Intelligence isn’t about parameter count. It’s about time.

Intelligence isn’t about parameter count. It’s about time. - Amazon Science

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Machine learning

Intelligence isn’t about parameter count. It’s about time.

As AI models grow larger, they become less insightful, not more. To ensure that they continue to learn, we need to reduce their inference time.

By Stefano Soatto , Alessandro Achille

February 25, 2026

11 min read

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When we prompt a large language model (LLM) to solve a complex polynomial equation, it does not just return an answer but uses its “chain of thought” to work through a solution. In a sense, the LLM behaves like a computer , a machine that computes the solution. But this machine is quite unlike what Alan Turing described as a universal model of computation almost 90 years ago.

Stefano Soatto is a vice president and distinguished scientist in the Amazon Web Services (AWS) Agentic AI organization. Credit: UCLA Samueli

In what sense can an LLM be thought of as a computer? Can it be universal , that is, able to solve any computable task, as a Turing machine does? If so, how does it learn this ability from finite data? Current theories of machine learning are of little help in answering these questions, so we need new tools. In an earlier Amazon Science post , we argued that AI agents and the LLMs that power them are transductive-inference engines, despite being trained inductively in the mold of classical machine learning theory. Induction seeks generalization , or the ability to behave on future data as one did on past data. To achieve generalization, one must avoid memorization, i.e., overfitting the training data. This works in theory, under the condition that both past and future data are drawn from the same distribution . In practice, however, such a condition cannot be verified, and in general, it doesn’t apply to high-value data in business, finance, climate science, and even language. That leaves us with no handle to explain how an LLM might learn how to verifiably solve a general computable task. With transduction, by contrast, one seeks to reason through past data to craft solutions to new problems. Transduction is not about applying past solutions in the hope that they generalize; rather, it is about being able to retrieve portions of memory that matter when reasoning through new solutions. In transduction, memorization is not a stigma but a value. Using the test data, along with memory, to craft a solution during transductive inference is not overfitting but adaptive, query-specific computation — i.e., reasoning . Inductive generalization is the kind of behavior one is forced to adopt when pressed for time. Such automatic, reactive behavior is sometimes referred to as “ system-1 ” in cognitive psychology. Transduction instead requires looking at all data and performing query-specific variable-length inference-time computation — chain-of-thought reasoning in an LLM, whose length depends on the complexity of the query. Such deliberative behavior is often referred to as...