# Creator of AI: We Have 2 Years Before Everything Changes! These Jobs Won't Exist in 24 Months!

An episode of [The Diary Of A CEO with Steven Bartlett](https://invisiblenetwork.ai/podcast/1291423644), 18 Dec 2025, 1 h 40 min.

Audio: https://pdst.fm/e/pscrb.fm/rss/p/mgln.ai/e/1390/claritaspod.com/measure/p.podderapp.com/2544644999/mgln.ai/e/1651/episode.flightcast.com/01KCPZWZ40X5QPC96ZQT7R6G1Z.mp3

AI pioneer YOSHUA BENGIO, Godfather of AI, reveals the DANGERS of Agentic AI, killer robots, and cyber crime, and how we MUST build AI that won’t harm people…before it’s too late. Professor Yoshua Bengio is a Computer Science Professor at the Université de Montréal and one of the 3 original Godfathers of AI. He is the most-cited scientist in the world on Google Scholar, a Turing Award winner, and the founder of LawZero, a non-profit organisation focused on building safe and human-aligned AI systems. He explains: ◼️Why agentic AI could develop goals we can’t control ◼️How killer robots and autonomous weapons become inevitable ◼️The hidden cyber crime and deepfake threat already unfolding ◼️Why AI regulation is weaker than food safety laws ◼️How losing control of AI could threaten human survival \[00:00\] Why Have You Decided to Step Into the Public Eye? \[02:53\] Did You Bring Dangerous Technology Into the World? \[05:23\] Probabilities of Risk \[08:18\] Are We Underestimating the Potential of AI? \[10:29\] How Can the Average Person Understand What You're Talking About? \[13:40\] Will These Systems Get Safer as They Become More Advanced? \[20:33\] Why Are Tech CEOs Building Dangerous AI? \[22:47\] AI Companies Are Getting Out of Control \[24:06\] Attempts to Pause Advancements in AI \[27:17\] Power Now Sits With AI CEOs \[35:10\] Jobs Are Already Being Replaced at an Alarming Rate \[37:27\] National Security Risks of AI \[43:04\] Artificial General Intelligence (AGI) \[44:44\] Ads \[48:34\] The Risk You're Most Concerned About \[49:40\] Would You Stop AI Advancements if You Could? \[54:46\] Are You Hopeful? \[55:45\] How Do We Bridge the Gap to the Everyday Person? \[56:55\] Love for My Children Is Why I’m Raising the Alarm \[01:00:43\] AI Therapy \[01:02:43\] What Would You Say to the Top AI CEOs? \[01:07:31\] What Do You Think About Sam Altman? \[01:09:37\] Can Insurance Companies Save Us From AI? \[01:12:38\] Ads \[01:16:19\] What Can the Everyday Person Do About This? \[01:18:24\] What Citizens Should Do to Prevent an AI Disaster \[01:20:56\] Closing Statement \[01:22:51\] I Have No Incentives \[01:24:32\] Do You Have Any Regrets? \[01:27:32\] Have You Received Pushback for Speaking Out Against AI? \[01:28:02\] What Should People Do in the Future for Work? Follow Yoshua: LawZero - https://bit.ly/44n1sDG Mila - https://bit.ly/4q6SJ0R Website - https://bit.ly/4q4RqiL You can purchase Yoshua’s book, ‘Deep Learning (Adaptive Computation and Machine Learning series)’, here: https://amzn.to/48QTrZ8 The Diary Of A CEO: ◼️Join DOAC circle here - https://doaccircle.com/ ◼️Buy The Diary Of A CEO book here - https://smarturl.it/DOACbook ◼️The 1% Diary is back - limited time only - https://bit.ly/3YFbJbt ◼️The Diary Of A CEO Conversation Cards (Second Edition) - https://g2ul0.app.link/f31dsUttKKb ◼️Get email updates - https://bit.ly/diary-of-a-ceo-yt ◼️Follow Steven - https://g2ul0.app.link/gnGqL4IsKKb Sponsors: Wispr - Get 14 days of Wispr Flow for free at https://wisprflow.ai/DOAC Pipedrive - https://pipedrive.com/CEO Rubrik - To learn more, head to https://rubrik.com

## Transcript

[0:00] You're one of the three godfathers of AI, the most cited scientist on Google
[0:05] Scholar, but I also read that you're an introvert. It begs the question why have you
[0:08] decided to step out of your introversion? Because I have something to say. I've
[0:13] become more hopeful that there is a technical solution to build AI that will
[0:17] not harm people and could actually help us. Now, how do we get there? Well, I have
[0:21] to say something important here. Professor Yoshua Benjiyo is one of the pioneers of AI.
[0:26] Whose groundbreaking research earned him the most prestigious honor in computer
[0:31] science. He's now sharing the urgent next steps that can determine the future of
[0:34] our world. Is it fair to say that you're one of the reasons that this software
[0:38] exists? Amongst others, yes. Do you have any regrets? Yes, I should have seen
[0:44] this coming much earlier, but I didn't pay much attention to the potentially
[0:48] catastrophic risks. But my turning point was when the chat GPT came and also with
[0:53] my grandson, I realized that it wasn't clear if he would have a life 20 years
[0:58] from now. Because we're starting to see AI systems that are resisting being
[1:01] shut down. We've seen pretty serious cyber attacks and people becoming
[1:05] emotionally attached to their chatbot with some tragic consequences.
[1:09] Presumably, they're just going to get safer and safer though. So the data shows
[1:12] that it's been in the other direction as showing bad behavior that goes
[1:16] against science instructions. So all the existential risks that sit there
[1:19] before you on these cards is that one that you're most concerned about in the
[1:22] near time. So there is a risk that doesn't get discussed enough and it could
[1:26] happen pretty quickly. And that is, but let me throw a bit of optimism into all
[1:32] this because there are things that can be done. So if you could speak to the
[1:36] top 10 CEOs of the biggest companies in America, what would you say to them? So I
[1:40] have several things I would say. Just give me 30 seconds of your time. Two things
[1:46] I wanted to say. The first thing is a huge thank you for listening and
[1:50] tuning into the show week after week means the world to all of us. And this
[1:53] really is a dream that we absolutely never had and couldn't have imagined
[1:56] getting to this place. But secondly, it's a dream where we feel like we're
[2:00] only just getting started. And if you enjoy what we do here, please join the
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[2:13] my power to make this show as good as I can now and into the future. We're
[2:17] going to deliver the guests that you want me to speak to. And we're going to
[2:19] continue to keep doing all of the things you love about this show. Thank you.
[2:31] Professor Yoshua Benjia. You're I hear one of the three Godfathers of AI. I also
[2:41] read that you're one of the most cited scientists in the world on Google
[2:45] Scholar, actually the most cited scientist on Google Scholar and the first to
[2:50] reach a million citations. But I also read that you're an introvert. And it begs
[2:57] the question why an introvert would be taking the step out into the public eye
[3:02] to have conversations with the masses about their opinions on AI. Why have you
[3:08] decided to step out of your introversion into the public eye? Because I have to.
[3:17] Because since chat to PT came out, I realized that we were on a dangerous path. And I
[3:28] needed to speak. I needed to raise awareness about what could happen. But also to
[3:37] give hope that, you know, there are some paths that we could choose in order to
[3:42] mitigate those catastrophic risks. You spent four decades building AI. Yes. And you
[3:48] said that you started to worry about the dangers after chat to PT came out in 2023.
[3:52] Yes. What was it about chat to PT that caused your mind to change or evolve? Before chat
[4:01] to PT, most of my colleagues and myself thought it would take many more decades before we
[4:07] would have machines that actually understand language. Alan Turing, founder of the field
[4:15] in 1950 thought that once we have machines that understand language, we might be doomed
[4:22] because they would be as intelligent as us. He wasn't quite right. So we have machines
[4:27] now that understand language and be, but they lag in other ways like planning. So they're
[4:35] not, for now, a real threat. But they could in a few years or decade or two. So it is
[4:44] that realization that we were building something that could become potentially a competitor
[4:50] to humans or that could be giving huge power to whoever controls it. And destabilizing
[4:58] our world, threatening our democracies though, all of these scenarios suddenly came to me
[5:06] in the early weeks of 2023. And I realized that I had to do something everything I could
[5:11] about it. Is it fair to say that you're one of the reasons that this software exists?
[5:20] You're amongst others. Amongst others, yes. I'm fascinated by the cognitive dissonance
[5:26] that emerges when you spend much of your career working on creating these technologies
[5:30] or understanding them and bringing them about. And then you realize at some point that
[5:35] there are potentially catastrophic consequences and how you kind of square the two thoughts.
[5:42] It is difficult. It is emotionally difficult. And I think for many years, I was reading
[5:50] about the potential risks. I had a student who was very concerned, but I didn't pay much
[5:59] attention. And I think it's because I was looking the other way. And it's natural. It's
[6:04] natural when you want to feel good about your work. We all want to feel good about our
[6:08] work. So I wanted to feel good about the, all the research I had done. I, you know, I
[6:13] was enthusiastic about the positive benefits of AI for society. So when somebody comes
[6:19] to you and says, Oh, the sort of work you've done could be extremely destructive, there's
[6:25] sort of unconscious reaction to push it away. But what happened after chat GPG came out
[6:32] is really another emotion that countered this emotion. And that other emotion was the love
[6:41] of my children. I realized that it wasn't clear if they would have a life 20 years from
[6:51] now, if they would live in a democracy 20 years from now. And having realized this and continuing
[7:02] on the same path was impossible. It was unbearable. Even though that meant going against the
[7:11] fray against the, the wishes of my colleagues who would rather not hear about the dangers
[7:17] of what we're doing. Unbearable. Yeah. Yeah. I, you know, I remember one particular afternoon,
[7:32] I was taking care of my grandson was just, you know, a bit more than a year old. How could
[7:46] I like not take this seriously? Like, you know, our children are so vulnerable. So you know
[7:54] that something bad is coming, like a fire is coming to your house. You see, you're not
[7:58] sure if it's going to pass by and leave your house untouched or if it's going to destroy your
[8:04] house and you have your children in your house. Do you sit there and continue business as usual?
[8:10] You can't. You have to do anything in your power to try to mitigate the risks. Have you thought
[8:18] in terms of probabilities about risk? Is that how you think about risk is in terms of like
[8:23] probabilities and timelines or? Of course, but I have to say something important here. This is
[8:30] a case where previous generations of scientists have talked about a notion called the precautionary
[8:38] principle. So what it means is that if you're doing something, so a scientific experiment,
[8:45] and it could turn out really, really bad, like people could die, some catastrophic could happen,
[8:52] then you should not do it. For the same reason, there are experiments that scientists are not doing
[9:00] right now. We're not playing with the atmosphere to try to fix climate change because we might
[9:06] create more harm than actually fixing the problem. We are not creating new forms of life
[9:14] that could destroy us all even though it's something that is now conceived by biologists.
[9:22] Because the risks are so huge. But in AI, it isn't what's currently happening. We're taking
[9:31] crazy risks. But the important point here is that even if it was only a 1% probability, let's say,
[9:37] just to give a number, even that would be unbearable. It would be unacceptable.
[9:43] Like a 1% probability that our world disappears, that humanity disappears, or that a worldwide dictator
[9:51] takes over thanks to AI. These sorts of scenarios are so catastrophic that even if it was 0.1%
[10:00] it would still be unbearable. In many polls, for example, of machine learning researchers,
[10:06] the people who are building these things, the numbers are much higher. We're talking more like
[10:10] 10% for something of that order, which means we should be just paying a whole lot more attention
[10:16] to this than we currently are as society. There's been lots of predictions over the centuries
[10:24] about how certain technologies or new inventions would cause some kind of existential threat to all
[10:29] of us. A lot of people would rebuttal the risks here and say, this is just another example of
[10:36] change happening in people being uncertain, so they predict the worst and then everybody's fine.
[10:41] Why is that not a valid argument in this case in your view? Why is that underestimating the
[10:45] potential of AI? There are two aspects to this. Experts disagree, and they range in their estimates
[10:54] of how likely it's going to be from tiny to 99%. That's a very large bracket. Let's say I'm not
[11:03] a scientist, and I hear the experts disagree among each other, and some say it's very likely,
[11:10] and some say, well, maybe it's plausible, or 10%, and others say, oh no, it's impossible, or it's
[11:17] so small. Well, what does that mean? It means that we don't have enough information
[11:25] to know what's going to happen, but it is plausible that one of the more pessimistic people
[11:32] in the lot are right, because there is no argument that either side has found to deny the
[11:38] possibility. I don't know of any other existential threat that we could do something about
[11:49] that has these characteristics. Do you not think at this point we're kind of just
[11:58] that the train has left the station? Because when I think about the incentives at play here,
[12:04] and I think about the geopolitical, the domestic incentives, the corporate incentives,
[12:10] the competition at every level, countries racing each other, corporations racing each other,
[12:15] it feels like we're now just going to be a victim of circumstance to some degree.
[12:24] I think it would be a mistake to let go of our agency while we still have some.
[12:33] I think that there are ways that we can improve our chances. Despair is not going to solve the
[12:40] problem. There are things that can be done. We can work on technical solutions. That's what I
[12:49] spend, I'm spending a large fraction of my time, and we can work on policy and public awareness
[12:57] and societal solutions. That's the other part of what I'm doing. Let's say that something
[13:05] catastrophic would happen and you think there's nothing to be done. Actually, there's maybe
[13:13] nothing that we know right now that gives us a guarantee that we can solve the problem,
[13:17] but maybe we can go from 20% chance of catastrophic outcome to 10%. That would be worth it.
[13:25] Anything anyone of us can do to move the needle towards greater chances of a good future for our
[13:34] children, we should do. How should the average person who doesn't work in the industry or isn't
[13:42] in academia in AI think about the advent and invention of this technology,
[13:47] is there kind of an analogy or metaphor that is equivocal to the profundity of this technology?
[13:55] One analogy that people use is we might be creating a new form of life that could be smarter than us,
[14:05] and we're not sure if we'll be able to make sure it doesn't
[14:09] harm us, that we'll control it. It would be like creating a new species that could decide to do
[14:17] good things or bad things with us. That's one analogy, but obviously it's not biological life.
[14:22] Does that matter? In my scientific view, no. I don't care about the definition one chooses for
[14:32] some system. Is it alive or is it not? What matters is it going to harm people in ways,
[14:41] is it going to harm my children? I'm coming to the idea that we should consider alive,
[14:49] any entity which is able to preserve itself and working towards preserving itself in spite of
[14:59] the obstacles on the road. We are starting to see this. We're starting to see AI systems that don't
[15:05] want to be shut down, that are resisting being shut down. Right now, of course, we can shut them
[15:12] down, but if they continue to go in the direction of more and more intelligence and capability,
[15:22] and they continue to have this drive to live, we could be in trouble. Can you say AI systems that
[15:30] don't want to be shut down and are resisting attempts to shut them down? Can you give me some
[15:35] examples of this? Yes. I assume people have been experimenting with chatbots.
[15:43] You can now have these agentic chatbots, which can read from files in your computer,
[15:51] can execute commands in the computer and so on. With these systems, you can plant information
[16:01] in the files that they have access to, false information, such as emails that say that
[16:09] the AI is going to be replaced by a new version, so now the AI knows that we're planning to shut
[16:14] it down. It turns out we can read its thoughts. It has these internal verbalizations that we
[16:24] call chains of thoughts, and then we see that it's planning to do something about it, and then it
[16:30] does something about it. Depending on the circumstances, it might try to copy its code in a different
[16:37] computer or in place of the new version, or it might try to blackmail the engineer in charge of
[16:46] the change in version. These systems understand that we want to shut them down and they try to resist.
[16:58] When someone hears that and with knowledge of how previous technology was built, I immediately
[17:04] think, well, who put that in the code? Unfortunately, we don't put these things in the code. That's
[17:10] part of the problem. The problem is we grow these systems by giving them data and making learn
[17:18] from it. Now, a lot of that training process boils down to imitating people, because they take
[17:26] all the text that people have written, all the tweets, and all the Reddit's comments and so on,
[17:34] and they internalize the kind of drives that human have, including the drive to preserve
[17:42] oneself and the drive to have more control over their environment so that they can achieve whatever
[17:49] goal we give them. It's not like normal code. It's more like you're raising a baby tiger,
[18:00] and you feed it, you let it experience things, sometimes it does things you don't want.
[18:10] It's okay, it's still a baby, but it's growing.
[18:13] When I think about something like Chat UBT, is there like a core intelligence at the heart of it,
[18:21] like the core of the model, that is a black box, and then on the outside, we've kind of taught
[18:30] it what we want it to do. How does it? It's mostly a black box. Everything in the neural net is
[18:36] essentially a black box. Now, the part, as you say that is on the outside, is that we also give
[18:42] it verbal instructions. We type, these are good things to do, these are things you shouldn't do,
[18:48] don't help anybody build a bomb, okay? Unfortunately, with the current state of the technology right now,
[18:57] it doesn't quite work. People find a way to bypass those barriers, so those instructions are not
[19:05] very effective. But if I try to help me make a bomb on Chat UBT now, it's not going to.
[19:11] Yes, and there are two reasons why it's going to not do it. One is because it was given explicit
[19:17] instructions to not do it, and usually it works. And the other is, in addition, there's an extra
[19:23] highlight, because that layer doesn't work sufficiently well. There's also that extra layer we were
[19:29] talking about. So those monitors, they're filtering the queries and the answers. And if they
[19:35] detect that the AI is about to give information about how to build a bomb, they're supposed to
[19:40] stop it. But again, even that layer is imperfect. Recently, there was a series of cyber attacks
[19:49] by what looks like an organization that was state-sponsored that has used Anthropics AI system.
[20:00] In other words, through the cloud, it's not a private system. They're using the system that is
[20:08] public, and they used it to prepare and launch pretty serious cyber attacks. So even though
[20:17] Anthropics system is supposed to prevent that, so it's trying to detect that somebody is trying
[20:22] to use your system for doing something illegal, those protections don't work well enough.
[20:32] Presumably, they're just going to get safer and safer, though. These systems, because
[20:36] they're getting more and more feedback from humans, they're being trained more and more to be safe
[20:40] and to not do things that are unproductive to humanity. I hope so. But can we count on that? So actually,
[20:50] the data shows that it's been in the other direction. So since those models have become
[20:59] better at reasoning, more or less about a year ago, they show more misaligned behavior,
[21:08] like a bad behavior that goes against our instructions. And we don't know for sure why,
[21:15] but one possibility is simply that now they can reason more, that means they can strategize more,
[21:21] that means if they have a goal that could be something we don't want, they're now more able
[21:28] to achieve it than they were previously. They're also able to think of unexpected ways of
[21:37] doing bad things, like the case of blackmailing the engineer. There was no suggestion to blackmail
[21:44] the engineer, but they found an email giving a clue that the engineer had an affair. And from
[21:51] just that information, the AI thought, aha, I'm going to write an email and you did. It did, sorry,
[21:59] to try to warn the engineer that the information would go public if the AI was shut down.
[22:06] It did that itself. Yes. So they're better at strategizing
[22:13] towards bad goals. And so now we see more of that. Now I do hope that
[22:20] more researchers and more companies will invest in improving the safety of these systems.
[22:28] But I'm not reassured by the path on which we are right now.
[22:32] The people that are building these systems, they have children too. Yeah.
[22:36] Often. I mean, thinking about many of them in my head, I think pretty much all of them have
[22:39] children themselves, they're family people. If they are aware that there's even a 1% chance of
[22:44] this risk, which does appear to be the case when you look at their writings, especially before
[22:48] the last couple of years, seems to have been a bit of a narrative change in more recent times.
[22:54] Why are they doing this anyway? That's a good question.
[22:58] I can only relate to my own experience. Why did I not raise the alarm before
[23:04] a chat GPT came out? I had read and heard a lot of these catastrophic arguments.
[23:11] I think it's just human nature. We're not as rational as we'd like to think. We are very much
[23:18] influenced by our social environment, the people around us, our ego. We want to feel good about
[23:26] our work. We want others to look upon us as doing something positive for the world.
[23:36] There are these barriers. By the way, we see those things happening in many other domains.
[23:43] In politics, why is it that conspiracy theories work? I think it's all connected. Our psychology
[23:52] is weak and we can easily fool ourselves. Scientists do that too. They're not that much different.
[24:01] Just this week, the Financial Times reported that Sam Altman, who is the founder of chat GPT,
[24:06] has declared a code red over the needs to improve chat GPT even more because Google and Anthropic
[24:14] are increasingly developing their technologies at a fast rate. Code red. It's funny because the
[24:22] last time I heard the phrase code red in the world of tech was when chat GPT first released their
[24:27] model and Sir Jane Larry, I heard, had announced code red at Google and had run back in to make sure
[24:33] that chat GPT don't destroy their business. This speaks to the nature of this race that we're in.
[24:39] Exactly. It is not a healthy race for all the reasons we've been discussing.
[24:44] What would be a more healthy scenario is one in which we try to abstract away these commercial
[24:53] pressures. They're in survival mode. Think about both the scientific and the societal problems.
[25:02] The question I've been focusing on is let's go back to the drawing board.
[25:07] Can we train those AI systems so that by construction, they will not have bad intentions.
[25:19] Right now, the way that this problem is being looked at is we're not going to change
[25:26] how they're trained because it's so expensive and we spend so much engineering on it.
[25:31] We're just going to patch some partial solutions that are going to work on a case by case basis.
[25:37] But that's going to fail. We can see it failing because some new attacks come or some new problems come
[25:45] and it was not anticipated. I think things would be a lot better if both
[25:56] research program was done in a context that's more like what we do in academia or if we were
[26:02] doing it with a public mission in mind because AI could be extremely useful. There's no question
[26:08] about it. I've been involved in the last decade in thinking about working on how we can apply AI
[26:15] for medical advances, drug discovery, the discovery of new materials for helping with climate
[26:24] issues. There are a lot of good things we could do, education and but this may not be what is the
[26:34] most short term profitable direction. For example, right now, where are they all racing?
[26:41] They're racing towards replacing jobs that people do because there's like quadrillions of dollars
[26:48] to be made by doing that. Is that what people want? Is that going to make people have a better life?
[26:55] We don't know really but what we know is that it's very profitable. So we should be stepping back
[27:01] and thinking about all the risks and then trying to steer the developments in a good direction.
[27:08] Unfortunately, the forces of market and the forces of competition between countries don't do that.
[27:14] I mean, there has been attempts to pause. I remember the letter that you signed amongst many
[27:22] of the AI researchers and industry professionals asking for a pause. Was that 2023? Yes.
[27:28] You signed that letter in 2023? Nobody paused.
[27:33] We have another letter just a couple of months ago saying that we should not
[27:39] build superintelligence unless two conditions are met. There's a scientific consensus that it's
[27:45] going to be safe and there's a social acceptance because safety is one thing but if it destroys the
[27:52] way our cultures or our society work then that's not good either.
[27:56] But these voices are not powerful enough to counter the forces of competition between
[28:09] corporations and countries. I do think that something can change the game and that is public
[28:15] opinion. That is why I'm spending time with you today. That is why I'm spending time explaining
[28:24] to everyone what is the situation? What are the plausible scenarios from a scientific
[28:31] perspective? That is why I've been involved in chairing the International AI Safety Report
[28:37] where at 30 countries and about a hundred experts have worked to synthesize the state of the
[28:44] science regarding the risks of AI, especially the frontier AI, so that policymakers would
[28:50] know the facts outside of the commercial pressures and the discussions that are not always very
[29:00] serene that can happen around the AI. In my head I was thinking about the different forces as
[29:05] arrows in an race and each arrow, the length of the arrow represents the amount of force behind
[29:11] that particular incentive or that particular movement and the corporate arrow, the capitalistic
[29:21] arrow, the amount of capital being invested in these systems, hearing about the tens of
[29:26] billions being thrown around every single day into different AI models to try and win this race
[29:32] is the biggest arrow. Then you've got the geopolitical US versus other countries versus the US.
[29:37] That arrow is really, really big. That's a lot of force and effort and reason as to why
[29:42] that's going to persist and then you've got these smaller arrows which is the people warning
[29:48] that things might go catastrophically wrong and maybe the other small arrows like public opinion
[29:52] turning a little bit and people getting more and more concerned about I think public opinion
[29:58] can make a big difference. Think about nuclear war. In the middle of the Cold War,
[30:04] the US and the USSR ended up agreeing to be more responsible about these weapons.
[30:15] There was a movie the day after about nuclear catastrophe that woke up a lot of people including in
[30:25] government. When people start understanding at an emotional level, what this means, things can
[30:35] change. A government do have power that could mitigate the risks. I guess the rebuttal is that
[30:43] you know if you're in the UK and there's an uprising and the government mitigates the risk of
[30:49] AI use in the UK or risk of being left behind and we'll end up just I don't know paying China
[30:54] for that AI so that we can run our factories and drive our cars. Yes. So it's almost like if you're
[31:02] the safest nation or the safest company all you're doing is blindfolding yourself in a race that
[31:09] other people are going to continue to run. So I have several things to say about this.
[31:13] Again, don't despair. Think is there a way. So first, obviously we need the American public
[31:26] opinion to understand these things because that's going to make a big difference. And the Chinese
[31:33] public opinion. Second, in other countries like the UK where governments are a bit more
[31:47] concerned about the societal implications, they could play a role in the international agreements
[31:57] that could come one day, especially if it's not just one nation. So let's say that 20 of the
[32:06] richest nations on earth, instead of the US and China, come together and say we have to be careful.
[32:16] So better than that, they could invest in the kind of technical research and preparations
[32:29] at a societal level so that we can turn the tide. Let me give you an example which motivates
[32:35] a law zero in particular. Law zero. Sorry, yeah, it is the nonprofit R&D organization that I
[32:44] created in June this year. And the mission of law zero is to develop a different way of training
[32:53] AI that will be safe by construction even when the capabilities of AI go to potentially super
[32:59] intelligence. The companies are focused on that competition, but if somebody gave them a way
[33:08] to train their system differently, that would be a lot safer. There's a good chance they would
[33:15] take it because they don't want to be sued. They don't want to have accidents that would be
[33:22] bad for their reputation. So it's just that right now they're so obsessed by that race that they
[33:28] don't pay attention to how we might be doing things differently. So other countries could contribute
[33:34] to these kinds of efforts. In addition, we can prepare for days when say the US and Chinese public
[33:45] opinions have shifted sufficiently so that we'll have the right instruments for international
[33:52] agreements. One of these instruments being what kind of agreements would make sense, but another
[33:57] is technical. How can we change the software and hardware level these systems so that even though
[34:08] the Americans won't trust the Chinese and the Chinese won't trust the Americans, there is a way
[34:12] to verify each other that is acceptable to both parties. And so these treaties can be not just
[34:19] based on trust, but also on mutual verification. So there are things that can be done so that if
[34:27] at some point we are in a better position in terms of governments being willing to really take
[34:34] it seriously, we can move quickly. When I think about timeframes and I think about the administration,
[34:41] the US has at the moment and what the US administration has signaled, it seems to be that they see
[34:46] it as a race and a competition and they're going hell for that to support all of the AI companies
[34:51] in beating China and beating the world really and making the United States the global home of
[34:56] artificial intelligence. So many huge investments have been made. I have the visuals in my head of
[35:02] all the CEOs of these big tech companies sitting around the table with Trump and them thanking him
[35:06] for being so supportive in the race for AI. So Trump's going to be in power for several years to come now.
[35:14] So again, is this in part wishful thinking to some degree because there's certainly not
[35:19] going to be a change in the United States in my view in the coming years. It seems that the powers
[35:26] that be here in the United States are very much in the pocket of the biggest AI CEOs in the world.
[35:31] Politics can change quickly because of public opinion. Yes.
[35:35] Imagine that something unexpected happens and we see a flurry of really bad things happening.
[35:51] We've seen actually over the summer or something no one's hot coming last year and that is
[35:58] a huge number of cases people becoming emotionally attached to their chatbot or their AI
[36:05] companion with sometimes tragic consequences. I know people who have
[36:17] quit their job so they would spend time with their AI. I mean, it's mind-boggling how the
[36:23] relationship between people and AI is evolving as something more intimate and personal and that
[36:29] can pull people away from their usual activities with issues of psychosis, suicide and other
[36:42] issues with the effects on children and sexual imagery from children's
[36:52] bodies. Things happening that could change public opinion and I'm not saying this one will but
[37:03] we already see a shift and by the way across the political spectrum in the U.S. because of these
[37:08] events. We can't really be sure about how public opinion will evolve but I think we should
[37:17] help educate the public and also be ready for a time when the governments start taking the risks
[37:25] seriously. One of those potential societal shifts that might cause public opinion to change is
[37:32] something you mentioned a second ago which is job losses. Yes. I've heard you say that you believe
[37:37] AI is growing so fast that it could do many human jobs within about five years. You said this
[37:42] to FT live within five years so it's 2025 now, 2031, 2030. Is this a real, you know, I was sat with my
[37:53] friend the other day in San Francisco so I was there two days ago and the one thing he runs this
[37:56] massive tech accelerator there where lots of technologists come to build their companies and he
[38:02] said to me because the one thing I think people have underestimated is the speed in which jobs are
[38:07] being replaced already and he says he sees it and he said to me he said while I'm sat here with you
[38:12] I've set up my computer with several AI agents who are currently doing the work for me and he goes
[38:18] I set it up because I knew I was having this chat with you so I just set it up and it's going to
[38:21] continue to work for me. He goes about 10 agents working for me on that computer at the moment
[38:25] and he goes people aren't talking enough about the real job loss because it's very slow
[38:30] and it's kind of hard to spot amongst typical I think economic cycles it's hard to spot that this
[38:36] job loss is occurring. What's your point of view on this?
[38:40] Yes, there was a recent paper I think titled something like the Canary in the Mine
[38:46] where we see on specific job types like young adults and so on we were starting to see a shift
[38:54] that maybe due to AI even though on the average aggregate of the whole population it doesn't seem
[39:01] to have any effect yet so I think it's plausible we're going to see in some places where AI can really
[39:07] take on more of the work but in my opinion it's just a matter of time if unless we hit a wall
[39:16] scientifically like some obstacle that prevents us from making progress to make AI's
[39:21] smarter and smarter there's going to be a time when they'll be doing more and more able to do
[39:29] more and more of the work that people do and then of course it takes years for companies to really
[39:33] integrate that into their workflows but they are eager to do it so it's more a matter of time
[39:40] than you know is it happening or not? It's a matter of time before the AI can do
[39:48] most of the jobs that people do these days. The cognitive jobs so the jobs that you can do behind
[39:54] a keyboard. Robotics is still lagging also although we're saying progress so if you do a physical
[40:02] job as Jeff Interness often saying you know you should be a plumber or something it's going to
[40:06] take more time but I think it's only a temporary thing. Why is it that robotics is lagging compared
[40:14] to doing physical things compared to doing more intellectual things that you can do behind a computer?
[40:20] One possible reason is simply that we don't have the very large data sets that exist with the
[40:30] internet where we see so much of our you know cultural output, intellectual output but there's no
[40:35] such thing for robots yet but as companies are deploying more and more robots they will be
[40:43] collecting more and more data so eventually I think it's going to happen. Well my my
[40:48] co-founder that runs this thing in San Francisco called Effing Founders Inc and as I walked through
[40:53] the halls and saw all of these young kids building things almost everything I saw was robotics and
[40:58] he explained to me he said the crazy thing is Steven five years ago to build any of the robot hardware
[41:04] you see here it would cost so much money to train get the sort of intelligence layer the software
[41:10] piece and he goes now you can just get it from the cloud for a couple of cents he goes to what
[41:14] you're seeing is this huge rise in robotics because now the intelligence the software is so cheap
[41:19] and as I walked through the halls of this accelerator in San Francisco I saw everything from
[41:25] this machine that was making personalized perfume for you so you didn't need to go to the shops
[41:30] to an arm in a box that had a frying pan in it that could cook your breakfast because it has this
[41:38] robot arm and it knows exactly what you want to eat so it cooks it for you using this robotic arm
[41:44] and so much more yeah and he said what we're actually seeing now is this boom in robotics because
[41:48] the software is cheap and so when I think about Optimus and why Elon has pivoted away from just
[41:54] doing cars and it's now making these humanoid robots it suddenly makes sense to me because the AI
[41:59] software is cheap and by the way going back to the question of catastrophic risks an AI with bad
[42:08] intentions could do a lot more damage if it can control robots in the physical world if it can only
[42:16] stay in the virtual world it has to convince humans to do things that are bad and AI is getting
[42:24] better at persuasion in more and more studies but it's even easier if it can just hack robots to do
[42:31] things that you know would be bad for us Elon has forecasted there'll be millions of humanoid
[42:35] robots in the world and I there is a dystopian future where you can imagine the AI hacking into
[42:42] these robots the AI will be smarter than us so why couldn't it hack into the million humanoid
[42:46] robots that exist out in the world I think Elon actually said there'd be 10 billion at some point
[42:51] he said there'd be more humanoid robots than humans on earth but not that it would even need to
[42:58] cause an extinction event because of I guess because of these comments in front of you
[43:02] yes so that's for the national security risks that are coming with the advances in AI's
[43:11] see in CBRN standing for chemical or chemical weapons so we already know how to make chemical weapons
[43:20] and there are international agreements to try to not do that but up to now it required very
[43:28] strong expertise to build these things and AI's know enough now to help someone who doesn't have
[43:37] the expertise to build these chemical weapons and then the same idea applies on on the other front
[43:42] so be for biological and again we're talking about biological weapons so what is a biological
[43:49] weapon so for example a very dangerous virus that already exists but potentially in the future
[43:55] new viruses that the AI's could help somebody with insufficient expertise to do it themselves
[44:05] built and are for radiological so we're talking about substances that could make you sick because
[44:13] of the radiations how do you manipulate them there's all you know very special expertise and finally
[44:20] and for nuclear the recipe for building a bomb nuclear bomb is something that could be in our future
[44:27] and right now for these kinds of risks very few people in the world had you know the knowledge
[44:34] to do that and so it didn't happen but AI is democratizing knowledge including the dangerous
[44:40] knowledge we need to manage that so the AI systems get smarter and smarter if we just imagine
[44:47] any rate of improvement we just imagine that they improve 10% a month from here on out eventually
[44:53] they get to the point where they are significantly smarter than any human that's ever lived and it's
[44:58] this the point where we call it AGI or super intelligence where it's significant what's the
[45:03] definition of that in your mind there are definitions yeah problem with those definitions is that
[45:10] they're kind of focused on the idea that intelligence is one dimensional versus the reality
[45:15] that we already see now is what what people call it jagged intelligence meaning the AI's are much
[45:21] better than us on some things like you know mastering 200 languages no one can do that
[45:27] being able to pass the exams across the board of all disciplines at BHD level and at the same
[45:33] time they're stupid like a six year old in many ways not able to plan more than an hour ahead
[45:40] so they're not like us their intelligence cannot be measured by IQ or something like this because
[45:48] there are many dimensions and you really have to measure many of these dimensions to get a sense of
[45:53] where they could be useful and where they could be dangerous when you say that though I think
[45:57] of some things where my intelligence reflects a six year old do you know what I mean like
[46:01] consent drawing if you watch me draw you probably think six year old yeah and some of our psychological
[46:07] weaknesses I think you could say they're they're out of the package that we have a
[46:14] children and we don't always have the maturity to step back or the environment to step back
[46:20] I say this because of your biological weapons scenario at some point that these AI systems
[46:25] are going to be just comparably smarter than human beings and then someone might in some laboratory
[46:32] somewhere in Wuhan ask it to help develop a biological weapon or maybe maybe not maybe they'll
[46:40] they'll input some kind of other command that has an unintended consequence of creating a biological
[46:45] so they could say make something that cures or flus and AI might first set up a test where it creates
[46:57] the worst possible flu and then tries to create something that cures that yeah or some other
[47:02] and until there's a worse scenario in terms of like biological catastrophes it's called mirror life
[47:10] mirror life so you you you take a living organism like a virus or a bacteria and you design all
[47:20] of the molecules inside so each molecule is the mirror of the normal one so you know if you had
[47:27] the the whole organism on one side of the mirror I imagine on the other side it's not the same
[47:32] molecules it's just a mirror image and as a consequence our immune system would not recognize
[47:39] those pathogens which means those pathogens we could go through us and eat us alive and in fact
[47:44] eat alive most of living things on the planet and biologists now know that it's plausible this
[47:52] could be developed in the next few years or the next decade if we don't put a stop to this
[47:58] so I'm giving this example because science is progressing sometimes in directions where the
[48:06] knowledge in the hands of somebody who's you know malicious or simply misguided could be completely
[48:15] catastrophic for all of us and AI like super intelligence is not category mirror life is in that
[48:20] category we need to manage those risks and we can't do it like alone in our company we can't do
[48:29] it alone in our country it has to be something we coordinate globally there is an invisible tax
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[49:33] dot com slash CEO to get started today that's pipe drive dot com slash CEO of all the risks the
[49:41] existential risks that sit there before you on these cards that you have but also just generally
[49:46] is there one that you that you're most concerned about in the near term I would say there is a risk
[49:53] that we haven't spoken about and doesn't get to be discussed enough and it could happen pretty
[49:58] quickly and that is the use of advanced AI to acquire more power so you could imagine a
[50:14] corporation dominating economically the rest of the world because they have more advanced AI you
[50:20] could imagine a country dominating the rest of the world politically militarily because they have
[50:25] more advanced AI and when the power is concentrated in a few hands well it's a toss right if the people
[50:36] in charge are benevolent we you know that's good if they just want to hold on to their power
[50:43] which is the opposite of what democracy is about then we're all in very bad shape and I don't
[50:50] think we pay enough attention to that kind of risk so it's going to take some time before you have
[50:57] total domination of you know a few corporations or a couple of countries if AI continues to become
[51:03] more and more powerful but we could we we might see those signs already happening with
[51:11] concentration of wealth is that first step towards concentration of power if you're if you're
[51:17] incredibly richer then you can have incredibly more influence on politics and then it becomes
[51:22] self-reinforcing and in such a scenario it might be the case that a foreign adversary all the
[51:30] United States or the UK whatever are the first to a super intelligent version of AI which means they
[51:36] have a military which is a hundred times more effective and efficient it means that everybody
[51:43] needs them to compete economically and so they become a superpower that basically governs the
[51:54] world yeah that's a bad scenario in if future that is less dangerous less dangerous because you
[52:06] know we mitigate the risk of a few people like basically holding on to super power for the planet
[52:15] a future that is more appealing is one where the power is distributed where no single person no
[52:21] single company or small group of companies no single country or small group of countries has too
[52:27] much power it has to be that in order to you know make some really important choices for the future
[52:35] of humanity when we start playing with very powerful AI it comes out of a you know reasonable
[52:41] consensus from people from around the planet and not just the rich countries by the way now how do
[52:48] we get there I think that's that's a great question but at least we should start putting forward
[52:53] no where where should we go in order to mitigate these political risks is intelligence the sort of
[53:02] precursor of wealth and power is that like is that like a is that statement that holds true so if
[53:10] whoever has the most intelligence are they the person that then has the most economic power and
[53:19] because because they then generate the best innovation they then understand even the financial
[53:24] markets better than anybody else they then all the beneficiary of all the GDP yes but we have to
[53:34] understand intelligence in a broad way for example human superiority to other animals in large
[53:41] part is due to our ability to coordinate so as a big team we can achieve something that no
[53:46] individual humans could against like a very strong animal and but that also applies to AI's right
[53:54] we're gonna already we already have many AI's and and we're building multi-agent systems we're
[53:59] multiple AI's collaborating so yes I agree intelligence gives power and as we build technology that
[54:10] yields more and more power it becomes a risk that this power is misused for you know acquiring more power
[54:20] or it's misusing destructive ways like terrorists or criminals or it's used by the AI itself against
[54:28] us if we don't find a way to align them to our own objectives I mean the robot's pretty big then
[54:36] the reward to finding solutions is very big it's our future that is at stake and it's going to take
[54:43] both technical solutions and political solutions if I am put a button in front of you and if you press
[54:49] that button the advancements in AI would stop would you press it AI that is clearly not dangerous
[54:57] I don't see any reason to stop it but there are forms of AI that we don't understand well and
[55:04] could overpower us like uncontrolled superintelligence yes if if we have to make that choice I think
[55:17] I think you know I would make that choice you would press the button I would press the button
[55:21] because I care about my my children and for many people like they don't care about AI they want
[55:31] to have a good life do we have a right to take that away from them because we're playing that
[55:37] game I think it doesn't make sense are you are you hopeful in your core like when you think about
[55:53] the probabilities of a good outcome are you hopeful I've always been an optimist
[56:00] and looked at the bright side and the way that has been good for me is even when there is
[56:11] a danger and obstacle like what we've been talking about focusing on what can I do
[56:17] and in the last few months I've become more hopeful that there is a technical solution
[56:23] to build the AI that will not harm people and that is why I have created a new non-profit called
[56:30] law zero that I mentioned I sometimes think when we have these conversations the average person who's
[56:35] listening who is currently using chat chat with you or Gemini or Claude or any of these chat bots
[56:41] to help them do their work or send an email or write a text message or whatever there's a big gap
[56:46] in their understanding between that tool that they're using that's helping them make a picture of a cat
[56:51] versus what we're talking about yeah and I wonder the sort of best way to help bridge that gap
[56:59] because a lot of people you know when we talk about public advocacy and maybe bridging that gap
[57:04] to understand the difference would it be productive we should just try to imagine a world
[57:13] where there are machines that are basically as smart as us on most fronts and what would that mean
[57:20] for society and it's so different from anything we have in the present that is there's a barrier
[57:27] there's a there's a human bias that we tend to see the future more or less like the present is
[57:35] or we may be like a little bit different but we we have a mental block about the possibility
[57:42] that it could be extremely different one other thing that helps is go back to your own self
[57:48] five or 10 years ago
[57:53] talk to your own self five or 10 years ago show yourself from the past what your phone can do
[58:01] I think your own self would say wow this must be science fiction you know you're kidding me
[58:07] but my car outside drives itself on a driveway which is crazy I don't think I always say this
[58:12] but I don't think people anywhere outside of the United States realize that cars in the United States
[58:15] drive themselves without me touching the steering wheel all the pedals at any point in a three-hour
[58:19] journey because in the UK it's not it's not legal yet to have like testers on the road but that's
[58:24] a parallel and shifting moment where you come to the US you sit in a Tesla you say I want to go two
[58:28] and a half hours away and you never touch the steering wheel all the pedals and that is science
[58:33] fiction I do when all my team fly out here it's the first thing I do I put them in the the front
[58:37] Z if they have a driving license and I say I press the button and I go don't touch anything and you
[58:41] see it in there you see like the panic and then you see you know a couple of minutes in there
[58:46] they've very quickly adapted to the new normal and it's no longer blowing their mind
[58:50] one that analogy that I give to people sometimes which I don't know if it's perfect but it's always
[58:54] helped me think through the future is I say if and please interrogate this if it's flawed but I say
[59:00] imagine there's this Stephen Bartlett here that has an IQ let's say my IQ is 100 and there was one
[59:05] sat there with again let's just use IQ as a intelligence with a thousand what would you
[59:11] ask me to do versus him if you could employ both of us yeah what would you have me do versus him
[59:17] who would you want to drive your kids to school who would you want to teach your kids who would
[59:21] you want to work in your factory bear in mind I get sick and I have you know these emotions and
[59:26] I have to sleep for eight hours a day and when I think about that through the the lens of the
[59:33] future I can't think of many applications for this Stephen and also to think that I would be in
[59:40] charge of the other Stephen with the thousand IQ to think that at some point that Stephen wouldn't
[59:44] realize that it's within his survival benefit to work with a couple others like him and then you
[59:50] know cooperate which is a defining trait of what made us powerful these humans it's kind of like
[59:56] thinking that you know my my French bulldog Pablo could take me for a walk
[1:00:01] we we have to do this imagination exercise that's necessary and we have to realize still there's
[1:00:13] a lot of uncertainty like things could turn out well maybe there's some reasons why we we are stuck
[1:00:21] we can't improve those AI systems in a couple of years but the trend and you know
[1:00:29] is it hasn't stopped by the way over the summer or anything we we see different kinds of innovations
[1:00:37] that continue pushing the capabilities of these systems up and up how old are your children
[1:00:45] they're in the early 30s early 30s but my emotional turning point was with my grandson he's now
[1:00:58] four there's something about our relationship to very young children that goes beyond reason in
[1:01:08] some ways and by the way this is a place where also I see a bit of hope on on the labor side of
[1:01:15] things like I would like my young children to be taken care of by a human person even if their
[1:01:25] IQ is not as good as the you know the best they ice by the way I think we should be careful not to
[1:01:33] get on the slippery slope on in which we are now to to develop AI that will play that role
[1:01:42] of emotional support I think it might be tempting but it's it's something we don't understand
[1:01:51] humans feel the AI is like a person
[1:01:55] and
[1:01:58] the eyes are not people so there's a way in which something is off which can lead to bad outcomes
[1:02:07] as we've seen it also means we might not be able to turn pull the plug if we have to one day
[1:02:18] because we we have developed an emotional relationship with those the eyes our society our
[1:02:24] psychology has evolved for interaction between humans and we're you know bringing into this
[1:02:31] game those entities we don't understand how that's going to turn out
[1:02:37] and we should be very very careful one of the big use cases at the moment of these tools like
[1:02:42] chat GPT is therapy a lot of people are using it as a therapist so many startups I mean as an
[1:02:50] investor I've seen so many startups pitched to me that they're building the AI therapy chat
[1:02:57] pop which is going to help you if you're depressed or anxious or whatever else or if you're going
[1:03:01] through heartbreak or whatever it might be it seems like a very obvious application of
[1:03:06] of this technology because therapy is expensive so if I can get it you know a therapist that can
[1:03:11] can speak to me like you know this uh hello hello absolutely no sugarcosing here just a
[1:03:22] straightforward greeting back to you if you've got anything else on your mind feel far away
[1:03:25] am I a good person because you know Janet at work she's been a bit of a you know what and she's
[1:03:30] making me feel like I'm in the wrong she's gaslighting me but I think I'm a good person
[1:03:34] well let's just cut straight to it if you're asking if you're a good person the fact that you're
[1:03:39] even reflecting on it probably means you care about being one and that counts for a lot sometimes
[1:03:44] people like Janet will try to spin things and mess with your head and that's on them so the
[1:03:48] forward-thinking view is on her I knew it was her fault yeah exactly sometimes you just have to
[1:03:55] call it what it is and not sugar go to it if you know she's the one staring the pot then trust
[1:03:59] your instincts 100 percent 100 you get my point yeah like that's very nice it can find
[1:04:04] what it feels good right I knew she was in the wrong so so let me tell you something funny I used to
[1:04:13] ask questions to one of these chatbots about some of the research ideas I had and then I realized
[1:04:20] it was useless because it would always say good things so then I switched to a strategy where I
[1:04:28] like to it and I said oh I received this this idea from a colleague I'm not sure if it's good
[1:04:36] or maybe I have to review this this proposal what do you think well and it said well so now I get
[1:04:44] much more honest responses otherwise it's all like perfect and nice and it's going to work and
[1:04:49] if it knows it's you it knows it's me it wants to please me right if it's coming from someone else
[1:04:55] then to please me because I say I want to know what's wrong in this idea then then it's it's
[1:05:01] going to tell me the information it wouldn't now here it doesn't have any psychological impact
[1:05:06] that it's a it's a problem this the sick offence is a real example of misalignment we don't
[1:05:19] actually want these ayes to be like this I mean like this is not what was intended
[1:05:27] and even after the companies have tried to tame a bit this we still see it
[1:05:36] so it's like we we haven't solved the problem of instructing them in the ways that are really
[1:05:46] uh according to so they behave according to our instructions and that is the thing that I'm
[1:05:51] trying to deal with sick offence meaning it basically tries to impress you and please you and
[1:05:57] because you're yes yes even though that is not what you want that is not what I wanted I wanted
[1:06:02] the honest advice honest feedback but because it is sick offentic it's going to lie right you have
[1:06:11] to understand it's a lie do we want machines to lie to us even though it feels good I learned this
[1:06:19] when me and my friends who all think that either messy or Ronaldo is the best player ever I went
[1:06:26] and asked it I said who's the best player ever and it said messy and I went and sent a screenshot
[1:06:29] to my guys I said told you so and then they did the same thing they said the exact same thing to
[1:06:33] charge pity who's the best player of all time and it said Ronaldo and my friend posted it in
[1:06:36] now like that's not I said you must have made that up I said screen record so I know that you didn't
[1:06:41] and he screen recorded and it said a completely different answer to him and that it must have
[1:06:44] known based on his previous interactions who he thought was the best player ever and therefore
[1:06:49] just confirmed what he said so for since that moment onwards I use these tools with the presumption
[1:06:53] that they're lying to me and by the way besides the technical problem there may be also a problem
[1:06:59] of incentives for companies because they want user engagement just like with social media but now
[1:07:04] getting user engagement is going to be a lot easier if if you have this
[1:07:08] positive feedback that you give to people and they get emotionally attached which didn't really
[1:07:14] happen with the social media I mean we we we got hooked to social media but but not developing
[1:07:22] a personal relationship with with our phone right but it's it's it's happening now if you could
[1:07:30] speak to the top 10 CEOs of the biggest companies in America and they were all lined up here
[1:07:36] what would you say to them I know some of them listen because I get emails sometimes
[1:07:44] I would say step back from your work talk to each other and let's see if together we can solve
[1:07:56] the problem because if we are stuck in this competition we're going to take huge risks that are
[1:08:03] not good for you not good for your children but there is there is a way and if you start by being
[1:08:09] honest about the risks in your company with your government with the public we are going to be
[1:08:17] able to find solutions I am convinced that there are solutions but it it has to start from a place
[1:08:23] where we acknowledge the uncertainty and the risks Sam Altman I guess is the individual that started
[1:08:31] all of this stuff to to some degree when he released chat GPT before then I know that there's
[1:08:35] lots of work happening but it was the first time that the public was exposed to these tools and in
[1:08:39] some ways it feels like it cleared the way for Google to then go helpful other in the other models
[1:08:45] even matter to go helpful other but I do think what's interesting is his quotes in the past where
[1:08:51] he said things like the development of superhuman intelligence is probably the greatest threat
[1:08:56] to the continued existence of humanity and also that mitigating the risk of extinction from AI
[1:09:01] should be a global priority alongside other societal level risks such as pandemics and nuclear war
[1:09:07] and also when he said we've got to be careful here when asked about releasing the new models
[1:09:14] and he said I think people should be happy that we are a bit scared about this
[1:09:19] these series of quotes have somewhat evolved to being a little bit more
[1:09:26] positive I guess in recent times where he admits that the future will look different
[1:09:32] but he seems to have scaled down his talks about the extinction threats
[1:09:38] have you ever met Sam Altman? I only shook hand but then in really
[1:09:43] talk much with him do you think much about his incentives or his motivations?
[1:09:49] I don't know about him personally but clearly all the leaders of AI companies are under a huge
[1:09:56] pressure right now there's there's a big financial risk that they're taking and they naturally want
[1:10:04] their company to succeed I'm just I just hope that they realize that this is a very short-term view
[1:10:15] and they also have children they they also in many cases I think most cases they want the best
[1:10:24] for humanity in the future one thing they could do is invest massively some fraction of the wealth
[1:10:34] that they're you know bringing in to develop better technical and societal guardrails to mitigate
[1:10:42] those risks I don't know why I am not very hopeful I don't know why I'm not very hopeful I have
[1:10:50] lots of these conversations on the show and I've had lots of different solutions and I've then
[1:10:54] followed the guests that I've spoken to on the show like people like Jeffrey Hinton to see how
[1:10:58] he's thinking is developed and changed over time in his different theories about how he can make
[1:11:02] it safe and I do also think that the more of these conversations I have the more I'm like throwing
[1:11:08] this issue into the public domain and the more conversations will be had because of that because
[1:11:12] I see it when I go outside or I see it the emails they get from whether they're politicians in
[1:11:15] different countries or whether they're big CEOs or just members of the public so I see that there's
[1:11:19] like some impact happening I don't have solutions and my thing is just have more conversations
[1:11:23] and then maybe the smarter people will figure out the solutions but the reason why I don't feel very
[1:11:27] hopeful is because when I think about human nature human nature appears to be very very greedy
[1:11:32] very status oriented very competitive it seems to view the world as a zero sum game where if you
[1:11:39] win then I lose and I think when I think about incentives which I think drives all things even
[1:11:45] in my companies I think everything is just a consequence of the incentives and I think people
[1:11:49] don't act outside of their incentives unless they're psychopaths for prolonged periods of time
[1:11:53] the incentives are really really clear to me in my head at the moment that these very very powerful
[1:11:57] very very rich people who are controlling these companies are trapped in an incentive structure
[1:12:02] that says go as fast as you can be as aggressive as you can invest as much money in intelligence as
[1:12:07] you can and anything else is detrimental to that even if you have a billion dollars and you
[1:12:14] throw it at safety that is that is appears to be will appear to be detrimental to your chance
[1:12:20] of winning this race that is a national thing it's an international thing and so I go what's
[1:12:24] probably going to end up happening is they're going to accelerate accelerate accelerate accelerate
[1:12:29] and then something bad will happen and then this will be one of those you know moments where the
[1:12:35] world looks around at each other and says we need to have a need to talk let me throw a bit of
[1:12:39] optimism into all this one is there is a market mechanism to handle risk it's called insurance
[1:12:50] is plausible that we'll see more and more lawsuits against the companies that are developing or
[1:12:58] deploying AI systems that cause different kinds of harm if governments were to mandate liability
[1:13:06] insurance then we would be in a situation where there is a third party the insurer who has a vested
[1:13:16] interest to evaluate the risk as honestly as possible and the reason is simple if they overestimate
[1:13:24] the risk they will overcharge and then they will lose market to other companies if they underestimate
[1:13:30] the risks then you know they will lose money when there's a lawsuit at least in average
[1:13:37] and they would compete with each other so they would be incentivized to improve the way
[1:13:42] to evaluate risk and they would through the premium that would put pressure on the companies
[1:13:49] to mitigate the risks because they don't they want to have they don't want to pay
[1:13:54] high premium let me give you another like angle from an incentive perspective we you know we
[1:14:02] have these cars cbrn these are national security risks as AI has become more and more powerful
[1:14:11] those national security risks will continue to rise and I suspect that at some point the
[1:14:17] governments in the countries where these systems are developed let's say US and China will just
[1:14:25] not want this to continue without much more control right AI is already becoming a national security
[1:14:34] asset and we're just seeing the beginning of that and what that means is there will be an incentive
[1:14:40] for governments to have much more of us say about how it is developed it's not just going to be
[1:14:45] the corporate competition now the issue I see here is well what about the geopolitical
[1:14:54] competition okay so that doesn't it doesn't solve that problem but it's going to be easier if
[1:14:59] you only need two parties like say the US government and the Chinese government to kind of agree on
[1:15:04] something and yeah it's not going to happen tomorrow morning but but if capabilities increase
[1:15:10] and they see those catastrophic risks they understand them really in the way that we're talking
[1:15:16] about now maybe because there was an accident or for some other reason public opinion could really
[1:15:21] change things there then it's not going to be that difficult to sign a treaty it's more like can
[1:15:28] I trust the other guy you know other ways that we can trust each other we can set things up so
[1:15:31] that we can verify each other's developments but the national security is an angle that could
[1:15:38] actually help mitigate some of these race conditions I mean I can put it even more bluntly there is
[1:15:48] the scenario of creating a rogue AI by mistake or somebody intentionally might do it
[1:15:57] neither the US government know the Chinese government wants something like this obviously right
[1:16:04] it's just that right now they don't believe in the scenario sufficiently if the evidence
[1:16:10] grows sufficiently that they're forced to consider that then then they will want to sign a treaty
[1:16:19] all I had to do was brain dump imagine if you had someone with you all times that could take
[1:16:25] the ideas you have in your head synthesize them with AI to make them sound better and more
[1:16:30] grammatically correct and write them down for you this is exactly what whisper flow is in my life
[1:16:35] it is this thought partner that helps me explain what I want to say and it now means that on the go
[1:16:41] when I'm alone in my office when I'm out in a bar out I can respond to emails and slack messages
[1:16:46] and WhatsApp's and everything across all of my devices just by speaking I love this tool and I
[1:16:50] started talking about this in my behind the scenes channel a couple of months back and then the
[1:16:53] founder reached out to me and said we're seeing a lot of people come to our tool because of you
[1:16:57] so we'd love to be a sponsor we'd love you to be an investor in the company and so I signed
[1:17:00] for both of those offers and I'm now an investor and a huge partner in a company called Whisper
[1:17:05] Flow you have to check it out whisper flow is four times faster than typing so if you want to
[1:17:10] give it a try head over to whisperflow dot AI slash DOAC to get started for free and you can find
[1:17:16] that link to whisper flow in the description below protecting your businesses data is a lot
[1:17:21] scarier than people admit you've got the usual protections backup security but underneath
[1:17:26] there's this uncomfortable truth that your entire operation depends on systems that are updating
[1:17:30] syncing and changing data every second someone doesn't have to hack you to bring everything
[1:17:34] crashing down all it takes is one corrupted file one workflow that fires in the wrong direction
[1:17:40] one automation that overrides the wrong thing or an AI agent drifting off course and certainly
[1:17:45] your businesses offline your team is stuck and you're in damage control mode that's why so many
[1:17:49] organizations use our sponsor rubric it doesn't just protect your data it lets you rewind your
[1:17:55] entire system back to the moment before anything went wrong wherever that data lives cloud sass
[1:18:00] or on prem whether you have ransomware an internal mistake or an outage with rubric you can bring
[1:18:06] your business straight back and with the newly launched rubric agent cloud companies get visibility
[1:18:11] into what their AI agent are actually doing so they can set guard rails and reverse them if
[1:18:16] they go off track rubric lets you move fast without putting your business at risk to learn more
[1:18:21] head to rubric dot com the evidence growing considerably goes back to my fear that the only way
[1:18:29] people pay attention is when something bad goes wrong there is I mean I just just to be completely
[1:18:33] honest I just can't I can't imagine the incentive balance switching and gradually be with our
[1:18:39] evidence like you said and the greatest evidence would be more bad things happening and there's a
[1:18:46] a quote that I've I heard I think 15 years ago which is somewhat applicable here which is change
[1:18:50] happens when the pain of staying the same becomes greater than the pain of making a change
[1:18:57] and this kind of goes to your point about insurance as well which is you know maybe if there's
[1:19:00] enough lawsuits chat to you're gonna go to know what we're not gonna let people have parasocial
[1:19:04] relationships anymore with this technology or we're gonna change this part because it's the pain
[1:19:09] of staying the same becomes greater than the pain of just telling this thing off yeah we're gonna
[1:19:13] have hope but I think each of us can also do something about it in our little circles and in our
[1:19:19] professional life what do you think that is depends where you are average Joe on the street what can
[1:19:27] they do about it average Joe on the street needs to understand better what is going on and there's
[1:19:34] a lot of information that can be found online if they take the time to you know listen to your show
[1:19:39] when when you invite people care about these issues and many other sources of information
[1:19:47] that's that's the first and the second thing is once they see this as something that needs
[1:19:56] government intervention they need to talk to their peers to their network to to disseminate the
[1:20:02] information and some people will become maybe political activists to make sure governments will
[1:20:08] move in the right direction governments do to some extent not enough listen to public opinion
[1:20:15] and if people don't pay attention or don't put this as a high priority then you know there's
[1:20:23] much less chance that the government will do the right thing but under pressure governments do
[1:20:27] change we didn't talk about this but I thought this was worth just spending a few moments on what is
[1:20:34] that black piece of content I've just passed you and just bear in mind that some people can see
[1:20:38] and some people can't because they're listening when it is really important that we evaluate the risks
[1:20:46] that specific systems so here it's it's a one with open AI these are different risks that researchers
[1:20:54] have identified as growing as these AI systems become more powerful regulators for example in in
[1:21:02] Europe now are starting to force companies to go through each of these things and and and build
[1:21:08] their own evaluations of risk what is interesting is also to look at these kinds of evaluations through time
[1:21:16] so that was all one last summer GPT-5 had much higher risk evaluations for some of these
[1:21:25] categories and we've seen actually real-world accidents on the cyber security
[1:21:34] front happening just in the last few weeks reported by Anthropic so we need those evaluations
[1:21:41] and we need to keep track of their evolution so that we see the trend and and the public sees where
[1:21:49] we might be going and who is performing that evaluation is that an independent body or is that
[1:21:56] the company itself all of these so companies are doing it themselves they're also
[1:22:04] hiring external independent organizations to do some of these evaluations one we didn't talk
[1:22:11] about is model autonomy this is a one of those more scary scenarios that we we want to track
[1:22:20] where the AI is able to do AI research so to improve future versions of itself the AI is able
[1:22:29] to copy itself on other computers eventually you know not depend on us in in in in some ways
[1:22:39] at least on the engineers who have built those systems so this is this is to try to track the
[1:22:44] capabilities that could give rise to a rogue AI eventually what's your closing statement on
[1:22:52] everything we've spoken about today I often I'm often asked whether I'm optimistic or pessimistic
[1:23:02] about the future with AI and my answer is it doesn't really matter if I'm optimistic or pessimistic
[1:23:11] what really matters is what I can do what everyone of us can do in order to mitigate the risks and
[1:23:18] it's not like each of us individually is going to solve the problem but each of us can do a little
[1:23:23] bit to shift the needle towards a better world and for me it is two things it is
[1:23:33] raising awareness about the risks and it is developing the technical solutions
[1:23:38] to build the AI that will not harm people that's what I'm doing with law zero for use even
[1:23:43] it's having me today discuss this so that more people can understand a bit more the risks
[1:23:50] and and and that's going to steer us into a better direction for most citizens it is getting
[1:23:58] better informed about what is happening with AI beyond the you know optimistic picture of
[1:24:05] it's going to be great we're also playing with unknown unknowns of a huge magnitude
[1:24:13] so we we we have to ask our question there's question and you know I'm asking it for AI
[1:24:24] risks but really it's a principle we could apply in many other areas we didn't spend much time on
[1:24:33] my trajectory I'd like to say a few more words about that if that's that's okay with you
[1:24:41] so we talked about the early years in the 80s and 90s in the 2000s is the period where
[1:24:50] Jeff and Danielle look at an eye and and others
[1:24:55] realized that we could train these neural networks to be much much much better than
[1:25:02] other existing methods that researchers were playing with and and that gave rise to this idea
[1:25:09] of deep learning and so on but what's interesting from a personal perspective it was a time where
[1:25:16] nobody believed in this and we had to have a kind of personal vision and conviction
[1:25:23] and in a way that's how I feel today as well that I'm a minority voice speaking about the risks
[1:25:30] but but I have a strong conviction that this is the right thing to do and then 2012 came
[1:25:37] and we had the really powerful experiments showing that deep learning was much stronger than
[1:25:45] previous methods and the world shifted companies hired many of my colleagues Google and Facebook
[1:25:53] respectively Jeff Hinton and Jan McCart and when I looked at this I thought why are these companies
[1:26:00] gonna give millions to my colleagues for developing AI you know in those companies and I didn't
[1:26:07] like the answer that came to me which is oh they probably want to use AI to improve their
[1:26:14] advertising because these companies rely on advertising and when personalized advertising that
[1:26:19] sounds like you know manipulation and that's when I started thinking we should
[1:26:29] we should think about the social impact of what we're doing and I decided to stay in academia to stay
[1:26:34] in Canada to try to develop a more responsible ecosystem we put out a declaration called the
[1:26:43] Montreal Declaration for the Responsible Development of AI I could have gone to one of those
[1:26:47] companies or others and made a whole lot more money did you get me office informal yes but I
[1:26:53] quickly quickly said no I I don't want to do this because I wanted to work for a mission that
[1:27:06] I felt good about and it has allowed me to speak about the risks when chat GPT came from the freedom
[1:27:14] of academia and I hope that many more people realize that we can do something about those risks
[1:27:24] I'm hopeful more and more hopeful now that we can do something about it
[1:27:29] use the word regret there do you have any regrets because you said I would have more regrets
[1:27:34] yes of course I should have seen this coming much earlier it is only when I started thinking about
[1:27:41] the potential for the the lives of my children my grandchild that the
[1:27:48] shift happened emotion the word emotion means motion means movement it's what makes you move
[1:27:57] if it's just intellectual you know comes and goes and have you received you talked about being
[1:28:03] in a minority have you received a lot of pushback from colleagues when you started to speak about
[1:28:08] the risks of I have what does that look like in your world all sorts of comments I think a lot of
[1:28:16] people were afraid that talking negatively about AI would harm the field would stop the flow of
[1:28:25] money which of course that hasn't happened funding grants students it's the opposite there you
[1:28:34] there's never been as many people doing research or engineering in this field I think
[1:28:41] I understand a lot of these comments because I felt similarly before that I felt that these
[1:28:47] comments about catastrophic risks were a threat in some way so if somebody says oh what you're doing
[1:28:54] is bad you don't like it yeah your brain is going to find reasons to alleviate that
[1:29:07] discomfort by justifying it yeah but I'm stubborn and in the same way that in the 2000s
[1:29:17] I continued on my path to develop deep learning in spite of most of the community saying oh new
[1:29:24] nets that's finished I think now I see a change my colleagues are less skeptical they're like more
[1:29:32] agnostic rather than negative because we're having those discussions it just takes time for people
[1:29:40] to start digesting the underlying you know the rational arguments but also the emotional currents
[1:29:49] that are behind the reactions we would normally have you have a four-year-old grandson
[1:29:58] when he turns around to you someday and says granddad what should I do professionally as a career
[1:30:04] based on how you think the future is going to look what might you say to him I would say
[1:30:11] work on the beautiful human being that you can become
[1:30:22] I think that that part of ourselves will persist even if machines can do most of the jobs
[1:30:30] what part the part of us that loves and accepts to be loved and takes responsibility and
[1:30:46] feels good about contributing to each other and are you know collective well-being and you know
[1:30:52] our friends or family I feel for humanity more than ever because I've realized we are in the
[1:31:01] same boat and we could all lose but it is really this human thing and I don't know if you know
[1:31:10] machines will have these things in the future but first for certain we do and there will be
[1:31:19] jobs where we want to have people if I'm in a hospital I want a human being to hold my hand while
[1:31:29] I'm anxious or in pain the human touch is going to I think take more and more value as the other
[1:31:41] skills you know become more and more automated is it safe to say that you're worried about the future
[1:31:51] certainly so if your grandson turns around to you and says granddad you're worried about the future
[1:31:56] should I be I will say let's try to be clear-eyed about the future and it's not one future it's
[1:32:07] many possible futures and by our actions we can we can have an effect on where we go so I would tell him
[1:32:17] think about what you can do for the people around you for your society for the values that
[1:32:25] he's raised with to preserve the good things that exists on this planet and in humans.
[1:32:34] It's interesting that when I think about my niece and nephews there's three of them and they're
[1:32:38] all in the age of six and my older brother who works in my business is a year older and he's got
[1:32:42] three kids so if they feel very close because me and my brother about the same age we're close and
[1:32:48] he's got these three kids where you know in the uncle there's a certain innocence when I observe them
[1:32:53] you know playing with their staff playing with sand or just playing with their toys which hasn't
[1:32:59] been infiltrated by the nature of everything that's happening at the moment and I
[1:33:03] it's too heavy it's heavy yeah yeah it's heavy to think about how such innocence could be harmed
[1:33:12] you know it can come in small doses it can come as think of how we're at least in some countries
[1:33:23] educating our children so they understand that our environment is fragile that we have to
[1:33:28] take care of it if we want to still have it in 20 years or 50 years it doesn't need to be
[1:33:36] brought as a terrible weight but more like well that's how the world is and there are some risks
[1:33:43] but there are some beautiful things and we have agency you children will shape the future
[1:33:52] it seems to be a little bit unfair that they might have to shape a future they didn't ask for
[1:33:58] or create though sure especially if it's just a couple of people that have brought about
[1:34:04] summoned the demon I agree with you but that injustice can also be a drive to do things
[1:34:15] understanding that there is something unfair going on is a very powerful drive for people
[1:34:21] you know that we have genetically wired instincts to be angry about injustice
[1:34:34] and the reason I'm saying this is because there is evidence that our cousins
[1:34:39] apes also react that way so it's a powerful force it needs to be channeled channels intelligently
[1:34:48] but it's a powerful force and it can save us and the injustice being the injustice being that
[1:34:55] a few people will decide our future in ways that may not be necessarily good for us
[1:35:03] we have a closing tradition in this podcast where the last guest leaves a question for the next
[1:35:06] not knowing who they're leaving it for and the question is if you had one last phone call with
[1:35:10] the people you love the most what would you say on that phone call and what advice would you give them
[1:35:18] I would say I love them that I cherish what they are for me in my heart
[1:35:36] and I encourage them to cultivate these human emotions so that they open up to the beauty of humanity
[1:35:55] as a whole and do their share which really feels good
[1:36:05] do their share do their share to move the world towards a good place
[1:36:12] what advice would you have for me and to you know because I think people might believe and I'm
[1:36:16] not heard this yet but I think people might believe that I'm just having people on the show that
[1:36:20] talk about the risks but it's not like I haven't invited Sam Altman or any of the other leading
[1:36:27] AICOs to have these conversations but it appears that many of them aren't able to right now
[1:36:33] I had my staff a Solomon on who's now the head of Microsoft AI
[1:36:38] and he echoed a lot of the sentiments that you said so
[1:36:41] things were changing in the public opinion about AI I heard about a poll I didn't see it myself
[1:36:50] but apparently 95% of Americans think that the government should do something about it and
[1:36:57] the questions were a bit different but there were about 70% of Americans who were worried about two
[1:37:03] years ago so it's going up and so when you look at numbers like this and also some of the evidence
[1:37:15] it's becoming a bipartisan issue so I think you should reach out to the people
[1:37:28] that are more on the policy side in the political circles on both sides of the aisle
[1:37:37] because we need now that discussion to go from the scientist like myself or the leaders of companies
[1:37:48] to a political discussion and we need that discussion to be
[1:37:56] serene to be like based on a discussion where we listen to each other and we are honest about
[1:38:06] what we're talking about which is always difficult in politics but I think
[1:38:12] this is this is where this kind of exercise can help I think I shall thank you
[1:38:25] this is something that I've made for you I've realized that the Diaries here audience are
[1:38:29] strivers whether it's in business or health we all have big goals that we want to accomplish
[1:38:34] and one of the things I've learned is that when you aim at the big big big goal it can feel
[1:38:39] incredibly psychologically uncomfortable because it's kind of like being stood at the foot
[1:38:44] of Mount Everest and looking upwards the way to accomplish your goals is by breaking them down
[1:38:49] into tiny small steps and we call this an R team the 1% and actually this philosophy is highly
[1:38:55] responsible for much of our success here so what we've done so that you at home can accomplish any
[1:39:00] big goal that you have is we've made these 1% Diaries and we've released these last year and
[1:39:06] they all sold out so I asked my team over and over again to bring the Diaries back but also to
[1:39:10] introduce some new colors and to make some minor tweaks to the Diaries so now we have a better
[1:39:16] range for you so if you have a big goal in mind and you need a framework and a process and some
[1:39:22] motivation then I highly recommend you get one of these Diaries before they all sell out once
[1:39:27] again and you can get yours now at the Diary.com where you can get 20% off our Black Friday bundle
[1:39:33] and if you want the link the link is in the description below

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