Aito.ai - Value, quality, or growth: who was right?Value, quality, or growth: who was right?<br>#PredictiveDatabase<br>#MachineLearning<br>#LLM<br>#Calibration<br>#Investing<br>#StructuredData

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Antti Rauhala<br>Co-founder<br>July 14, 2026 • 15 min read

The question

I was taught the efficient market hypothesis as gospel. Prices already reflect everything known, so nobody can beat the market for long, so a rational person just buys the index and stops trying. It is a clean theory and it is mostly good advice.

It has also never quite matched what I saw. A handful of investors have beaten the market for decades, and not quietly. Benjamin Graham did it. Warren Buffett did it, and in 1984 he wrote an essay, "The Superinvestors of Graham-and-Doddsville," arguing that his cluster of value investors beating the market for decades was too consistent to be luck. Joel Greenblatt wrote a whole book called "The Little Book That Beat the Market" with a ranked, mechanical formula and a backtest to match. The theory says this should not happen. The record says it did.

So the honest question is not "is the market efficient." It is: can you actually earn long-term alpha, and if you can, whose philosophy is right? I am a value investor, so instead of arguing about it, I built a test.

Three investors, three bets

The debate has three classic poles, and it is cleanest to give each one a name.

Graham, value. Buy things that are statistically cheap and demand a margin of safety. The bet is on price. A cheap enough asset does not have to be wonderful to make you money.

Buffett, quality. Buy a wonderful business at a fair price and let it compound. The bet is on the business: a durable moat, honest and able management, disciplined capital allocation. Price still matters, but quality is what carries the decade.

Fisher, growth. Philip Fisher, whose "Common Stocks and Uncommon Profits" shaped a generation, bet on companies with long runways of growth and a genuine secular tailwind. The bet is on the future getting bigger. Buffett himself once said he is "85% Benjamin Graham and 15% Phil Fisher," which tells you these poles are rivals and ingredients at the same time.

My own prior was Buffett's. I believed a fair-priced quality business steamrolls a cheap low-quality one over ten years, because compounding is exponential and a bad business stays a bad business. I wanted to see it in the numbers.

The setup

Take the S&P 500 as it actually stood in 2014, 2017, and 2020, reconstructed from the historical index so there is no survivorship bias. For each company, read its 10-K and proxy from that year and grade it on the qualitative factors an analyst cares about: moat, market position, market quality, leadership, capital allocation. Add the quantitative buckets: valuation, growth, leverage, profitability, momentum, volatility. Each philosophy is now a family of features. Graham owns valuation. Buffett owns the quality grades. Fisher owns growth and secular sectors.

The grading is done by an LLM reading the filings alone, instructed to use no knowledge from after the date being judged. Then every graded company goes into Aito, a predictive database, and the question becomes a query.

"from": "companies",<br>"where": {<br>"moat_strength": "wide",<br>"valuation_bucket": "fair",<br>"growth_bucket": "high",<br>"sector": "Information Technology"<br>},<br>"predict": "outcome_bucket"

There is no training step. The answer comes back as a calibrated probability across five outcome buckets, great to disaster, with a breakdown of which factors moved it. That breakdown is what lets us referee the three philosophies at all.

The numbers, first the ranking

Start with the accuracy, because it is humbling. On 1,294 held-out observations, cross-validated with companies grouped by ticker so none is ever trained on its own outcome, the model gets the exact bucket right about 35% of the time, against a 27% base rate. A real lift, but a modest one. This is not a machine that tells you precisely how a company will do.

Now the fact that matters. Rank every company by expected outcome, form a top-20 and a bottom-20 fund, and measure their actual returns over the following decade:

The market returned 7.9% a year.

The top-20 fund returned 20.6% a year.

The bottom-20 fund lost money, at minus 4.1% a year.

A model only a third accurate at the label still separated winners from losers by roughly 25 points of annual return. The value is in the ordering, not the precise call. Which is the whole point of calibrated prediction: you do not need high accuracy, you need an honest ranking and a model that knows when it is guessing.

Predicted versus realised frequency by confidence decile. When the engine says 80 percent, it is right about 80 percent of the time. Brier score 0.18, where random is 0.25 and perfect is 0.

So who was right?

Point the database at itself and ask which factors actually associate with a great outcome. Aito's relate query returns a lift per...