I’ve been studying the early history of electricity’s discovery as a map for our current discovery of artificial intelligence. The smartest people alive back then, including Isaac Newton, who may have been the smartest person who ever lived, had confident theories about electricity’s nature that were profoundly wrong. In fact, despite the essential role of electrical charges in the universe, everyone who worked on this fundamental force was profoundly wrong for a long time. All the pioneers of electricity — such as Franklin, Wheatstone, Faraday, and Maxwell — had a few correct ideas of their own (not shared by all) mixed in with notions that mostly turned out to be flat out misguided. Most of the discoveries about what electricity could do happened without the knowledge of how they worked. That ignorance, of course, drastically slowed down the advances in electrical inventions.

In a similar way, the smartest people today, especially all the geniuses creating artificial intelligence, have theories about what intelligence is, and I believe all of them (me too) will be profoundly wrong. We don’t know what artificial intelligence is in large part because we don’t know what our own intelligence is. And this ignorance will later be seen as an impediment to the rate of progress in AI.

A major part of our ignorance stems from our confusion about the general category of either electricity or intelligence. We tend to view both electricity and intelligence as coherent elemental forces along a single dimension: you either have more of it or less. But in fact, electricity turned out to be so complicated, so complex, so full of counterintuitive effects that even today it is still hard to grasp how it works. It has particles and waves, and fields and flows, composed of things that are not really there. Our employment of electricity exceeds our understanding of it. Understanding electricity was essential to understanding matter. It wasn’t until we learned to control electricity that we were able to split water — which had been considered an element — into its actual elements; that enlightened us that water was not a foundational element, but a derivative compound made up of sub elements.

It is very probable we will discover that intelligence is likewise not a foundational singular element, but a derivative compound composed of multiple cognitive elements, combined in a complex system unique to each species of mind. The result that we call intelligence emerges from many different cognitive primitives such as long-term memory, spatial awareness, logical deduction, advance planning, pattern perception, and so on. There may be dozens of them, or hundreds. We currently don’t have any idea of what these elements are. We lack a periodic table of cognition. 

The cognitive elements will more resemble the heavier elements in being unstable and dynamic. Or a better analogy would be to the elements in a biological cell. The primitives of cognition are flow states that appear in a thought cycle. They are like molecules in a cell which are in constant flux, shifting from one shape to another. Their molecular identity is related to their actions and interactions with other molecules. Thinking is a collective action that happens in time (like temperature in matter) and every mode can only be seen in relation to the other modes before and after it. It is a network phenomenon that makes it difficult to identify its borders. So each element of intelligence is embedded in a thought cycle, and requires the other elements as part of its identity. So each cognitive element is described in context of the other cognitive modes adjacent to it.

I asked ChatGPT5Pro to help me generate a periodic table of cognition given what we collectively know so far. It suggests 49 elements, arranged in a table so that related concepts are adjacent. The columns are families, or general categories of cognition such as “Perception”, “Reasoning”, “Learning”, so all the types of perception or reasoning are stacked in one column. The rows are sorted by stages in a cycle of thought. The earlier stages (such as “sensing”) are at the top, while later stages in the cycle (such as “reflect & align”) are at the bottom. So for example, in the family or category of “Safety” the AIs will tend to do the estimation of uncertainty first, later do verification, and only get to a theory of mind at the end.

The chart is colored according to how much progress we’ve made on each element. Red indicates we can synthesize that element in a robust way. Orange means we can kind of make it work with the right scaffolding. Yellow reflects promising research without operational generality yet.

I suspect many of these elements are not as distinct as shown here (taxonomically I am more of a lumper than a splitter), and I would expect this collection omits many types we are soon to discover, but as a start, this prototype chart serves its purpose: it reveals the complexity of intelligence. It is clear intelligence is compounded along multiple dimensions. We will engineer different AIs to have different combinations of different elements in different strengths. This will produce thousands of types of possible minds. We can see that even today different animals have their own combination of cognitive primitives, arranged in a pattern unique to their species’ needs. In some animals some of the elements — say long-term memory — may exceed our own in strength; of course they lack some elements we have.

With the help of AI, we are discovering what these elements of cognition are. Each advance illuminates a bit of how minds work and what is needed to achieve results. If the discovery of electricity and atoms has anything to teach us now, it is that we are probably very far from having discovered the complete set of cognitive elements. Instead we are at the stage of believing in ethers, instantaneous action, and phlogiston – a few of the incorrect theories of electricity the brightest scientists believed.

Almost no thinker, researcher, experimenter, or scientist at that time could see the true nature of electricity, electromagnetism, radiation and subatomic particles, because the whole picture was hugely unintuitive. Waves, force fields, particles of atoms did not make sense (and still does not make common sense). It required sophisticated mathematics to truly comprehend it, and even after Maxwell described it mathematically, he found it hard to visualize.

I expect the same from intelligence. Even after we identify its ingredients, the emergent properties they generate are likely to be obscure and hard to believe, hard to visualize. Intelligence is unlikely to make common sense. 

A century ago, our use of electricity ran ahead of our understanding of it. We made motors from magnets and coiled wire without understanding why they worked. Theory lagged behind practice. As with electricity, our employment of intelligence exceeds our understanding of it. We are using LLMs to answer questions or to code software without having a theory of intelligence. A real theory of intelligence is so lacking that we don’t know how our own minds work, let alone the synthetic ones we can now create.

The theory of the atomic world needed the knowledge of the periodic table of elements. You had to know all (or at least most) of the parts to make falsifiable predictions of what would happen. The theory of intelligence requires knowledge of all the elemental parts, which we have only slowly begun to identify, before we can predict what might happen next.