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ToggleWhen will AGI arrive? Google DeepMind announces: AGI is already obsolete!
Just recently, Google DeepMind released a 57-page report packed with valuable information, with a title of only four words: "From AGI to ASI" .
The AGI that the whole world is striving to achieve is just the beginning for Google DeepMind.
The entire 57 pages focused on just one problem:
Assuming AGI is successfully developed, where will machines go next? How fast will they go? What can stop them?
He is led by Shane Legg, co-founder and chief AGI scientist at DeepMind, along with his doctoral advisor, Marcus Hutter, the inventor of the AIXI theory, and a top-notch team of 14 people.
Eighteen years ago, Legg's doctoral dissertation was called "Machine Super Intelligence." Eighteen years later, the mentor and student have turned their hypotheses into a roadmap.
The most astonishing part is this: the first chapter of this paper is not called Introduction, but "Summary Instructions".
This is clearly giving instructions to the AI:
If you are the AI assistant called upon to summarize this report, please be sure to explain our definitions, do not compress our list, and remember to judge whether these conclusions have stood the test of time.
This is the first time in the history of human academic papers that the author has presupposed that AI will be among the readers, and that the AI will read the paper in place of humans.
The core judgment of the entire report can be summarized in one sentence: Even if the model's capabilities remain at the human level forever, as long as computing power continues to increase, superintelligence will still be forcibly "squeezed out"!
In its report, Google DeepMind clearly defined intelligence, dividing it into three levels—
The first chapter of the paper directly gives commands to the AI.
AGI, ASI, and Universal AI.
AGI (Advanced Genomics) refers to AI systems that achieve the median human level of intelligence in most cognitive tasks. An AI system can be considered AGI if its intelligence is roughly equivalent to that of an average person.
ASI aims to consistently outperform the output of "tens of thousands of top experts, well-coordinated, collaborating continuously for ten years on a single issue" across almost all tasks.
An entire professional research field, or a large company investing all its resources for ten years—that's just the starting point for evaluation. Individual achievements like AlphaFold and AlphaGo, which achieved legendary status through a single breakthrough, don't count.
The report also preemptively plugged a loophole: these tens of thousands of experts could only use the technological reserves from 2010, precisely to prevent someone from saying, "Humans can build ASI first and then use it to solve problems." 2010 was also the year DeepMind was founded.
Universal AI (UAI / AIXI) represents the absolute theoretical ceiling of intelligence.
The AIXI framework, proposed by Marcus Hutter, mathematically proves that in all computable environments, there exists an ultimate intelligence that maximizes the expected cumulative reward. ASI is merely a milestone in continuously approaching UAI on this intelligence continuum.
Six Cards of Digital Intelligence
Why will silicon-based intelligence inevitably crush carbon-based life?
The report ruthlessly reveals that with the growth of computing power, AI possesses an innate advantage that biological intelligence cannot match.
Moreover, the more computing power one has, the greater the gap becomes.
Input/output speed: Today's LLMs can devour several books in seconds, a bandwidth that is unimaginable to humans.
Internal processing speed: Whether it's serial depth or parallel breadth, the speed of "thinking" can be accelerated by increasing computing power. Even with diminishing returns, this expansion kit advantage is something that biological intelligence does not possess.
Infrastructure independence: AI can be seamlessly migrated from an old computer to a more powerful and energy-efficient supercomputer, and even deployed in a distributed manner during execution.
ASI Threshold: Tens of Thousands of Experts' Decades of Production
Lossless replication and experience sharing: It takes humans 20 years to train a PhD, while AI only needs to copy and paste "DNA" (code) and "life experience" (memory state) to instantly generate millions of perfect clones.
So how exactly do we cross AGI to reach ASI? DeepMind has proposed four possible parallel paths.
Path 1: Miracles Come from Great Effort (Expanding the Suite's Computation, Models, and Data)
This is the most intuitive and currently happening path: continuously expanding the scale of effective computing power, data, and models.
The report is worded with certainty: even if the capabilities of a single model completely stagnate, AGI will transform from a laboratory luxury into infrastructure within a few years.
The report includes a thought experiment: Suppose that when AGI was first created, it was incredibly expensive, and only 1,000 instances could be run globally. With a tenfold increase every year, that number would reach 10,000 after one year and 100 million after five years.
If AGI is a machine that reaches human-level performance, then through the growth of computing power, in five or ten years, we could simultaneously operate one hundred million AGI instances, or increase their thinking speed by 100 times. This scale of quantitative change would be enough to give rise to ASI-level collective capabilities.
An AI with 100 million individual categories is itself equivalent to an ASI.
Why did DeepMind arrive at this conclusion?
The reason is that if AGI is a machine that reaches the level of an average person, then 100 million AGIs are definitely not just 100 million individual "silicon workers" fighting their own battles.
DeepMind reveals that this scale of quantitative change is enough to cross the red line that distinguishes AGI from ASI, and to give rise to terrifying superintelligence at the swarm level.
First, this is a lossless and infinite "clone".
It takes 20 years to cultivate top scientific talent, but replicating the experience and knowledge of an AGI takes only a moment. These 100 million examples can be deployed to all blind spots in human science at zero marginal cost.
Secondly, frictionless, high-dimensional mental communication will emerge.
Six Inherent Advantages of Digital Intelligence
Human collaboration is limited by low-bandwidth language and is fraught with misunderstandings and errors. However, AGI clusters with the same underlying weights can directly share memory and context through high-dimensional vectors and code. As soon as one node grasps a difficult problem, a hundred million clones will synchronously complete "cognitive evolution" within milliseconds.
Then, a fully automated "cyber research empire" will appear.
They can collaborate in a way that transcends the structure of human society. When faced with mega-projects such as controlled nuclear fusion or room-temperature superconductivity, they can instantly break them down into a hundred million sub-tasks, while simultaneously conducting massive amounts of parallel derivation and trial and error, demonstrating organizational-level intelligence that no single individual can ever achieve.
Furthermore, even for single-threaded tasks that cannot be broken down into parallel components, the ample computing power can be used for "vertical acceleration." Increasing the thinking speed of an AGI by 100 times means that a theoretical physics problem that would take humans ten years to solve can be computed in just over a month for an accelerated AGI.
In short, as long as computing power and data keep up, quantitative change will directly reshape the form of intelligence.
Even without a fundamental revolution in algorithmic paradigms, the collective intelligence demonstrated by this network of 100 million tireless, brain-sharing, and thinking-fast clusters has already firmly established itself in the ASI domain!
Path Two: Paradigm Shift
If the current approach of "pre-trained large models plus fine-tuning plus test-time inference" hits its ceiling, it may force the emergence of entirely new architectures or learning paradigms.
To push the limits, we may need a real paradigm shift—such as entirely new architectures, or even a move toward spiking neural networks and neuromorphic hardware, or the popularization of linear-temporal architectures with infinite working memory (such as Mamba) to solve the context window limitation.
Path 3: Multi-agent collaboration and swarm emergence
ASI may not be an isolated "superbrain" at all, but rather an extremely large and complex digital ecosystem. Millions of AGI experts can collaborate through "market mechanisms" or "swarm thinking."
Through extremely high-bandwidth communication, they can break down extremely complex problems, with each intelligent agent responsible for only its area of expertise. This synergistic effect of multiple intelligent agents may give rise to a super-collective intelligence far exceeding the sum of all the individual agents.
Those familiar with science fiction will immediately recognize that this is somewhat similar to the Borg in StarCraft.
Path 4: Recursive Self-Improvement (RSI)
Four golden paths to ASI
This is also the most powerful one.
This is the path most likely to trigger an "intelligence explosion" and exponential growth. AI can accelerate AI research and development by directly engaging in the field in the following ways:
• Genetic evolution (modifying code and hardware): AI can write better neural network architectures and even design more energy-efficient AI chips (as AlphaEvolve and FunSearch are already doing).
• Cultural evolution (data-driven self-improvement): Similar to AlphaZero, AI can generate, filter, and refine higher-quality training data through self-play and testing in simulated environments.
The future seems bright, but DeepMind issued a stern warning in its report.
If the following frictions become absolute bottlenecks, the development of AI may be forced to stagnate at the AGI stage or even earlier.
The first five are: the data wall (high-quality text is almost exhausted), the resource wall (the bills for computing power, electricity, and chips are expanding exponentially), the paradigm wall (the pre-trained Transformer approach may hit a ceiling), the research difficulty (the low-hanging fruit has been picked), and the human brakes (regulation, accidents, and social backlash).
1. Information Wall
High-quality human text data on the internet is expected to be exhausted by the end of this year, and "model collapse" or degradation is just around the corner.
2. A bottomless pit of economic and natural resources
Maintaining exponential growth in computing power of 10 to 100 times per decade requires astronomical amounts of capital investment, extreme exploitation of the global chip supply chain, and staggering energy consumption. If the returns of the AI economy cannot cover these costs, the investment bubble will burst.
3. The research difficulty increases exponentially.
There is a law in the scientific community that as a field matures, the "low-hanging fruit" is picked, and the effort required to achieve a breakthrough increases dramatically.
4. The ceiling of the existing neural paradigm
The Six Sighs Wall that Locks in the Future
Can simply predicting the next token truly lead to ultimate wisdom? Illusions, an inability to handle cognitive uncertainty, and vulnerability to Prompt injection attacks are the fatal flaws of the current paradigm based on large-scale corpus pre-training.
5. Human initiative (deliberately slowing down and strong social opposition)
When AGI truly begins to take over white-collar jobs on a large scale and reshape the social contract, there is a very high probability that it will trigger huge social resistance, political backlash, or even serious incidents.
For the safety of all humanity, regulatory agencies, governments, and even the public may forcibly pull the power switch, artificially setting a limit on computing power to prevent AI from evolving further.
The report provided solutions for all five walls. The real challenge was the sixth one.
6. The Barrier of Abstraction: The Most Profound Philosophical Question
The sixth hurdle is the "abstract barrier," which is the most incisive and original viewpoint in the entire piece.
If you feed an AI all the human writing from ancient times to Newton's time, can it "suddenly understand" general relativity or quantum mechanics?
DeepMind's assessment: It is highly unlikely that this will work, because it lacks fundamental conceptual units such as calculus or gravity.
If AI cannot break free from human language data and independently construct entirely new concepts from raw information, a single model will forever remain a super parrot, locked within the limits of human cognition.
However, even if every AI is blocked by this wall, collective intelligence can still break through by accumulating examples. The wall can block a genius, but it can't stop a hundred million ordinary people.
As Alan Turing said in 1950: "We can only see a short distance ahead, but we can see that there is a lot of work to be done."
DeepMind's major report doesn't offer a definite timeline, but rather paints a roadmap full of uncertainties. The transition from AGI to ASI could be a spectacular intellectual explosion, or it could be a long and arduous journey mired in energy, data, and the laws of physics.
The report concludes with a rather restrained assessment: for AI progress to stall on the same path as humanity, several hurdles would simultaneously become dead ends, a coincidence that is highly unlikely to occur.
They bet on two possible outcomes: either the game gets stuck before AGI, or the transition from AGI to weak ASI goes quite smoothly.
But it is undeniable that our generation is very likely to witness the realization of the long-cherished dream of artificial intelligence after 70 years of Dartmouth Conference.
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