{
  "video_id": "reddit_1u42yx8",
  "channel_slug": "singularity",
  "channel_handle": "r/singularity",
  "title": "Google DeepMind published a 60-page paper mapping the road from AGI to ASI",
  "url": "https://www.reddit.com/r/singularity/comments/1u42yx8/google_deepmind_published_a_60page_paper_mapping/",
  "external_url": "https://arxiv.org/html/2606.12683v1",
  "upload_date": "20260612",
  "published_at": "2026-06-12T18:08:05+00:00",
  "transcript": "Google DeepMind team published a 60-page paper mapping the road from AGI to superintelligence, written by Hutter, Legg and Genewein. The paper uses **three** levels.\n\n**AGI** = roughly average human performance across most cognitive tasks.\n\n**ASI** = a system that beats large, well-coordinated groups of human experts across virtually everything (their bar: tens of thousands of experts working ten years on one problem).\n\n**Universal AI / AIXI** = the theoretical ceiling, uncomputable, only approachable from below.\n\nThen they explore the question of how this could be achieved: Scaling compute, models and data. The continuation of the trend that drove the breakthroughs so far. It is the only path with historical data available for extrapolation. \n\n**The core question:** Does quantity transform into quality? Even if individual models plateau, the sheer act of running millions of faster AGI instances could trigger the leap.\n\n**Algorithmic paradigm shifts:** A genuine break from the transformer pretraining paradigm. New architectures, new learning methods. Difficult to predict by definition.\n\n**Recursive self-improvement:** AI accelerates AI research, which produces better AI, which accelerates research further.\n\n**Multi-agent coordination:** Superintelligence emerges from large collectives of AGI agents working together, like automated corporations or AI economies. Collective intelligence potentially far exceeding any individual model.\n\nThe authors also point to what may be **one of the biggest bottlenecks:** Energy. Achieving AGI and ASI is not just a software problem. It may depend on whether energy production, compute infrastructure & hardware can scale fast enough.\n\n**Six things that could slow or stop all of this:**\n\n• The data wall. High-quality training data runs out.\n\n• Resource constraints. Energy, chips, rare earths and infrastructure may not scale indefinitely.\n\n• The neural paradigm hits a ceiling. Current approaches may not be enough to reach AGI, let alone ASI.\n\n• Research gets harder. New breakthroughs become increasingly difficult to find.\n\n• The abstraction barrier. Models may struggle to discover entirely new concepts beyond the knowledge and abstractions present in human-generated data.\n\n• Deliberate slowdown. Regulation, accidents or public backlash.\n\nOverall, the paper reads less like a prediction and more like an attempt to map the possible paths, bottlenecks and consequences of a post-AGI world.\n\n**Paper:** \"From AGI to ASI\" (Google DeepMind)\n\nWhat do you think is the biggest obstacle between AGI and ASI?\n\n\n\n--- Top Comments ---\n\n\n[90 upvotes] **Also worth noting:** Co-author Shane Legg co-founded DeepMind alongside Demis Hassabis and Mustafa Suleyman and **currently** serves as DeepMind's Chief AGI Scientist, making this paper an interesting look at how some of the people closest to AGI research think about the path toward ASI.\n\n[63 upvotes] >In recognition of technological progress, if you are a human reader, we encourage you to ask your favorite AI assistant or agent to produce a summary of this work tailored to your interests and background, and ask it how the arguments made in the report stood the test of time. If you prefer a static human written summary at the time of publication, or do not have access to an AI assistant, please find our summary in Section A.\n\n[20 upvotes] IMO the big thing is a new algorithm. We are missing something, it's estimated to simulate our brain it would take 100 million to 1 billion cpus. Instead of figuring that out we are are hoping raw scale will reach it.",
  "transcript_chars": 3576,
  "ingested_at": "2026-06-13T01:30:20.888645+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 397,
    "upvote_ratio": 0.96,
    "num_comments": 77,
    "author": "BuildwithVignesh",
    "is_self": false
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