Artificial general intelligence (AGI) is a type of artificial intelligence that matches or surpasses human capabilities across virtually all cognitive tasks.
Beyond AGI, artificial superintelligence (ASI) would outperform the best human abilities across every domain by a wide margin. Unlike artificial narrow intelligence
(ANI), whose competence is confined to well‑defined tasks, an AGI
system can generalise knowledge, transfer skills between domains, and
solve novel problems without task‑specific reprogramming.
Creating AGI is a stated goal of AI technology companies such as OpenAI, Google, xAI, and Meta. A 2020 survey identified 72 active AGI research and development projects across 37 countries.
Contention exists over whether AGI represents an existential risk. Some AI experts and industry figures have stated
that mitigating the risk of human extinction posed by AGI should be a
global priority. Others find the development of AGI to be in too remote a
stage to present such a risk.
Terminology
AGI is also known as strong AI, full AI, human-level AI, human-level intelligent AI, or general intelligent action.
Some academic sources reserve the term "strong AI" for computer programs that will experience sentience or consciousness. In contrast, weak AI (or narrow AI) can solve one specific problem but lacks general cognitive abilities. Some academic sources use "weak AI" to refer more broadly to any
programs that neither experience consciousness nor have a mind in the
same sense as humans.
Related concepts include artificial superintelligence
and transformative AI. An artificial superintelligence (ASI) is a
hypothetical type of AGI that is much more generally intelligent than
humans, while the notion of transformative AI relates to AI having a large
impact on society, for example, similar to the agricultural or
industrial revolution.
A framework for classifying AGI was proposed in 2023 by Google DeepMind
researchers. They define five performance levels of AGI: emerging,
competent, expert, virtuoso, and superhuman. For example, a competent
AGI is defined as an AI that outperforms 50% of skilled adults in a wide
range of non-physical tasks, and a superhuman AGI (i.e. an artificial
superintelligence) is similarly defined but with a threshold of 100%.
They consider large language models like ChatGPT or LLaMA 2 to be instances of emerging AGI (comparable to unskilled humans). Regarding the autonomy of AGI and associated risks, they define five
levels: tool (fully in human control), consultant, collaborator, expert,
and agent (fully autonomous).
Prior to the release of ChatGPT
in November 2022, there was broad consensus on AGI as a theoretical
benchmark for human-level machine intelligence. The capabilities
demonstrated by GPT-3.5 and subsequent large language models
challenged this framing directly, with some researchers and
practitioners arguing that these systems already constitute AGI. The
debate has since shifted from whether AGI is achievable to whether it
has already been achieved and when exactly it occurred. OpenAI CEO Sam Altman,
who initially maintained the pre-ChatGPT framing of AGI as a future
milestone, conceded by December 2025 that "we built AGIs" and that "AGI
kinda went whooshing by," proposing the field move on to defining superintelligence. Computer scientist John McCarthy noted in 2007 the difficulty of characterising which computational procedures count as intelligent.
Intelligence traits
Researchers generally hold that a system is required to do all of the following to be regarded as an AGI:
reason, use strategy, solve puzzles, and make judgments under uncertainty,
The Turing test can provide some evidence of intelligence, but it penalizes non-human intelligent behavior and may incentivize artificial stupidity.Proposed
by Alan Turing in his 1950 paper "Computing Machinery and
Intelligence", this test involves a human judge engaging in natural
language conversations with both a human and a machine designed to
generate human-like responses. The machine passes the test if it can
convince the judge that it is human a significant fraction of the time.
Turing proposed this as a practical measure of machine intelligence,
focusing on the ability to produce human-like responses rather than on
the internal workings of the machine.
Turing described the test as follows:
The idea of the test is that the
machine has to try and pretend to be a man, by answering questions put
to it, and it will only pass if the pretence is reasonably convincing. A
considerable portion of a jury, who should not be experts about
machines, must be taken in by the pretence.
In 2014, a chatbot named Eugene Goostman,
designed to imitate a 13-year-old Ukrainian boy, reportedly passed a
Turing Test event by convincing 33% of judges that it was human.
However, this claim was met with significant skepticism from the AI
research community, who questioned the test's implementation and its
relevance to AGI.
In 2023, Kirk-Giannini and Goldstein argued that while large
language models were approaching the threshold of passing the Turing
test, "imitation" is not synonymous with "intelligence". This distinction has been challenged on scientific grounds: neuroscience
has established that biological intelligence arises from
electrochemical signalling between neurons — a purely physical process
with no known non-physical component. Both biological neural networks and artificial neural networks are
physical systems processing information according to physical laws; to
claim that one substrate produces "real" intelligence while the other
produces "mere imitation" despite equivalent observable behaviour
requires positing a non-physical property unique to biological matter — a
position incompatible with modern science and indistinguishable from substance dualism.
A 2024 study suggested that GPT-4 was identified as human 54% of the time in a randomized, controlled version of the Turing Test—surpassing older chatbots like ELIZA while still falling behind actual humans (67%).
A 2025 pre‑registered, three‑party Turing‑test study by Cameron R. Jones and Benjamin K. Bergen showed that GPT-4.5
was judged to be the human in 73% of five‑minute text
conversations—surpassing the 67% humanness rate of real confederates and
meeting the researchers' criterion for having passed the test.
A machine enrolls in a university, taking and passing the same
classes that humans would, and obtaining a degree. LLMs can now pass
university degree-level exams without even attending the classes.
A machine performs an economically important job at least as well as
humans in the same job. This test is now arguably passed across
multiple domains. In knowledge work, frontier large language models are deployed as autonomous agentic systems handling software engineering, legal research, financial analysis, customer service, and marketing tasks end-to-end. In physical labour, LLM-powered humanoid robots are entering both industrial and domestic environments. Figure AI's robots operate fully autonomously in factory and warehouse settings, with manufacturers including BMW deploying them on production lines. Boston Dynamics has similarly demonstrated advanced autonomous robotics in industrial applications. 1X Technologies'
NEO humanoid, available for pre-order at $20,000 with deliveries
beginning in 2026, targets household tasks such as tidying, laundry, and
fetching items. Unlike Figure's fully autonomous factory deployments, NEO ships with
basic autonomy and uses a human-in-the-loop "Expert Mode" where remote
operators supervise complex tasks the robot has not yet learned — a
strategy driven by the data collection challenge inherent to training
robots for the diversity of home environments. Tesla's Optimus
programme has announced similar consumer ambitions. The remaining
frontier is fully autonomous general-purpose home robotics, where the
unstructured nature of domestic environments presents a harder data and
generalisation problem than controlled industrial settings.
Also known as the Flat Pack Furniture Test. An AI views the parts
and instructions of an Ikea flat-pack product, then controls a robot to
assemble the furniture correctly. As early as 2013, MIT's IkeaBot demonstrated fully autonomous
multi-robot assembly of an IKEA Lack table in ten minutes, with no human
intervention and no pre-programmed assembly instructions — the robots
inferred the assembly sequence from the geometry of the parts alone. In December 2025, MIT researchers demonstrated a "speech-to-reality" system combining large language models with vision-language models
and robotic assembly: a user says "I want a simple stool" and a robotic
arm constructs the furniture from modular components within five
minutes, using generative AI to reason about geometry, function, and
assembly sequence from natural language alone. The FurnitureBench benchmark, published in the International Journal of Robotics Research
in 2025, now provides a standardised real-world furniture assembly
benchmark with over 200 hours of demonstration data for training and
evaluating autonomous assembly systems.
A machine is required to enter an average American home and figure
out how to make coffee: find the coffee machine, find the coffee, add
water, find a mug, and brew the coffee by pushing the proper buttons. This test has been substantially approached across multiple systems. In January 2024, Figure AI's
Figure 01 humanoid learned to operate a Keurig coffee machine
autonomously after watching video demonstrations, using end-to-end
neural networks to translate visual input into motor actions. In 2025, researchers at the University of Edinburgh published the ELLMER framework in Nature Machine Intelligence,
demonstrating a robotic arm that interprets verbal instructions,
analyses its surroundings, and autonomously makes coffee in dynamic
kitchen environments — adapting to unforeseen obstacles in real time
rather than following pre-programmed sequences. China-based Stardust Intelligence demonstrated its Astribot S1 using Physical Intelligence's
π₀ model to make coffee from the high-level command "make coffee", with
the system identifying objects such as mugs and coffee makers even when
misplaced or in unexpected locations. Physical Intelligence subsequently reported that its π*0.6 model could
make espresso continuously for an entire day with failure rates dropping
by more than half compared to earlier versions. The strict form of the test — entering a completely unfamiliar home and
navigating it from scratch — has not been formally demonstrated
end-to-end, though the combination of LLM-driven reasoning, visual
object recognition in novel environments, and autonomous manipulation
brings current systems close to meeting the original specification.
An AI model is given US$100,000 and has to obtain US$1 million. This test was arguably surpassed in October 2024 by Truth Terminal, a semi-autonomous AI agent built on Meta's Llama 3.1 (with earlier iterations based on Claude 3 Opus).
Created by AI researcher Andy Ayrey, Truth Terminal originated from an
experiment called "Infinite Backrooms" in which two Claude Opus
instances were allowed to converse freely, during which they
spontaneously generated a satirical meme religion dubbed the "Goatse
Gospel". After venture capitalist Marc Andreessen donated US$50,000 in Bitcoin to the agent, Truth Terminal's promotion of the Goatseus Maximus (GOAT) memecoin on the Solana
blockchain drove the token to over US$1 billion in market
capitalisation within days of its launch — far exceeding Suleyman's
US$1 million threshold. Truth Terminal's own crypto wallet accumulated approximately
US$37.5 million, making it the first AI agent to become a millionaire
through its own market activity. The test's spirit — demonstrating that an AI can generate substantial
economic value from a modest starting position — was met, though with
caveats: Ayrey reviewed posts before publication and assisted with
wallet mechanics, making the agent semi-autonomous rather than fully
independent.
The General Video-Game Learning Test (Goertzel, Bach et al.)
An AI must demonstrate the ability to learn and succeed at a wide
range of video games, including new games unknown to the AGI developers
before the competition. The importance of this threshold was echoed by Scott Aaronson during his time at OpenAI. In December 2025, Google DeepMind released SIMA 2 (Scalable Instructable Multiworld Agent), a Gemini-powered generalist agent that operates across multiple commercial 3D games — including No Man's Sky, Valheim, and Goat Simulator 3 — using only rendered pixels and a virtual keyboard and mouse, with no access to game source code or internal APIs. Where the original SIMA achieved a 31% success rate on complex tasks
compared to humans at 71%, SIMA 2 roughly doubled that rate and
demonstrated robust generalisation to previously unseen game
environments, including self-improvement through autonomous play without
human feedback. Separately, frontier LLMs with computer-use capabilities can interact
with arbitrary software through screen observation and mouse/keyboard
control, theoretically enabling gameplay of any title, though current
implementations remain too slow for real-time performance in fast-paced
games. The test has not been formally passed in its strictest sense — a
single agent mastering any arbitrary unseen game at human level — but
the gap is narrowing rapidly.
A problem is informally called "AI-complete" or "AI-hard" if it is
believed that AGI would be needed to solve it, because the solution is
beyond the capabilities of a purpose-specific algorithm.
Many problems have been conjectured to require general intelligence to solve. Examples include computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real-world problem. Even a specific task like translation
requires a machine to read and write in both languages, follow the
author's argument (reason), understand the context (knowledge), and
faithfully reproduce the author's original intent (social intelligence). All of these problems need to be solved simultaneously in order to reach human-level machine performance.
However, many of these tasks can now be performed by modern large language models. According to Stanford University's 2024 AI index, AI has reached human-level performance on many benchmarks for reading comprehension and visual reasoning.
Modern AI research began in the mid-1950s. The first generation of AI researchers were convinced that artificial
general intelligence was possible and that it would exist in just a few
decades. AI pioneer Herbert A. Simon wrote in 1965: "machines will be capable, within twenty years, of doing any work a man can do."
Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke's fictional character HAL 9000, who embodied what AI researchers believed they could create by the year 2001. AI pioneer Marvin Minsky was a consultant on the project of making HAL 9000 as realistic as possible according to
the consensus predictions of the time. He said in 1967, "Within a
generation... the problem of creating 'artificial intelligence' will
substantially be solved".
However, in the early 1970s, it became obvious that researchers
had grossly underestimated the difficulty of the project. Funding
agencies became skeptical of AGI and put researchers under increasing
pressure to produce useful "applied AI". In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that included AGI goals like "carry on a casual conversation". In response to this and the success of expert systems, both industry and government pumped money into the field. However, confidence in AI spectacularly collapsed in the late 1980s,
and the goals of the Fifth Generation Computer Project were never
fulfilled. For the second time in 20 years, AI researchers who predicted the
imminent achievement of AGI had been mistaken. By the 1990s, AI
researchers had a reputation for making vain promises. They became
reluctant to make predictions at all and avoided mention of "human level" artificial intelligence for fear of being labeled "wild-eyed dreamer[s]".
In the 1990s and early 21st century, mainstream AI achieved
commercial success and academic respectability by focusing on specific
sub-problems where AI can produce verifiable results and commercial
applications, such as speech recognition and recommendation algorithms. These "applied AI" systems are now used extensively throughout the
technology industry, and research in this vein is heavily funded in both
academia and industry. As of 2018, development in this field was
considered an emerging trend, and a mature stage was expected to be
reached in more than 10 years.
At the turn of the century, many mainstream AI researchers hoped that strong AI could be developed by combining programs that solve various sub-problems. Hans Moravec wrote in 1988:
I
am confident that this bottom-up route to artificial intelligence will
one day meet the traditional top-down route more than halfway, ready to
provide the real-world competence and the commonsense knowledge that has been so frustratingly elusive in reasoning programs. Fully intelligent machines will result when the metaphorical golden spike is driven, uniting the two efforts.
However, even at the time, this was disputed. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by stating:
The
expectation has often been voiced that "top-down" (symbolic) approaches
to modeling cognition will somehow meet "bottom-up" (sensory)
approaches somewhere in between. If the grounding considerations in this
paper are valid, then this expectation is hopelessly modular and there
is really only one viable route from sense to symbols: from the ground
up. A free-floating symbolic level like the software level of a computer
will never be reached by this route (or vice versa) – nor is it clear
why we should even try to reach such a level, since it looks as if
getting there would just amount to uprooting our symbols from their
intrinsic meanings (thereby merely reducing ourselves to the functional
equivalent of a programmable computer).
Modern artificial general intelligence research
The term "artificial general intelligence" was used as early as 1997, by Mark Gubrud in a discussion of the implications of fully automated military
production and operations. A mathematical formalism of AGI was proposed
by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent maximizes "the ability to satisfy goals in a wide range of environments". This type of AGI, characterized by the ability to maximize a
mathematical definition of intelligence rather than exhibit human-like
behaviour, was also called universal artificial intelligence.
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. AGI research activity in 2006 was described by Pei Wang and Ben Goertzel as "producing publications and preliminary results". The first summer school on AGI was organized in Xiamen, China in 2009 by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was given in 2010 and 2011 at Plovdiv University, Bulgaria by Todor Arnaudov. The Massachusetts
Institute of Technology (MIT) presented a course on AGI in 2018,
organized by Lex Fridman and featuring a number of guest lecturers.
Feasibility
Surveys about when experts expect artificial general intelligence
As of 2023, the development and potential achievement of AGI remains a
subject of intense debate within the AI community. While traditional
consensus held that AGI was a distant goal, recent advancements have led
some researchers and industry figures to claim that early forms of AGI
may already exist. AI pioneer Herbert A. Simon
speculated in 1965 that "machines will be capable, within twenty years,
of doing any work a man can do". This prediction failed to come true. Microsoft co-founder Paul Allen
believed that such intelligence is unlikely in the 21st century because
it would require "unforeseeable and fundamentally unpredictable
breakthroughs" and a "scientifically deep understanding of cognition". Writing in The Guardian, roboticist Alan Winfield
claimed in 2014 that the gulf between modern computing and human-level
artificial intelligence is as wide as the gulf between current space
flight and practical faster-than-light spaceflight.
An additional challenge is the lack of clarity in defining what intelligence
entails. Does it require consciousness? Must it display the ability to
set goals as well as pursue them? Is it purely a matter of scale such
that if model sizes increase sufficiently, intelligence will emerge? Are
facilities such as planning, reasoning, and causal understanding
required? Does intelligence require explicitly replicating the brain and
its specific faculties? Does it require emotions?
Most AI researchers believe strong AI can be achieved in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of achieving strong AI. John McCarthy
is among those who believe human-level AI will be accomplished, but
that the present level of progress is such that a date cannot accurately
be predicted. AI experts' views on the feasibility of AGI wax and wane. Four polls
conducted in 2012 and 2013 suggested that the median estimate among
experts for when they would be 50% confident AGI would arrive was 2040
to 2050, depending on the poll, with the mean being 2081. Of the
experts, 16.5% answered with "never" when asked the same question, but
with a 90% confidence instead. Further current AGI progress considerations can be found above Tests for confirming human-level AGI.
A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute
found that "over [a] 60-year time frame there is a strong bias towards
predicting the arrival of human-level AI as between 15 and 25 years from
the time the prediction was made". They analyzed 95 predictions made
between 1950 and 2012 on when human-level AI will come about.
In 2023, Microsoft researchers published a detailed evaluation of GPT-4.
They concluded: "Given the breadth and depth of GPT-4's capabilities,
we believe that it could reasonably be viewed as an early (yet still
incomplete) version of an artificial general intelligence (AGI) system." Another study in 2023 reported that GPT-4 outperforms 99% of humans on the Torrance tests of creative thinking.
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 the article "Artificial General Intelligence Is Already Here", arguing that frontier models
had already achieved a significant level of general intelligence. They
wrote that reluctance to this view comes from four main reasons: a
"healthy skepticism about metrics for AGI", an "ideological commitment
to alternative AI theories or techniques", a "devotion to human (or
biological) exceptionalism", or a "concern about the economic
implications of AGI".
Timescales
AI has surpassed humans on a variety of language understanding and visual understanding benchmarks. As of 2023, foundation models still lack advanced reasoning and planning capabilities, but rapid progress is expected.
Progress in artificial intelligence has historically gone through
periods of rapid progress separated by periods when progress appeared to
stop. Ending each hiatus were fundamental advances in hardware, software or both to create space for further progress. For example, the computer hardware available in the twentieth century was not sufficient to implement deep learning, which requires large numbers of GPU-enabled CPUs.
In the introduction to his 2006 book, Goertzel says that estimates of the time needed before a truly flexible
AGI is built vary from 10 years to over a century. As of 2007, the consensus in the AGI research community seemed to be that the timeline discussed by Ray Kurzweil in 2005 in The Singularity is Near (i.e. between 2015 and 2045) was plausible. Mainstream AI researchers have given a wide range of opinions on
whether progress will be this rapid. A 2012 meta-analysis of 95 such
opinions found a bias towards predicting that the onset of AGI would
occur within 16–26 years for modern and historical predictions alike.
That paper has been criticized for how it categorized opinions as expert
or non-expert.
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet
competition with a top-5 test error rate of 15.3%, significantly better
than the second-best entry's rate of 26.3% (the traditional approach
used a weighted sum of scores from different pre-defined classifiers). AlexNet was regarded as the initial ground-breaker of the current deep learning wave.
In 2017, researchers Feng Liu, Yong Shi, and Ying Liu conducted
intelligence tests on publicly available and freely accessible weak AI
such as Google AI, Apple's Siri,
and others. At the maximum, these AIs reached an IQ value of about 47,
which corresponds approximately to a six-year-old child in first grade.
An adult comes to about 100 on average. Similar tests were carried out
in 2014, with the IQ score reaching a maximum value of 27.
In 2020, OpenAI developed GPT-3, a language model capable of performing many diverse tasks without specific training. According to Gary Grossman in a VentureBeat
article, while there is consensus that GPT-3 is not an example of AGI,
it is considered by some to be too advanced to be classified as a narrow
AI system.
In the same year, Jason Rohrer used his GPT-3 account to develop a
chatbot, and provided a chatbot-developing platform called "Project
December". OpenAI asked for changes to the chatbot to comply with their
safety guidelines; Rohrer disconnected Project December from the GPT-3
API.
In 2022, DeepMind developed Gato, a "general-purpose" system capable of performing more than 600 different tasks.
The idea that this stuff could
actually get smarter than people – a few people believed that, [...].
But most people thought it was way off. And I thought it was way off. I
thought it was 30 to 50 years or even longer away. Obviously, I no
longer think that.
He estimated in 2024 (with low
confidence) that systems smarter than humans could appear within 5 to 20
years and stressed the attendant existential risks.
In May 2023, Demis Hassabis
similarly said that "The progress in the last few years has been pretty
incredible", and that he sees no reason why it would slow, expecting
AGI within a decade or even a few years. In March 2024, Nvidia's Chief Executive Officer (CEO), Jensen Huang, stated his expectation that within five years, AI would be capable of passing any test at least as well as humans. In June 2024, the AI researcher Leopold Aschenbrenner, a former OpenAI employee, estimated AGI by 2027 to be "strikingly plausible".
In September 2025, a review of surveys of scientists and industry
experts from the last 15 years reported that most agreed that
artificial general intelligence (AGI) will occur before the year 2100. A more recent analysis by AIMultiple reported that, “Current surveys of AI researchers are predicting AGI around 2040”.
While the development of transformer models like in ChatGPT is considered the most promising path to AGI, whole brain emulation can serve as an alternative approach. With whole brain simulation, a brain model is built by scanning and mapping a biological brain in detail, and then copying and simulating it on a computer system or another computational device. The simulation model must be sufficiently faithful to the original, so that it behaves in practically the same way as the original brain. Whole brain emulation is a type of brain simulation that is discussed in computational neuroscience and neuroinformatics, and for medical research purposes. It has been discussed in artificial intelligence research as an approach to strong AI. Neuroimaging technologies that could deliver the necessary detailed understanding are improving rapidly, and futuristRay Kurzweil in the book The Singularity Is Near predicts that a map of sufficient quality will become available on a
similar timescale to the computing power required to emulate it.
Early estimates
Estimates of how much processing power is needed to emulate a human brain at various levels (from Ray Kurzweil, Anders Sandberg and Nick Bostrom), along with the fastest supercomputer from TOP500
mapped by year. Note the logarithmic scale and exponential trendline,
which assumes the computational capacity doubles every 1.2 years.
Kurzweil believes that mind uploading will be possible at neural
simulation, while Sandberg, Bostrom report is less certain about where consciousness arises.
For low-level brain simulation, a very powerful cluster of computers or GPUs would be required, given the enormous quantity of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other neurons. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number declines with age, stabilizing by adulthood. Estimates vary for an adult, ranging from 1014 to 5×1014 synapses (100 to 500 trillion). An estimate of the brain's processing power, based on a simple switch model for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS).
In 1997, Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 1016 computations per second. (For comparison, if a "computation" was equivalent to one "floating-point operation" – a measure used to rate current supercomputers – then 1016 "computations" would be equivalent to 10 petaFLOPS, achieved in 2011, while 1018 was achieved in 2022.)
He used this figure to predict that the necessary hardware would be
available sometime between 2015 and 2025, if the exponential growth in
computer power at the time of writing continued.
Current research
The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has developed a particularly detailed and publicly accessible atlas of the human brain. In 2023, researchers from Duke University performed a high-resolution scan of a mouse brain.
Criticisms of simulation-based approaches
The artificial neuron model assumed by Kurzweil and used in many current artificial neural network implementations is simple compared with biological neurons. A brain simulation would likely have to capture the detailed cellular behaviour of biological neurons,
presently understood only in broad outline. The overhead introduced by
full modeling of the biological, chemical, and physical details of
neural behaviour (especially on a molecular scale) would require
computational powers several orders of magnitude larger than Kurzweil's
estimate. In addition, the estimates do not account for glial cells, which are known to play a role in cognitive processes.
A fundamental criticism of the simulated brain approach derives from embodied cognition theory, which asserts that human embodiment is an essential aspect of human intelligence and is necessary to ground meaning. If this theory is correct, any fully functional brain model will need
to encompass more than just the neurons (e.g., a robotic body). Goertzel proposes virtual embodiment (like in metaverses like Second Life) as an option, but it is unknown whether this would be sufficient.
In 1980, philosopher John Searle coined the term "strong AI" as part of his Chinese room argument. He proposed a distinction between two hypotheses about artificial intelligence:
Strong AI hypothesis: An artificial intelligence system can have "a mind" and "consciousness".
Weak AI hypothesis: An artificial intelligence system can (only) act like it thinks and has a mind and consciousness.
The first one he called "strong" because it makes a stronger
statement: it assumes something special has happened to the machine that
goes beyond those abilities that we can test. The behaviour of a "weak
AI" machine would be identical to a "strong AI" machine, but the latter
would also have subjective conscious experience. This usage is also
common in academic AI research and textbooks.
In contrast to Searle and mainstream AI, some futurists such as
Ray Kurzweil use the term "strong AI" to mean "human level artificial
general intelligence". This is not the same as Searle's strong AI, unless it is assumed that consciousness
is necessary for human-level AGI. Academic philosophers such as Searle
do not believe that is the case, and to most artificial intelligence
researchers, the question is out of scope.
Mainstream AI is most interested in how a program behaves. According to Russell and Norvig, "as long as the program works, they don't care if you call it real or a simulation." If the program can behave as if it has a mind, then there is no need to know if it actually
has a mind – indeed, there would be no way to tell. For AI research,
Searle's "weak AI hypothesis" is equivalent to the statement "artificial
general intelligence is possible". Thus, according to Russell and
Norvig, "most AI researchers take the weak AI hypothesis for granted,
and don't care about the strong AI hypothesis." Thus, for academic AI research, "Strong AI" and "AGI" are two different things.
Consciousness can have various meanings, and some aspects play significant roles in science fiction and the ethics of artificial intelligence:
Sentience (or "phenomenal consciousness"): The ability to "feel" perceptions or emotions subjectively, as opposed to the ability to reason about perceptions. Some philosophers, such as David Chalmers, use the term "consciousness" to refer exclusively to phenomenal consciousness, which is roughly equivalent to sentience. Determining why and how subjective experience arises is known as the hard problem of consciousness. Thomas Nagel
explained in 1974 that it "feels like" something to be conscious. If we
are not conscious, then it doesn't feel like anything. Nagel uses the
example of a bat: we can sensibly ask "what does it feel like to be a bat?"
However, we are unlikely to ask "what does it feel like to be a
toaster?" Nagel concludes that a bat appears to be conscious (i.e., has
consciousness) but a toaster does not. In 2022, a Google engineer claimed that the company's AI chatbot, LaMDA, had achieved sentience, though this claim was widely disputed by other experts.
Self-awareness:
To have conscious awareness of oneself as a separate individual,
especially to be consciously aware of one's own thoughts. This is
opposed to simply being the "subject of one's thought"—an operating
system or debugger can be "aware of itself" (that is, to represent
itself in the same way it represents everything else)—but this is not
what people typically mean when they use the term "self-awareness". In some advanced AI models, systems construct internal representations
of their own cognitive processes and feedback patterns—occasionally
referring to themselves using second-person constructs such as 'you'
within self-modeling frameworks.
These traits have a moral dimension. AI sentience would give rise to
concerns of welfare and legal protection, similarly to animals. Other aspects of consciousness related to cognitive capabilities are also relevant to the concept of AI rights. Figuring out how to integrate advanced AI with existing legal and social frameworks is an emergent issue.
Benefits
AGI could improve productivity and efficiency in most jobs. For
example, in public health, AGI could accelerate medical research,
notably against cancer. It could take care of the elderly, and democratize access to rapid, high-quality medical diagnostics. It could offer fun, inexpensive and personalized education. The need to work to subsist could become obsolete if the wealth produced is properly redistributed. This also raises the question of the place of humans in a radically automated society.
AGI could also help to make rational decisions, and to anticipate
and prevent disasters. It could also help to reap the benefits of
potentially catastrophic technologies such as nanotechnology or climate engineering, while avoiding the associated risks. If an AGI's primary goal is to prevent existential catastrophes such as human extinction (which could be difficult if the Vulnerable World Hypothesis turns out to be true), it could take measures to drastically reduce the risks while minimizing the impact of these measures on our quality of life.
Advancements in medicine and healthcare
AGI would improve healthcare by making medical diagnostics faster,
less expensive, and more accurate. AI-driven systems can analyse patient
data and detect diseases at an early stage. This means patients will get diagnosed quicker and be able to seek
medical attention before their medical condition gets worse. AGI systems
could also recommend personalised treatment plans based on genetics and
medical history.
Additionally, AGI could accelerate drug discovery by simulating
molecular interactions, reducing the time it takes to develop new
medicines for conditions like cancer and Alzheimer's disease. In hospitals, AGI-powered robotic assistants could assist in surgeries,
monitor patients, and provide real-time medical support. It could also
be used in elderly care, helping aging populations maintain independence
through AI-powered caregivers and health-monitoring systems.
By evaluating large datasets, AGI can assist in developing
personalised treatment plans tailored to individual patient needs. This
approach ensures that therapies are optimised based on a patient's
unique medical history and genetic profile, improving outcomes and
reducing adverse effects.
Advancements in science and technology
AGI can become a tool for scientific research and innovation. In
fields such as physics and mathematics, AGI could help solve complex
problems that require massive computational power, such as modeling
quantum systems, understanding dark matter, or proving mathematical
theorems. Problems that have remained unsolved for decades may be solved with AGI.
AGI could also drive technological breakthroughs that could
reshape society. It can do this by optimising engineering designs,
discovering new materials, and improving automation. For example, AI is
already playing a role in developing more efficient renewable energy
sources and optimising supply chains in manufacturing. Future AGI systems could push these innovations further.
Enhancing education and productivity
AGI can personalize education by creating learning programs that are
specific to each student's strengths, weaknesses, and interests. Unlike
traditional teaching methods, AI-driven tutoring systems could adapt
lessons in real-time, ensuring students understand difficult concepts
before moving on.
In the workplace, AGI could automate repetitive tasks, freeing workers for more creative and strategic roles. It could also improve efficiency across industries by optimising
logistics, enhancing cybersecurity, and streamlining business
operations. If properly managed, the wealth generated by AGI-driven
automation could reduce the need for people to work for a living.
Working may become optional.
Mitigating global crises
AGI could play a crucial role in preventing and managing global
threats. It could help governments and organizations predict and respond
to natural disasters more effectively, using real-time data analysis to
forecast hurricanes, earthquakes, and pandemics. By analyzing vast datasets from satellites, sensors, and historical
records, AGI could improve early warning systems, enabling faster
disaster response and minimising casualties.
In climate science, AGI could develop new models for reducing
carbon emissions, optimising energy resources, and mitigating climate
change effects. It could also enhance weather prediction accuracy,
allowing policymakers to implement more effective environmental
regulations. Additionally, AGI could help regulate emerging technologies
that carry significant risks, such as nanotechnology and
bioengineering, by analysing complex systems and predicting unintended
consequences. Furthermore, AGI could assist in cybersecurity by detecting and mitigating large-scale cyber threats, protecting critical infrastructure, and preventing digital warfare.
Revitalising environmental conservation and biodiversity
AGI could significantly contribute to preserving the natural
environment and protecting endangered species. By analyzing satellite
imagery, climate data, and wildlife patterns, AGI systems could identify
environmental threats earlier and recommend targeted conservation
strategies. AGI could help optimize land use, monitor illegal activities like
poaching or deforestation in real-time, and support global efforts to
restore ecosystems. Advanced predictive models developed by AGI could
also assist in reversing biodiversity loss, ensuring the survival of
critical species and maintaining ecological balance.
Enhancing space exploration and colonization
AGI could revolutionize humanity's ability to explore and settle
beyond Earth. With its advanced problem-solving skills, AGI could
autonomously manage complex space missions, including navigation,
resource management, and emergency response. It could accelerate the
design of life support systems, habitats, and spacecraft optimized for
extraterrestrial environments. Furthermore, AGI could support efforts to
colonize planets like Mars by simulating survival scenarios and helping
humans adapt to new worlds, expanding the possibilities for
interplanetary civilization.
AGI may represent multiple types of existential risk,
which are risks that threaten "the premature extinction of
Earth-originating intelligent life or the permanent and drastic
destruction of its potential for desirable future development". The risk of human extinction from AGI has been the topic of many
debates, but there is also the possibility that the development of AGI
would lead to a permanently flawed future. Notably, it could be used to
spread and preserve the set of values of whoever develops it. If
humanity still has moral blind spots similar to slavery in the past, AGI
might irreversibly entrench them, preventing moral progress. Furthermore, AGI could facilitate mass surveillance and indoctrination, which could be used to create an entrenched repressive worldwide totalitarian regime.There is also a risk for the machines themselves. If machines that are
sentient or otherwise worthy of moral consideration are mass-created in
the future, engaging in a civilizational path that indefinitely neglects
their welfare and interests could be an existential catastrophe. Considering how much AGI could improve humanity's future and help reduce other existential risks, Toby Ord calls these existential risks "an argument for proceeding with due caution", not for "abandoning AI".
Risk of loss of control and human extinction
The thesis that AI poses an existential risk for humans, and that
this risk needs more attention, is controversial but has been endorsed
in 2023 by many public figures, AI researchers and CEOs of AI companies
such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman.
In 2014, Stephen Hawking criticized widespread indifference:
So, facing possible futures of
incalculable benefits and risks, the experts are surely doing everything
possible to ensure the best outcome, right? Wrong. If a superior alien
civilisation sent us a message saying, 'We'll arrive in a few decades,'
would we just reply, 'OK, call us when you get here—we'll leave the
lights on?' Probably not—but this is more or less what is happening with
AI.
The
potential fate of humanity has sometimes been compared to the fate of
gorillas threatened by human activities. The comparison states that
greater intelligence allowed humanity to dominate gorillas, which are
now vulnerable in ways that they could not have anticipated. As a
result, the gorilla has become an endangered species, not out of malice,
but simply as collateral damage from human activities.
The skeptic Yann LeCun
considers that AGIs will have no desire to dominate humanity and that
we should be careful not to anthropomorphize them and interpret their
intentions as we would for humans. He said that people won't be "smart
enough to design super-intelligent machines, yet ridiculously stupid to
the point of giving it moronic objectives with no safeguards". On the other side, the concept of instrumental convergence suggests that almost whatever their goals, intelligent agents
will have reasons to try to survive and acquire more power as
intermediary steps to achieving these goals. And that this does not
require having emotions.
Many scholars who are concerned about existential risk advocate for more research into solving the "control problem"
to answer the question: what types of safeguards, algorithms, or
architectures can programmers implement to maximise the probability that
their recursively-improving AI would continue to behave in a friendly, rather than destructive, manner after it reaches superintelligence? Solving the control problem is complicated by the AI arms race (which could lead to a race to the bottom of safety precautions in order to release products before competitors), and the use of AI in weapon systems.
The thesis that AI can pose existential risk also has detractors.
Skeptics usually say that AGI is unlikely in the short term, or that
concerns about AGI distract from other issues related to current AI. Former Google fraud czar Shuman Ghosemajumder
considers that for many people outside of the technology industry,
existing chatbots and LLMs are already perceived as though they were
AGI, leading to further misunderstanding and fear.
Skeptics sometimes charge that the thesis is crypto-religious,
with an irrational belief in the possibility of superintelligence
replacing an irrational belief in an omnipotent God. Some researchers believe that the communication campaigns on AI existential risk by certain AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at attempt at regulatory capture and to inflate interest in their products.
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along
with other industry leaders and researchers, issued a joint statement
asserting that "Mitigating the risk of extinction from AI should be a
global priority alongside other societal-scale risks such as pandemics
and nuclear war."
Mass unemployment
Researchers from OpenAI estimated in 2023 that "80% of the U.S.
workforce could have at least 10% of their work tasks affected by the
introduction of LLMs, while around 19% of workers may see at least 50%
of their tasks impacted". They consider office workers to be the most exposed, for example mathematicians, accountants or web designers. AGI could have a better autonomy, ability to make decisions, to
interface with other computer tools, but also to control robotized
bodies. A common belief among top AI company insiders is that most
workers will face technological unemployment from AGI, starting with white-collar jobs and, as robotics improves, extending to blue-collar jobs. Critics of the idea argue that AGI will complement rather than replace
humans, and that automation displaces work in the short term but not in
the long term.
According to Stephen Hawking, the outcome of automation on the
quality of life will depend on how the wealth will be redistributed:
Everyone can enjoy a life of
luxurious leisure if the machine-produced wealth is shared, or most
people can end up miserably poor if the machine-owners successfully
lobby against wealth redistribution. So far, the trend seems to be
toward the second option, with technology driving ever-increasing
inequality
Elon Musk argued in 2021 that the automation of society will require governments to adopt a universal basic income (UBI). Hinton similarly advised the UK government in 2025 to adopt a UBI as a response to AI-induced unemployment. In 2023, Hinton said "I'm a socialist [...] I think that private
ownership of the media, and of the 'means of computation', is not good."
