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. The interesting claim buried in that is not about diligence. It is that 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.

Two of his other habits translate less cleanly than they appear to. 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. A company that shifts from an engineering culture to a managerial one does not usually notice at the time; it experiences the change as improved efficiency, right up until the consequences arrive. 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.

Whether any of this works depends, as ever, on the quality of the data and the soundness of the underlying models, and it needs robust risk management, human oversight, and a clear-eyed view of the regulatory perimeter — 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 — and 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.