The development of artificial intelligence is reaching a decisive phase. As tech companies around the world battle for supremacy in AI development, one key question remains: when will Artificial General Intelligence (AGI) become a reality? A fascinating interview at the Google I/O conference between Alex Kantrowitz, Sergey Brin (Google co-founder) and Demis Hassabis (CEO of Google DeepMind) provides surprising insights into this vision of the future.
What began as a planned one-on-one interview with Hassabis turned into a spontaneous three-way conversation when Brin unexpectedly took the stage. The discussion revealed not only differing views on the AGI timeline, but also deep insights into Google’s strategy for the next phase of the AI revolution.
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The great AGI debate: 2030 as a turning point?
Hassabis’ cautious definition
Demis Hassabis, who is considered one of the leading AGI researchers, defines AGI as a system that can achieve “the range of intellectual performance of the best humans in history”. His vision goes far beyond everyday human abilities: “What Einstein achieved, what Mozart could compose, what Marie Curie discovered” – an AGI system with the same brain architecture should be able to do all of this.
Hassabis is particularly critical of the current consistency of AI systems: “Any of us can find obvious weaknesses in today’s chatbots and assistants within a few minutes. Simple math problems from high school or basic games that they can’t master.” For real AGI, it would have to take teams of experts several months to identify such weaknesses.
His timeline? Just after 2030.
Brin’s optimistic forecast
Sergey Brin, who says he has returned to Google because of the “unique time in computer history”, is more optimistic: AGI before 2030. “Honestly, every computer scientist should not be retired right now, but working on AI,” Brin emphasizes the historical significance of the current moment.
Brin’s clear statement is particularly noteworthy: “We fully intend for Gemini to be the very first AGI”. This declaration of intent shows Google’s ambitions in the AGI race against OpenAI, Anthropic and other competitors.
Scaling versus algorithmic breakthroughs
The two-pillar strategy
A key point of discussion was whether larger data centers and more computing power alone are enough to achieve AGI. Hassabis advocates a balanced approach: “You need both. You need to scale the known technologies to the maximum and at the same time work on the next innovations that could enable a 10-fold leap in six months or a year”.
Brin sees algorithmic advances as even more crucial: “Historically, algorithmic improvements have outpaced computational advances, even with Moore’s Law. If I had to guess, I would say that algorithmic advances are likely to be even more significant”.
Deep Think: Thinking as a breakthrough
A concrete example of these algorithmic innovations is Google’s “Deep Think” system, which was introduced at the presentation. Hassabis explains the concept using AlphaGo as an example: “We had versions of AlphaGo with ‘thinking’ turned off – just the model that communicated its first idea. That was about master level. With thinking on, it was well above world champion level – about 600 Elo points difference”.
This “thinking time” could be crucial for complex, valuable tasks: “The more time you give the system, the better it gets. For very difficult, valuable tasks, it will be worth letting it think for a very long time”.
Self-improving AI: the path to the intelligence explosion?
Alpha Evolve as a harbinger
Hassabis confirmed Google’s work on “Alpha Evolve”, a system that can improve algorithms by itself. When asked whether Google wanted to trigger an “intelligence explosion”, he replied: “Not an uncontrolled one. It’s an interesting first experiment combining different techniques – in this case evolutionary programming – with the latest foundation models”.
The parallels with AlphaZero are remarkable: “We know that self-improvement is possible. AlphaZero learned chess and Go from scratch within 24 hours. But these were limited game domains. The real world is far more complex”.
Security as a priority
Despite the progress, Hassabis emphasizes the importance of security: “It is important that the first AGI systems are built reliably and securely. After that, we can imagine developing many systems with secure architectures”.
The future of human-machine interaction
Smart glasses as an AGI platform
One fascinating aspect of the conversation was Google’s vision for smart glasses. Hassabis sees this as the perfect platform for AGI: “I think the universal assistant is the killer app for smart glasses. That’s what will make it work”.
The strategy behind it is clear: AGI needs to understand the physical world in order to be truly useful. “We want it to be useful in everyday life for everything, not just on the computer or a device. It has to come along and understand the physical context”.
Robotics as the next step
Hassabis sees the current generation of AI as the breakthrough for robotics: “I think we’re at a really exciting moment where finally with these latest versions, especially Gemini 2.5, the software intelligence is there to finally make robotics work.”
The bottleneck was never the hardware, but the software: “The bottleneck in robotics was not so much the hardware, but the software intelligence that has always held robotics back”.
Social transformation and philosophical questions
The web of the future
When asked about the web in ten years’ time, both experts were cautious. Brin: “I think ten years is so far beyond what we can see because of the rate of progress in AI. Not just the web – I think we really don’t know what the world will look like in ten years’ time”.
Hassabis adds, “The web is going to change quite a bit if you think about an agent-centric web. It doesn’t necessarily have to see renderings of how we humans use the web.”
Simulation or reality?
The conversation took a surprising turn when discussing Simulation Theory. Hassabis: “I don’t think we live in a simulation, as Nick Bostrom and others talk about. But I do think that the underlying physics is ultimately information theory. So we live in a computational universe, but it’s not a simple simulation”.
Conclusion: A race with an uncertain outcome
The interview reveals the tensions and ambitions at the forefront of AI development. While Hassabis and Brin disagree on the exact timeline, there is consensus on the transformative power of the coming AGI systems.
Google’s strategy is becoming clear: a combination of massive scaling and algorithmic breakthroughs, coupled with a focus on practical applications such as smart glasses and robotics. The emphasis on security and gradual development shows an awareness of the enormous risks.
Whether AGI is achieved in 2029 or 2031 may seem secondary. What is crucial is that both experts see the 2030s as the decade of the AGI revolution. For companies, governments and society, this means: preparations must begin now.
The question is no longer whether AGI is coming – but who will develop it first and how it will change the world.
Sources
- YouTube interview: “DeepMind CEO Demis Hassabis + Google Co-Founder Sergey Brin: AGI by 2030?” by Alex Kantrowitz
- Axios coverage of AGI timeline
- VentureBeat article on Google’s AGI ambitions
