Google DeepMind CEO Demis Hassabis + Google Co-Founder Sergey Brin: Scaling AI, AGI Timeline, Simulation Theory

Summary

Big Technology Podcast Summary Host: Alex Kantrowitz
Guests: Demis Hassabis (CEO of DeepMind) and Sergey Brin (Co-founder of Google)
Episode Title: Google DeepMind CEO Demis Hassabis + Google Co-Founder Sergey Brin: Scaling AI, AGI Timeline, Simulation Theory
Release Date: May 21, 2025

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1. Introduction

The episode opens with Alex Kantrowitz welcoming the audience to a live-streamed session featuring two prominent figures in the tech world: Demis Hassabis, CEO of DeepMind, and Sergey Brin, co-founder of Google. The focus of the discussion centers on the frontiers of Artificial Intelligence (AI), scaling AI technologies, the timeline for Artificial General Intelligence (AGI), and engaging topics like simulation theory.

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2. Frontier Models and Their Potential

Demis Hassabis emphasizes the remarkable progress in AI, highlighting recent advancements showcased in DeepMind's keynote. He asserts, “I think we’re seeing incredible gains with the existing techniques, pushing them to the limit... to get all the way to something like AGI, I think may require one or two more new breakthroughs” (01:33).

Key Points:

• Continued Progress: Existing AI techniques are being maximized.
• Need for New Breakthroughs: Achieving AGI may necessitate one or two significant innovations.
• Future Innovations: Promising ideas are being developed for DeepMind’s Gemini branch.

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3. Scale vs. Innovation in AI Development

The discussion shifts to the role of scaling in AI advancements. Alex Kantrowitz asks whether scale remains the primary driver or if it has become a supporting factor.

Demis Hassabis responds by advocating for a balanced approach: “You need both. You need to scale to the maximum the techniques that you know about... and at the same time, you want to spend a bunch of effort on what’s coming next...” (02:16).

Sergey Brin adds, “I think that historically, the algorithmic advances have actually beaten out the computational advances... algorithmic advances are probably going to be even more significant than the computational advances” (03:29).

Key Points:

• Dual Approach: Importance of scaling current techniques and investing in future innovations.
• Algorithm vs. Compute: Algorithmic improvements may offer more substantial benefits than mere computational scaling.

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4. Data Centers and Compute Needs

When questioned about the reliance on larger data centers and more chips, Demis Hassabis acknowledges the necessity but also points to new demands beyond training, such as serving and inference. He notes, “We’re going to need a lot of data centers for serving and also for inference, time, compute, giving” (03:41).

Key Points:

• Infrastructure Expansion: Increased need for data centers to handle both training and serving/inference.
• Performance Enhancement: Emphasis on improving performance for tasks requiring extensive computation.

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5. Reasoning Paradigm and Thinking Systems

Alex Kantrowitz delves into the reasoning paradigm, a year-old concept that integrates reasoning capabilities with traditional Large Language Models (LLMs).

Demis Hassabis elaborates on the “thinking paradigm,” comparing it to DeepMind’s early work with AlphaGo and AlphaZero: “With the thinking turned on, it’s way beyond world champion level... potentially even bigger gains by adding this thinking type of paradigm on top” (05:00).

Sergey Brin agrees, highlighting the significant advantages reasoning adds to AI systems: “It’s obviously a huge advantage... we’re just at the tip of the iceberg right now” (06:10).

Key Points:

• Thinking Systems: Incorporating reasoning processes significantly boosts AI capabilities.
• Historical Success: Demonstrated through superior performance in complex games.
• Future Potential: The reasoning paradigm could lead to even greater advancements in real-world applications.

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6. AGI: Definitions and Timelines

The conversation turns to defining AGI and predicting its arrival.

Demis Hassabis stresses the importance of a clear definition, distinguishing between typical human intelligence and a more theoretical construct that matches the full range of human capabilities: “For something to be called AGI, it would need to be consistent, much more consistent across the board than it is today” (09:25).

Sergey Brin speculates on the timeline, suggesting AGI could emerge before 2030: “2030. Boy, you really kind of put it on that fine line. I’m going to say before” (26:33).

Demis Hassabis cautiously aligns just after 2030, highlighting the complexities involved: “I’m just after” (26:49).

Key Points:

• Clarifying AGI: Differentiating between general and typical intelligence is crucial.
• Timeline Predictions: Variance in predictions, but a probable emergence around the early 2030s.

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7. Emotion in AGI

The discussion explores whether AI needs to possess emotions to be considered AGI.

Demis Hassabis believes understanding emotion is essential but is uncertain about mimicking human-like emotional reactions: “I think you will need to understand emotion... it might be different or it might be not necessary...” (13:50).

Key Points:

• Emotional Understanding: AGI should comprehend emotions.
• Design Considerations: Uncertainty about whether AGI should emulate human emotional responses.

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8. Self-Improving Systems and Intelligence Explosion

Alex Kantrowitz raises the topic of self-improving AI systems, referencing "Alpha Evolve," which enhances algorithms and training methods.

Demis Hassabis clarifies that while self-improvement is a method of advancement, DeepMind aims to control such processes: “No, not an uncontrolled one... It remains to be seen if that type of approach can work in a more general way” (14:52).

Key Points:

• Self-Improvement: Potential for AI to enhance its own capabilities.
• Controlled Advancement: Emphasis on ensuring self-improvement loops are managed to prevent uncontrolled intelligence growth.

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9. Race to Develop AGI and Google's Position

Sergey Brin discusses the competitive landscape, suggesting that the first entity to achieve AGI could set the stage for subsequent developments: “It’s like a constant leapfrog... I do think there’s an inspiration element that you see...” (11:50).

Demis Hassabis echoes the sentiment, highlighting the importance of responsible development: “It is important that those first systems are built reliably and safely” (12:51).

Key Points:

• Competitive Environment: Multiple entities racing to develop AGI.
• Responsibility: Ensuring early AGI systems are safe and reliable.

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10. Smart Glasses and Multimodal AI

The conversation shifts to hardware innovations, specifically smart glasses.

Demis Hassabis explains the rationale behind multimodal AI for smart glasses: “AGI needs to understand the physical environment... It needs to come around you and understand your physical context” (20:50).

Sergey Brin reflects on past experiences with Google Glass, noting advancements in technology and partnerships that make current smart glasses more feasible: “The technology gap... Now we have great partners that are helping us build this” (21:07).

Key Points:

• Multimodal AI: Integrating visual understanding with AI assistants.
• Product Evolution: Learning from Google Glass to develop more advanced and user-friendly smart glasses.

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11. Video Generation and Model Quality

Alex Kantrowitz inquires about the implications of AI-generated video content on training models.

Demis Hassabis addresses concerns about model quality degradation through AI-generated data: “We’re very rigorous with our data quality management... we attach synth ID to them” (23:44). He explains mechanisms to maintain data integrity and prevent "model collapse."

Key Points:

• Data Integrity: Implementing robust data quality and watermarking to distinguish AI-generated content.
• Synthetic Data Utilization: Careful integration of synthetic data to enhance training without compromising model quality.

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12. Future of the Web and AGI Before 2030

In a rapid-fire segment, Sergey Brin expresses uncertainty about predicting the web's future due to the rapid pace of AI advancements: “I don’t think we really know what the world looks like in 10 years” (25:56).

Demis Hassabis suggests a transformative shift towards an "agent-first" web: “Things will be pretty different in a few years” (26:16).

Key Points:

• Uncertainty: Difficulty in forecasting the exact future of the web.
• Transformation: Anticipation of significant changes driven by AI-driven agents.

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13. AI in Job Interviews

Alex Kantrowitz poses a playful question about hiring candidates who utilize AI during interviews.

Demis Hassabis responds conditionally, indicating that the appropriateness depends on the extent of AI usage: “Depends how they used it” (27:16).

Sergey Brin humorously admits to never conducting job interviews: “I never interviewed at all, so I don’t know” (27:32).

Key Points:

• Conditional Acceptance: Potential acceptance of AI usage in interviews based on context and extent.
• Personal Insight: Both guests highlight their lack of direct experience in the hiring process.

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14. Simulation Theory Discussion

The episode concludes with an intriguing discussion on whether reality might be a simulation.

Demis Hassabis clarifies his stance: “Not in the way that Nick Bostrom and people talk about... underlying physics is information theory” (28:13). He suggests that while the universe may be computational, it isn't a straightforward simulation.

Sergey Brin adds a philosophical perspective, questioning the recursive nature of simulations and the anthropocentric view: “If we’re in a simulation... there’s got to be some stopping criteria” (29:03).

Key Points:

• Computational Universe: The idea that underlying physics may be based on information theory.
• Philosophical Implications: Addressing the complexities and paradoxes of simulation theory.

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15. Conclusion

Alex Kantrowitz wraps up the conversation, expressing gratitude to Demis Hassabis and Sergey Brin for their insightful participation. The episode offers a comprehensive exploration of AI's current state, future trajectories, and philosophical considerations, providing valuable insights for listeners interested in the evolving landscape of artificial intelligence.

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Notable Quotes:

• Demis Hassabis: “I think we’re seeing incredible gains with the existing techniques, pushing them to the limit...” (01:33)
• Sergey Brin: “The algorithmic advances are probably going to be even more significant than the computational advances.” (03:29)
• Demis Hassabis: “We need both scaling and innovation to drive AI forward.” (02:16)
• Sergey Brin: “AI is going to be vastly more transformative than the web and mobile phones combined.” (16:11)
• Demis Hassabis: “For something to be called AGI, it would need to be consistent, much more consistent across the board than it is today.” (09:25)
• Sergey Brin: “I’m going to say before [2030].” (26:33)
• Demis Hassabis: “We’re very rigorous with our data quality management and curation.” (23:44)

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This summary encapsulates the key discussions and insights from the "Big Technology Podcast" episode featuring Demis Hassabis and Sergey Brin, providing a comprehensive overview for those who have not listened to the full episode.

Transcript

[Leah Smart]

From LinkedIn News. I'm Leah Smart, host of Everyday Better, an award winning podcast dedicated to personal development. Join me every week for captivating stories and research to find more fulfillment in your work and personal life. Listen to Everyday better on the LinkedIn podcast network, Apple Podcasts, or wherever you get your podcasts from.

[Jessi Hempel]

LinkedIn News. I'm Jessi Hempel, host of the hello Monday Podcast. Start your week with the hello Monday Podcast. We'll navigate career pivots. We'll learn where happiness fits in. Listen to hello Monday with me, Jessi Hempel on on the LinkedIn podcast network or wherever you get your podcasts.

[Alex Canceroitz]

All right, everybody, we have an amazing crowd here today. We're going to be live streaming this, so let's hear you make some noise so everybody can hear that you're here. Let's go. I'm Alex Canceroitz, I'm the host of Big Technology Podcast and I'm here to speak with you about the frontiers of AI with two amazing guests. Demis Hassabis, the CEO of DeepMind is here. Google DeepMind. Good to see you, Demis.

[Demis Hassabis]

Good to see you too.

[Alex Canceroitz]

And we have a special guest, Sergey Brin, the co founder of Google is also here. All right, so this is going to be fun. Let's start with the frontier models. Demis, this is for you. With what we know today about frontier models, how much improvement is there left to be unlocked? And, and why do you think so many smart people are saying that the gains are about to level off?

[Demis Hassabis]

I think we're seeing incredible progress. You've all seen it today, all the amazing stuff we showed in the keynote. So I think we're seeing incredible gains with the existing techniques, pushing them to the limit. But we're also inventing new things all the time as well. And I think to get all the way to something like AGI, I think may require one or two more new breakthroughs. And I think we have lots of promising ideas that we're cooking up and we hope to bring into the main branch of the Gemini branch.

[Alex Canceroitz]

All right, and so there's been this discussion about scale. Does scale solve all problems or does it not? So I want to ask you, in terms of the improvement that's available today, is scale still the star or is it a supporting actor?

[Demis Hassabis]

I think I've always been of the opinion you need both. You need to scale to the maximum the techniques that you know about, you want to exploit them to the limit, whether that's data or compute scale. And at the same time, you want to Spend a bunch of effort on what's coming next maybe six months, a year down the line. So you have the next innovation that might do a 10x leap in some way to kind of intersect with the scale. So you want both in my opinion. But I don't know. Sergey, what do you think?

[Sergey Brin]

I mean, I agree it takes both. You know, you can have algorithmic improvements and simply compute improvements. Better chips, more chips, more power, bigger data centers. I think that historically, if you look at things like the N body problem and simulating just gravitational bodies and things like that as you plot it, the algorithmic advances have actually beaten out the computational advances. Even with Moore's law, if I had to guess, I would say the algorithmic advances are probably going to be even more significant than the computational advances. But both of them are coming up now, so we're kind of getting the benefits of both.

[Alex Canceroitz]

And demis, do you think the majority of your improvement is coming from building bigger data centers and using more chips? Like there's talk about how the world will be just wallpapered with data centers. Is that your vision?

[Demis Hassabis]

Well, no, look, I mean, we're definitely going to need a lot more data centers. It's amazing that, you know, it still amazes me from a scientific point of view, we turn sand into thinking machines. It's pretty incredible. But actually it's not just for the training. It's now we've got these models that everyone wants to use, you know, and actually we're seeing incredible demand for 2.5 pro and I think Flash. We're really excited about how performant that is for the incredible sort of low cost. I think the whole world's gonna want to use these things. And so we're gonna need a lot of data centers for serving and also for inference, time, compute, giving. You know, you saw, you saw deep think today, 2.5 pro, deepthink. The more time you give it, the better it will be. And certain tasks, very high value, very difficult tasks, you'll wanna, it will be worth letting it think for a very long time. And we're thinking about how to push that even further. And again, that's gonna require a lot of chips at runtime.

[Alex Canceroitz]

Okay, so you brought up test time, compute. We've been about a year into this reasoning paradigm and you and I have spoken about it twice in the past as something that you might be able to add on to traditional LLMs to get gains. So I think this is like a pretty good time for me to be like, what's happening? Can you help us contextualize the magnitude of improvement we're seeing from reasoning.

[Demis Hassabis]

We've always been big believers in what we're now calling this thinking paradigm. If you go back to our very early work on things like AlphaGo and AlphaZero, our agent work on playing games, they all had this type of attribute of a thinking system on top of a model. And actually you can quantify how much difference that makes if you look at a game like chess or Go. We had versions of AlphaGo and AlphaZero with the thinking turned off. So it was just the model telling you its first idea. And it's not bad, it's maybe master level, something like that. But then if you turn the thinking on, it's way beyond world champion level. It's like a 600 Elo plus difference between the two versions. So you can see that in games, let alone for the real world, which is way more complicated. And I think the gains will be potentially even bigger by adding this thinking type of paradigm on top. Of course, the challenge is that your models and I talked about this earlier in the talk, need to be a kind of world model. And, and that's much harder than building a model of a simple game, of course, and it has errors in it and yet those can compound over longer term plans. But I think we're making really good progress on all those fronts.

[Sergey Brin]

Yeah, look, I mean, as Demis said, DeepMind really pioneered a lot of this reinforcement learning work. And what they did with AlphaGo and AlphaZeros you mentioned showed, as I recall, something you would take 5,000 times as much training to match what you were able to do with still a lot of training. And the inference time compute that you were doing would go. So it's obviously a huge advantage. And obviously like most of us, we get some benefit by thinking before we speak. Although not always. I always get reminded to do that. But I think that the AIs obviously are much stronger once you add that capability. And I think we're just at the tip of the iceberg right now in that sense. It's been less than a year than these models have really been around.

[Demis Hassabis]

Especially if you think about obviously with an AI during its thinking process, it can also use a bunch of tools or even other AIs during that thinking process to improve what the final output is. So I think it's going to be an incredibly powerful paradigm.

[Alex Canceroitz]

DeepThink is very interesting. I'm going to describe it, I'm trying to describe it right. It's basically a bunch of parallel reasoning processes working and then checking each other, and then it's like reasoning on steroids. Now, Demis, you mentioned that the industry needs a couple more advances to get to AGI. Where would you put this type of mechanism? Is this one of those that might get the industry closer?

[Demis Hassabis]

I think so. I think it's maybe part of one, should we say, and there are others, too, that we need to. Maybe this can be part of improving reasoning. Where does true invention come from? Where you're not just solving a math conjecture, you're actually proposing one or hypothesizing a new theory in physics. I think we don't have systems yet that can do that type of creativity. I think they're coming and these types of paradigms might be helpful in that. Things like thinking and then probably many other things. I mean, I think we need a lot of advances on the accuracy of the world models that we're building. I think you saw that with Veo. The potential VO3 of how it amazes me how it can intuit the physics of the light and the gravity. Having someone. I used to work on computer games, not just the AI, but also graphics engines in my early career, and remember having to do all of this by hand and program all of the lighting and the shaders and all of these things. Incredibly complicated stuff we used to do in early games. And now it's just intuiting it within the model. It's pretty astounding.

[Alex Canceroitz]

I saw you shared an image of a frying pan with some onions and some oil.

[Demis Hassabis]

Hope you all like that.

[Alex Canceroitz]

There was no subliminal messaging about that.

[Demis Hassabis]

No, not really. Not really. Just maybe a subtle, subtle message.

[Alex Canceroitz]

Okay, so we said the word AG or the acronym AGI a couple times. There's, I think, a movement within the AI world right now to say, let's not say AGI anymore. The term is so overused as to be meaningless. But, Demis, it seems like you think it's important. Why?

[Demis Hassabis]

Yeah, I think it's very important. But I think, I mean, maybe I need to write something about this also with Shane Legg, who's our chief scientist, who was one of the people that invented the term 25 years back. I think there's sort of two things that are getting a little bit conflated. One is like, what can a typical person do, an individual do? And we can, you know, we're all very capable, but we can only do however capable we are. There's only a certain slice of things that one is expert in. Right. And. Or, you know, you could say what can you do what like 90% of humans can do? That is obviously going to be economically very important, and I think from a product perspective, also very important. So it's a very important milestone. So maybe we should say that's like typical human intelligence. But what I'm interested in and what I would call AGI is really a more theoretical construct, which is what is the human brain as an architecture able to do? Right? And the human brain is an important reference point because it's the only evidence we have maybe in the universe that general intelligence is possible. And there it would have to be able to. You would have to show your system was capable of doing the range of things even the best humans in history were able to do with the same brain architecture. It's not one brain, but the same brain architecture. So what Einstein did, what Mozart was able to do, or Marie Curie and so on. And that it's clear to me today's systems don't have that. And then the other thing that, why I think it's sort of overblown. The hype today on AGI is that our systems are not consistent enough to be considered, to be fully general yet. They're quite general. So they can do, you know, thousands of things. You see many impressive things today, but every one of us have experience with today's chat bots and assistants. You can easily, within a few minutes, find some obvious flaw with them, some high school math thing that it doesn't solve, you know, some basic game it can't play. It's not very difficult to find that, those holes in the system. And for me, for something to be called AGI, it would need to be consistent, much more consistent across the board than it is today. It's. It should take like a couple of months for maybe a team of experts to find a hole in it, an obvious hole in it. Whereas today it takes an individual minute to find that.

[Alex Canceroitz]

Sergey, this is a good one for you. Do you think that AGI is going to be reached by one company and it's game over? Or could you see Google having AGI, OpenAI having AGI, Anthropic having AGI, China having AGI?

[Sergey Brin]

Wow, that's a great question. I mean, I guess I would suppose that one company or country or entity will reach AGI first. Now, it is a little bit of a, you know, kind of a spectrum. It's not like a completely precise thing. So it's conceivable that there will be more than one roughly in that range at the same time. After that, what Happens, I mean, I think it's very hard to foresee, but you could certainly imagine there's going to be multiple entities that come through. And in our AI space we've seen when we make a certain kind of advance, other companies are quick to follow and vice versa. When other companies make certain advances, it's kind of a constant leapfrog. So I do think there's an inspiration element that you see and that would probably encourage more and more entities to cross that threshold.

[Alex Canceroitz]

Demis, what do you think?

[Demis Hassabis]

Well, I think we probably do. I think it is important for the field to agree on a definition of AGI, so maybe we should try and help that to coalesce. Assuming there is one. There probably will be some organizations that get there first and I think it's important that those first systems are built reliably and safely. And, and I think after that, if that's the case, we can imagine using them to shard off many systems that have safe architectures built provably underneath them. And then you could have personal AGIs and all sorts of things happening. But it's quite difficult, as Sergey says, it's pretty difficult to predict, sort of see beyond the event horizon to predict what that's going to be like.

[Alex Canceroitz]

Right, so we talked a little bit about the definition of AGI and a lot of people have said AGI must be knowledge, right? The intelligence of the brain. What about the intelligence of the heart? Demis, briefly, does, does AI have to have emotion to be considered AGI? Can it have emotion?

[Demis Hassabis]

I think you will need to understand emotion. I don't know if I think it will be a sort of almost a design decision if we wanted to mimic emotions. I think there's no, I don't see any reason why it couldn't in theory, but it might be different or it might be not necessary or in fact not desirable for them to have the sort of emotional reactions that we do as humans. So I think again it's a bit of an open question as we get closer to this AGI timeframe sort of event, which I think is more on a 5 to 10 year time scale. So I think we have a bit of time, not much time, but some time to research those kinds of questions.

[Alex Canceroitz]

When I think about how the timeframe might be shrunk, I wonder if it's going to be the creation of self improving systems. And last week I almost fell out of my chair reading this headline about something called Alpha Evolve, which is an AI that helps design better algorithms and even improve the way LLMs train. So Demis, are you trying to cause an intelligence explosion?

[Demis Hassabis]

No, not an uncontrolled one. Look, I think it's an interesting first experiment. It's an amazing system, great team that's working on that, where it's interesting now to start pairing other types of techniques, in this case evolutionary programming techniques, with the latest foundation models which are getting increasingly powerful. And I actually want to see in our exploratory work a lot more of these kind of combinatorial systems and sort of pairing different approaches together. And you're right, that is one of the things, a self improvement, someone discovering a kind of self improvement loop would be one way where things might accelerate further than they're even going today. And we've seen it before with our own work with things like Alphazero, learning chess and go and any two player game from scratch within less than 24 hours starting from random with self improving processes. So we know it's possible. But again those are in quite limited game domains which are very well described. So the real world is far messier and far more complex. So it remains to be seen if that type of approach can work in a more general way.

[Alex Canceroitz]

Sergey, we've talked about some very powerful systems and it's a race, it's a race to develop these systems. Is that why you came back to Google?

[Sergey Brin]

I mean, I think as a computer scientist it's a very unique time in history. Like honestly, anybody who's a computer scientist should not be retired right now, should be working on AI. That's what I would just say. I mean there's just never been a greater sort of problem and opportunity, a greater cusp of technology. So I wouldn't say it's because of the race, although we fully intend that Gemini will be the very first AGI clarify that. But to be immersed in this incredible technological revolution, I mean, it's unlike I went through sort of the Web 1.0 thing. It was very exciting. And whatever we had mobile, we had this, we had that. But I think this is scientifically far more exciting and I think ultimately the impact on the world is going to be even greater. And as much as the web and mobile phones have had a lot of impact, I think AI is going to be vastly more transformative.

[Alex Canceroitz]

So what do you do day to day?

[Sergey Brin]

I think I torture people like Demis, who's amazing by the way he tolerated me crashing this fireside. I'm across the street pretty much every day and they're just people who are working on the key Gemini text models on the pre training on the post training, mostly those. I periodically delve into some of the multimodal work VO3 as you've all seen. But I tend to be pretty deep in the technical details and that's a luxury I really enjoy, fortunately, because guys like Demis are, you know, minding the shop and yeah, that's just where you know, my scientific interest is. It's deep in the algorithms and how they can evolve.

[Alex Canceroitz]

Okay, let's talk about the products a little bit, some that were introduced recently. I just want to ask you a broad question about agents, Demis, because when I look at other tech companies building agents, what we see in the demos is usually something that's contextually aware, has a disembodied voice, is often interacted, you often interact with it on a screen. When I see DeepMind and Google demos, oftentimes it's through the camera. It's very visual. There was an announcement about smart glasses today, so talk a little bit about if that's the right read, why Google is so interested in having an assistant or companion that is something that sees the world as you see it.

[Demis Hassabis]

Well, it's for several reasons. Several threads come together. So as we talked earlier, we've always been interested in agents. That's actually the heritage of DeepMind. Actually we started with agent based systems in games. We are trying to build AGI, which is a full general intelligence. Clearly that would have to understand the physical environment, the physical world around you. And two of the massive use cases for that, in my opinion are a truly useful assistant that can come around with you in your daily life, not just stuck on your computer or one device. It needs to, we want it to be useful in your everyday life for everything. And so it needs to come around you and understand your physical context. And then the other big thing is I've always felt for robotics to work, you sort of want what you saw with Astra on a robot. And I've always felt that the bottleneck in robotics isn't so much the hardware, although obviously there's many, many companies and working on fantastic hardware and we partner with a lot of them. But it's actually the software intelligence that I think is always what's held robotics back. But I think we're in a really exciting moment now where finally, with these latest versions, especially 2.5 Gemini and more things that we're going to bring in this kind of VO technology and other things, I think we're going to have really exciting algorithms to make robotics finally work in its sort of realize Its potential, which could be enormous. So I think this. And then in the end, AGI needs to be able to do all of those things. So for us, and that's why you can see we always had this in mind. That's why Gemini was built from the beginning, even the earliest versions, to be multimodal. And that made it harder at the start because it's harder to make things multimodal than just text only. But in the end, I think we're reaping the benefits of those decisions now. And I see many of the Gemini team here in the front row of the correct decisions we made. They were the hardest decisions, but we made the right decisions. And now you can see the fruits of that with all of what you've seen today.

[Alex Canceroitz]

Actually, Sergey, I've been thinking about whether to ask you a Google Glass question far away. What did you learn from Glass that Google might be able to apply today, now that it seems like smart glasses have made a reappearance?

[Sergey Brin]

Wow. Yeah, great question. I learned a lot. I mean, that was. I definitely feel like I made a lot of mistakes with Google Glass. I'll be honest, I am still a big believer in the form factor, so I'm glad that we have it now. And now it's like, looks like normal glasses doesn't have the thing in front. I think there was a technology gap, honestly. Now in the AI world, the things that these glasses can do to help you out without constantly distracting you, that capability is much higher. There's also just. I just didn't know anything about consumer electronic supply chains, really, and how hard it would be to build that and have it be at a reasonable price point, managing all the manufacturing, so forth. This time we have great partners that are helping us build this. So that's another step forward. What else can I say? I do have to say I miss the airship with the wingsuiting skydivers for the demo. Honestly, it would have been even cooler here at Shoreline Amphitheater than it was up in Moscone back in the day. But maybe we'll have to. We should probably polish the product first this time. It's ready and available, and then we'll do a really cool demo. So that's probably a smart move.

[Demis Hassabis]

Yeah. What I will say is, I mean, look, we've got obviously an incredible history of Glass devices and smart devices. We can bring all those learnings to today. And very excited about our new glasses. As you saw, what I was always talking to our team and Shuram and the team about is that I Mean, I don't know if Sergey would agree, but I feel like the Universal Assistant is the killer app for smart glasses and I think that's what's going to make it work. Apart from the fact that the hardware technology has also moved on and improved a lot, I feel like this is the actual killer app, the natural killer app for it.

[Alex Canceroitz]

Okay, briefly on video generation. I sat in the audience in the keynote today and was like fairly blown away by the level of improvement we've seen from these models. And I mean, you had filmmakers talking about it in the presentation. I want to ask you Demis specifically about model quality. If the Internet fills with video that's been made with artificial intelligence, does that then go back into the training and lead to a lower quality model than if you were training just from human generated content?

[Demis Hassabis]

Yeah, look, there's a lot of worries about this so called model collapse. Video is just one thing, but in any modality, text as well. There's a few things to say about that. First of all, we're very rigorous with our data quality management and curation. We also, at least for all of our generative models, we attach synth ID to them. So there's this invisible AI actually made watermark that is pretty, very robust, has held up now for a year, 18 months since we released it. And all of our images and videos are embedded with this watermark so we can detect, and we're releasing tools to allow anyone to detect these watermarks and know that that was an AI generated image or video. And of course that's important to combat deep fakes and misinformation. But it's also of course you could use that to filter out if you wanted to whatever was in your training data. So I don't actually see that as a big problem. Eventually we may have video models that are so good you could put them back into the loop as a source of additional data, synthetic data it's called. And there you just gotta be very careful that you're actually creating from the same distribution that you're gonna model. You're not distorting that distribution somehow the quality's high enough. We have some experience of this in a completely different with things like AlphaFold where there wasn't actually enough real experimental data to build the final alphafold. So we had to build an earlier version that then predicted about a million protein structures. And then we selected it had a confidence level on that. We selected the top three, 400,000 and put them back in the training data. So there's lots of. It's very cutting edge research to like mix synthetic data with real data. So there are also ways of doing that. But on the terms of the video sort of generated stuff, you can just exclude it if you want to. At least with our own work and hopefully other gen media companies follow suit and put robust watermarks in also obviously first and foremost to combat deep fakes and misinformation.

[Alex Canceroitz]

Okay, we have four minutes. I got four questions left. We now move to the miscellaneous part of my questions. Let's see how many we can get through and as fast as we can get through them. Let's go to Sergey with this one. What does the web look like in 10 years?

[Sergey Brin]

What does the web look like in 10 years? I mean, one minute, boy. I think 10 years because of the rate of progress in AI is so far beyond anything we can see, not just the web. I don't think we really know what the world looks like in 10 years.

[Alex Canceroitz]

Okay, Demis.

[Demis Hassabis]

Well, I think that's a good answer. I do think the web, I think in nearer term the web is going to change quite a lot. If you think about an agent first web, does it really need to. It doesn't necessarily need to see renders and things like we do as humans using the web. So I think things will be pretty different in a few years.

[Alex Canceroitz]

Okay, this is kind of an under over question. AGI before 2030 or after 2030?

[Sergey Brin]

2030. Boy, you really kind of put it on that fine line. I'm going to say before.

[Alex Canceroitz]

Before.

[Sergey Brin]

Yeah.

[Alex Canceroitz]

Demis.

[Demis Hassabis]

I'm just after.

[Alex Canceroitz]

Just after. Okay.

[Sergey Brin]

No pressure.

[Demis Hassabis]

Demis. Exactly. I have to go back and get working harder is that I can ask for it.

[Sergey Brin]

He needs to deliver it.

[Demis Hassabis]

Exactly.

[Sergey Brin]

Bob Sandbagger.

[Demis Hassabis]

We need that.

[Alex Canceroitz]

I'll come to the review. All right. So would you hire someone that used AI in their interview? Demis?

[Demis Hassabis]

Oh, in their interview. Depends how they used it. I think using today's models tools, probably not, but I think that would be what? Depends how they would use it, actually. I think it's probably the answer.

[Alex Canceroitz]

Sergey.

[Sergey Brin]

I mean, I never interviewed at all, so I don't know. I feel it would be hypocritical for me to judge people exactly how they interview.

[Demis Hassabis]

Yeah, I haven't either actually. So snap on that. I've never done a job interview.

[Alex Canceroitz]

Okay, so Demis, I've been reading your tweets. You put a very interesting tweet up where there was a prompt that created some sort of natural scene. Oh yeah, Here was the tweet. Nature to simulation at the press of a button does make you wonder with a couple of emojis. And people ran with that and wrote some headlines saying demis thinks we're in a simulation. Are we in a simulation?

[Demis Hassabis]

Not in the way that Nick Bostrom and people talk about. I do think though this. So I don't think this is some kind of game, even though I wrote a lot of games. I do think that ultimately underlying physics is information theory. So I do think we're in a computational universe, but it's not just a straightforward simulation. I can't answer you in one minute. But, but I think. I think the fact that these systems are able to model real structures in nature is quite interesting and telling. And I've been thinking a lot about our work we've done with AlphaGo and AlphaFold and these types of systems. I've spoken a little about it. Maybe at some point I'll write up a scientific paper about what I think that really means in terms of what's actually going on here in reality.

[Alex Canceroitz]

Sergey, you want to make a headline?

[Sergey Brin]

Well, I think that argument applies recursively. Right. If we're in a simulation, then by the same argument, whatever beings are making the simulation are themselves in a simulation for roughly the same reasons, and so on and so forth. So I think you're going to have to either accept that we're in an infinite stack of simulations or that there's got to be some stopping criteria.

[Alex Canceroitz]

And what's your best guess?

[Sergey Brin]

I think that we're taking a very anthropocentric view, like when we say simulation in the sense that some kind of conscious being is running a simulation that we are then in, and that they have some kind of semblance of desire and consciousness that's similar to us. I think that's where it kind of breaks down for me. So I just don't think that we're really equipped to reason about sort of one level up in the hierarchy.

[Alex Canceroitz]

Okay. Well, Demis, Sergey, thank you so much. This has been such a fascinating conversation. Thank you. Thank you all. Alex, thank you.

[Sergey Brin]

Sergei, pleasure.

[Alex Canceroitz]

Sam.