Everyone from AI executives and researchers to doomsayers, politicians, and activists is talking about Artificial General Intelligence (AGI). Yet, they often don't seem to agree on its exact definition. One common definition of AGI is an AI that can do everything a human can do, but are humans truly general? In this paper, we address what's wrong with our conception of AGI, and why, even in its most coherent formulation, it is a flawed concept to describe the future of AI. We explore whether the most widely accepted definitions are plausible, useful, and truly general. We argue that AI must embrace specialization, rather than strive for generality, and in its specialization strive for superhuman performance, and introduce Superhuman Adaptable Intelligence (SAI). SAI is defined as intelligence that can learn to exceed humans at anything important that we can do, and that can fill in the skill gaps where humans are incapable. We then lay out how SAI can help hone a discussion around AI that was blurred by an overloaded definition of AGI, and extrapolate the implications of using it as a guide for the future.
Figures & tables
Figure 1: A two-dimensional semantic map organizing prominent definitions for AGI and other North Star measures of artificial intelligence, along two axes. The vertical axis represents the source of intelligence, ranging from performance-based capabilities (DO, bottom) to learning and adaptability (LEARN, top). The horizontal axis represents the scope of tasks, from universal/open-ended domains (left) to human-centric and economically-focused domains (right). Definitions cluster into three categories: Adaptive Generalists (teal) emphasize learning efficiency and generalization in open environments; Cognitive Mirrors (violet) focus on replicating human-level cognitive capabilities across broad task domains; Economic Engines (orange) prioritize practical utility and economic value in human-relevant tasks. Superhuman Adaptable Intelligence (SAI) falls into the realm of adaptable AI that can do anything that is important both inside and outside the human realm.
AGI Definition
Failure mode
Explanation
“Match or exceed the cognitive versatility and proficiency of a well-educated adult.” ( Hendrycks et al., 2025 )
Not Consistent
Human cognition is not general in any meaningful way. This definition is also unnecessarily narrow
“Highly autonomous systems that outperform humans at most economically valuable work .” ( Morris et al., 2024 )
Not Consistent
The focus here is explicitly on a subset of tasks that are of economic worth. Clearly not General
“A system that should be able to do pretty much any cognitive task that humans can do .” — Demis Hassabis (DeepMind CEO) ( Mitchell, 2024 )
Not Consistent
This definition is not actually general both in its focus on humans, and also in its focus on ”cognitive tasks”, which seems to be to the exclusion of physical tasks like locomotion
“We need precise, quantitative definitions and measures of intelligence – in particular human-like general intelligence…. …The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty” ( Chollet, 2019 )
Not Consistent
Chollet himself admits as much, calling human cognition ’only “general” in a limited sense’, a contradiction of terms
“We define AGI as a system that demonstrates broad generality (performing a wide range of tasks) and high performance (matching or exceeding human levels).” ( Morris et al., 2024 )
Not Feasible
While they acknowledge generality requires exceeding human levels, with a focus on direct performance over adaptation, such a system is not realizable with finite resources.
“Intelligence measures an agent’s ability to achieve goals in a wide range of environments .” ( Legg and Hutter, 2007 )
Not Feasible
Legg and Hutter define the domain of environments as all that are computable. They further emphasize ability over adaptability. Strong ability on such a vast set of tasks is not realizable with finite resources
Table 1: The failure of most AGI definitions. Note: some definitions fail for multiple reasons, but we only highlight one.
Figure 2: Illustration of the task space overlap between the human domain and the AI domain within the universal task space.
Figure 3: Illustration of autoregressive model divergence
Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations. Achieving this goal would have profound and far-reaching impacts on human society, which raises many complex questions for the decade ahead. This report investigates how AI itself might continue to develop in a post-AGI world along the continuum of machine intelligence. The endpoint of this continuum, Universal AI, is theoretically well understood, which provides some formal grounding for the main focus of this report: the transition from human-level AGI to artificial general superintelligence, which can intuitively be understood as a system that is more intelligent and cognitively capable than large organisations of humans. After characterizing ASI, the report discusses four potential pathways from AGI to ASI: scaling AGI, AI paradigm shifts, recursive improvement, and ASI emerging from large-scale multi-agent collectives. The report then discusses possible frictions and bottlenecks along these pathways. Determining whether the impact of these frictions will be negligible or substantial raises a number of concrete open research questions. Due to large uncertainties for predicting ASI progress, it cannot be ruled out that AI progress might continue to accelerate over the next years. This could imply that the image of a single transformative step change, caused by the introduction of human-level AGI into our society, could be inaccurate. More apt might be the prospect of a series of transformative societal changes caused by AI-enabled progress and breakthroughs across many areas of science and technology. Preparing for this prospect requires a massively interdisciplinary endeavour of global scope and interest.
Tim Genewein, Matija Franklin, Alexander Lerchner +11
Google DeepMind · University of Waterloo (work conducted while at Google DeepMind) · Australian National University +1
Is monolithic scaling the only path to AGI? This paper challenges the dogma that purely scaling a single model is sufficient to achieve Artificial General Intelligence. Instead, we identify Agentic AI as a necessary paradigm for mastering the complex, heterogeneous distribution of real-world tasks. Through rigorous theoretical derivations, we contrast the optimization constraints of monolithic learners against the efficiency of Agentic systems, progressing from simple routing mechanisms to general Directed Acyclic Graph (DAG) topologies. We demonstrate that Agentic AI achieves exponentially superior generalization and sample efficiency. Finally, we discuss the connection to Mixture-of-Experts, reinterpret the instability of current multi-agent frameworks, and call for greater research focus on Agentic AI.
Junwei Liao, Shuai Li, Muning Wen +2
1Shanghai Jiao Tong University 2Shanghai Innovation Institute · 1Shanghai Jiao Tong University · University College London.
Krakauer, Krakauer, and Mitchell (2025) distinguish between emergent capabilities and emergent intelligence, arguing that true intelligence requires efficient coarse-grained representations enabling diverse problem-solving through analogy and minimal modification. They contend that intelligence means doing "more with less" through compression and generalization, contrasting this with "vast assemblages of diverse calculators" that merely accumulate specialized capabilities. This paper examines whether their framework accurately characterizes human intelligence and its implications for conceptualizing artificial general intelligence. Drawing on empirical evidence from cognitive science, I demonstrate that human expertise operates primarily through domain-specific pattern accumulation rather than elegant compression. Expert performance appears flexible not through unifying principles but through vast repertoires of specialized responses. Creative breakthroughs themselves may emerge through evolutionary processes of blind variation and selective retention rather than principled analogical reasoning. These findings suggest reconceptualizing AGI as an "archipelago of experts": isolated islands of specialized competence without unifying principles or shared representations. If we accept human expertise with its characteristic brittleness as genuine intelligence, then consistency demands recognizing that artificial systems comprising millions of specialized modules could constitute general intelligence despite lacking KKM's emergent intelligence.
Daniel Kilov
Machine Intelligence and Normative Theory (MINT) Lab, Research School of Social Sciences, Australian National University