Forget six fingers. AI’s bigger story is quantity

TWO years ago, every argument about artificial intelligence was about quality. The machines hallucinated court cases, absorbed biases, drew hands with six fingers, and could not count the letters in “strawberry”. Those complaints faded so steadily that few people noticed them leave.

A few still arrive every morning. Someone finds two em dashes in a stranger’s article and declares it slop. One caught error becomes a verdict on everything AI does. Each catch assumes that everyone has a top professional on call at a fair price. Most people never have.

The machine need not be the best person in the world to be the best help someone has ever had. The work cleared the bar of usefulness while we were busy counting fingers.

The new story is quantity. There is an old, grim saying that one death is a tragedy, and a million is a statistic. Science has just met its own version.

In September, one lab set roughly 10,000 agents loose on one of the hardest open problems in mathematics: whether the equations of fluid flow always behave. In under four days, they produced a proof that specialists are still arguing over.

That is a headline. It has a hero, a famous prize and seminar rooms checking every line.

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Weeks later, the same lab posted more than 700 manuscripts in one go, each result costing a few hours of machine thinking. That is a statistic. Hundreds of results fit neither in a headline nor a mind.

Silent progress

The jump from one to hundreds took a few weeks. Thousands, and eventually a million, are a matter of budget. Nothing requires any of them to be announced.

The same recipe works for anyone who can state a problem precisely and score an answer automatically: circuits, molecules, materials, schedules and routes.

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Mathematics was only the loudest example. A programming tool of half a million lines was rewritten into a new language in 11 days, a job its creator reckoned would take three expert engineers an entire year.

One model surfaced thousands of unknown security flaws in major operating systems within weeks, one of which had hidden from human auditors for 27 years.

A genome atlas predicted the effects of all nine billion possible single-letter changes in human DNA.

Each made a day of news. When each quarter doubles the last, the doubling stops being an event and becomes weather.

Critics still count how many of a hundred machine answers are wrong. Bias and hallucination followed the same path, with the error count falling while the volume kept climbing.

The models doing this work are also the weakest the world will use from here on.

We call these systems language models because the first thing they did was write. But language was only the doorway. They are a mathematical revolution that finds patterns along long chains of reasoning, which is why they travel from chemistry to code to law without asking permission.

Their results also refuse to sit in a pile. Each useful answer becomes a foothold for the next, often in another field, often inside a company that never publishes.

Flood meets world

Now the harder question: Who receives it all? The quality debate was comfortable because every failure came with a remedy. Quantity offers no such fix.

Any remedy has to be built from human attention, trust and time. None of the three improves exponentially.

Our institutions were built for a trickle. Peer review rations the hours of qualified readers, a court’s docket the hours of judges. Producing something worth considering was itself an expensive filter.

Remove that cost, and the rationer becomes a dam asked to pass its design flow by a thousand times, holding back mostly true water.

Anyone who has run a sell-side research department, like this writer, knows the feeling in miniature. At first, every complaint is about too little. Later, it is about too much. Better research does not manufacture another hour in the reader’s day.

The maintainers of curl, a small programme inside countless devices, ended their bug bounty after a flood of poor AI submissions. Yet, their complaints have since changed. The AI-assisted reports are now largely accurate and arriving in overwhelming volume.

Imaginary flaws could be dismissed. Real ones create obligations. A deluge of falsehood is a scandal. A deluge of truth is a climate.

The physical world is the last brake. One AI system proposed some 380,000 stable crystal structures, against roughly 20,000 found in all of human experimentation.

The machines can dream a thousand times faster than the world can build. Whoever owns the last physical step owns a veto over the first thousand intellectual ones.

For business, this means exploding innovation. What follows is continuous, compounding and highly complex creative destruction. Few sitting ducks will stay comfortable as the crossfire intensifies.

Much of the progress will arrive as a thousand ordinary costs that quietly stop being necessary, filed under operations, while the AI bill arrives neatly itemised. Expect the argument about returns to intensify, as returns lag.

Organisations that learn to sort, verify and act on abundance will pull away from those still built for a trickle. The greatest risk sits with those who decide not to attempt. The next greatest sits with those who attempt and build no way to take delivery.

The moment a tsunami becomes visible is the last comfortable moment. Today, the water is warm. The headlines are about em dashes.

Quality was the question we knew how to argue about. Quantity is the one we will have to live with.

This is an adaptation of an article that first appeared on GenInnov’s blog.

The writer is founder and portfolio manager of GenInnov Asset Management in Singapore.