
Google DeepMind CEO Predicts AI Will Help Humans Colonise The Galaxy Starting 2030
Google DeepMind CEO and Nobel laureate Demis Hassabis said that starting 2030 humans will be able to colonise the galaxy and artificial intelligence will power this revolution.
2024 Nobel Prize winner and Google DeepMind CEO Demis Hassabis told a news outlet recently that humans will be able to 'colonise the galaxy" starting 2030 and the revolution will be powered by artificial intelligence (AI). The Nobel chemistry laureate told WIRED that AI will lead humanity to far into the universe while turbocharging human productivity.
Hassabis, who was jointly awarded the Nobel Prize with David Baker 'for computational protein design", said the 'golden era' was only five years away and that AI models set to bring about a renaissance in human existence.
'If everything goes well, then we should be in an era of radical abundance, a kind of golden era. AGI can solve what I call root-node problems in the world, curing terrible diseases, much healthier and longer lifespans, finding new energy sources," Hassabis was quoted as saying in an interview with WIRED.
AGI, or Artificial General Intelligence, refers to an AI system with human-like cognitive abilities, capable of understanding, learning, and applying knowledge across a wide range of tasks.
'If that all happens, then it should be an era of maximum human flourishing, where we travel to the stars and colonise the galaxy. I think that will begin to happen in 2030," he said.
When asked whether abundance through AI would still result in unequal distribution, Demis Hassabis said the technology could make the world feel 'like a non-zero-sum game."
Although AGI has the potential to open vast new frontiers for humanity, Hassabis has previously expressed concern that society may not be prepared for its impact and admitted that the risks and consequences of such powerful technology often keep him up at night.
'It's a sort of like probability distribution. But it's coming, either way it's coming very soon and I'm not sure society's quite ready for that yet. And we need to think that through and also think about these issues that I talked about earlier, to do with the controllability of these systems and also the access to these systems and ensuring that all goes well," he said.
He has also advocated for creating a UN-style global body to oversee the development and governance of AGI.
'I would advocate for a kind of CERN for AGI, and by that, I mean a kind of international research-focused high-end collaboration on the frontiers of AGI development to try and make that as safe as possible," he further added.
Hashtags

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


The Hindu
12 minutes ago
- The Hindu
To survive AI and global geopolitics, India should become a hub of knowledge creation, not just knowledge processing
Earlier this month, Tata Consultancy Services (TCS), India's largest IT services firm, confirmed it will lay off 12,000 employees. For decades, companies such as TCS symbolised India's prowess in IT-enabled services — a low-cost, high-scale model that rode the wave of globalisation. But that model is now under existential strain. The era of labour arbitrage is drawing to a close, and the age of artificial intelligence (AI) is rewriting the rules of economic competitiveness. Generative AI, machine learning, and automation are fast replacing the very tasks that once gave India its edge: coding, data entry, support services, and even parts of analytics. The decline in headcount is not a blip; India's core export, white-collar digital labour, is being disrupted. And the country does not seem prepared as we see problems in absorbing science and engineering talent newly entering the job market. Simultaneously, the manufacturing-led catch-up route is narrowing. For years, economists argued India could do what China did in the 1990s — turn industrial policy and export-led manufacturing into mass employment and structural transformation. But that ship has largely sailed. Countries such as Vietnam and Bangladesh have already captured the low-cost manufacturing space. Add to that rising automation and India's own infrastructure bottlenecks, the feasibility of China-style manufacturing resurgence diminishes rapidly. What, then, is India's pathway to sustained economic relevance? The answer lies upstream — in innovation, discovery science, and a smart, coordinated science, technology, and innovation (STI) policy. If India wants to be a rule-maker rather than a rule-taker in the AI-driven global economy, it must invest urgently in becoming a hub of knowledge creation, not just knowledge processing. This will only be achievable with a new national compact that starts from STEM but goes above and beyond embracing STEPS — an integration of STEM with policy and society. This means building a generation of technologists who understand not just how to build systems, but how those systems affect entrepreneurship, business model and scaling, ethics, governance, and inclusion. It also means reforming curricula to include data governance, AI ethics, climate-tech, innovation economics, and intellectual property policy. Finally, it also means urgent, mission mode requirement of an integrated, State-agnostic approach where we will see not just southern India having a head start in STEM and STEPS. The New Education Policy (NEP), 2020 provides some groundwork, but implementation must go further and deeper. From IITs to State universities, India will need a deliberate shift toward interdisciplinary innovation and doctoral-level research capacity. India's innovation-to-education pipeline is currently too weak to sustain a 21st century knowledge economy. And sadly in this, as noted above, manufacturing likely will not save India any more. The dream of becoming the 'next China' in manufacturing is now largely unrealistic. India's manufacturing sector contributes just 14-16% of the GDP — a figure that has barely budged in a decade. More worryingly, global manufacturing is undergoing its own AI-led transformation: smart factories, predictive maintenance, and robotic assembly lines are shrinking the need for cheap labour. Competing on cost is now a losing battle. Moreover, global supply chains are also realigning around strategic resilience and digital integration, not just wage arbitrage. India's challenge is not to attract the next garment factory but to build the next quantum computing lab or climate-resilient agri-tech platform. Which brings us to the question of how a Triple Helix approach might be India's best shot at future-readiness. To get there, India will need a clear National Science and Innovation Strategy underpinned by deep collaboration between government, industry, and academia. No single actor can deliver the transformation needed and increasingly the need of the hour will be science-based entrepreneurship and scientist entrepreneurs. It has been done before like by Vijay Chandru, inventor of Simputer and founder of Strand Genomics, also a former IISc Professor, but one Vijay Chandru is hardly enough for a country of 1.3 billion. Blue-sky science Government also must invest in blue-sky science, reform its R&D funding structures, and design enabling regulatory frameworks for frontier tech (AI, biotech, semiconductors, and so on). Universities must evolve into innovation hubs, not just exam factories. They must work closely with industry, build tech transfer offices, and reward risk-taking. Industry also must move beyond short-term returns and co-invest in long-horizon research, from chip design to synthetic biology refusing to accept modest productivity gains with a middling equilibrium mindset. Global lessons abound. The U.S.'s DARPA ecosystem, Germany's Fraunhofer Institutes, and Israel's Start-Up Nation playbook all demonstrate how strategic state support and institutional coordination can turn ideas into global advantage. India can build from their lessons, leverage on the current global geopolitical headwinds and create a national consciousness around science and innovation. It is not just investment in science that will matter, but investment in the science of innovation itself brings in a critical evaluation mindset for upgrading based on evidence. India lacks a coherent framework to measure what works: which R&D models yield translational success? How do tech incubators perform over time? Where does research funding leak or stagnate? A National Science of Science and Innovation Policy (NSIP) platform — a cross-ministerial, data-driven approach to governing the innovation ecosystem — could be a way forward. NITI Aayog's AI strategy and the recent National Research Foundation are steps in the right direction, but coordination and scale remain insufficient. This effort must include dedicated funding for AI safety, public interest technologies, twin transition policies and sovereign computational infrastructure. The stakes are high: if India does not develop its own AI stack, algorithms, chips, cloud, data protocol, it will remain captive to technological colonialism. Stagnation, inequality The fallout from the AI transition is not hypothetical as we see in the TCS situation mentioned above. If India does not invest in science, technology, and evidence-based policy today, it will face economic stagnation, rising inequality, and geopolitical irrelevance tomorrow. The global economy will not wait for India to catch up and in fact, catching-up economies are looking for the country's leadership in these areas. This is particularly concerning since already, a handful of countries — mostly in West and East Asia — are monopolising AI patents, funding, and talent. Without a deliberate national push, India will continue to supply coders to other nations' AI empires rather than building its own. The good news is that India has the ingredients: a young demographic, a robust start-up ecosystem, and scientific institutions with proven excellence. What we need now is leadership, vision, and a strategic shift in mindset — from cost to creativity, from services to science, from political populism to real performance. India can still leapfrog into the global innovation vanguard. But only if it recognises that science, technology, and smart policy are not luxuries — they are our last, best bet in the age of AI. The dragon is roaring already, will the elephant wake up? Chirantan Chatterjee is a Professor of Development Economics, Innovation and Global Health at the University of Sussex


Hindustan Times
7 hours ago
- Hindustan Times
AI Robs My Students of the Ability to Think
One of the things I love about teaching political communications is my students' eagerness to take up the art and craft of the work at hand. Shame seldom cast its shadow on our classroom conversations. Last year that changed. More than half the nonnative English-speaking students and a notable number of native English speakers told me that after relying on AI to draft their papers and emails, their ability to write, speak and conduct basic inquiry is slipping away. They tell me this as if they have done something wrong, never considering that it is their professors, not they, who should carry that burden. I am no stranger to the effect of technology on language and literacy, nor am I shocked by its bland patterns of enthusiastic advent, which always give way to shabbiness and decay. Google promised the ability to search—a word that has terrific depth and meaning—and delivered a crass advertiser-led sorting system. Facebook started as proto-Tinder before a revamp that said we'd get Woodstock-style digital communes. Then it locked us in a space where people scream at each other. Through it all, I have tinkered with, embraced, studied, used, thrown away and taught about more forms of technology than I can remember, from letterpress printing to podcast production. But no new technology has produced such a terrifying admission of stark and fundamental disempowerment by my students as AI has. For all its promise, AI is being developed and used in ways that are disabling. There is little evidence that senior university faculty are committed to tamping down the rampant overuse of AI. Instead, it is the paperweight on a pile of evidence that at an ethical level, universities are too timid or ignorant to insist that students use the core skills we are supposed to be teaching them. Perhaps willful ignorance is the better phrase—these core skills are no mystery. They involve an ability to sift through information and understand who created it, then organize and pull it together with logic, reason and persuasion. When teachers dream of our students' successes, we want to see these skills help them thrive. For that to happen, students must gain the ability to synthesize information. They must be able to listen, read, speak and write—so they can express strategic and tactical thinking. When they say AI is eroding their ability to speak and write, this is what they're losing, often before they've ever fully gained it. It's the result of disturbing trends. One is the general decline in educators' commitment to seeing communications as a fundamental skill that all courses should develop. I often write a page of notes in response to a page of graduate student homework, describing not only what the student should do but why and how to do so in the future. Too often, the reply is: 'I haven't gotten this much feedback since high school.' Compliments are nice, but these asides don't fill me with joy. Nor do the many excuses academics give for this collective failure—from financial and time constraints to the old hyperliteralist trope that we must respond to student demands—even when they're unwittingly against their own educational interests—and torch everything else. Along with this decline in teaching, I am often told (as though I write with a quill) that technology is eclipsing our need to teach these skills to the expert degree we once did. This is the voice of technological evangelism in higher education, and its adherents encourage a deeper embrace of AI, even though—with a few exceptions—they have little to no ability to lead students to any kind of mastery. The types of academics who engage in this kind of boosterism aren't known for their subtlety, so I am constantly inundated with—and told to celebrate—new faculty-created AI tools for everything from the art of cutting text down (try putting the 'I Have a Dream' speech into an AI shortener) to the mortifying practice of using AI to summarize student course reviews or even grade assignments. When confronted, these evangelists often push back with hypersimplified examples of handy AI shortcuts, such as customer service-style bots to answer students' questions about crucial aspects of course management. But I'm struck by their tone, which often presumes that current teaching methods and student engagement are some kind of drudgery that has entitled us to AI-based relief, even if it comes at the expense of our students' learning. All the while, its use is indiscriminate and widespread. Just ask the students who are using it to the point where a tool now has mastery over them and is robbing them of language. In my work, which ranges from negotiation to disability policy, the implications of this disempowerment are frightening. Colin Powell once told my colleagues and me that he often winced at how people would, with a choice of words so poorly attuned to the other side of a negotiation, walk into a room and convert 'an adversary into an enemy.' His observation reflects the depth and breadth of intentionality that humans must possess to do the careful work that can be a matter of life and death for others. Contrary to what AI enthusiasts claim, the human possession of these skills will never become irrelevant if we value life, society and governance. For students to grow into professionals who have those skills, they must first develop them. What it will take for their teachers to defend that right when those teachers already possess the knowledge and power to do so, I do not know. Mr. Green teaches at Harvard's Kennedy School and is author of 'A Perfect Turmoil: Walter E. Fernald and the Struggle to Care for America's Disabled.'


Hans India
9 hours ago
- Hans India
Google DeepMind CEO: AGI Still Years Away as AI Struggles with Simple Mistakes
Big Tech giants like Google, Meta, and OpenAI are locked in a high-stakes race to develop artificial general intelligence (AGI) — AI systems capable of thinking, planning, and adapting on par with humans. But according to Google DeepMind CEO Demis Hassabis, that goal remains distant, as current AI still makes surprisingly simple errors despite impressive achievements. Speaking on the Google for Developers podcast, Hassabis described today's AI as having 'jagged intelligence' — excelling in certain domains but stumbling in basic ones. He cited Google's latest Gemini model, enhanced with DeepThink reasoning technology, which has reached gold-medal-level performance in the International Mathematical Olympiad — one of the toughest math competitions worldwide. Yet, that same model can still make avoidable mistakes in high school-level math or fail at simple games. 'It shouldn't be that easy for the average person to just find a trivial flaw in the system,' Hassabis remarked. This inconsistency, he explained, is a sign that AI is far from human-level intelligence. He argued that simply scaling up models with more data and computing power will not bridge the gap to AGI. Instead, fundamental capabilities like reasoning, planning, and memory — areas still underdeveloped in even the most advanced AI — must be strengthened. Another challenge, Hassabis noted, is the lack of rigorous testing. Many standard AI benchmarks are already saturated, creating the illusion of near-perfect performance while masking weaknesses. For example, Gemini models recently scored 99.2% on the AIME mathematics benchmark, leaving minimal room for measurable improvement. However, these results don't necessarily mean the model is flawless. To overcome this, Hassabis called for 'new, harder benchmarks' that go beyond academic problem-solving to include intuitive physics, real-world reasoning, and 'physical intelligence' — the ability to understand and interact with the physical world as humans do. He also stressed the need for robust safety benchmarks capable of detecting risks such as deceptive behavior in AI systems. 'We're in need of new, harder benchmarks, but also broader ones, in my opinion — understanding world physics and intuitive physics and other things that we take for granted as humans,' he said. While Hassabis has previously suggested AGI might arrive within five to ten years, he now emphasizes caution. He believes AI companies should first focus on perfecting existing models before chasing full AGI. The path ahead, he implied, is less about winning a race and more about ensuring AI's capabilities are reliable, safe, and truly intelligent across the board. For now, despite breakthroughs in reasoning and problem-solving, the dream of AI that matches human intelligence remains a work in progress — and one that may take longer than the industry's most optimistic predictions.