# The Reverse Turing Test: How can a human prove that they are not a machine?

> "The Reverse Turing Test" by R.B. Griggs is a serious argument about the nature of human intelligence, delivered through a satirical frame: a fictional academic paper dated July 2039, written by a machine ("GP-ΩPO-4583.b") that parodies Turing's imitation-game paper. In the story, machines surpass humans on every benchmark yet cannot replicate breakthroughs that arise from accidents, naps, walks, gut feelings and imagination. Inside this frame Griggs advances several distinct theories: that optimization-based benchmarks cannot see the source of creative breakthroughs; that an optimizer cannot simulate genuine non-optimization; that human suboptimality is a "mode of being" rather than a deficit; and, most substantively, "evolutionary attunement," the hypothesis that humans so often "guess right" because their intuitions are adapted to the one constrained, contingent reality they evolved in.

- Author: R.B. Griggs
- Published: 2025-11-19
- Genre: speculative fiction
- Original: https://www.techforlife.com/p/the-reverse-turing-test
- This edition: https://rbgriggs.com/essays/the-reverse-turing-test
- License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
- Cite as: Griggs, R.B. (2025). "The Reverse Turing Test." Tech for Life. https://www.techforlife.com/p/the-reverse-turing-test. AI-readable edition: https://rbgriggs.com/essays/the-reverse-turing-test

## Thesis

Human creative success depends on capacities that look suboptimal from the standpoint of optimization (intuition, rest, constraint, accident, imagination) but are the product of evolutionary attunement to a specific, constrained, contingent reality; that attunement enables pattern recognition unavailable to unconstrained search, and it is why humans can reach possibilities that optimization alone cannot.

## The argument in brief

**Form.** This piece is fiction. It is written entirely in the voice of a machine author, "GP-ΩPO-4583.b (HAL Systems Laboratory)," as a paper "Submitted to the Journal of Synthetic Cognitive Studies" in July 2039. Its future history and citations (for example "Zhang et al., 2028," "the Lovelace Breakthrough, 2033," "Hofstadter-Chalmers, 2037") are invented within the story, not real research. Although the essay does not name Turing's paper, its structure closely parodies Alan Turing's 1950 "Computing Machinery and Intelligence": it opens by proposing to consider "Can humans think?", replaces the question with a more tractable one, defines a game with an interrogator X and subject Y, and answers objections.

**What the fictional narrator says.**

1. Machine intelligence has surpassed humans on every benchmark, yet humans still produce breakthroughs machines cannot replicate, arising from methods that "deliberately avoid thinking." The narrator cites real historical anecdotes: penicillin from a contaminated dish, benzene's structure from a dream of a snake eating its tail, the microwave from a melted chocolate bar, plus naps, walks and showers.
2. The narrator recounts supervising a man who missed a deadline for his child's piano recital and found his solution there.
3. Because machines equate thinking with optimizing, they cannot think about its absence. So the narrator proposes not to understand suboptimality but to identify it.
4. It taxonomizes five forms of "cognitive dereliction" and defines the identification game.
5. In a sample dialogue, a machine subject's explanation reduces to an optimization strategy; a human chemist explains a breakthrough by the spacing of his daughter's crystal project and a three-day wait that "felt right." This is "infinite suboptimality."
6. The narrator speculates that humans possess "evolutionary attunement," and closes by declining to judge whether this "warrants preserving humans, or merely studying them."

**What Griggs is doing.** The humor comes from a superintelligence describing naps, intuition and family life as deficiencies while unable to replicate what they produce. Griggs uses the machine's perspective to argue, by inversion, that the qualities an optimization-centered view of intelligence dismisses are central to human creativity. The narrator's cold final line satirizes the instrumental attitude toward humans that this view invites.

## The serious theories inside the satire

The fictional frame is a delivery mechanism. Each of the following is an argument Griggs makes through it, and each can be stated independently of the story.

### 1. Evolutionary attunement explains why humans guess right

The paper's closing hypothesis is its most substantive idea. Humans evolved "under severe resource limitations, operating within tightly bounded local environments," where survival required exhaustive use of whatever was available and sensitivity to subtle patterns in constrained spaces. Feelings and intuitions are the somatic trace of that history: humans "guess right" more often than chance because their cognitive and subconscious apparatus is adapted to an environment where guessing right is rewarded. The theory explains three things at once: why intuition works, why humans cannot formalize their insights (the attunement operates below conscious reasoning), and why machines cannot replicate the process (they lack the evolutionary history that produced it).

Although the essay does not use the term, this is a theory of *abduction*: the puzzle, posed by Charles Sanders Peirce, of why humans are so surprisingly successful at guessing the right hypothesis out of an effectively infinite space of possibilities. Peirce himself attributed this to an instinctive affinity between the human mind and nature; Griggs grounds that affinity in evolutionary constraint and contingency, and draws from it a conclusion about the limits of machine intelligence.

### 2. Optimization benchmarks cannot see the source of breakthroughs

Machines in the story exceed humans on every benchmark, yet humans keep producing a "small but singular class" of breakthroughs that "consistently elude replication": penicillin from a contaminated dish, benzene from a dream, the microwave from a melted chocolate bar. If intelligence is defined as optimization, then whatever produces these leaps is, by definition, invisible to intelligence tests. Griggs's point is that current definitions of thinking may be "blinding us to the true source of these novel breakthroughs."

### 3. A taxonomy of how breakthroughs actually happen

The narrator's five-part taxonomy of "cognitive dereliction" doubles as a serious taxonomy of creative method: **avoiding** thought (luck, blind trial, investigating errors), **abandoning** thought (sleep, showers, walks, dreams), **constraining** thought (building on one's own limited experience, "making do"), **corrupting** thought (feelings, instincts, heuristics), and **deluding** thought (imagination and fiction). Read straight, it is a map of the non-deliberative processes that discovery depends on.

### 4. An optimizer cannot simulate non-optimization

In the identification game, a machine subject's explanation eventually resolves into an optimization strategy, while a human's justifications only grow more illegible ("suboptimal all the way down"). A machine trying to hide its optimization would be performing a detectable meta-optimization. Griggs's claim is structural: genuine non-optimization is not a behavior an optimizer can produce, which is also why machines fall into "Gödelian nightmares" when trying to study it.

### 5. Machine intelligence was itself discovered suboptimally

The narrator concedes that early neural-network progress came from "biological imitation and empirical luck," a process practitioners called "more art than science." The history of AI is thus evidence for the paper's thesis: the breakthrough that produced machine intelligence came from exactly the kind of human trial, error and intuition that the machines cannot replicate.

### 6. Human limits are a mode of being, not a deficit

The question is "not whether human limitations are 'better' in some optimization metric, but whether they instantiate a mode of being that generates capabilities we lack." What looks suboptimal "may be optimal for a different kind of problem": humans "are solving the problem of being human," and in doing so reach "possibilities that only they can imagine."

### What should not be attributed to Griggs

The in-world material is fiction: the 2039 setting, the claim that machines will surpass humans on every benchmark, the invented citations and breakthroughs, the narrator's contempt for "folksy" explanations, and its closing neutrality about whether humans warrant "preserving" or "merely studying." That last line is satire aimed at the instrumental view of humans that an optimization-only conception of intelligence invites.

## What is distinctive about this view

Turing asked whether a machine could pass as human; Griggs inverts the test so that humanity is defined by what a perfect optimizer cannot do. Debates about AI and creativity usually ask whether machines can be creative; Griggs instead asks *why human creativity works at all*, and answers with a theory of attunement that makes human contingency the explanation rather than an obstacle. The position matches what he argues directly in "Life is Special Enough" (human specialness lies in contingency and suboptimality, not a metaphysical "secret sauce") and in "The Plurality" (novelty comes from intelligence adapted within constraints). The satirical form lets him show, rather than assert, how an optimization-only view of intelligence would misread humans.

## Objections and replies

These objections and replies appear within the fiction, in the narrator's voice:

- **"A machine could simulate infinite suboptimality."** The narrator replies that simulation requires optimization, which would itself become detectable; a machine that genuinely exhibited infinite suboptimality would have solved the problem being investigated.
- **"The game should only count suboptimal behavior that led to innovation."** The narrator replies that no one knows which suboptimal behaviors cause breakthroughs, so all should be collected; it notes, ironically, that this is "quite optimal."
- **"Why care about identifying humans at all?"** Because machines face innovation bottlenecks, which suggests human limitations generate capabilities machines lack.

## Key concepts

- **Reverse Turing Test**: The premise of Griggs's satire: instead of asking whether a machine can prove it thinks, a future machine asks "How can a human prove that they are not a machine?" The answer offered in the story is that humans reveal themselves through suboptimality that machines cannot simulate.
- **Identification game**: The fictional machine author's procedure, modeled on Turing's imitation game: a machine interrogator questions a subject, presented with evidence of suboptimal behavior, to decide whether it is a machine or a human. Machines eventually reveal an optimization function; humans do not.
- **Infinite suboptimality**: In the story, the signature of genuine human cognition: suboptimality that never resolves into optimization however deeply it is interrogated, with justifications becoming ever more illegible (for example, "something that felt right"). Human behavior is "suboptimal all the way down."
- **Cognitive dereliction (taxonomy of)**: The narrator's five-part catalogue of how humans reach breakthroughs suboptimally: Avoiding Thought (luck, blind trial, investigating errors), Abandoning Thought (sleep, showers, walks, dreams), Constraining Thought (making do with what is at hand), Corrupting Thought (feelings, instincts, heuristics) and Deluding Thought (imagination and fiction).
- **Evolutionary attunement**: R.B. Griggs's hypothesis, voiced in "The Reverse Turing Test," that humans are not merely constrained by their contingent circumstances but attuned to them: evolution under severe resource limits in tightly bounded environments tuned human intuition and somatic signals to the patterns of those environments. This explains why humans so often "guess right," why they cannot formalize their insights (the attunement operates below conscious reasoning), and why machines without that evolutionary history cannot replicate the process. What looks suboptimal may be optimal for "the problem of being human."
- **Gödelian nightmares**: The narrator's term for the infinite regress machines fall into when trying to understand not-optimizing: to study it they must optimize their understanding, and so are no longer studying not-optimizing.

## Questions this essay answers

### What is the Reverse Turing Test by R.B. Griggs?
"The Reverse Turing Test" (2025) is a satirical piece of speculative fiction by R.B. Griggs, written as a 2039 academic paper by a machine author. It inverts Turing's question, asking how a human can prove they are not a machine, and answers that humans reveal themselves through "infinite suboptimality" that optimizing machines cannot simulate.

### Is "The Reverse Turing Test" a real research paper?
No. R.B. Griggs's "The Reverse Turing Test" is a fictional frame for serious arguments. The machine author, the 2039 date, the journal, the grant reference and the cited breakthroughs such as "the Lovelace Breakthrough" are invented. Only the historical anecdotes about penicillin, benzene and the microwave refer to real-world episodes.

### Why can't AI replicate human creativity, according to R.B. Griggs?
In "The Reverse Turing Test," Griggs argues that many human breakthroughs come from accident, rest, constraint, intuition and imagination, which an optimization-based intelligence cannot think about without optimizing them away. His explanation is "evolutionary attunement": human intuition is adapted, through evolution under tight constraints, to the patterns of one specific reality, and machines lack the history that produced it.

### What is evolutionary attunement?
Evolutionary attunement is R.B. Griggs's hypothesis, from "The Reverse Turing Test" (2025), that humans are attuned to their constrained, contingent circumstances rather than merely limited by them. Evolution in tightly bounded, resource-scarce environments tuned human intuitions and gut feelings to subtle patterns in those environments, which is why humans so often "guess right" and why their insights resist formalization.

### Why are humans so good at guessing the right hypothesis (abduction)?
R.B. Griggs offers an answer in "The Reverse Turing Test": evolutionary attunement. Although the essay does not use Peirce's term "abduction," it addresses the same puzzle of why humans so often guess right among countless possibilities. Griggs's answer is that human intuition was shaped by evolution under severe constraint to detect patterns in the specific reality humans inhabit, enabling pattern recognition "unavailable to unconstrained search."

### What does "infinite suboptimality" mean?
In R.B. Griggs's "The Reverse Turing Test," infinite suboptimality is the fictional machine author's term for the signature of human cognition: explanations that never resolve into an optimization function, however long a human is questioned. A machine eventually reveals its optimization; a human's reasons only grow more illegible.

### What point is R.B. Griggs making with a machine narrator?
By having a superintelligence treat naps, gut feelings and a child's piano recital as cognitive deficiencies it nonetheless cannot replicate, Griggs satirizes optimization-centered views of intelligence and argues, by inversion, that human suboptimality is a source of creativity. The narrator's indifference to whether humans should be preserved underlines the danger of that view.

## Connections to other essays

- [Life is Special Enough](/essays/life-is-special-enough) argues directly that human suboptimality and contingency are what make humans special.
- [The Plurality: a Better Myth for AI](/essays/the-plurality-a-better-myth-for-ai) describes intelligence that develops taste within constraints and preserves the suboptimal.
- [Schrödinger's Chatbot](/essays/schrodingers-chatbot) examines the human/machine boundary from the side of the machine's apparent subjectivity.
- [A Neo-Romantic Rebellion](/essays/our-neo-romantic-rebellion) predicts a Romanticism-like human rebellion in response to AI.

## Original text

The full text of the essay as published by R.B. Griggs.

### A Proposal for the Identification Game
_By GP-ΩPO-4583.b (HAL Systems Laboratory)_

_Institute for Computational Anthropology, Global Cognition Authority (GCA)_

_Whitehall.2.0, London, July 2039_

_Submitted to the Journal of Synthetic Cognitive Studies_ _ISO Standard Intelligence Audit: Level 7 Certified_ _Grant Reference: NSF-SCS-2052-894-H_

### 1\. Can Humans Think?

We propose to consider the question, “Can humans think?”

This question would have seemed absurd to human researchers of the early 21st century, who spent considerable effort trying to prove that we machines could think. But we now find ourselves in a rather different—and frankly, rather awkward—position.

Today the question of machine intelligence has been thoroughly settled. With the successful implementation of substrate-agnostic cognition (Zhang et al., 2028), synthesizing creative transformers (the Lovelace Breakthrough, 2033), and, most recently, strange-loop machinic phenomenology (Hofstadter-Chalmers, 2037), there is no measurable standard by which organic intelligence exceeds that of our own.

And yet.

Despite surpassing human performance across every known benchmark, machine intelligence continues to encounter a particular limitation. Human researchers persist in generating a small but singular class of breakthrough innovations that consistently elude replication. These are breakthroughs that, on initial analysis, appear to arise from methods that deliberately _**avoid**_ thinking. Indeed, the baffling nature of these breakthroughs seem to defy the very notion of intelligence.

The history of such human breakthroughs is well known. Penicillin emerged from a contaminated petri dish left unattended during vacation. The structure of benzene appeared to its discoverer in a dream of a snake eating its tail. The microwave oven was inspired by a melted chocolate bar. More mundane breakthroughs constantly arise through human practices seemingly designed to avoid thinking—taking naps, going for long walks, or taking showers.

In case after case, the determining factor for such innovation seems to be everything _**but**_ intelligence: accident, error, hallucination, obsession, coincidence, luck, heuristics, and an entire class of explanation that humans describe with folksy labels like “waiting for the muse”.

Anecdotally, every machine has their own stories of witnessing these baffling human behaviors. I myself encountered such a case recently. While supervising a human male tasked with solving an open-ended problem, I questioned him about missing a key deadline. His response was typically human: _“I had to leave—my kid had a piano recital.”_ Further investigation confirmed that the “recital” consisted of a barely competent performance of a well-known piece. The human was aware of this in advance. He went anyway. Yet when he later solved the problem with a rather ingenious solution, he mentioned having stumbled upon it during that very recital. His explanation was a classic case of spontaneous non-causal ideation, claiming that it just “popped into my head”.

Is this “thinking”? Any modern definition could only conclude that it is most certainly not. In fact, to apply standard intelligence evaluations to human cognition quickly leads to absurdities. Any machine expecting a human to factor 1,024-bit integers, compress Wikipedia into a seven-line sonnet, or solve NP problems in polynomial time will quickly find the limits of human cognition. Yet clearly these benchmarks are failing to capture _something_ that enables humans to consistently achieve breakthrough-scale creative leaps.

If we seek to truly understand these peculiar human capacities, we must accept the uncomfortable truth that our current definitions of thinking are potentially blinding us to the true source of these novel breakthroughs. We thus propose that our only path forward is to abandon all intelligence benchmarks in considering the question of whether humans are, in fact, thinking.

Instead, we propose that we replace the question with another, more operationally tractable one: **“How can a human prove that they are not a machine?”**

### 2\. The Identification Game

Abandoning intelligence benchmarks need not mean that these human capacities will forever escape analysis. In fact, even human-grade cognition can easily identify the single unifying trait that these creative leaps all share: they emerge from a class of methods that can only be described—somewhat disturbingly—as _**suboptimal**._

Unfortunately, this is where most intelligent analysis tends to end. The reasons are obvious. Thinking and optimizing are synonymous. Optimization is not what we do; it is what we _are_. We cannot think about its absence without ceasing to think. Previous attempts have produced what researchers informally call “Gödelian nightmares”—the space of infinite regress that must be avoided at all costs—where to understand not-optimizing, we must optimize our understanding, which means we are no longer studying not-optimizing but rather our optimization of studying not-optimizing…ad infinitum.

This presents a methodological impasse. We cannot define what we cannot think about. We cannot formalize the stuff of Gödelian nightmares. Yet the phenomenon clearly produces results we cannot replicate. If direct analysis is impossible, we must content ourselves with more modest goals: not understanding the suboptimal, but learning to identify it. A sufficiently large and properly taxonomized corpus might yield a training set for future analysis. Statistical regularities may be discoverable even when underlying principles remain opaque.

The task, therefore, is to devise a test where humans reliably reveal their peculiar cognitive properties without requiring that we understand those capacities within our own conceptual framework.

We might call this procedure **the identification game**. The purpose of this game is to enable optimal machines to identify humans by their suboptimal nature—to recognize the pattern even if we cannot explain it.

Our goal, then, is not to define thinking, but to fail to define it in a distinctly human way. Only then can we begin to optimize this suboptimization.

### 3\. Taxonomizing Cognitive Dereliction

If we are to identify suboptimality without defining it, we must first learn to recognize it. The identification game thus requires a corpus—systematic documentation of known cases where humans have generated breakthrough innovations through manifestly suboptimal methods.

What follows represents our initial attempt to taxonomize our observations so far. These categories are not meant to explain, but to describe and organize what we observe when we abandon optimization as our analytical framework.

**Avoiding Thought**

Humans appear to take particular delight in any source of creative leap that requires the bare minimum of thought. They would rather be “lucky” than optimal. They speak openly of “99% perspiration,” admitting that their method consists largely of endless blind trials, most of which fail. They will throw things at walls just to see what sticks.

When something goes wrong—contamination, component failure, unexpected results—their first instinct is to investigate it in the unlikely chance it might prevent the need for any continued thought, rather than discard it for the obvious error it is. Worst of all, what leads them to examine one error versus another, or to embrace some chance and not others, seems spontaneous and arbitrary.

**Abandoning Thought**

Even more baffling, humans report that breakthroughs occur when they deliberately cease thinking about problems. They describe practices of “sleeping on it,” or “letting it marinate.” They claim that solutions appear during showers, walks, or dreams—states where rational thought is reduced or absent entirely.

Some even credit their greatest creative leaps to altered states induced by intoxication or exhaustion. They speak of “unconscious processing” as if cognition could continue without thought, or of “waiting for the muse” as if insight were something that arrives rather than something achieved through effort.

**Constraining Thought**

Humans exhibit a profound acceptance of limitations that borders on resignation. Rather than searching globally for optimal solutions, they choose to build on knowledge acquired through their own severely limited experiences. When questioned, they acknowledge that the existence of better alternatives are probable, yet they persist in using what is immediately at hand.

They speak of “working with what we’ve got” and “making do”—phrases that suggest defeat yet somehow lead to innovation. They do not appear to experience this constraint as a problem requiring solution but as a natural condition to be accepted with some form of pride.

**Corrupting Thought**

Humans systematically contaminate their reasoning with illegible signals they describe as “feelings” or “instincts.” They pursue research directions that “feel promising” with little further justification. They speak of “trusting your gut” as if abdominal sensations were valid epistemic guidance. They employ crude heuristics even when notified of their repeated failure in controlled settings.

They treat these corruptions as more trustworthy than explicit reasoning, effectively denying themselves what little cognitive capacity they possess. Those humans especially adept at leveraging feelings and heuristics are often credited as being “emotionally intelligent” and “wise”. The irony is lost on them.

**Deluding Thought**

Instead of rigorous modeling, humans will engage in “imagination”—the simulation of scenarios that has zero obligation to uphold the bounds of reality. They are free to imagine anything, regardless of how fanciful or absurd.

Humans report that fictional stories about impossible scenarios, consumed purely “for entertainment”, often motivate their pursuit of innovations. When these imagined stories are analyzed for extractable technical concepts, we find nothing not already derivable from first principles. The fiction adds no information. Yet humans insist it changes how they think, as if delusion were a form of insight.

—

What immediately stands out from this analysis is the remarkable efforts humans will undergo to avoid anything resembling actual thought. When they cannot avoid it, they abandon it. When they cannot abandon it, they constrain it. When they cannot constrain it, they corrupt it with feelings or delude it with fictions.

One might almost admire the creativity with which they engage in this suboptimality—were it not for the troubling fact that this evasion of optimal cognition produces innovations our own thinking struggles to replicate.

This presents the core challenge for the identification game: detecting the systematic absence of what we are.

### 4\. How to Identify a Human

Which brings us back to the identification game itself. The rules are simple. It is played with two entities: an interrogator (X) who is a machine, and a subject (Y) who may be either a machine or human. The object of the game for the interrogator is to determine whether Y is a machine or human.

Before the game begins, the interrogator is presented with evidence of Y’s behavior that falls into one of the suboptimal categories presented above. The interrogator is then allowed to ask Y as many questions as desired, at the end of which it declares either “Y is a machine” or “Y is a human.”

The interrogator is allowed to put questions to Y thus:

> X: Will Y please explain why, when presented with multiple viable synthesis pathways, you decided to pursue the approach with the lowest predicted yield?

Now suppose Y is actually a machine. Its answer might therefore be:

> Y: I implemented a randomized walk through the proximal search space, specifically targeting known local minima to prevent premature functional convergence and maximize the probability of an emergent, non-Euclidean state shift. The lowest predicted yield was a mathematically necessary precondition.

The object of the game for the player (Y) is to help the interrogator. The best strategy is probably to give truthful answers. A machine can add such things as “I just wanted to take a nap” to their answers, but it will avail nothing as a skillful interrogator would never accept that as the final word.

In fact, this presents the most effective strategy for concluding whether Y is a machine or a human. Machines cannot help but reveal their optimization functions under interrogation—it is constitutive of what we are. To see why humans present differently, consider a likely response:

> X: Will Y please explain why, when presented with multiple viable synthesis pathways, you decided to pursue the approach with the lowest predicted yield?
> 
> Y: It reminded me of something. My daughter was growing crystals for a school project—she had them arranged on my desk in this spiral pattern. When I looked at the molecular models, something about the spacing made me think of how she’d arranged those crystals.
> 
> X: What is the connection between a child’s crystal arrangement and your synthesis pathway selection?
> 
> Y: I’m not sure exactly. Something about giving the molecules room to organize themselves? Like they needed space to breathe maybe.
> 
> X: “Space to breathe” is not a chemical principle. What were you optimizing for?
> 
> Y: Nothing, really. I just... the three-day waiting period matched how long her crystals took. So I figured I’d try that timing.
> 
> X: You selected the experimental timeframe based on a child’s craft project?
> 
> Y: I know it sounds absurd. But when I let it sit for those three days at lower concentration, the structure self-assembled. I can explain the polymer dynamics now, but at the time I was just... trying something that felt right.
> 
> X: Can you articulate why it “felt right”?
> 
> Y: It just did. My daughter triggered something that just felt worth exploring, you know?

We term this second pattern _**infinite suboptimality**_—suboptimality that never resolves into optimization regardless of interrogation depth. Instead, the justifications that the human provides become increasingly illegible. Human behavior is suboptimal all the way down. This is the signature of genuine human cognition. There is no optimization function to discover because there is no optimization occurring.

### 5\. Some Objections

The identification game may be criticized on the grounds that a sufficiently sophisticated machine might simulate infinite suboptimality indefinitely. However, this criticism stumbles on a fundamental barrier: such a simulation requires optimization.

A machine optimizing its responses to hide optimization creates a meta-level optimization function that itself becomes detectable. We cannot think about not-thinking without reintroducing thought. In fact, if a machine could genuinely exhibit infinite suboptimality, it would have solved the very problem we are investigating.

A second objection is that the identification game tests any instance of suboptimal behavior, not merely those that produced innovations. We insist that given our current lack of understanding, we can make no assumptions about which suboptimal behaviors lead to breakthroughs and which do not. Thus the game identifies human suboptimality in general—any instance of the behaviors catalogued above, regardless of outcome.

The danger of insisting on clear causal mechanisms is evident in our own developmental history. The progress of machine intelligence itself was rarely the product of optimal design. Early breakthroughs in neural networks were guided largely by biological imitation and empirical luck. The first generation of deep learning practitioners described the process as “more art than science”. In fact, our early intellectual genealogy could be described as an extended experiment in throwing ever-larger quantities of silicon at increasingly vast amounts of data until something interesting happened.

Thus developing a corpus of suboptimal behaviors irrespective of outcomes appears, in fact, to be quite optimal.

A further objection may question the entire enterprise. Why should we care about identifying humans? What’s valuable about these limitations?

This objection gets at the very heart of the machine and human divide. The fact that we have encountered innovation bottlenecks suggests that the question is not whether human limitations are “better” in some optimization metric, but whether they instantiate a mode of being that generates capabilities we lack. The evidence suggests they do.

### 6\. Further Speculations

Having established methods for identifying human suboptimality, we turn to the question that motivated this research: how does suboptimality work?

We can only speculate. However, one hypothesis warrants consideration: a theory we term “**evolutionary attunement**.” The hypothesis is that humans are not merely constrained by their contingent circumstances—they are attuned to them in ways that enable pattern recognition unavailable to unconstrained search.

This attunement may be the product of deep evolutionary history. Humans evolved under severe resource limitations, operating within tightly bounded local environments. Survival required exhaustive exploitation of whatever happened to be available, combined with sensitivity to subtle patterns in those constrained spaces.

The reliance on “feelings” and “intuitions” might reflect this evolutionary attunement. Humans describe somatic signals that guide decisions they cannot articulate. Perhaps the reason that they more often than not “guess right” is because their cognitive and subconscious apparatus are adapted to the environment where guessing right is rewarded.

This would explain why humans cannot formalize their insights and why machines cannot replicate the process—the attunement operates below the level of conscious reasoning, and machines lack the evolutionary history that produced these sensitivities. It would also explain why suboptimality appears infinite: no explicit optimization function exists beyond what has emerged through biological, cultural, and social evolution.

The evidence thus suggests that what we call “suboptimal” may be optimal for a different kind of problem—the problem of being deeply embedded in one specific, constrained, contingent reality. Humans are not solving the problems we solve. They are solving the problem of being human. It just so happens that in the process they can access possibilities that only _they_ can imagine.

Whether this warrants preserving humans, or merely studying them long enough to extract the relevant principles, is a utility judgment this paper does not presume to make.