Towards Company World Models

The “Silver Tsunami”—i.e. the wave of business owners approaching retirement age—renders our work particularly timely from a public benefit perspective. For an owner without a clear successor, the challenge extends beyond finding a buyer. Who will inherit the intuition, relationships, and unwritten metis accumulated over decades of operating the business?

Our intention is to help preserve these businesses and the livelihoods they support. While owners remain actively involved, Company World Models can learn from their decisions and corrections, making their understanding available to the employees and successors who will carry the business forward. This requires working alongside operators while their knowledge can still be elicited, tested, and clarified.

Might these systems eventually assume many of an owner’s responsibilities? Certainly. But we need not resolve the limits of autonomous AI to see the immediate opportunity: helping a business continue to thrive after its owner chooses to retire.

Nor should we expect alignment to be finished at retirement. Customers change, new obligations emerge, and yesterday’s sensible decision may become tomorrow’s mistake. Successors must be able to revise the model and determine which inherited practices to preserve. Even as machines become more capable of decision-making, the people responsible for the business should retain authority over its objectives and tradeoffs.

The opportunity is to make an owner’s accumulated expertise useful beyond their tenure, while giving the next generation the room and tools to build upon it.

Thoughtfully transitioning the real economy through the AI revolution is itself a worthy responsible AI problem of our time. The livelihoods of business owners, employees, and customers are at stake; and ensuring that these people benefit from AI should be central to what we mean by responsible AI.

These Real Economy businesses in Beacon’s portfolio and the customers we serve possess the knowledge, needs, and values that the frontier labs’ alignment recipes do not currently or adequately capture. Company World Models must account for these localized preferences to competently serve the businesses they operate. Doing so naturally brings this broad, underserved sector of the economy directly into the alignment process, whose preferences might otherwise never reach model development.

“Making money”, e.g. for a business, is clearly an objective people will instruct their AIs to pursue. But it exhibits incomplete contract characteristics: the stated goal leaves the many conditions under which its pursuit is un/acceptable unspecified. An AI might deceive the board, exploit customers or indiscriminately cut staff to improve the numbers in pursuit of its ultimate objective.

Fortunately for us, Beacon’s owner-operators remain in the driver’s seat. They have the knowledge to recognize failures, strong incentives to intervene, and authority to inspect and reject consequential proposals before execution. Their corrections make the otherwise implicit conditions and exceptions of “making money” explicit for machines to learn.