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​What Happens When AI Performance Asymptotes?​

In the past, the bigger the AI model, the better the performance. Across OpenAI’s models for example, parameters have grown by 1000x+ & performance has nearly tripled.

But model performance will soon asymptote - at least on this metric.

This is a chart of many recent AI models’ performance according to a broadly accepted benchmark called MMLU. ¹ MMLU measures the performance of an AI model compared to a high school student.

I’ve categorized the models this way :

• Large : > 100 billion parameters
• Medium : 15 to 100b parameters
• Small : < 15b parameters

Over time, the performance is converging rapidly both across model sizes & across the model vendors.

What happens when Facebook’s open-source model & Google’s closed-source model that powers Google.com & OpenAI’s models that power ChatGPT all work equally well?

Computer scientists have been challenged distinguishing the relative performance of these models with many different tests. Users will be hard-pressed to do better.

At that point, the value in the model layer should collapse. If a freely available open-source model is just as good as a paid one, why not use the free one? And if a smaller, less expensive to operate open-source model is nearly as good, why not use that one?

The rapid growth of AI has fueled a surge of interest in the models themselves. But pretty quickly, the infrastructure layer should commoditize, just as it did in the cloud where three vendors command 65% market share : Amazon Web Services, Azure, & Google Cloud Platform.

The applications & the developer tooling around the massive AI commodity brokers is the next phase of development - where product differentiation & distribution differentiate rather than brilliant, raw technical advances.²

¹ MMLU measures 57 different tasks including math, history, computer science & other topics. It’s one measure of many & it’s not perfect - like any benchmark. There are others including the Elo system. Here’s an overview of the differences.. Each benchmark grades the model on a different spectrum : bias, mathematical reasoning are two other examples.

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