{
  "id": 15830560,
  "title": "Google may already be showing us its route to AGI",
  "url": "https://urgent.news/2026/10/21/google-may-already-be-showing-us-its-route-to-agi",
  "topic": "business",
  "section": "Business",
  "published": "2026-10-21T01:00:04.000Z",
  "source": {
    "name": "e27",
    "slug": "e27",
    "url": "https://e27.co/google-may-already-be-showing-us-its-route-to-agi-20260916/"
  },
  "original_language": "en",
  "account": "In 2015, Demis Hassabis reflected on a decision he made in 2004 when he realized that DeepMind's technology was not yet ready for general intelligence. By 2026, Hassabis outlined a more comprehensive set of components: Gemini as a world model, AlphaGo-style search and planning, specialized tools, memory, reasoning, and systems capable of acting in the physical world. This suggests that Google may already possess the necessary elements to realize its long-standing theory of artificial general intelligence (AGI).\n\nDeepMind's early focus was on agents learning to act, rather than generating language. In 2013, a neural network was able to navigate Atari games, make decisions based on pixel input, and improve its performance based on rewards. Hassabis described this progression from \"pixels to action\". Later, AlphaGo integrated neural networks with search algorithms to determine the best course of action. By 2016, Hassabis emphasized the importance of combining neural networks with planning approaches. DeepMind also began exploring imagination-augmented agents that simulated possible futures before executing actions, and developed memory and continual learning systems.\n\nWhile AlphaGo was highly successful in its specific domain but lacked general applicability, large language models (LLMs) changed the landscape. Google Brain's Transformer architecture, introduced in 2017, provided a foundation for models trained on vast quantities of language, code, images, and other data. These models demonstrated broad competence across various subjects and tasks, a capability not present in earlier DeepMind agents. However, LLMs were still limited in their ability to autonomously pursue objectives over long sequences of actions.\n\nIn 2023, Google consolidated its AI efforts by merging Google Brain and DeepMind into Google DeepMind. The resulting Gemini system was described as multimodal, designed to work with tools, and intended to eventually incorporate memory and planning capabilities. Hassabis framed Gemini as a synthesis of AlphaGo-type systems and large language models, suggesting that this could be the key to realizing AGI.\n\nGoogle's recent projects increasingly resemble the components DeepMind previously investigated separately. Hassabis has emphasized the importance of a \"world model, not just a language model\". Google's Genie project explores this direction by generating interactive environments where actions affect subsequent events. SIMA (Simulation-based Model of Agency) complements Gemini by allowing an AI to interpret objectives, reason about them, and act within 3D environments. Deep Think addresses the limitations of conventional language models by exploring multiple reasoning paths before committing to an answer. Robotics further extends this approach into the physical world.\n\nThe integration of these components into a cohesive system may be crucial for creating an artificial worker that functions similarly to human employees. An AI capable of understanding objectives, choosing appropriate tools, interpreting their outputs, and making informed decisions could potentially replace workers in various roles. While the economic threshold required for AGI is higher than current systems, the economic impact of AI systems that can perform key tasks more efficiently may be significant.",
  "summary": "In 2015, Demis Hassabis looked back at a decision he had made more than a decade earlier. By 2004 he was already thinking about what would eventually become DeepMind, but concluded that the technology was not ready. As he later put it, “we still didn’t have enough of the components.” Deep learning had not yet […] The post Google may already be showing us its route to AGI appeared first on e27 .",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}