The Buffett Algorithm

Agentic systems and the next superinvestors

Warren Buffett’s success is largely attributed to a reading habit: five or six hours a day, several hundred pages, a quotidian influx of primary material out of which the investment judgements emerge. Buried in that habit is a bolder claim than diligence. A sufficiently broad and patient intake of raw information, held in one mind long enough, produces something that looks like insight.

That is a description of a process, and processes can be delegated.

Agentic AI systems can construct plans of action in response to complex and shifting problems, and pursue an assigned objective with some independence. Applied to markets, such a system can ingest filings, trends, disclosures and reporting continuously rather than for six hours a day, and can work directly from 10-K filings, balance sheets and cash flow statements rather than from other people’s summaries of them. Buffett’s insistence on primary sources over opinion translates unusually cleanly into a design principle: prioritise the granular data, discount the commentary.

His other two habits, patience and discipline, translate unevenly. Patience is straightforward to specify: a system can be told to wait, and unlike a person it will not get bored or frightened while waiting. Discipline is harder, because the discipline that matters is not adherence to criteria but knowing which criteria to abandon and when. That judgement is the thing being automated, and it is the thing least well captured by the record of past decisions.

The same capability extends past portfolios. In executive search, where the quality of leadership makes or breaks an enterprise, systems that can weigh performance history, leadership style and cultural fit against each other might identify candidates that a conventional process would never surface, the analogue of Buffett’s preference for businesses with strong management and durable advantage.

But there is a failure mode worth naming, and Boeing is its illustration. After its 1997 merger with McDonnell Douglas, Boeing drifted from an engineering culture to a managerial one without much noticing at the time; it experienced the change as improved efficiency, right up until the 737 MAX crashes of 2018 and 2019. Organisations that pursue AI-driven optimisation without deliberately preserving room for contrarian thinking risk exactly that trajectory: highly efficient, internally consistent, and progressively less able to recognise the thing that will undo them. The most successful ventures tend to rest on premises that were non-consensus and correct, and an optimiser trained on consensus is poorly placed to find those.

All of this depends on sound data and sound models, and none of it removes the need for human oversight: insider trading rules do not become more negotiable because an agent did the reading.

Still, the shape of the opportunity is real. Buffett turned an appetite for reading into an unmatched record over decades. A system that can read everything, continuously, without fatigue or ego, is a genuinely new instrument. The constraint on it will not be how much it can process, but whether anyone can tell the difference between its judgement and its confidence.