Chimps, Humans, and AI: A Deceptive Analogy

The prospect of smarter-than-human artificial intelligence (AI) is often presented and thought of in terms of a simple analogy: AI will stand in relation to us the way we stand in relation to chimps. In other words, AI will be qualitatively more competent and powerful than us, and its actions will be as inscrutable to humans as current human endeavors (e.g. science and politics) are to chimps.

My aim in this essay is to show that this is in many ways a false analogy. The difference in understanding and technological competence found between modern humans and chimps is, in an important sense, a zero-to-one difference that cannot be repeated.

How are humans different from chimps?

A common answer to this question is that humans are smarter. Specifically, at the level of our individual cognitive abilities, humans, with our roughly three times larger brains, are just far more capable.

This claim no doubt contains a large grain of truth, as humans surely do beat chimps in a wide range of cognitive tasks. Yet it is also false in some respects. For example, chimps have superior working memory compared to humans, and apparently also beat humans in certain video games, including games involving navigation in complex mazes.

But researchers who study human uniqueness actually provide some rather different, more specific answers to this question. If we focus on individual mental differences in particular, researchers have found that, crudely speaking, humans are different from chimps in three principal ways: 1) we can learn language, 2) we have a strong orientation toward social learning, and 3) we are highly cooperative (among our ingroup, compared to chimps).

These differences have in turn resulted in two qualitative differences in the abilities of humans and chimps in today’s world.

I. Symbolic language

The first is that we humans have acquired an ability to think and communicate in terms of symbolic language that represents elaborate concepts. We can learn about the deep history of life and the universe, as well as the likely future of the universe, including the fundamental limits to future space travel and future computations. Any educated human can learn a good deal about these things whereas no chimp can.

Note how this is truly a zero-to-one difference: no symbolic language versus an elaborate symbolic language through which knowledge can be represented and continually developed (see chapter 1 in Deacon, 1997). It is the difference between having no science of physics versus having an elaborate such science with which we can predict future events and put hard limits on future possibilities.

This zero-to-one difference cannot really be repeated. Given that we already have physical models that predict, say, the future motion of planets and the solar system to a fairly high degree of accuracy, the best one can do in this respect is to (slightly) improve the accuracy of these predictions. Such further improvements cannot be compared to going from zero physics to current physics.

The same point applies to our scientific understanding more generally: we currently have theories that work decently well at explaining most of the phenomena around us. And though one can significantly improve the accuracy and sophistication of many of these theories, any such further improvement would be much less significant than the qualitative leap from absolutely no conceptual models to the entire collection of models and theories we currently have.

For example, going from no understanding of evolution by natural selection to the elaborate understanding of biology we have today cannot be matched, in terms of qualitative and revolutionary leaps, by further refinements in biology. We have already mapped out the core basics of biology (in fact a great deal more than that), and this can only be done once.

This is not an original point. Robin Hanson has made essentially the same point in response to the notion that future machines will be “as incomprehensible to us as we are to goldfish”:

This seems to me to ignore our rich multi-dimensional understanding of intelligence elaborated in our sciences of mind (computer science, AI, cognitive science, neuroscience, animal behavior, etc.).

… the ability of one mind to understand the general nature of another mind would seem mainly to depend on whether that first mind can understand abstractly at all, and on the depth and richness of its knowledge about minds in general. Goldfish do not understand us mainly because they seem incapable of any abstract comprehension. …

It seems to me that human cognition is general enough, and our sciences of mind mature enough, that we can understand much about quite a diverse zoo of possible minds, many of them much more capable than ourselves on many dimensions.

Ramez Naam has argued similarly in relation to the idea that there will be some future time or intelligence that we are fundamentally unable to understand. He argues that our understanding of the future is growing rather than shrinking as time progresses, and that AI and other future technologies will not be beyond comprehension:

All of those [future technologies] are still governed by the laws of physics. We can describe and model them through the tools of economics, game theory, evolutionary theory, and information theory. It may be that at some point humans or our descendants will have transformed the entire solar system into a living information processing entity — a Matrioshka Brain. We may have even done the same with the other hundred billion stars in our galaxy, or perhaps even spread to other galaxies.

Surely that is a scale beyond our ability to understand? Not particularly. I can use math to describe to you the limits on such an object, how much computing it would be able to do for the lifetime of the star it surrounded. I can describe the limit on the computing done by networks of multiple Matrioshka Brains by coming back to physics, and pointing out that there is a guaranteed latency in communication between stars, determined by the speed of light. I can turn to game theory and evolutionary theory to tell you that there will most likely be competition between different information patterns within such a computing entity, as its resources (however vast) are finite, and I can describe to you some of the dynamics of that competition and the existence of evolution, co-evolution, parasites, symbiotes, and other patterns we know exist.

Chimps cannot understand human politics and science to a similar extent. Thus, the truth is that there is a strong disanalogy between the understanding chimps have of humans versus the understanding that we humans — thanks to our conceptual tools — can have of any possible future intelligence (in physical and computational terms, say).

Note that the qualitative leap reviewed above was not one that happened shortly after human ancestors diverged from chimp ancestors. Instead, it was a much more recent leap that has been unfolding gradually since the first humans appeared, and which has continued to accelerate in recent centuries, as we have developed ever more advanced science and mathematics. In other words, this qualitative step has been a product of cultural evolution just as much as biological evolution. Early humans presumably had a roughly similar potential to learn modern language, science, mathematics, etc. But such conceptual tools could not be acquired in the absence of a surrounding culture able to teach these innovations.

Ramez Naam has made a similar point:

If there was ever a singularity in human history, it occurred when humans evolved complex symbolic reasoning, which enabled language and eventually mathematics and science. Homo sapiens before this point would have been totally incapable of understanding our lives today. We have a far greater ability to understand what might happen at some point 10 million years in the future than they would to understand what would happen a few tens of thousands of years in the future.

II. Cumulative technological innovation

The second zero-to-one difference between humans and chimps is that we humans build things. Not just that we build things, but that we refine our technology over time. After all, many non-human animals use tools in the form of sticks and stones, and some even shape primitive tools of their own. But only humans improve and build upon the technological inventions of their ancestors.

Consequently, humans are unique in expanding their abilities by systematically exploiting their environment, molding the things around them into ever more useful self-extensions. We have turned wildlands into crop fields; we have created technologies that can harvest energy — from oil, gas, wind, and sun — and we have built external memories far more reliable than our own, such as books and hard disks.

This is another qualitative leap that cannot be repeated: the step from having absolutely no cumulative technology to exploiting and optimizing our external environment toward our own ends. The step from having no external memory to having the current repository of stored human knowledge at our fingertips, and from harvesting absolutely no energy (other than through individual digestion) to collectively harvesting and using hundreds of quintillions of Joules every year.

To be sure, it is possible to improve on and expand these innovations. We can harvest greater amounts of energy, for example, and create even larger external memories. Yet these are merely quantitative differences, and humanity indeed continually makes such improvements each year. They are not zero-to-one differences that only a new species could bring about. And what is more, we know that the potential for making further technological improvements is, at least in many respects, quite limited.

Take energy efficiency as an example. Many of our machines and energy harvesting technologies have already reached a significant fraction of the maximally possible efficiency. For example, electric motors and pumps tend to have around 90 percent energy efficiency, and the best solar panels have an efficiency greater than 40 percent. So as a matter of hard physical limits, many of our technologies cannot be made orders of magnitude more efficient; in fact, a large number of them can at most be marginally improved.

In sum, we are unique in being the first species that systematically sculpted our surrounding environment and turned it into ever-improving tools, many of which have near-maximal efficiency. This step cannot be repeated, only expanded further.


Just like the qualitative leap in our symbolic reasoning skills, the qualitative leap in our ability to create technology and shape our environment emerged, not between chimps and early humans, but between early humans and today’s humans, as the result of a cultural process occurring over thousands of years. In fact, the two leaps have been closely related: our ability to reason and communicate symbolically has enabled us to create cumulative technological innovation. Conversely, our technologies have allowed us to refine our knowledge and conceptual tools, by enabling us to explore and experiment, which in turn made us able to build even better technologies with which we could advance our knowledge even further, and so on.

This, in a nutshell, is the story of the growth of human knowledge and technology, a story of recursive self-improvement (see Simler, 2019, “On scientific networks”). It is not really a story about the individual human brain per se. After all, the human brain does not accomplish much in isolation (nor is it the brain with the largest number of neurons; several species have more neurons in the forebrain). It is more a story about what happened between and around brains: in the exchange of information in networks of brains and in the external creations designed by them. A story made possible by the fact that the human brain is unique in being by far the most cultural brain of all, with its singular capacity to learn from and cooperate with others.

The range of human abilities is surprisingly wide

Another way in which an analogy to chimps is frequently drawn is by imagining an intelligence scale along which different species are ranked, such that, for example, we have “rats at 30, chimps at 60, the village idiot at 90, the average human at 98, and Einstein at 100”, and where future AI may in turn be ranked many hundreds of points higher than Einstein. According to this picture, it is not just that humans will stand in relation to AI the way chimps stand in relation to humans, but that AI will be far superior still. The human-chimp analogy is, on this view, a severe understatement of the difference between humans and future AI.

Such an intelligence scale may seem intuitively compelling, but how does it correspond to reality? One way to probe this question is to examine the range of human abilities in chess. The standard way to rank chess skills is with the Elo rating system, which is a good predictor of the outcomes of chess games between different players, whether human, digital, or otherwise.

An early human beginner will have a rating around 300, a novice around 800, and a rating in the range 2000-2199 is ranked as “Expert”. The highest rating ever achieved is 2882 by Magnus Carlsen.

How large is this range of chess skills in an absolute sense? Remarkably large, it turns out. For example, it took more than four decades from when computers were first able to beat a human chess novice (the 1950s), until a computer was able to beat the best human player (1997, officially). In other words, the span from novice to Kasparov corresponded to more than four decades of progress in hardware — i.e. a million times more computing power — and software. This alone suggests that the human range of chess skills is rather wide.

Yet the range seems even broader when we consider the upper bounds of chess performance. After all, the fact that it took computers decades to go from human novice to world champion does not mean that the best human is not still ridiculously far from the best a computer could be in theory. Surprisingly, however, this latter distance does in fact seem quite small. Estimates suggest that the best possible chess machine would have an Elo rating around 3600, which means that the relative distance between the best possible computer and the best human is only around 700 Elo points (the Elo rating is essentially a measure of relative distance; 700 Elo points corresponds to a winning percentage of around 1.5 percent for the losing player).

This implies that the distance between the best human (Carlsen) and a chess “Expert” (someone belonging to the top 5 percent of chess players) is similar to the distance between the best human and the best possible chess brain, while the distance between a human beginner and the best human is far greater (2500 Elo points). This stands in stark contrast to the intelligence scale outlined above, which would predict the complete opposite: the distance from a human novice to the best human should be comparatively small whereas the distance from the best human to the optimal brain should be the larger one by far.


It may be objected that chess is a bad example, and that it does not really reflect what is meant by the intelligence scale above. But the question is then what would be a better measure. After all, a similar story seems to apply to other games, such as shogi and go: the human range of abilities is surprisingly wide and the best players are significantly closer to optimal than they are to novice players.

In fact, one can argue that the objection should go in the opposite direction, as human brains are not built for chess, and hence we should expect even the best humans to be far from optimal at it. We should expect to be much closer to “optimal” at solving problems that are more important for our survival, such as social cognition and natural language processing — skills that most people are wired to master at super-Carlsen levels.

Regardless, the truth is that humans are mastering ever more “games”, literal as well as figurative ones, at optimal or near-optimal levels. Not because evolution “just so happened to stumble upon the most efficient way to assemble matter into an intelligent system”, but rather because it created a species able to make cultural and technological progress toward ever greater levels of competence.

The cultural basis of the human capability expansion

The intelligence scale outlined above misses two key points. First, human abilities are not a constant. Whether we speak of individual abilities (e.g. the abilities of elite chess players) or humanity’s collective abilities (e.g. building laptops and sending people to the moon), it is clear that our abilities have increased dramatically as our culture and technology have expanded.

Second, because human abilities are not a constant, the range of human abilities is far wider, in an absolute sense, than the intelligence scale outlined above suggests, as it has grown and still continues to grow over time.

Chess is a good example of this. Untrained humans and chimps have the same (non-)skill level at chess. Yet thanks to culture, some people can learn to master the game. A wealthy society can allow people to specialize in chess, and makes it possible for knowledge to accumulate in books and experts. Eventually, it enables learning from super-human chess engines, whose innovations we can adopt just as we do those of other humans.

And yet we humans expand our abilities to a much greater extent than the example of increased human chess abilities suggests, as we not only expand our abilities by stimulating our brains with progressively better forms of practice and information, but also by extending ourselves directly with technology. For example, we can all use a chess engine to find great chess moves for us. Our latest technologies enable us to accomplish ever-more tasks that no human could ever accomplish unaided.

Worth noting in this regard is that this self-extension process seems to have slowed down in recent decades, likely because we have reaped most low-hanging fruits already, and in some respects because it is impossible to improve things much further (we already mentioned energy efficiency as an example where we are getting close to the upper limits in many respects).

This suggests that not only is there not a qualitative leap similar to that between chimps and modern humans ahead of us, but that even a quantitative growth explosion, with relative growth rates significantly higher than what we have seen in the past, should not be our default expectation either (for some support for this claim, see “Peak growth might lie in the past” in Vinding, 2017).

Why this is relevant

The errors of the human-chimp analogy are worth highlighting for a few reasons. First, the analogy can lead us to overestimate how much everything will change with AI. It leads us to expect qualitative leaps of sorts that cannot be repeated.

Second, the human-chimp analogy makes us underestimate how much we currently know and are able to understand. To think that intelligent systems of the future will be as incomprehensible to us today as human affairs are to chimps is to underestimate how extensive and universal our current knowledge of the world in fact is — not just when it comes to physical and computational limits, but also in relation to general economic and game-theoretic principles. We know a good deal about economic growth, for example, and this knowledge has a lot to say about how we should expect future intelligent systems to grow. In particular, it suggests that local AI-FOOM growth is unlikely.

The analogy can thus have an insidious influence by making us feel like current data and trends cannot be trusted much, because look how different humans are from chimps, and look how puny the human brain is compared to ultimate limits. I think this is exactly the wrong way to think about the future. We should base our expectations on a deep study of past trends, including the actual evolution of human competences — not simple analogies.

Relatedly, the human-chimp analogy is also relevant in that it can lead us to grossly overestimate the probability of an AI-FOOM scenario. That is, if we get the story about the evolution of human competences so wrong that we think the differences we observe today between chimps and modern humans reduce mostly to a story about changes in individual brains, then we are likely to have similarly inaccurate expectations about what comparable innovations in some individual machine are able to effect on their own.

If the human-chimp analogy leads us to (marginally) overestimate the probability of a FOOM scenario, it may nudge us toward focusing too much on some single, concentrated future thing that we expect to be all-important: the AI that suddenly becomes qualitatively more competent than humans. In effect, the human-chimp analogy can lead us to neglect broader factors, such as cultural and institutional developments.

Note that the above is by no means a case for complacency about risks from AI. It is important that we get a clear picture of such risks, and that we allocate our resources accordingly. But this requires us to rely on accurate models of the world. If we overemphasize one set of risks, we are by necessity underemphasizing others.

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