Mustafa Suleyman: Empathy in AI

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Okay. It takes a little bit of naivety to declare That your mission is to build artificial general intelligence ethically and safely. The goal of pie is not to take a lot of time and to distract you from human beings. The goal of Pi is to help you.

In your interactions of human beings. How do I learn more? How do I plan better? How do I get more efficient? And now you're gonna have a personal AI. That will be working for you in the background. Mm. people go like well I'm just gonna sit down and watch television have my pie deal with everything. It's like, No no, that's not actually in fact the design.

The world gets built the way we intentionally design and build it. As creators of technology, we suddenly have a new way of creating very, very magical experiences for people. That was Mustafa Suleiman and Reed Hoffman, co founders of Inflection AI. I'm executive producer Chris McLeod.

Reed recently sat down with Mustafa to discuss the ever-changing landscaped of artificial intelligence. as well as the ideals that were essential in creating the AI assistant pie. And we're so excited to share this interview with you today. because it's the perfect prologue to our upcoming mini-series, AI and you.

where Reed will talk with an array of AI leaders, including Mustafa. To explore how you can harness AI to scale your productivity, your business, and yourself while staying safe in the process. Mustafa Suleiman has been an AI leader for decades. before he co-founded Inflection AI with Reed, he was co-founder of the AI company Deep Mind. Which was acquired by Google in twenty fourteen.

Mustafa's recent book, The Coming Wave, explores the seismic changes that are on the horizon for humanity and what we should be doing to build a future that is safe and secure for everyone. The interview you're about to hear is a peek inside the minds of two of the foremost AI leaders. They'll give a clear eyed but optimistic view on how AI will revolutionize our lives.

Now Reed's interview with Mustafa Suleiman. Well, I have been looking forward to this masters of scale interview for years. Mustafa Siliman, my co-founder of Inflection, a friend who I have learned from in multiple vectors. Ranging from poker.

To Actually in fact really serious things that matter. So Mustafa Sulliman, welcome to Masters of Scale. Thank you, Reed. It is super exciting to be here. I have obviously learned infinite amounts from you over the last decade, and so I'm super excited to be here having this conversation. It's been a long time coming. And so let's start.

With When you dropped out of Oxford. And we're thinking, Okay, how do I make a contribution? You're like, Okay, I'm gonna create a what you know, in the in the US is called a five oh one C three a nonprofit, but a kind of a equivalent of a NGO. For helping society, you worked Mayor's office.

What got you into artificial intelligence? What made you suddenly realize Oh gosh. This. is something that I should be Yeah.

Yeah, I've I guess I've always been a systems thinker. I love thinking about the relationship between complex ideas over time. And when I dropped out of Oxford, it was to start a nonprofit uh charity, a telephone counseling service. And I was really interested in the question, how do we have the biggest possible impact at scale? To make the world a better place. And that was genuinely back in two thousand and two. you know, my main motivation and for the first, you know, five years of my career

I worked in nonprofits, I worked in local government, I ended up co-founding a conflict resolution firm and working all over the world as a facilitator and a negotiator, you know, at the UN for big companies, uh for local governments. And all of that experience led me to realize that actually It's technology that really has an outsized impact in the world. And technology was accelerating at an incredible pace, even in those days. I think it was Two thousand and eight or so when I really started to pay attention to Facebook's growth.

And it was that realization that this product had gone from a complete non entity two or three years earlier to then having I think it was like a hundred million monthly active users at that time. I was like Wow.

technology really is the thing that is gonna transform our world and I want to try and participate and try and steer it to, you know, deliver the best possible impact that it can for our species. So say a little bit about the conversations that you started with Demis and Shane. Yeah, so I mean, the best way to, you know, get involved in a completely new alien paradigm is to throw yourself in at the deep end. And so

I ended up hanging out with Shane Leg and Demis Sasabis. At the Gatsby Computational Neuroscience Unit at University College London. And they very kindly snuck me in through the back door to listen in to the lunchtime lecture series. uh which was about how neuroscience could be a tool for inspiring new machine learning algorithms. And I have to say, whilst I didn't understand all of it, I found it deeply inspiring. And, you know, we started going for lunch together, the three of us at Carluccio's, which is an Italian restaurant, a couple minutes round the corner from Russell Square in London. And we started thinking big and planning, you know, what it would be like if we put together

An AGI company, and over the course of about six months, started reading papers, reading textbooks. you know, really teaching myself. to understand at a high level you know, how these machine learning systems work, what their objective function is, um, you know, how they're trained I think the really cool thing that we had spotted at the time was that models that had the ability to learn were in fact starting to gain traction.

And artificial neural networks had been around since the eighties, you know, proposed in various different formats. And traditionally A lot of the AI models had been handcrafted. So they were kind of just complex sets of rules that interact with one another. And everyone uh realized that that wasn't how the brain worked. What everyone was looking for was sort of more brain inspired type algorithms and

And the idea, of course, was that you know a reward signal from an environment could Update a set of weights in a network. to ensure that the network was a better fit. for the learning objective, eventually now building the kind of models that you know, we see with large language models and

You know, I think looking back It takes a little bit of naivety to be bold enough to declare that you know your mission is to build artificial general intelligence ethically and safely. That was the strapline of our twenty five page business plan, which we took to Silicon Valley in the summer of two thousand and ten and ended up successfully pitching Peter Thiel at the time for a

minuscule one point five million pounds, which we were over the moon about. And that that then became our series A. Yep. And then What were some of the kind of the key insights that you were getting from the deep mind time That kind of shed a light forward to where we are now.

What was that what was the lens, you know, kind of deep mind forward? From the early days. what the team were trying to do was use these models to generate a novel example of a black and white handwritten digit. Right. So the the AI would read lots and lots of examples of images. I I think they were sort of like three hundred pixels by three hundred pixels, black and white.

handwritten digits And then it would try to generate a brand new seven that didn't exist in the data set, for example, that that wasn't a match. It was a novel style of handwriting. And you know, we we sort of saw this almost like a a small video, like a short twenty seconds. as the video goes from complete black and white pixelated fuzziness and slowly resolves to, you know, a clear black background with a very distinct seven, you know, emerging out of the darkness. And

It sounds really small, but it was a really mind blowing moment for me. And you know, in many ways All of the subsequent breakthroughs, whether they were learning to play Atari games or AlphaGo or Alpha Fold, or many of the health applications that we did that produced best in the world class of X ray recognition or

Ophthalmology. eye disease diagnoser Each of these was really a a continuation of that very early work back in two thousand eleven, two thousand and twelve. And when did you start seeing That scale compute.

was gonna be important part of the equation. What was the the beginning of that realization. Well, we certainly didn't see scale back in two thousand and thirteen in the way that we think of scale now. Even though looking back, they were really large amounts of compute that were used to train

the Atari model, for example. So The Atari model used Two petaflops of compute. A flop. is a floating point operation, it's a unit of computation.

And a peta flop, peta P E T A refers to A million billion operations. So at the time it used two million billion operations, w you know, which is sounds like an enormous number. But to put that into perspective Every year since then

The cutting edge of AI models. Haz ten times more compute. Over the last ten years the amount of compute used to train the best and the biggest models in the world Has ten X.

ten orders of magnitude in ten years. So that gives you a sense of the trajectory that we've been on over the last decade. It's it's kind of hard to comprehend. And so when When did you kind of shift to kind of the large language model? Focus and what brought you kind of Silicon Valley.

as a way of kind of building that. Well, I moved to work at Google full time in twenty twenty, and I was lucky enough to be able to work on an earlier version of Lambda, which was called Mina at the time. And I joined a team that was

maybe five or six people strong at the time. And it was really just a small research project. And the model was you know, s significantly smaller than GPT two, it was you know, sort of almost incoherent But you could see glimmers of really impressive text. Like every now and then it would string together a sentence or two sentences and, you know, it seemed really, really impressive. And

Over the course of my time there once we renamed it Lambda and we scaled it up. It was just Unbelievable to see how good it had become so quickly.

And I think what we built with Lambda was an interactive back and forth agent. So in many ways there was chat GPT way before chat GPT, and we were completely blown away with how good your seventh and tenth turn of conversation was with the model.

Because it obviously had the prior turns of interaction in its working memory. And I think that was the key insight that made us realise that actually agents, you know, were really going to be the future of this next wave of technology. Yep, so and this is where

at the very earliest glimmers. The inflection and pie. Story begins. Because that conversation about like

Wait. The agent interaction is part of what unlocks All of this capability. into human experience.

And creates this productive Amplifier in a number of different ways. And obviously one of the things that you were trying to do at the time was kind of say, okay, I'm I'm trying to get this productized and launched to Google. Yeah, it's part of my job here.

And you know, Google at the time until you know Chat GBT kind of kick them in the rear end and got them moving. was kinda like no no no no no we're we we don't want to launch anything that threatens our search business and and we don't know what how people will respond to this so let's just keep this as an R D project. And you're like, Well, but wait, this could be so important. For humanity.

We tried really hard to get that launched at the time, but there just wasn't the appetite for uh taking the kind of risk. And it was pretty clear, I think, to a lot of people at Google that this was potentially going to unseat you know, Google's existing search business. And so it's super hard for a company to try to compete with itself and upend itself from within. But I think, you know, the interactive component of it was just so obvious. I mean, once we had hooked it up to search, we were trying to sort of ground the generations that the AI produced in the context of the search results and try and make it more factual.

Buy sort of training Lambda to reference a search result when it produced an output. And actually that's what you now see in Bard. you know, Google's new AI It's pretty much exactly the same model.

So it's very clear to me that actually it was gonna be conversation that is the new user interface, right? I think that the future of the web and future of digital interactions in general Will be that you just ask your AI. I think everybody is gonna have a personal AI. And what you really want is to make sure that it is

aligned with your interests, on your team, in your corner. Because you're gonna turn to your AI for all sorts of important And exciting and entertaining and even sensitive moments in your life. Uh, it's not just gonna be about directions or getting factual information. It's also gonna be about, you know

venting your frustrations. It's gonna be, you know, about asking a stupid question that you're sort of too embarrassed to ask a friend or a colleague. Or, you know, it's gonna be sharing in a quiet private moment when you sort of want to reflect. And so I think that that new user interface gives us a a kind of claim it's like a new design material as creators of technology, we suddenly have

a new way of creating very, very magical experiences for people. And as a result, these sort of more relationship based, interactive based AI agents are are what I think is gonna be the future. So say a little bit about We have multiple theories of scale. That we're going to actually get to in this discussion, because there's a scale on a number of different interesting vectors, which may surprise some people.

But let's start with a micro, which is The design of pie. What did you think were the kind of the key things in an agent Uh what are the some of the places where people can understand pie is different from chat GPT and other things that it might have heard of? What's the The as it were, the one on one.

Or the tactile You know, kind of design. of how pi currently operates. Yeah, so we've Designed pie, which by the way stands for personal intelligence.

To be really sensitive and kind and supportive. Right. Our thesis was What makes for great conversation? And that really is gonna be the backbone of the new set of surfaces in web in apps. um and in all of your devices. It's gonna be conversational.

So Pi needed to be Very respectful. Very patient. Very kind. Always curious.

Right? It's super important that Pi seeks to understand your intent. Right. It doesn't make assumptions. You know, it doesn't stay firm when it's wrong. So it was really important to us that Pi uh is able to back down and seek feedback and ask clarifying questions.

And I think that's slightly different to the other AIs on the market, which try to be um, you know, sort of more of a uh a sort of conversational Wikipedia giving you facts and lists and assuming that it knows the right answer straight away. And Yeah, that was the core hypothesis is that if we can design a personal AI that, you know, really gets to know you over time, that remembers the conversations that you've had in the past, then increasingly it will be able to personalize

It's style and tone to your style and tone. And therefore give you, you know, a much better quality experience. So let's now Broaden that out some. to the kind of the view of the universe.

In which, you know, pie is a is a stepping stone in coming. The kind of the personal intelligence universe and What that means for amplifying zone when this vision gets the scale. What does that vision look like?

Well I think that Everybody over the next decade is going to have access to a personal intelligence, a pie. You know I think there will be many, many different types of AI in the world. Some of those AIs will represent brands

Some of them will represent businesses or digital influencers. Some of them would be trying to sell you stuff. Maybe you'll have a healthcare AI or a a lawyer AI. Um, even governments will have their own AIs, you know, that help you with government services and your tax return. And so what you want as a consumer is an AI that is really on your side, that can represent you and that can interact with Other

you know, AIs that are trying to sort of sell you something or persuade you of something. And really that's what a personal AI is. I think over time it will start to feel like uh a browser for your life, right? So if you imagine what a browser is today. It represents the sum total of your you know, digital curiosities, right? Some of your tabs are gonna be clusters, you know, where you're researching a

I don't know, a new camera that you want to buy, another set of tabs will be like you, you know, trying to book a new holiday. Another set of tabs will be like you doing some research for work, or maybe you're looking for a new job. Each of these are threads. Or lines of inquiry. In your life. that require you to maintain state. You sort of have to remember where am I at on this little mini journey.

And how do I learn more? How do I plan better? How do I get more efficient? How do I keep up the rhythm of this little inquiry or investigation in my life? And now you're gonna have a personal AI to help you maintain state, to help you dig deeper and learn more. that will be working for you in the background. You know, your pie is gonna go off and Find, you know, new articles, give you summaries.

Find you nice how to's and little videos. little snippets to um, you know, help you keep progressing on each of these lines of inquiry. And that's kind of how I see it, preserving state across all these different um sort of areas of your interest. And helping make you smarter and and save you time.

And you know, it it naturally brings a kind of a comedian kind of perspective is like well my pile's gonna talk to your pile and we'll sort it out. And intersect that with the kind of the human amplification, the humanism that's at the kind of the core of the design of this, because Obviously to some degree people go like well I'm just gonna sit down, watch television, have my pie deal with everything. It's like no no that's not actually in fact the design. The world

gets built the way we intentionally design and build it. How is that play into the human amplification side? Well Pi is clearly In the end, in the next few years

going to save people vast amounts of time, right? It is going to give you back time. And I think it'll be a question for you as to how you want to spend that time. I hope that it means that it will free us up. Two spend more time with our kids and our friends and our family and our loved ones and be out in the world because, you know, you're gonna spend less time doing mundane administrative tasks online. You know, in many ways

Pi is gonna be like a chief of staff. for you, or like a PA or a secretary organizing Planning Booking, buying, arranging The amount of time that we all spend

Just processing payments online. Or just like ordering the groceries when it's a run of the mill order and we know what we need. There's a lot of wastage in front of our screens. So I think Trying to make your life more efficient and more productive.

hopefully frees you up to spend more time Interacting with other people. Equally, it could be that, you know, you now have more time to pursue your hobbies and your passions and your new learning interests. Or it might be that you're trying to you know, upskill yourself to change jobs, switch careers, move cities.

Each of those things will be something that, you know, your personal AI can help you with too. Yeah, and and one of the ways that I put a different lens on the same point you've just made is Is The goal of pie is not to take a lot of time and to distract you from human beings.

The goal of pie is to help you. In your interaction as a human being. So if You come in like you know, you can I have this difficult discussion with a friend. And you come and start talking to Pi. It's not, oh, just talk to me and I'll make you feel totally better and you can ignore your friend totally. It's like no, let me help you.

Like kind of like how you engage with your friend and understand each other and and have a great conversation as part of the The design ethos. It's to help you. Being your best self. and navigate the world you're in, whether it's life or work or anything else, but from you out.

Is I think You know, another lens into what you just said. Yeah, totally. I mean that's that's a great way of putting it. I mean, in many ways pie is kind of there to absorb your tiredness and help you, you know Translate your sort of um your frustrations.

On to pie. rather than onto, you know, other people in your life. And I think No, that's just a it's hard to wrap your head around, you know, it really is a completely different experience. And that's where I think we have to sort of really try to make the right choices, really think very, very carefully. I mean, one of the things that you and I have talked about a lot and that we're very clear on is that, you know, we

We don't want to let pie be used for romantic relationships, right? So, you know, that's other people will build those things potentially. It's just not our bag. You know, so now if you know, someone sort of approaches that kind of style of conversation with Pi Pi will be really deliberate and clear that that's kind of off limits. It'll be super respectful. You know, that's the nice thing about Pi. It never judges you no matter What values or views you come with.

It'll try to talk it through with you and, you know, present both sides of the argument. But it's never going to put you down or reject you, but it does have its own boundaries and it has its own values and they may not work for everybody, but you know, we s we certainly we hope that, you know, it'll be a healthy way to proceed for most people. Yeah, indeed. When you build substantial wealth through your business, it's often tied up in a single equity position. The upside is real, but so is the risk, and knowing when to act isn't always obvious.

Creative planning works with business owners to build a strategy around concentrated equity. When to diversify, how to manage tax risk, and how to protect what you've spent years building. Creative planning where wealth works together. learn more at creative plating dot com slash masters of scale The Wired Newsroom is known for award winning reporting on how technology shapes our world.

On Wired's Uncanny Valley, we take that curiosity even further. Each week, journalists from Wired break down the biggest stories in tech while speaking directly with the people building, challenging, and reshaping the future. Is the AI boom sustainable? How do you protect your privacy in an age of constant surveillance? Uncanny Valley tackles the questions driving today's tech debates and lighting up your group chats.

Listen to new episodes every Thursday. Wherever you get your podcasts. Hey there, it's Jill Slessenger. I'm launching a new show. It's called Money Moves, and your money is going to move. We're gonna help you make better financial decisions. We're gonna call out the BS you're finding all over social media. We're gonna give you actionable guidance to make your financial life clearer, less stressful. We're gonna answer your financial questions and take the mystery out of your financial life.

Follow and listen to Money Moves with Jill Schlesinger wherever you get your podcast. Now let's go to another version of scale, which is the compute behind this. Say a little bit about What the scale of compute is what inflection is. We just had an announcement recently about

You know, H one hundred clusters. Say a little bit about this. Right, yeah. I mean, so this entire story of large language models is a story of Of scale. It's really quite surreal. So The training data sets, for example. Use hundreds and hundreds of billions

Or you can think of them as words. Um, you know, more texts than any one of us has ever read in our lifetimes or ever could, even if it was the only thing that we did. And you're right, in terms of compute, that is really the core engine powering the progress of this new revolution. And you know, the workhorse of that engine is the NVIDIA

GPU uh graphics processing unit, these chips that were previously primarily used for gaming and turned out to be brilliant parallel processors when it comes to running computations for neural networks. Um, so we, as you mentioned have managed to gather together a pretty impressive super computer.

Recently we announced our new supercomputer of um NVIDIA H one hundreds, and on the open source ML performance benchmark. NVIDIA and our partner Corwe And us actually demonstrated that it was the fastest computer in the world. Uh which was pretty remarkable in itself. And uh we also just announced our uh new funding round uh where we've been lucky enough to be able to raise one point three billion dollars.

And um we'll be using that to build the largest supercluster in the world, which we'll have up and running by the the autumn of this year. And that's incredibly exciting. I mean it's just quite surreal that us as a forty person one year old startup has been able to build the largest cluster in the world. Say a little bit about what the kind of that size of cluster is relative to how people talk about exoscale computers and supercomputers and so forth.'Cause it's a it's another of the lens of scale here is

is on the amazing of the scale compute. Well, one way to think about it is that for each of the words being read by the AI The AI needs to learn the relationship between that word And the previous words in a sentence and there could be billions and billions of possible combinations of previous words in a sentence. So you can think of it as kind of like learning an all-to-all relationship between all the words that it has read.

In order to give you The likelihood that the next word in a sentence,'cause it's predicting or generating the next word in a sentence It gives you the the likelihood that one word will appear over another. Right. So for any sentence that Pi

Generates It's also generating Scores of other words that could come next, right? You can think of it for each word, there's like a ranking of the next ten words and then ten words and then ten words. So

It's kind of surreal that that You know, albeit sort of simple method. Could produce you know, complexity and fluency uh the like what we've got, which is just amazing. That maybe gives you a bit of an intuition for the case that the more compute you have

The more times you can run those computations and you know, kind of the machine sort of chews over all the possibilities, if you like. And that's why we, you know, you and I have been pursuing scale over the last year. No, exactly. So let's go to another lens of scale.

Which is The governance. Of this. You know it's one of the things I said at the very outset of our interview is that there's just a ton of stuff that I've been learning from you, and one of the things that You know is I think our

Maybe Third serious conversation. Very early was the question of what are the right governance mechanisms and how does this work. And let's start the governance conversation with your coming book. And I deliberately, you know, punned on that the coming wave. Uh say a little bit about the coming wave and and why you went very retro.

In writing a book. Yeah, I mean, really writing a book was an excuse to think deeply and seriously about what was happening. It was kind of a meditation or a reflection. And The key question I was sort of trying to answer with the book, um, The Coming Wave was What? technology trajectory are we actually on?

Right, so what does it mean? But Everything. In the history of our species that we have produced has actually

Got cheaper. And easier to use The more it's demanded. So if it's useful. Everybody wants it.

Everybody there for Dries to Produce it. That drives the price down. And that means that it spreads far and wide.

That is true for every technology that has been valuable to us. In centuries. And what was clear to me sort of during the pandemic when I had a little bit of time to sort of not fly and not travel so much and and really reflect. was that if this was true for AI

That is both going to be the most incredible boost to creativity and productivity in the history of our species. But also potentially An incredibly destabilizing time. Because anybody who has

An agenda. Who wants to amplify their narrative, their ideology, whether it's political or commercial. Anybody is now going to have a tool or an aid. At their side.

to turbocharge that agenda. And you know, I'm a huge optimist. for technology and that's why I build it. But I also believe That those who are concerned about the potential

Dark side. Need to be actively participating in building and creating and shaping it to the best possible outcomes. Well let's talk a little bit about because your book is And our dialogues are actually one of the places where

I treat the concerns most seriously because most of the folks who Go concerns, go everything from the relatively laughable and silly six month pause letter. Which is kind of like wrong in its theory of human nature, wrong in its theory of technological development, wrong in its theory, like just kind of like all of that. Two

Oh my God, the sky is falling or something else. And None of that is constructive or positive towards getting to a better universe. The thing is actually not just Let's steer away from this potential landmine. But here's how we steer to positive

and possible futures. Hence techno optimism. So say a little bit about kind of like what that steering course needs to look like. you know how you're hoping that the book coming out in September Will help people frame that and understand that.

There's a kind of Extreme anxiety and doomerism that is kind of bubbling up at the moment, which I think is being Amplified and and encouraged by this kind of Fear. that suddenly the sky is gonna fall in. And I think that's a really dangerous attitude. I mean, if you look around you,

It's really technology that has delivered all of the progress and benefits that we see that has created stability and order and civilization and extended our you know, life expectancy by double over the last century and a half, and you know, lifted billions of people out of poverty and you know, enabled all of us to be connected and and educated. I mean, it it really it's almost ridiculous that we have to Make that defense and quantify all of those benefits.

At the same time. we have to be eyes wide open about the potential for this to cause instability. But there's many more practical near term threats to stability, which we should be focused on, rather than talking about existential risk and AGI suddenly emerging and exploding and taking over and manipulating us and taking over armies and all of this kind of fear mongering.

Um Um sorry, I'm just getting a phone call. Something about that. Um It is a classic in recorded interviews. Yeah, well obviously I would have m it it it turned on airplane, but I'm um Connecting to you and of course it's a spam call.

Of course, what else could it be? It's an AI, uh uh robo calling from something. Wait, I wanna be part of this conversation. Yeah. Where were we? Um Well, so it's it's the um Where where you were was on the existential risk. And part of it, of course, is the fact that

One of the places where the most of the existential risk people are doing a disservice and causing an increased likelihood of dystopia. is that by focusing on kind of future robot overlords. Versus how human beings use this technology.

Including by the way. Criminals, crazy people, bad state actors, all the rest of this. This is the destabilization point. Actually in fact Increases Dystopic outcomes

Because they mislead the focus. Yeah, I mean that that's totally right. I mean so there's a The near-terets include things like a massive spread of misinformation, which has the potential to destabilize elections. A massive reduction in the barrier to entry. to causing cyber attacks.

And you know, they're a very practical security and anti misinformation steps that we should be taking But the challenge for those who are sort of more negative about the existential risk and so on Is that actually that requires very kind of practical and operational, you know, roll your sleeves up and get in and build solutions and make things safe and secure.

This is eminently doable. It's gonna require A huge lift and probably a lot of changes to the way that content is moderated on platforms. It will require new algorithms. And of course it will require new regulation. of types, right? Over the next few years. And that's the messy hard work of trying to operationalise change and make things just

incrementally a little bit better. And I I think some people have got slightly caught up in the sci fi conversation which is easy to trigger a nice dopamine hit when you're having a you know a dinner with friends or something and and that might be distracting from the practical work we need to get on and do. So obviously we agree intensely that governments need to be involved you know, part of the the slogan that I've been using and talking to governments, cause you know, generally speaking, if you listen to the press and listen to the committees of people that like, oh, we should slow these big tech companies down. Is like well actually in fact

That's a bad participation because we have line of sight to a medical assistant, a tutor, you know, on every phone. Think about the human suffering that you alleviate, the human potential that you enable. Um, you know, with these kinds of things. The real question is how do we get there? And your real question is the government Isn't You know, um, how do we slow this stuff down? It's how do we shape it so we minimize risks.

How do we shape it so that We get these benefits to as the bulk of our people as fast as possible. Let's shift to this. to another subject that we've talked about in a lot of depth. You know, like many things, since we've talked about this for ten plus years.

Um Trust. That trust is really important. Trust in technology, trust in pie, trust in You know, governance. What are the key things that

all of the actors here should be doing. In everything from Product development. Two you know, companies reading products, communicating people.

We can talk about inflection, specifically what kinds we're doing. Um And then also like media and government so that The elevation of trust is gonna be extremely important and obviously we want it to be a well founded trust.

Trust with good purpose. Look, I I think fundamentally we are at the beginning of a new revolution in the history of our species. There's it gonna be a completely different quality of object arriving in our world, right? I mean just as hardware over the last sixty years.

has gone from, you know, huge big T V screens in your living room to tiny devices in your pocket that stream H D. We're on the same trajectory for access to intelligence. That is going to completely change The landscape of

society, culture, politics, religion. It's really gonna change what it means to be human, it's gonna change what governance looks like and what it means to earn an income, you know, it's gonna change what national boundaries actually look like over the next thirty to forty years. it's sort of hard to fathom how profound this change is going to be. And so in amongst all of that, really the most important thing is being able to trust the technology that arrives in your environment.

And the way that we form trust, I think, is that we observe Behaviours consistently over time. You know, you trust that your iPhone is going to perform well because it's consistent. It's reliable. It does the same thing over and over again. And at the moment, You can't yet fully trust.

These large language models. They're not reliable. They're not robust. They're fun, they're good, they can be super useful, you can learn a lot from them. But they still make mistakes. And I think that it's gonna take us another two or three years to really iron out these these weaknesses in the models.

And so over time you'll build more trust in the model as you can observe their behaviors. consistently over time. And and and what that enables is us to train these models to be very boundary and respectful and make sure that humans are always in control And at the top of the food chain. And say a little bit about kind of what are the things'cause you know, part of obviously the tech industry

Has been Screwing up. on maintaining trust And they're antagonism agents'cause you know, people Always

Attack. And he Any pillar that's happening in society that has a differential raise in power, whether it's you know, finance or banking or oil or whatever. It always happens that way. And so the tech has that too, but on the other hand, of course

you know, in my view, it's partially'cause they're terrible communicators. Right, and saying here's how we're designing it, here's what our goals are, here's what our purposes are, and we're listening to your concerns. And and part of how we show that we're listening is we're reflecting. What we hear about your concerns to you as we're doing it. But that that trust between the builders of the technology and the rest of the society all is really important. to keep that communication and trust going.

What's your current criticism and one what's your suggestions for improvement? What I guess my my main criticism of technology of the platform companies so far is that they have really tried to make the argument that the platform is neutral. that it doesn't actually have responsibility for the content that appears on the platform. And this is the long debated section two thirty discussion about who's liable for content, right?

And I think that instead of arguing Twenty. that you know content really had no publisher liability if you're a platform creator. What we should have done is say, okay, look, None of the existing paradigms work. It's clearly not fair to say that a platform Is a publisher like a newspaper and anything that it it hosts on its platform it's responsible for. But it's also clear completely unreasonable to go in the other direction.

And I think that had we taken a little bit more time to debate that and think it through, then some of the emergent effects that have to do with this kind of, you know, spreading of misinformation, this very polarized and outrage driven environment that we now find ourselves in. I think potentially some of those could have been a little bit avoided. And so I think the other component is really thinking about the business model. You know, the commercial relationship between you

And the content that you consume really, really matters. And if you don't appear to be paying anything for it. Then you're probably part of the commercial process. Like your attention makes you the product. Right. And I think that that means that your interests On a line.

with the content that you're seeing. Somebody else is paying to put that in front of you, even if it doesn't look like look and sound like an advert. um and it's not explicitly named as an advert, it's clearly pushing a particular idea. And you know, ranking is very much a form of persuasion. The order in which content appears in a feed really shapes what you end up seeing and you know, because you never get to the bottom of your infinite scroll. So I think in this new wave, we have to think about how to address both of those questions. How to minimize

Outrage. and minimize sort of alarmism and polarization and hatred for one another and anger because That isn't what we want to build as product designers. But also think really carefully about the business model. You know, I I don't think anybody wants a personal AI.

that is funded by advertising that is selling you you know, to the highest possible bidder. And sort of trying to persuade you to buy something. So that doesn't mean that there can't be sponsored content in the experience. You know, clearly ads sometimes can be exactly what you are looking for and really, really useful. So just getting this balance right, I think is going to be really, really critical to making sure that your personal AI always serves your interests First and foremost above all, above anything else.

So as part of kind of trust And governance. There's a lot of different things. It's everything from You know, kind of like you know, what kind of structures we should have in society. Well, one of the things that you've already been doing, and you know, both within the the British context and also within the American context. Is what's the governance of the organizations?

And so one of the things that was really key for how we were setting up inflection was the setup as a PVC. So Say a little bit about kind of why what a PBC is. How to To understand it what The inflection.

P V C And how this You know, is an effort to Provide good governance and to increase trust. Yeah, I'm glad you brought that up. So obviously when a business

goes around and produces products. typically it tends to only focus on its users and its customers, who's paying its bills. And so the existing corporate structure maximizes returns to the shareholder. without any legal consideration to the impact on the environment or to the impact on wider society in general.

Or for just doing good for humanity. And so when you and I got together to co-found Inflection, it was a no brainer that we would incorporate it as a public benefit corporation. So what this is, is a new type of corporate structure. which is still a company and our goal is still to make profits, but we as directors of the company have a fiduciary that is a legal obligation. to balance the interests of the shareholders with the interests of wider society. and people who are affected by our externalities.

You have to factor in. The long term consequences. for people who don't pay you, who aren't your customers, but for society in general And you have to try to do the right thing. And so it's not going to solve all of the issues of companies, but I think it's a first step. towards trying to create a more balanced social and commercial mission.

integrated into one organizational design. I mean as as you know well, but for listeners, you know, you've been a great support to me, uh personally over the last decade as I've tried to establish deep minds governance structure, both when we were acquired by Google, we had an ethics and safety board which of course you were on uh back in two thousand fourteen, which is pretty incredible first for its time. And then throughout my time at Deep Mind and at Google, you know, we try to create lots of different oversight boards and different structures to house the the future technology. Well,

As always, I learned stuff by talking to you. Mustafa Silliman. Thank you for being on Masters of Scale. Reed, thank you. This has been super fun. Hey listeners, Bob here. If you listen to Rapid Response on Masters of Scale, you may be missing half the show because every Friday we release a second Rapid Response exclusively in the Rapid Response feed. The guests and topics are just as compelling and timely for From Ford CEO to NASA's administrator to the lessons from The Devil Wears Prada. It takes about 10 seconds to find, just search Rapid Response wherever you listen to podcasts and hit follow to make sure you never miss an episode.

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