In March 2023, a viral challenge invited founders to let AI run their businesses for 30 days. Matt Leta took it on from the worst possible position. His agency had just posted a record revenue year before an audit exposed collapsing collections and mounting taxes, and a formal bankruptcy recommendation sat on his desk. He had $100 left. Rather than fold the company and walk away clean, he committed to repaying every debtor from new profits, with no legal protection, and started over with an AI chatbot as his working partner. The experiment was public from day one, documented on a thread that grew to 23,900 followers.
Three years later, Future Works is the AI-native transformation engine for Fortune 500 enterprise clients, running 12-week cycles instead of multi-year programs, with AI agents plus named experts and fees tied to what they produce.
The PartnerGPT reframe
The original challenge asked participants to follow AI instructions and act as its hands in the world. That framing did not make sense to him. An AI cannot take accountability for a bad call, feel it when a client is unhappy, or pick up the phone and repair a relationship. Leta called his version the PartnerGPT challenge and treated GPT-4 as a partner, not a boss.
He briefed a dedicated session on the business, treated its responses as input rather than instruction, and pushed back when the logic did not hold. It functioned, as he wrote at the time, as "better than assistant, but not strong enough to be a boss." The AI even named the company. "Future Works" came back in one of those sessions. It fit. He kept it.
What 30 days actually produced
By the end of 30 workdays, the experiment had generated $1.32M in total pipeline, with $556k closed or within 95% probability of closing. The account reached 23,900 new social media followers starting from zero. The model behind it was simple: small packaged projects offered to fast-moving buyers at 70 to 90% below what a traditional firm would charge, then built from there. No full-time staff were hired during the challenge. Contractors came in only after clients were signed.
Two things did not work: the existing communities and mailing lists from his previous agency yielded zero projects, and a self-serve ordering system went nowhere. Everyone still wanted a call. The relationship is what the tool ran on.
Accountability in the age of AI agents
Every call during those 30 days was Leta's. The AI helped him think more clearly, move faster, and test ideas he might have talked himself out of. The execution and the risk belonged to him. That is the operating principle that makes the model work at scale. Named experts own the decisions that matter. AI agents handle the throughput that used to consume weeks. As the team scaled from one person to a hundred, the accountability structure stayed the same.
From $100 to outcome-staked enterprise AI transformation
The firm signed its first Fortune 500 client before the end of month six, with no outside investors. The outcome-staked model that governs every engagement today started as a practical necessity. "Tying fees to results was the only honest way to ask someone to take a chance on us," Leta said. When fees are linked to what ships, the vendor has no financial reason to go slow. The firm crossed $2M in revenue with Fortune 100 clients on the roster. With no full-time staff and no overhead until clients were signed, $100 was enough to start.
The working model, from agent fleets to outcome-staked fees, is documented at future.works.