在500天创业生存测试中，仅有三款AI模型在起始资本之上完成

Researchers at Princeton University built CEO-Bench, a test where AI agents have to run a fictional software company for 500 simulated days. Most current models go broke, and a simple rule-based heuristic with no AI beats nearly all of them. AI agents are getting increasingly good at narrow tasks: fixing a bug, following a service policy in a conversation, or completing a web-based workflow. These tasks share a simple structure, according to the Princeton study: the agent gets a clear goal, acts briefly, and receives quick feedback. Many important real-world tasks look nothing like that. They involve long chains of decisions under uncertainty, where you have to set priorities, allocate limited resources, read noisy signals, and adapt to changing conditions. To test exactly these skills, the researchers developed CEO-Bench . The benchmark simulates a realistic example of this kind of long-horizon task: running a startup for 500 simulated days. The researchers point to a famous example: in 1997, Apple was 90 days from bankruptcy. Steve Jobs drew a simple two-by-two grid—consumer and pro, desktop and portable—and decided Apple would only build products for those four quadrants. The iMac, iPod, and iPhone followed. This type of strategic steering intelligence is fundamentally different from what AI agents do today, the authors argue. Agents are getting better at individual tasks fast. But steering an entire organization toward long-term goals? That's a different problem entirely. CEO-Bench is a first attempt at measuring exactly this "steering intelligence." In CEO-Bench, an agent runs a made-up subscription software company called NovaMind. It starts with zero customers and one million dollars in the bank. Performance is measured by remaining cash at the end. If the balance drops below zero even once, the company is bankrupt and the simulation ends. The agent controls the company through a Python API with 34 tools and a database of 19 tables. Instead of just issuing individual commands, it writes its own code, queries the database with SQL, and builds custom workflows from the results. That puts it in front of the same challenges a human CEO would face, the researchers say. There's a lot to decide: pricing and tiers, ad spend across channels, product quality and R&D, infrastructure capacity and customer support, plus multi-round negotiations with enterprise clients. On top of that, there's a simulated social network where the agent can read complaints, competitor news, and economic trends and post itself. What makes the task hard is time and uncertainty. Decisions play out on realistic business timelines: revenue only arrives at billing dates, R&D projects take days to weeks, and mistakes often don't show up until later through churn or damaged reputation. Costs hit right away. The agent has to spend money whose payoff might not show up for weeks. Much of the company's state stays hidden. The agent can't directly see customer satisfaction, willingness to pay, or minimum quality expectations. It has to piece these together from noisy signals like cancellations, support tickets, or reactions on the social network. The simulation models 26 customer segments and individual customers, each with their own budgets, price sensitivities, and expectations. The world keeps changing, too. Competitors periodically raise customer quality expectations, preferences shift over time, and a simulated business cycle affects demand and willingness to pay, so the agent has to keep adjusting. The researchers deliberately chose fixed, transparent rules rather than a language model as referee. They wanted to avoid a weakness they see in Vending-Bench , a test with a simulated vending machine : there, an AI-simulated supplier can reward an agent for unrealistic verbal promises.