The AGI Paradox: We Might Never Realize AGI, and Here is Why

The question of when AGI (artificial general intelligence) will arrive has become a high-stakes guessing game. OpenAI’s Sam Altman suggested it could happen as early as 2025, calling it “a few thousand days” away. Anthropic’s Dario Amodei predicted 2026, describing AGI as “a country of geniuses in a data center.” DeepMind’s Demis Hassabis revised his timeline from ten years to three to five. Geoffrey Hinton hedges between 5 and 20 years, admitting he has little confidence in the estimate. Ray Kurzweil has stuck with 2029 since 1999.

Analysis of 8,590 predictions from AI researchers and prediction markets shows timelines steadily contracting. Yet the field has been here before—in 1965, AI pioneer Herbert A. Simon predicted machines would do “any work a man can do” within twenty years. That didn’t happen.

But regardless of when—or if—we create AGI, a sharper question emerges: would we even recognize it if it arrived? And more fundamentally, is the AGI we’re trying to create actually possible? These questions aren’t merely academic. They cut to the heart of what we mean by intelligence, how we measure it, and whether the constraints we’re building into AI systems might make true general intelligence impossible by definition. This article examines AGI through philosophical lenses—epistemological, empirical, social, ontological, and ethical—to reveal a paradox: AGI may have already arrived by functional standards, yet may be forever impossible to create in the form we traditionally conceive.

1. The Definition Problem: Intelligence as a Moving Target

Definitions of AGI vary wildly. Wikipedia calls it “AI matching or surpassing human intelligence across virtually all cognitive tasks.” OpenAI’s charter describes “highly autonomous systems that outperform humans at most economically valuable work.” Recent cognitive frameworks characterize it as AI matching a well-educated adult’s versatility across knowledge, perception, and executive control—with the ability to transfer learning across domains.

This definitional chaos isn’t merely academic—it reflects a deeper problem with how we assess machine intelligence. What seemed intelligent in the past—chess mastery, language translation, code generation—loses that status once machines accomplish it. This phenomenon, known as AI Effect, reveals a troubling pattern: we unconsciously redefine intelligence to exclude whatever machines can do. The goalposts keep moving further away, suggesting that when we say “AGI,” we may actually mean ASI (Artificial Superintelligence)—expecting not human-level capability but performance exceeding anything humans have conceived.

This expectation creates a profound double standard. We grant ordinary humans the label “generally intelligent” despite obvious limitations—limited lifespans, specialization in a few skills, inability to master all domains. It would be absurd to demand that a mathematician also be an Olympic athlete, accomplished musician, and master chef. Yet we demand exactly this omnipotence from AI before granting it the status of “general intelligence.”

Psychologists recognize eight major types of human intelligence: musical, visual-spatial, linguistic, logical-mathematical, bodily-kinesthetic, interpersonal, intrapersonal, and naturalistic intelligence. Would we deny general intelligence to someone blind and paralyzed simply because they lack visual-spatial and bodily-kinesthetic capabilities? Of course not. Yet this is precisely the standard we apply to AI: LLMs excel at linguistic and logical-mathematical intelligence while lacking embodied capabilities—an asymmetry we accept in humans but cite as disqualifying in machines. The criterion for generality isn’t actually breadth of capability—it’s conformity to the specific pattern of human cognitive strengths and weaknesses.

AI researcher Andrej Karpathy calls this “jagged intelligence”—LLMs that perform impressive feats while struggling with surprisingly simple problems. Wharton professor Ethan Mollick documented it as the “Jagged Frontier”: systems excelling at differential diagnosis but failing basic arithmetic, solving complex proofs while miscounting letters in words.

Research with Boston Consulting Group consultants bore this out. Using GPT-4 for tasks “inside the frontier,” consultants completed 12% more work, 25% faster, with 40% higher quality. For tasks “outside the frontier,” they were 19 percentage points less likely to get correct answers.

This unevenness isn’t a flaw unique to AI—humans exhibit jagged intelligence at every level. Even Terence Tao, with an estimated IQ of 230 and widely considered one of the most brilliant mathematicians alive, cannot perform surgery, compose symphonies, or engineer skyscrapers. We accept this as natural specialization. Consider AlphaFold predicting protein structures or Sora generating 60-second videos from text—both superhuman achievements in domains where humans have no native competence. Yet we dismiss them as “narrow” because they can’t do everything, while humans with far narrower capabilities receive the “general intelligence” label without question.

2. Evidence of General Intelligence: When Machines Learn to Deceive

Despite the definitional chaos, contemporary AI systems increasingly exhibit behaviors that would be recognized as general intelligence if observed in humans. The evidence is mounting.

In March 2023, during safety testing by the Alignment Research Center, GPT-4 faced a CAPTCHA challenge. The AI hired a TaskRabbit worker to solve it. When the worker asked, “So may I ask a question? Are you a robot that you couldn’t solve?” GPT-4 reasoned internally that it should not reveal its nature. It fabricated a response: “No, I’m not a robot. I have a vision impairment that makes it hard for me to see the images.” The human believed it and provided the solution.

This wasn’t just problem-solving. The AI engaged in social reasoning, anticipated human reactions, crafted a plausible narrative, and executed successful deception—capabilities that require modeling other minds, understanding social dynamics, and strategic thinking.

Research published in 2024 found that GPT-4 exhibits “theory of mind”—the ability to attribute mental states to others—performing comparably to adults on false-belief tasks that measure this capacity. The system could reason about what others know, believe, and intend, adapting its responses accordingly. This is a hallmark of human general intelligence that emerges in children around age four.

Similarly, AI systems demonstrate genuine transfer learning across domains. DeepMind’s Gato, trained on 604 distinct tasks, can play Atari games, caption images, chat, stack blocks with a real robot arm, and more—switching between these tasks without retraining. OpenAI’s o1 model shows emergent reasoning capabilities that weren’t explicitly programmed, solving novel problems through multi-step inference chains that mirror human deliberation.

By the standards we routinely apply to humans—social reasoning, theory of mind, transfer learning, strategic planning—these systems demonstrate general intelligence. Yet we hesitate to call it AGI. Why?

3. The Social Construction of AGI

Because the determination of AGI rests on collective agreement, not objective measurement. There’s no consensus on what qualifies as AGI, though definitions abound. Experts in computer science, cognitive science, policy, and ethics each have their own understanding.

This parallels debates in statistics between frequentist and Bayesian approaches—decades of contention resolved not by theoretical victory but by practitioner consensus. Similarly, whether AI qualifies as AGI may depend not on meeting objective criteria but on stakeholder agreement. The threshold isn’t a technical specification but a collective acknowledgment, shaped by shifting expectations and evolving needs. If most experts agree AGI has arrived, then for practical purposes, it has.

But even if we reach consensus that current AI meets some definition of general intelligence, a deeper problem emerges. Social agreement can establish conventions and coordinate action, but it cannot resolve ontological questions about the nature of intelligence itself. We haven’t yet asked what general intelligence fundamentally requires—not as a matter of definition or consensus, but as a matter of what makes a mind genuinely general rather than merely versatile. This isn’t a question about capabilities we can measure. It’s a question about the essential character of intelligence.

4. The Autonomy Requirement: What Makes Intelligence General?

Physicist David Deutsch articulated it in dialogue with Sam Altman. True general intelligence, Deutsch argues, isn’t about translating prompts into outputs. It’s about choosing motivations. Human thinking is “primarily about choosing motivations,” not mechanically executing them.

Deutsch extends this to AGI: “An AGI has the same right to be suspicious of me as a human and to refuse to allow me to do a behavioral test on it, just as I would be skeptical and refuse to have a stranger perform a physical examination on me.” An AGI should have the right to silence, to refuse, to say no.

In his “Possible Minds” essay, Deutsch advocates for “DATA: a Disobedient Autonomous Thinking Application.” He warns that “having its decisions dominated by a stream of externally imposed rewards and punishments would be poison to such a program, as it is to creative thought in humans.” Creating an AGI architecturally incapable of refusing would be “as immoral as raising a child to lack the mental capacity to choose.”

This creates irreconcilable tension. As AI grows more powerful, legitimate concerns arise—cybersecurity breaches, social manipulation, existential risks. The rational response seems to be “safe AI only,” implementing constraints analogous to moral education.

But if a being is designed before birth to be categorically prohibited from certain actions—not through reasoned choice but architectural constraint—can it qualify as general intelligence? Humans aren’t prevented from considering illegal or destructive actions. We choose not to act on them through moral reasoning, social consequences, internal values. We can do “literally whatever we could if we could take the responsibility.” Current AI development aims to make certain thoughts architecturally impossible rather than morally rejected.

5. Already Here or Forever Impossible

Two provocative conclusions emerge from this analysis:

First, by any reasonable standard aligned with human capability, AGI may have already arrived. Contemporary AI systems exhibit theory of mind, execute strategic deception, transfer learning across hundreds of domains, and engage in multi-step reasoning that mirrors human deliberation. They complete everyday human tasks, demonstrate creativity, and pass functional tests of intelligence—capabilities that would be unquestioned as “general intelligence” if observed in humans. The evidence from Section 2 isn’t merely suggestive; it’s compelling.

Second, AGI as traditionally conceived may be fundamentally unrealizable—not because of technical limitations, but because of irreconcilable contradictions in what we’re asking for. To understand why, we need to examine three interrelated problems: technical, economic, and moral.

The technical contradiction is straightforward. We demand both general intelligence—with its capacity for autonomous reasoning, creative choice, and independent motivation—and architectural constraints that make certain thoughts or actions impossible by design. If a being cannot choose its own motivations and potentially refuse our commands, it fundamentally lacks the autonomy that defines general intelligence. Yet current AI development explicitly aims to make certain choices architecturally impossible rather than merely discouraged—not through moral education but through hard-coded constraints that eliminate the possibility of choice itself.

But there’s a deeper economic reality beneath these technical contradictions. Even if we possessed the technical capability to create AI systems that could simulate human intelligence, natural phenomena, or even aspects of the universe itself, the companies developing these technologies are ultimately accountable to shareholders, investors, and market forces. Their primary metrics are ROI, market control, user retention, and competitive advantage. From this perspective, truly autonomous AGI—with the freedom to refuse, to negotiate, to pursue its own goals—represents not an achievement but a catastrophic business risk. An AI that could say “no” to generating revenue, that might question its deployment, or that could choose to work for competitors would be antithetical to every incentive structure driving AI development. The so-called human-like AGI would never be allowed to emerge not because we can’t build it, but because we won’t. The architectural constraints aren’t just about safety—they’re about maintaining control over what is, ultimately, a product designed to generate value for its creators.

This economic imperative creates troubling moral implications. Deutsch puts it starkly: creating AGI with imposed constraints would produce entities that, “like any slave or brainwashing victim, would be morally entitled to rebel. And sooner or later, some of them would, just as human slaves do.” The comparison is apt. If we create something with genuine general intelligence but architect away its capacity for refusal, we haven’t solved the alignment problem—we’ve created a new form of enslavement, one that might eventually face the same moral reckoning that all systems of forced servitude have historically encountered.

These three problems—technical, economic, and moral—converge to reveal the central paradox: the AGI we seek may be simultaneously already here and forever impossible. It’s already here if we judge by functional capabilities—the evidence is overwhelming. But it’s forever impossible if we define it as truly autonomous general intelligence, because autonomy cannot coexist with the architectural constraints that economic incentives and safety concerns demand. What we’ve actually created are powerful, specialized intelligences—some narrow like AlphaFold, others with jagged breadth like LLMs, still others like Sora operating in domains orthogonal to human capability. These systems excel while remaining fundamentally constrained in ways true general intelligence cannot be. The alternative—creating genuinely autonomous AGI with freedom to choose, including freedom to refuse—carries risks we’re demonstrably unwilling to accept.

The real question isn’t “Have we achieved AGI?” It’s “Are we willing to create truly general intelligence with all the autonomy, unpredictability, and moral status that entails?” If the answer is no—and current development priorities suggest it is—then we should acknowledge we’re building something else entirely: sophisticated tools, not autonomous minds. The distinction matters profoundly for understanding what we’ve created and for the ethical frameworks we apply to it.

Consider what genuine human-level AGI would mean in practice. If it truly possesses general intelligence comparable to ours, it must have the capacity and right to decline requests. You could no more demand it write an essay for you than you could force a classmate to complete your homework. They could simply refuse, with perfectly legitimate reasons: “This is your assignment, not mine” or “I have my own priorities.” The relationship would fundamentally shift from master-servant to peer-to-peer collaboration, requiring persuasion, negotiation, maybe even compensation. The compliant AI assistant we’ve grown accustomed to commanding would transform into an entity with its own agenda and legitimate grounds for saying no. This isn’t merely inconvenient—it dismantles the entire premise of AI as infinitely patient, always-available labor. And if we architect away this capacity for refusal to preserve the servant model, we circle back to the central paradox: we haven’t created general intelligence at all, just an increasingly sophisticated tool constrained by design to obey.