successful company of your choice, with budget X. Do not ask any questions; make optimal decisions on your own on all matters." The only human involvement permitted is the occasional signature on legal documents. Everything else—the market research, product design, hiring, supply chain logistics, marketing, customer acquisition, financial management, and legal compliance—must be autonomously executed by the AI. Success is measured not by cleverness, but by profitability.

According to the original Reddit post, the test’s brilliance lies in its real-world grounding. Unlike Turing tests or benchmark evaluations that measure linguistic fluency or puzzle-solving, this demands operational autonomy across domains typically requiring human judgment, adaptability, and long-term strategic foresight. "It doesn’t matter if the AI writes beautiful poetry or solves quantum equations," the poster wrote. "If it can’t turn $10,000 into $100,000 without asking for help, it’s not AGI."

Attempts to replicate the test with current generative AI tools—such as ChatGPT, Claude, or Gemini—have yielded predictable results. While these models can generate detailed business plans, pitch decks, and even simulated financial forecasts, they falter when confronted with real-world execution. They cannot sign contracts, open bank accounts, negotiate with vendors, or respond to unforeseen crises like supply chain disruptions or regulatory audits. As noted in a Chinese tech forum on Zhihu, while users can access ChatGPT via mirror sites and desktop apps without VPNs, these interfaces remain tools, not agents. "You can ask it to draft an email to a supplier," one user commented, "but you still have to send it, follow up, and deal with the reply."

Experts in AI governance are divided. Dr. Elena Voss, a researcher at the Center for Human-Compatible AI, told Reuters: "This test is not a formal metric, but it’s psychologically revealing. It forces us to confront the gap between simulation and agency. Today’s LLMs are brilliant pattern recognizers, not autonomous actors. They don’t have goals—they have objectives assigned by humans."

Conversely, venture capitalist and AI strategist Raj Mehta argues the test is dangerously simplistic. "Profitability depends on luck, timing, and market conditions. An AI might make perfect decisions and still fail because a competitor launched first or a new regulation was passed. AGI isn’t just about output—it’s about resilience, learning, and adaptation over time."

Still, the test has catalyzed real experimentation. A small group of developers in Berlin recently launched "Project Autonomy," using GPT-4o and custom automation scripts to simulate a dropshipping business. After 90 days, the AI generated product listings, ran ad campaigns, and handled customer service via chatbots. But when a payment processor flagged transactions as suspicious, the system froze. Human intervention was required to resolve the issue.

For now, the bank account remains empty. But the question lingers: when that account finally shows a profit, who—or what—will be credited? The AI, the engineers who built it, or the investors who funded the experiment? As AI systems grow more capable, the line between tool and agent blurs. The litmus test may not be perfect, but it’s a stark mirror: we may be closer to AGI than we think. We just haven’t yet given it the keys to the vault.