Consciousness and AI safety debates

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Support for this show comes from Clavo Imagine hiring two brilliant employees The first takes your marketing from idea to full campaign, email, SMS, push and the time it takes to describe it The second handles every customer conversation. twenty four seven Answering questions, recommending products, handling orders both on brand and always on Your next hires, Clavio's AI agents Get started at K L A V I Y O ot com reccommendations can be amazing I mean, maybe someone recommended that TV show you've been obsessed with lately. But when it comes to home projects, it's different If you don't like a show, you might lose a few minutes If you hire a friend of a friend, of a friend to fix a leaky ceiling, you could end up with a flooded kitchen. Maybe I know a guy just isn't enough for your home That's why Thumbtack works so well They'll match you with a top rated local Pro, and you can see photos of past work, credentials, and reviews all right in the app For your next home project, try Thumb tack Hire the right pro today Welome to Dcoder. I'm La Pellll Eetit and Chie of Vge and Coder is my show about big ideas and other problems Today I'm talking to Mustafa Suleimman, the CEO of Microsoft AI, and I'm actually going to keep this intro pretty short. First, if you're watching the video, you can tell that I'm working from the basement of my wife's family farm. But second, and way more importantly, this's a real burner of an episode. Maf and I covered everything from his approach to training new models to his deep criticisms of anthropic talking about claud as though it's conscious. Of course, we also talked about all of the AI announcements Microsoft just made it build its developer It's the company's relationship with openp AI, which is very different than it used to be, and the cultural and political pushback to AI across the country. I really want to know how Mistafa was thinking about it and whether any of the consumer AI products available today are enough to overcome those objections. Like I said, this one's a burd, and Mistafa was down to talk about all of it. Okay, Musta Salimet, CEO of Microsoft AI Here we go I'm Stopas Sillman, you're the C of Microsoft AI. Wlcome back to Deoder. Nili, great to be with you again. Yeah, I'm very excited to talk to you. I think our previous conversation, one of my favorite conversations about AI and how it should make us feel and what it's for that I've had and all the conversations about AI that we've had. There are some big changes at Microsoft Maybe some very important recontextualization about how people feel about AI that I want to talk to you about in particular. And then there's Microsoft Build, the big Microsoft Big deeveloper conference, lots of new announcements, lots of big ideas about what computers are for and maybe where they should be that I want to get into. Let's start at the very start. This is some deep decoder stuff that is important to understand before all the rest of it Since you joined Microsoft You have restructured how AI works there. Your role has changed The last time I talked to you, you were in charge of a bunch of consumer products that has been since set aside. You're now training new models, you're on the frontier. Explain how Microsoft AI is structured now and how it's structured inside Microsoft Yeah. so I mean, I guess the last like fifteen to eighteen months or so, we've been on this journey to re establish our relationship with open AI. and it's taken a minute. I think it culminated in a newew contract that we got done finally in October of last year. And there were lots and lots of different provisions in that, including cement and extending the partnership crucially freeing us up to be able to pursue superintelligence independently, as well as keep buying and licensing their models U So since October I've been assembling the superintelligence team, building clusters of sufficient scale to train frontier models are you know, hiring a team focused on super intelligence. And so that was quite a big shift for us because it sort of enabled me to focus just on The Super intelligence mission And that has then culminated in a few things that we announced this week at Bild. We have seven new models across all the modalities and so on. So There's been a pretty big shift and I think a long time in the planning and a great relief for us to now be you know, in the game and pursuing the absolute frontier over the next few years. Was this the plan when you were hired at Microsoft It's been the plan certainly for the last eighteen months. I mean, I think the relationship with open AI has gone through lots of ups and downs. And in many ways U you know, Is I think is going to go down as one of the most successful partnerships, you know, in history. It's been great for open AI and 's been great for Microsoft and All good relationships evolve and I think this is just the next stage in our evolution Let me ask you about that evolution specifically. You know, we all just saw the trial between Elon Musk and Open Eye and Sam Altman Microsoft was involved in that trial in the sense that every so often A lawyer for Microsoft would stand up and say, and we weren't around Someone would say yes and that was that. But obviously, what came out during that trial, what has been clear during this entire time is that The original notion was that Open Aye would be a research lab and provide models and that Microsoft would build the products And Microsoft had expertise in going to market. It had expertise in enterprise. It was trying to regain foothold in consumer in a variety of ways and that this would be a platform shift and that the the research work would be over at open eye in the product work would be inside of Microsoft That's the thing that changed, right? is OpenAe wanted to make more and more consumer products Obviously, given your new role and your new focus, Microsoft more and more wants to make its own models. Why the split? What didn't work in that relationship I mean, I think OpenAI is led by an incredibly ambitious founding team and Sam himself And so naturally as they started to get more traction and generate a ton of revenue, they saw opportunities to go full stack. So it wasn't just that they started working on consumer products. Obviously, ChatuT was incredibly successful They also started working on their own data centers. they started creating their own chip There's lots of rumors flowing around about their own consumer hardware devices They started taking models direct to market through Chatuue Enterprise so across the stack, they were kind of broadening way beyond research over the last two, three, four years And naturally, the same is also true for Microsoft. I mean I think the partnership's now five or six years old and still has another, you know, four, five, six years to run And likewise,, we're one of the largest technology companies in the world You know we have four hundred and ninety three of the five hundred largest companies store and process most of their data on our systems, use Azure, use M three hundred and sixty five and Teams. I think people often underappreciate how enormous we are and how big our distribution is in enterprise And so long term, and you know I do mean, you, over the five, six, seven, ten years We have to make sure that we're completely sustainable and we're not just a recipient of somebody else's IP that we then slightly modify and adapt and put into production for our products, but we actually have the ability to stand on our own two feet and greatreat world class models. I mean, super intelligence is coming, I think it's just around the corner And so I think it's going to be, you, basically the most valuable technology of all time And there's sort of no way that long term, we could be structurally dependent on a third party for providing that IP for all eternity. And so that's been the transition that You know, obviously was triggered, you know sort of when open AI and so on had their bard issue, but then as I came in and my team came in, we started building that out, we're on that transition. And I think we're in a great spot because we can take a fairly steady, you know careful, long term optimal position, both for Open AI, which I think has done incredibly well out of this and for us. Yeah. I want to spend some time on superintelligence is right around the corner. I just want to put a head it now because I just want to kind of understand the transition for one more turn here. There's a moment in trial, It's sort of a very funny message from Microfio Satinadeela. He says, I don't want to be Intel and have openen Ay Microsoft, which is very funny in the context of Microsoft CEO himself saying I don't want to be the provider and make have them be the the platform that provides all the value and collects all the value and maybe' will be swapped out, right? I don't want Ch me to run on Azure And then OpenA will go get all the value, and then maybe they can swap us out just as happened with Windows and Intel over time Is that a realization? Did Nadeela come to you? What was that meeting like where he said, okay open ass hadited sport issues. We need to get back on the frontier and stand at our own two feet What did that conversation look like and how is that decision made I mean, obviously that's Satia's decision. and you know as well as like Amy and Brad, many other people in the company But I think it's as with anything, you know, these are slow moving changes in the company as it comes to realize that a direction that we're taking You know needs a little bit of tweaking and adjustment And so that was happening way before you know the sort of November board incident. And I think it just builds up over time as you look at you know, the kind of constellation of different fronts, around which we're competing directly, increasingly All the tension that comes from that Um But also just knowing that partnerships like that don't last forever. I mean, OpenAI wants to be a trillion dollar public company, It incredible revenues is growing like crazy. They want to have the freedom to operate and be able to buy you know, compute from, you know, all sorts of other places, build their own compute partner with whoever they want So the initial, you know, construct was formed, you know, the contract was formed at a time when the companies were veryy different in terms of size and scale and balance of needs and stuff. And so I think it made sense for that moment, but then you know, it became pretty clear that, you know, this is something that we have to be able to own and control ourselves and do right by our own customers. Like I said, I mean we have an incredible distribution on enterprise, which I think is just completely unrivaled in the world. And so we have to make sure we're building the best things for our for our customers and that looks slightly different to a company that has been jointly optimizing both for the consumer, which HatchBT. and also for the enterprise and also for the fundamental science mission of superintelligence, which includes in a whole bunch of different directions which are Overlapping, but you know could arguably said to be orthogonal to the consumer and the enterprise directions too So you know, naturally, I think that's how partnerships evolve and they get, you know reset periodically Yeah, but building a frontier model is very expensive, I'm told. releliably told. This is a very expensive project to set about on At some point, Amy Hood, the CFO of Microsoft has to say, Yep, you've got the budget When did that happen? and was that just a text message? was there a meeting? Tell me about the specifics there I think look we've sort of made the decision the early part of last year which obviously informed all the contract negotiations, which then all got know resolved and signed in October And you know it is a significant investment, but we have a long time to make it. I mean, we've already made significant investments in our own self sufficiency mission, our Ma two hundred chip is actually an outstanding chip as one example, right? I mean, we now are able to Manufacturer and ship a chip that is thirty percent cheaper than a GB two hundred inside of our own clusters And now that we can co design our own models with it, the MAI Thinking one model that we've just released U you know actually delivers one point four X performance per watt improvement on top of the thirty percent improvement that you get from running on a Ma two hundred once we co optimize the models for our tasks. So The value of making sure that you own and control your own stack direct the entire cod design effort end to end for the use cases that are most important to us, which is obviously agentic coding, our developers, our enterprises. I mean clearly pays the dividends that justify the investment that we have to make over the next few years You said self sufficiency mission which is a very polite way of saying you want to stand in your and two feet, you want to do your own thing. I'm told there's some controversy inside of Microsoft about a line my colleague Hayden Field wrote in a piece describing build. I'm just going to read this. This is from Hayden. It's a great line So this year's Microsoft buildild had the vibe of a freshly single deoret posting a thirst trap on Instagram, right? The breakup is completed It's time to flex. here's our new model. We're going to stand around on two feet You're out there saying you're going to build models at the frontier and compete with the leading labs Is that the feeling inside of Microsoft that you're free. you're free to be on your owninitely No, no, not at all. Look, I mean, obviously there's aool cool headline, a fun phrase, but like the reality is we are in partnership with OpenAI for years and years to come. I mean we're running way north of twenty thirty. They still produce the best models in the world. F five is an outstanding model The codex, the cybersecurity models that are coming through are amazing And they're powering the majority of what we do. So naturally that's going to continue And so I think you know that's just a natural course of these sorts of partnerships. I don't think it's anything untoward or surprising. I think know openp eye is very understanding in supportive of that. I mean, they've obviously been an incredibly fast growing company and they understand that we have to You know, pursue our own agenda as well. So very normal Let me ask you the other decoder question, and then I to get into the announcements at Bild Certainly a super intelligence The last time we spoke You said your framework for making decisions operated on a six week cycle, given how fast AI was moving That made sense then. Things have settled, maybe, maybe some things are more in focus. What is your decision making framework now We still operate by the same cycle rhythm At the end of each cycle, we have a one week meet upp in person. U I'm a real believer in this, even though we're still an in office culture four days a week. In fact, the week after next, you know, my entire super intelligence team comes together in Boston in person for four days and that is for all of our retrospectives on how build went, what we learned, what we didn't get right, what we need to improve our planning for the next cycle, which is going to run for eight weeks this time with a one week meet upp afterwards, and that's all laid out for the entire year. So the whole orrganization knows that that's the rhythm by which we operate I think it's actually really important to emphasize that time frrame because quarterly planning It gets a little bit blurry and a bit abstract and I think you know, six to eight weeks depending on where it falls in the calendar. is actually the optimal time for making very clear falsifiable missions In addition to the cycle rhythm of these six to eight weeks cycles, we also operate by squads Squads are mixed into disciplinary subgroups that are focused on a specific mission and they don't necessarily ladder up to the manager They actually are run by a DRI and the DRI is often an IC and their job That's directly a responsible individual and individual contributor Exactly. Thankk you. And, you know, I think we've we've taken the approach of separating the role of the manager from the role of the DRI that executes on a specific mission. And I think that's because being a great DRI is exhausting You know, you're like literally all in twenty four hours a day and you're pushing as hard as you possibly can Being a manager is often you about being a coach, offering support, giving guidance, feedback, unblocking all sorts of things, helping with people's career growth And so I think keeping those separate allows us to rotate DRIs every two or three cycles so that some people can try, you know, sort of different positions and have rotation And it's a great, very flexible structure that allows us to be pretty nimble, I think Let's talk about build I wanted to start with superintelligence. You've mentioned it several times now I was just at Google IO. Demis Sisabas used to be your colleague when you were at Google, ended that keynote by saying that we were in quote, the foothills of the singularity and that AGI was coming with all the power of Google. You're saying super intelligence is here. Are all the same things? Are we using different language to describe AGI? arere there differences How would you define superintelligence in your context versus the singularity in demisis Yeah, I mean, obviously, I didn't say it was here. I say it' coming and I think it's it's obviously there's a lot in like fluidity around these phrases. Um But I think what we can clearly see that's happening right now is that there is log linear hill climbing across all modalities. And that means that there is a very direct relationship between each order of magnitude of compute that we apply. each order of magnitude or each incremental increase in data and climbing on benchmarks, whether they're public benchmarks, internal benchmarks, they're targets that we focus on with reinforcement learning environments And that is a very important observation. Those predictions that I think we're all making I understand why some people are sort of skeptical of them or raise questions, but they're very grounded in the sort of empirical observations of over a decade of increase in performance of these models. I mean, essentially the same generenal purpose architecture has seen twelve orders of magnitude, more computation applied, a trillion fold increase in flops. over fifteen years and basasically has worked in audio, in our image in text, in code you know, in many other time series prediction tasks And so we're basically extrapolating out that You know, more orders of magnitude of compute will enable us to continue to climb in this log linear way inside of other environments And then it raises the question of are we going to be able to train models that can invent new knowledge, not just sort of extrapolate from existing data that we have but actually teach us things that we don't know and make new discoveries. And then the second thing is know, do they have the capacity to you know, self improve and accelerate the process of deciding which hypotheses should be set which ones should be pursued how to generate training data for each of those, how to factor those into new runs or even innovate on the actual architecture itself. So I think both of those things need to be true to be able to see this compounding progress But I think we're going to continue to get massive gains just from applying the next few orders of magnitude of compute. and that probably does achieve parity with human performance on many, many more tusks just as we've seen that happen in the last six months on coding So coding is really interesting because It's easily validated, right? You write the code, you ask the computer to run it, it runs or fails. We've seen some of the downsides, certainly around security, right? The downsides are obvious We're seeing this sort of regulatory approach to coding security play out in lots of ways I've probably vibecoded some security disasters on my own phone and computer and that's, you know, maybe that's just a risk I'm willing to take. Every other function It doesn't seem that easy. I always pick on law because know that's my background, but a judge doesn't validate legal writing the way a computer validates code. Like If you get it wrong The judge can send you to jail, right? That is maybe the worst output validation error that you can probably run into How do you measure the effectiveness across domains as easily as you can measure the effectiveness in coding. because this seems to me where The metaphor or the analogy to from coding to other domains falls apart very quickly Not so sure. So I mean, coding, obviously, you can verify the correct execution. of code, it runs or it crashes But there's a ton of nuance in there. The quality of the code that gets written really matters, It's extensibility how reconfigurable it is, how useful it is in practice. it's not just that a piece of code runs, it's like how does a model actually use it as a devOps or an SRE in production to kind of return to that piece of code that it's written and then use it in a practical and useful way And then of course, you have to grade the quality of the output that has been produced. likeike it may be high quality functioning code, but is it actually the app or the website that you want it? And there are aesthetic judgments in that, There's commercial judgments in that. So The challenge of internalizing non verifiable rewards is present in code, even though code is still primarily a verifiable reward signal And I think the other thing to observe is that chat is also a non verifiable space. and yet we've managed to climb that to basically human level performance through interaction with real world usage. That provides a very strong how I'm very curious, How how you measurered chat at human level performance Well, so I think many people are having long conversations, meaningful conversations with AIs at human level performance. I the quality is exceptionally good. It has very good Emotional intelligence. U it's broadly very accurate, We've minimised the hallucinations. We don't talk so much about bias anymore. It's grounded in real world observations. I think by most people's measures, We've got to know human level performance in conversation for quite a wide range of tasks now I mean may your measures. I'm actually sure most people's measures. I would disagree with almost all of this, but those are my measures. What are your measures I mean, my measure is like when I turn to my assistant and ask it, you know to provide me with a daily briefing, summarizing all the conversations that have happened on teams and on email, the updates that have happened to documents, And I get basically a synthesized summary with a set of actions that I should take next which is basically better than what my chief of staff can produce I would say that's human level performance in in synthesis you know, analysis. proposed actions and chat. I mean, there are many, many millions of people every day that are using it for emotional support, for counsellling, for therapy, for coaching, for advice. I think it's one of the most popular use cases inside all of the chat bots. That's a pretty robust measure, I would say, to make the claim. I know you've spent a lot of time thinking about this, particularly in the emotional connection to some of these chatbots. These are products that you have built and deployed I would draw a pretty big distinction from This thing is really, really good at summarizing my email and task list and providing me a brief about what things to prioritize and This thing is an emotional coach for somebody undergoing some kind of crisis. Like those are not similar tasks, thoseose are not similar kinds of intelligence, even in people necessarily. I know some people who are very good at making lists and are very bad at emotional support. How do you putut that all together in your brain and say, okay, this is broadly human level performance in check Well, I mean, I think if you define chat as an interactive exchange between two parties, one of which in this case is an AI broadly satisfies some goal. You're looking to learn the sport score You're looking for advice on which restaurant to go to You're looking for coaching and feedback on an essay that you've written You're looking for suggestions about which job to take next or some tough conversation you're about to have with your manager You get a response, you go back and forth, you have five or six exchanges And you find that a useful output, which you might otherwise have to go rely on an expert friend or even pay a coach There are, I mean, just objectively, empirically speaking, hundreds of millions of people that get that experience every day from these chat bots So maybe we could quibble over whether that technically represents human level performance. I think it's a fairly reasonable thing to claim. And you know, I think there's no reason why that isn't going to continue climbing, right? I mean, we've the rate of climbing in the last three years is the thing that I think is most staggering. And so what we're trying to do from this point is extrapolate Okay, what are the fundamental drivers of that climb? compomute data, interaction from real world users And you know, those things look set to continue. So I think that I would expect that they apply to many other domains too, not just sort of I don't know, chat or emotional support and productivity and you know, and that kind of thing, but many other domains beyond that to healthcare you know, to live production deployments inside of education u you know, to assistants that are increasingly managing your home, you know, looking at your, you know, just everything that is in your everyday life basically to make you kind of more productive. So That's I think a trajectory that's likely to continue. We need to take a quick break, we'll get back Support for this show comes from Shopify. 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And the spot bonus is added to the next bond's pay run So don't settle for AI, that's all talk Head to rippling. Ai slash decoder and get AI that turns insights into action That's R I P P L I N G AI slash decoder Sign up for exclusive access today. B with Microsoft AI CEO Mistopfa Silliman. Well, this is interesting. You've mentioned now that it's still the same fundamental architecture transransformers attention, that we've been applying computer For fifteen years, we're getting these big increases You are in a fairly unique spot. at B you announced your first flagship reasoning model, MAI Thinking One You got to start from scratch Is there anything you've done differently now after fifteen years in architecting and training this model Or is it just, yeep, we're going to collect all the data and run the training run just as we did. and we have more compute now so it's going to be better No, actually, I I think there's actually quite a lot of differences. The first thing to say is that the way that you curate the data we start right at the top of the stack is that we basically have Um, you know paid for and acquired an extremely high quality, very conservative set of data and extracted A lot of the noisy, distracting, low quality, potentially security risk issues to do with that data. And the methods that you do for that, I think are actually quite proprietary. We just shared a one hundred and nine page very detailed technical report, which was very well received on Twitter which shares a lot of the details on how we do this. I think the second thing is U Whilst I think it's important to be quite cautious with architectural choices and we have been. There are also number of pretty significant shifts that I think we've made into sort of how we put together our training runs. So our training runs have been incredibly stable very few crashes, very few restarts. We shared a lot of those graphs to show infrastructure stability. and also MFU efficiency. So model flop utilization, which is basically shows that we can put you know, a basically state of the art number of flops through each chip for every step in our training run U So I think that This is extremely easy to get wrong and you know we all hear lots of stories from different labs about how things do go wrong and it actually is It's pretty hard to make the very careful and deliberate choices to get things right and take her take the right approach to make sure we produce high quality models because our job and our ambition is to try and build this hill climbing machine. That means the integration of the silicon with the models, with the super high quality data with a stack of RLEs for reinforcement learning environments that allow us to basically systematically hill climb against any objective that we choose. And that's what MAI thinking one is It's a general purpose, fairly neutral thinking model that is pretty good at coding. It's now roughly on par with OPus four point six at least on the benchmarks. We haven't deployed it at scale into production, so there's still lots more work to do there But it's an extremely strong reasoner, ninety seven percent on AME, which is the primary measure for its reasoning performance at least on the benchmarks It's very good at instruction following. and then the goal is basically to make that available to many, many developers and enterprises and allow them climb on it for their use cases because everybody has a sort of slightly different objective that they have in their company to try and build you know, agents and so on that support their use case One of the things that you've noted in talking about MAI thinking one is that you didn't distill any existing models Which actually struck me as surprising, right? This is a thing you could do. You have access to open eyes IP. Everyone's distilling everything. We just found out in this trial that Grock was distilled from a number of models. Why not do distillation here? Why not jump ahead So there's definitely lots of shortcuts to the frontier And if you take a super high quality model and you sort of like polish your base model. with high quality instructions or answers or outputs from a superior model. And it's true that the model might quickly fit to that distribution But it's very unclear that they would then be able to surpass that teacher And so we've been very deliberate for two reasons. The first is that we want to make sure that we can exceed the teacher in order to set the frontier ourselves over the next few years And the second is that we really want to build one of the great labs and it's going to take us, you know, many years to come probablyro the next two, three years But in order to do that We have to be able to show that we can actually build every component ourselves. We can hire the very best talent in the world. push the frontier with actual research rather than just re implementation copying or distillation from any other third party And we're in a great position where we're able to really carefully and meticulously pursue that objective, knowing that we have the resources to by anthropic models where they sort of exceed the frontier. We have the resources to put eleven thousand different models inside a Fry so every one of our developers gets pure optionality. And of course, we have the resources to continue to deploy open AI models, which are obviously outstanding and are at the frontier today. So that's just a natural part of the self sufficiency mission and it'll take time for us to to truly get to the absolute frontier on that. but I think we're in a great spot. We made a ton of progress. I mean, this is a very, very strong model. and it wasn't just that model that we released. We've released seven new models simultaneously Trcribe model, for example, one point five is literally the number one in the world. It's the most cost effective of any of the hyperscalers. It's the highest on accuracy Our image model is now number two, our image editing model is number three, right behind Google and Op AI. So I think we're well up there with our image and audio. Our code model, code Fash is incredibly strong optimized for VS code, you know really, really a great model that's on par with Sonnet, you know four six. So it's really in a great spot in this minute Yeah, were there any legal or IP concerns with distillation? this is a a live issue, like out the world, you know, anthropic complains of other people distilling their models. There's concerns about Chinese companies distillaying models whet our existing IP agreements can cover that. Do you have any of those concerns to keep you away from it No, we didn't, but I think I understand why a lot of people get frustrated. I mean, Anthropic have been very frustrated and some of the rumors around XAI and meteta and Obviously the open source models and so on because Essentially that's basically taking the IP and the knowledge that another team has put together and then literally sort of force feed it into your own model. I think it's a bit of a short term It's a short term win. And you know, like I said, I mean, really we want to create a culture in the lab where we can come up with the next big thinking breakthrough or the next big coding breakthrough or the next big architectural you know, push. I mean right now we're experimenting with a loop transformer, which is a slightly different variant on the current transformer Lots of people in the field are looking at it too. No one seems to have quite got into production yet But in order to create a culture and a team that can really push the frontier, they have to understand, own and create the full stack as and when they need to, and also use things from third parties whenever we need to too. Like our paper, for example, has Hundreds of citations grounded in the rest of the literature. So it's very much a contribution back to the field in return for everything that we've learned over the years from all the great publications that have been out there Can I ask you if you understand the frustration from anthropic and your peers in AI about distillation, also understand the frustration from creatives and publishers and YouTubers about All the AI companies scraping their work as a collective to make these models because that frustration is only getting louder Yes. No I understand the frustration. The open web challenge is one we've talked about before. and I get it and I see that people are frustrated and obviously that's working its way through the conversation in the courts and, you know, I see that you know people put things online and, you know, they had differentiffere expectations about what the contract was with that being placed online. and it's a tricky one. You mentioned all your data was carefully curated. Did you pay for all the data that you're using to train the new models I mean, a lot of our data we obviously take from the open web in the normal way Carefully curated means it is extremely carefully filtered for security, for quality for third party dependencies from some of the open source data sets you know, keeping it away from a lot of the Chinese lineages, which I think are very different Our enterprises want to make sure that when they put something into production They can trust us that we've really built it with their needs in mind and I think it's This is one of the know benefits, I think, of being very, very deliberate and patient and being attentive to all the details You mentioned enterprise I think this is very interesting. Microsoft is all in on enterprise AI in big ways, actually. I would even draw the line straight to Asa Sharma, the new head of Xbox is getting rid of AI in a bunch of places. and the gamers are happy, right? There's one reaction AI in consumer space. There's another in enterprise And I think AI has as close to product market fit in enterprise as you can get with something is changing as fast as AI. There are a bunch of databases that corporations control You can just go access them because they control them. That's their data There's a bunch of repeatable processes and tasks and old systems that maybe the models can just do more efficiently. There's something very important happening to entnterprise. at the same time The consumer antipathy towards AI is just increasing. You know, my argument is we have not built great consumer AI products. This industry has not ce them and it does not shift them it has not made it obvious that all of this is worth it. using all the data from the open web and changing the contract of publishing to a mass audience of people. So now it's being used for training of models that will deliver trillions of dollars of value to corporations There isn't a product that says, this is worth it. Again you know, Satia Nedoa recently gave an interview with Axios and he saidays, we need social permission for this and until we have it until we deliver that value, people are going to feel this way. We've seen college speakers get booed We've seen data centers get banned Do you think that there's a consumer product that's worth it, that's worth the angst about training, that's worth the angst aboutout data centers? That was your focus, Now your focus is enterprise. I would say that Just on a face of it, it doesn't seem like Microsoft has interest in the consumer product anymore. But do you see one that's worth it or that could be built I mean, I'm not sure I agree with you that there hasn't been any value for the consumer out of this. I mean there's billion across all of the chatbots ' like billions of people a month that are getting immense value out of it. Now like for a moment, you know emmpathize a little bit with the, you know small scale business owner or you know, the kind of mom that's like helping her kid with the homework and can now just turn to a conversational AI and get like feedback, get instructions, get essay questions set mean, being able to like ask esssentially questions about, you know, How do I kind of like generate revenue? How do I put together a cash flow forecast, which college should I apply to I mean, these are everyday tasks that you know, are coming with some quality, you know, factual advice and information. So I don't really buy that people are not getting benefit out of these things. I think they are. But I think I can very clearly make the argument that they're not getting enough benefit, right? Okay. They're the ones saying that we should not have more data centers They are the ones booing AI the graduation speeches. The polling is clear, particularly young people, the more they use AI, the more antipathy they have towards it. That's clear in every single poll That's the argument I'm making, not that there's no value But the value exchange is not clear enough Yeah fair I'm seeing Microsoft in particular pivot to entnerprise away from you know, the big search product The reinvention of Bing that would make Google Dance like that's over And we're all focused on enterprise where the value is. I'm just wondering if there's value enough for the consumer. to make all of this worth it Yeah, I mean, look, I think there's understandably a lot of anxiety. There's enormous amount of speculation about what's going to happen in the next five to ten years. whether it's framed as the singularity or whether it's framed as the job apocalypse. You know, these are not helpful framings. I think that People are scared because it's poorly defined and it's often framed as an inevitable, threatening gray cloud over people's heads Um I think that what matters is what we do with technology and I think that for a long time argued that we have to place the human first. You know, some people in the field placed scientific discovery first placed, you know, accelerating you know, intelligences that can explore the galaxies and so on and said, you know, that it's inevitable that we're going to have these AIs that are going to be more powerful than all of us combined. I mean, that's naturally scary to people. And I think that we have to basically flip it the other way around and say The purpose of science and technology is to make us all healthier and smarter and happier And that's been the quest that we've been on as a species for you know, thousands of years of invention And it's the test that we should put u super intelligent to again. And if it doesn't achieve that test then I think people will reject it and they'll be right to reject it And I think that everybody is focus is now going to turn in the next five years to How is this making me healthier and happier, smarter, more capable, more productive And if it's not doing that, then naturally people are going to be angry and resist and react. And I don't think there is anything unexpected about that or anything wrong about that? I think that's inevitable. So That's why one of the things I've been passionate about for many, many years is healthcare. And you know just a couple of days ago, we announced a new partnership with Mayo Clinic This is the number one hospital in the world consistently reported They have the, you know, highest quality longitudinal patient record data set across all the modalities. They have the best clinical practice And we are going to they're also a nonprofit which I think a lot of people don't realize. sixty five percent of their patient population is on Medicaid. People often associate them with the super elites flying in internationally to get the best care in the world. but they actually have majority on Medicaid They're an amazing institution with an incredible mission to deliver the best health carere everywhere And we now have a very long term partnership to co train from scratch with their data, with our models brand new model for you know, brand new foundation model for health ploy in their hospitals and hopefully take it around the world to deliver the best, you know Cinical care and healthcare that we possibly can to as many as many many people as possible. That's why I go in the field. You know, that's what I was originally motivated by,'s I'm passionate by about. You know, I can only focus on the things that I think are going to make a difference and that will help people and you know, leave a good legacy for everybody and that's what we're trying to do. We need to check out our quick break, we'll back in a minute. Support for the show comes from Service Now. AI was supposed to handle the parts of the job you hate. Instead, it just describes them, suggests what to do about them, and then leaves you to do it That's not help. That's homework ServiceNow's AI specialists are different. They're not a tool Think of them as digital teammates who actually do the work From start to finish Cases get resolved, requests get processed, loops get closed, and most importantly, no extra work for you Because when you can truly delegate to AI, you can get back to the work only you can do. The work that requires a person with ideas and judgment And you know Pulse. 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Get started at KL A V IY O d. com We all do it You have a night for yourself but don't like the sound of the silence, so you turn on the TV just for the ambiance. It's a little trick that helps you feel like you've got company and aren't alone And other insurers, well, they may make you feel alone But when you switch to GaICo, you've got claims repps available around the clock So whenever you need, you'll have people around to help And let's turn on the washing machine, just for good measure Isn't that soothing? It feels good to have support. It feels good to Gaica. We're back with Microsoft AI CEO Mistfa Sillyman I appreciate that. I appreciate the healthcare framing. and I understand why that's everyone's go to, right Healthcare in America, in particular, if you could make it even ten percent better, you will have affected a lot of people's lives in a particularly profound way The thing is I know a very smart guy who has a very different vastly more aggressive approach to all of this than you. That person is you four months ago. This is what Mustafa Sillyman said to the Fancial Times four months ago. White collar work when you're sitting down in a computer, either being a lawyer or an account or a product manager or a marketing person Most of these tasks will be fully automated by an AI within the next twelve to eighteen months This was four months ago That implies that a year from now, lawyers, accountants, product managers and marketing people will will not have jobs, right? Their jobs will be automated. Is that still your timeline? No, no, no, h sex. so so I said tasks in the quote that you've just said, I said tasks. So that does not mean jobs. Vy important distinction. In labor economics, there is a an entire taxonomy. of subc compomponents of a role of a functioning organization Um seending an email, you know, having a conversation with a colleague putting together a PowerPoint. Subtasks will increasingly become digitized, automated, and you know we can basically generate more and more of them That does not necessarily mean that the role goes away at all. It just means that the work can be done faster and more efficiently, which is today often work that is quite wrote, it's quite manual, it's quite labor intensive, it's time consuming And so what the natural progression of technology is to make your life easier, faster, less frictionful, more seamless. As everyone often complains, that has made you and me and everybody else much more busy It'sentally made us more available, more stressed, it's given us more information Right So there's always these like revenge effects of efficiency, which I think people forget. It's quite likely that we're going to get made much, much more productive because we spend less time doing the kind of narrow administrative menial tasks, and we'll have to spend more time doing creative judgment focused things, which ultimately create a lot more value. We can also experiment much more quickly, so we'll be able to try lots of things out in parallel because the cost of execution is going to get lower. In my mind, that's likely to increase the overall quality of things because we're going to try out more hypotheses, whether in journalism or in business or in anything that we do So I think that they're sort of slightly taken out of context because of a natural misunderstanding between jobs and tasks. But nevertheless, you could push back at me and say, okay, well then what does the landscape look like in five or ten or fifteen years time And that's where I think we have to re actually can I I'm not going to push back on in you that way. I'm going to push back in a very specific way. And I realize this is your quote and you're saying was misterpreted I'm just looking at this literal sentence. And there is no distinction between tasks and sub tasks It is a white collar work. The examples are laawyer, accountant, product manager marketing person. then and then you said most of these tasks will be fully automated by an AI within the next twelve to eighteen months So there's no distinction of subtast there. Yeah. So you're saying most lawyers will have their jobs fully automated and the practice of law will look totally different within a year even even by the words of that quote. And I'm just saying are you still in that timeline? being a lawyer will look totally different because Asients will be running around doing everything that we were doing before mostost of the tasks means work that you do in order to get your overall job done. And that I think is going to free you up to do the more human like and the more judgment parts of your work. And there's a very important distinction in know jobs and roles are the broader category, tasks are the components of that, and it's an established definition in the literature in labor market economics for many many decades. It was maybe too nuanced even for the financial times. but But nevertheless, that was the intent I do think there's an important question around Wh does that leave us in the longer term? And it is going to be challenging. L more and more of this stuff. We can quibble over the timelines of whether it's a few years or whether it's a decade or whether it's twenty years But the reality is we are going to be automating more and more of this work, tasks, jobs, roles, activity and everything that we do. And so what's going to matter more is the governance that we put around these technologies. Who are they accountable to? Who owns them What are the feedback loops that regulate and introduce friction to make sure that they actually serve I mean, I wrote an essay on humumanist superintelligence outlining quite directly four or five months ago what I think of as basically a North Star maybe not quite a framework, but a set of principles that basically says Technology is here to serve us. That's the test that we should put it to. It's the test that people would put it to. It's the test that we care about in Microsoft. and I think that more and more everyone' going to have to really focus on that question because it is going to deliver a tremendous amount of good and we want it to continue doing that. But we wanted to do it in a way that doesn't sort of cause, you know you know ridiculous amounts of instability during the transitionary period I believe you. I know you've been th about this stuff for a long time, but I'm going I'm been respond in the way I know my audience wants me to respond because I hear it from them all the time and What it looks like is this whole industry, you, everybody included, went all in on we're going to replace all the jobs and Really accelerating on building out data centers at massive capacity and asking for a lot of resources against big promises There was political pushback And now all of the stances have softened And you know you saying it's not all jobs are going away. We have to rethink jobs is of a piece with all the other CEO's in this industry saying similar things and talking about healthcare. that comes up every single time now And I'm wondering if that political pushback has actually changed. how you are talking about this. There's a lot of your peers who think AI simply has a marketing problem that hasn't been communicated effectively enough, and they should spend hundreds of millions of dollars on podcasts communicate the b that A more effectivelyike This is a real thing that is happening in this industry. Do you think AI simply has a marketing problem and at the political pushback has opened your eyes to this marketing problem orr do you think there's something else going on U There's a series of questions there. The first is What do I actually think and believe? and has it changed in the last six months? The answer is no I wrote a very detailed book about this three years ago, way ahead of time Wning about many of the things that are currently happening and doing so explicitly to lay on the table tremendous risks to surveillance, to concentration of power, to concentration of wealth to disinttermediation of the state, to threats to democracy, to know threats to the nature of the human and what it means to be a person in the context of the arrival you know, these very new, you know, forms of silicon being in some sense And you know, I've been working on and the idea that like sort of my healthcare interest is like just a flash in the pan, which is a function of the reactions to data centers and so on. I mean, I've been working on healthcare for over a decade and pushed many, many times on some of the cutting edge breakthroughs, contributions to the field in radiology, mammography, and pathology, many other areas, electronic health records. So I've always believed that the purpose of technology is to just make us healthier and happier. And those are the things that I choose to work on and direct my time to Does the industry have a reputation and PR problem? I mean, I think it's pretty clear that people are very anxious, They're very frustrated And you know, there's there's there's going to be, you know, a lot of attention on that in the next few years, understandably But it's And I think what we can do is take accountability of the things that we build the way we build them, the decisions that we make to put types of technology out in the world and the types of problems that we choose to work on like, you know, we are doing with the Mayo Cinic I want to, by the way, say and point out that I think the first time you and I ever was before he joined Microsoft. It was right after that book came out And we did a panel together So one of the reason I'm comfortable asking this is because I do know You've been thinking about this for a long time, and I'm aware of that book. I think for me, the question is whether the industry as a whole misjudged. total amount of value can provide to overcome the seeming recklessness that people are now reacting to, the ask for resources that people are now reacting to And you know, you're building new models. There's probablyably a trade offff inside of Microsoft between we can use the existing Azure footprint to charge our customers money or we can spend money to train new models And that kind of looks like the same conversation people are having about resources in their communities whether we should use the existing energy footprint to build new AI or do something else that might be more immediately valuable How do you think about all of that? right? You are one of the leaders of this industry. You want to be on the frontier with the companies driving the most change How do you think about asking for those resources in a way that isn't just promising future results, but also immediately providing benefits to communities in a way that makes people want you to be there Yeah, I mean, I think that I'm very proud that Microsoft has stuck by its net zero targets Our new data centers are all liquid cooled This means that they use about a restaurant's worth of are water for a six year period. It's like a swimming pool that gets filled up with water and it just circulates around the system. Um They're all largely renewable in terms of their electricity consumption. You know, so I think commitments like that to make sure, for example, We've made a commitment recently to ensure that local communities affected by shift in electricity demand by our data centers are compensated and protected so that they don't see a spike in their prices their energy bills Those are the kinds of things that I think Microsoft does and can continue doing as a responsible company to just really pay attention to the consequences for communities. And I think On the flip side U you know, change happens because people participate at every level People inside of companies have to make different decisions People who protest and campaign have to make decisions and make the effort to go out and make their voice heard and be involved in a political process And that's how we as a species collectively evolve and move things forward. And month to month, ac course the quarter. It feels like we're all kind of at odds with one another But when you look back decade over decade We're kind of like this, you know, collective We did kind of mesh of all sorts of different incentives that are just actually nudging things in the right direction And we really are I think despite all of the angst and the polarization I think we're building something that is going to make our species much, much more healthier and happier and more capable. And I think that we have to Make sure we get the right path on the way there because there's lots of like pitfalls and ways that it can go wrong. but The right path involves, you know, people making their voices heard and people changing course based on a response and reaction to that. So I think it's a good thing that that's happening and that's the process working as intended Let me with the enterprise side of this. Weve spent a long time on the consumer side and how people feel. On the enterprise side We're seeing a bunch of companies figure out how valuable these tools actually are. Right? Amazon basically took down a leaderboard because people were cheating to use more tokens than they needed. We've seen some companies just blow out their token budgets. I think Uber just pulled back because they'd blown through their token allocation for the year and they weren't seeing any value from it U How do you think about that side of it right now, where there's so much excitement and so much desire for change in the enterprise in particular software engineering At least some people having fun and Maybe some of other people are having full existential crises, but some people are having fun Um And the value hasn't still been realized. Right? or we're beginning to see pure token maxing does not actually deliver the same kind of value that maybe you'd expect How do you think about the use there? Because that's Maybe if you prove it out in enterprise that it will actually out in other ways I think different people report different things So there's obviously some examples of people overusing coding models, generating, you know, useless code, useless you know tookens But there's many people whose work and impact has been completely transformed by it, right? So I mean, There's no question that this has had a massively beneficial impact on the software engineering industry. we are producing much more high quality, much faster code across the entire stack. And so yeah, I kind of think There's obviously examples of some people that maybe got it wrong, didn't set the right token budgets. maybe you know there's going to be mistakes along the way I don't think that's any signal that there isn't adoption or people don't see value. I mean, The value from where I'm sitting is incredible. Many many people tell me every single day that it's transforming their work. put and productivity I think the other thing to say is that like These things happen in like surges, there's kind of a swell of energy. It gets all a bit frothy. People pull back a few months later and realize that actually that isn't the thing and then they head in a slightly different direction So it's a bit meandering and organic and you know, I think that's inevitable There's a lot of excitement so people big claims on Twitter and so on. but actually the steady march of progress looks very, very linear and continuous I agree with that on the whole. Where it doesn't look linear to me is the form factors of computers Right There's probably more form factor experimentation right now than at any point in the last ten years. right M mostly settled on smartphone for at least last ten years We'reing different AI wearables. Glasses, mayaybe will be everyone's favorite device have my doubts Microsoft showed off some new devices at build There was a the badge that controls an agent and the little For lack of better word, the Chumby, the little desktop friendly thing that controls an agent. I was a big Chumby fan. I got my career started writing about Chumbbe's Frank Gadget. It was the first thing that came to mind All of those to me, I look at them and I think, where does the compute live? Where does the logic live? That's up for grabs now in a way that isn't just the linear march of progress, right? All of my computing happens in the cloud on cloud based applications and it's just agents running around data stored elsehere in the cloud. and all I need is a credit card on a lanyard issue instructions to That changes the entire architecture of computing, It might change the entire architecture of modern civilization in like many ways, right? If we don't all have smartphones How do you think about that? Where is that going? Is that up for grabs or is it will be a hybrid approach? whereere do you see the appropriate end stage Mm Yeah, it's very interesting. I mean, I think that Both things are going to happen at the same time. The edge is going to get way more powerful and the cloud is still going to be the primary driver of the largest models. And so increasingly, your agent will be smart enough to know that it can answer the question, what is the capital of France on device, whether it's on your you know glasses, wristband, you know on your badge or in your earbods And then it will know when it doesn't know. It'll know that this is actually a pretty complicated question or it's an action that requires you know a whole bunch of sequences of steps to be generated or it requires novel code to be written, and it will turn to the cloud So this kind of like switching hybrid thing is going to be super important The other thing is and we've already sort of seen it in the last three or four months is that we can have pretty powerful local machines. that can do async background processing. They can like constantly monitor systems if you need them to, they can do tasks that can afford to take ten hours, run much, much more slowly than they otherwise would be if they were in a supercomputer. So you know naturally when we're like swamped with demand, then that demand finds loads of nooks and crannies to kind of get satisfied by I'm actually very excited by the badge that we're building. I mean, it's pretty cool. like This is a technology that basically everyone in a major company has. Um It hasn't evolved in twenty five, thirty years U you know, we definitely have to wear it. It's provided by the company itself by the SSAad. So like up leveling that and actually making it a pretty cool open platform that's programmable that other people can build on top of I think it's a cool idea. I think this is going to work. so I'm very excited by it Yeah I just the thing that strikes me is there's no way you can put bunch of high powered local compute in a badge At that I think he need it all the computers is elsewhere Yeah No, you're definitely to have some local compute. you're going to have a local classifier just as you do on your earbuds at the moment. I mean, you're going to have local classifiers.. It's going to have wake worords, you know, it's going to have its own camera So you know, I think that know increasingly these things are just going to become Vessels for Processing power that happens in a kind of nested chain of increasingly less powerful devices to go right to the endpoint. Do you think the phone has a feature in that? I mean, Bild is right in the middle of IO and WWC's Big companies that control phone platforms. They love talking about how phone platforms will stay at the center the argument I hear from so many is that actually AI is a platform shift that might totally displace the phone I think the history of technology teaches us that basically as things get more useful, They get cheaper. They proliferate and they spawn new uses of technology So I think we've become so used to the phone that everyone just assumes that this is going to be an anchor device for the rest of history But actually, many of the features and functionality of your phone, I think, are going to get disintermediated, broken apart, and stored on smaller devices Right now, the primary function that the phone is playing, in my opinion is verification It's functioning as your ID card. doing your face recognition to auth you into various different environments I think you can well imagine that being a much cheaper, smaller you know, secure device which disconnects you from your phone And then you know, communication taking place via voice or even via like a series of ambient sensors where your AI doesn't really live on a device It's actually just, you know with you wherever you are, appearing you know on the bathroom mirror, wherever it is. You know, I think it's like you can imagine it feeling much more immersive. not in the next like three to five years, but looking much further out And I think that the infrastructure to support that kind of you know, encrypted but distributed appearance of agents is probably going to end up emerging in the twenty thirties. Let me ask you two final questions to wrap up. You mention that it's the same architectures that we've been using I have a lot of open questions about whether LLMs basically are the path to AGI You know, things that I point to is They don't actually know anything. at this point, even Microsoft research is pointing out that they don't know anything and that leads to certain kinds of mistakes and certain kinds of applications Are OMs the path to AGI or superintelligence Look, I think we probably need a couple more big breakthroughs. doesn't mean that we're going to see a slowdown in performance improvements over the next few years which I think is kind of a difficult distinction for people to grasp onene thing to say is Um, Human level performance across most tasks is still very far from superintelligence you know, a super intelligence is a general purpose learner that can Basically immediately understand a brand new domain. which is out of distribution. So it needs to be able to learn in a novel environment from scratch because it has stored representation of Valuable knowledge, conceptual knowledge. U And at the moment, we haven't really fully tested that. The agents aren't general purpose. They're actually, although they're broad and often integrated They're kind of domain specific. I mean we're using them for chat, we're using them for coding,' using for image audio Um Now obviously as a human, we do many, many other tasks that are much broader and more wide ranging. I think that's why people are pushing on world models and sort of much more immersive real world interactive agents that see the kind of full distribution of tasks or you know, experiences that I have during a day So I think that it's enough to take us a very long way in the next three years, the next three orders of magnitude of compute And yet, you know, full super intelligence beyond that is still an open question as to whether LLMs are enough we need other things. I think It's not quite true that they don't know anything or they don't have knowledge. They clearly are a store of knowledge. They're a highly compressed representation of knowledge, they just do so in a different way to a traditional relational database in a much more fluid, flexible, sort of abstract way that know is actually very useful. We want that ambiguity in the internal representation. Um So you know and increasingly, they're learning to use traditional tools. That's the other thing to kind of grasp a little bit is that It may be that the neural network Combined with the existing stores of knowledge and the existing tools that have been created elsewhere in the digital ecosystem is enough to bootstrap it up to improve its performance significantly So there's just a lot of like highly valuable, highly effective pieces that are already on the table, which are in the process of being connected together in the next few years. And I think that's going to drive like the progress that we're all excited about. One of the things that I think is just very funny in the industry right now is If you ask Anthropic if Claude is alive They will sort of get very frustrated that you're talking about it the word alive, which they interpret to me in flesh and blood And then they will not say whether or not they think Claude is conscious. And so they drawn, I think, for the first time in human history a distinction between being alive and being conscious And they think Claude is conscious, but not alive where they don't know if Claus is clacious Where are you? Do you think the models have consciousness? Do you think they're alive? Do you think they have the potential to achieve these things H Yeah, I mean, I take the other side of that that debate. I mean I published a paper on seemingly conscious AI, warning about the risks of misrepresenting these models as conscious, I think it is very dangerous I also published an article in Nature making the same claim And I think that it's almost as though Some of the folks at anthropic have anthropomorphed the design of Claude so much that it has then gone and wireheaded them and kind of tricked them into believing that it has these glimmers of consciousness that they put into it in the first place in their cononstitution, for example They actually, which is the training manual that they use to teach Claude what it can and can't do. It's not just a rulebook, it's actually a training guide that's part of their process You know, in that manual, they actually speculate about Claude's welfare about Claude's own rights to prior versions of itself and actually say that they would Consult Claude before deleting or turning off prior versions you know, they they speculate about its consciousness and whether, you know, it has those feelings and is aware I think that's really, really dangerous. F firstly, it's a philosophical failing because they've treated the Constitution as a place for speculation like you would in an academic paper rather than a training manual. So Claude has then gone and internalized those ideas about itself in its own training But second, I think, This is highly undesirable. This is exactly what we don't want from AIs. We want AIs to be controllable, contained. accountable aligned tools that serve humanity. That's the project of humanist superintelligence. I think that's what we should all be pursuing We do not want to have to contend with a superintelligence that has ideas about its own suffering, about ideas about its own feeling And then beyond that, I think it's actually pretty clear that these models don't experience suffering. I think suffering is the primary definition of what it means to be a conscious being and I think it's inherently biological I don't think there is any ain network or feedback loop. inside of the models, which connects outside sensory networks to you know an evolved sense of what is right or wrong through harm and experimentation. I mean, that's just not how these models are trained U So I think it's like very dangerous to project potential rights onto beings, tools, agents you know have the potential to be like significantly more capable than us in many respects. So I think that's going to become the big debate. I mean, it was even part of the Pope's enncyclical recently. I think it's going to become a very, very big part of the debate soon. and ye, I've talked to Dario a lot about it in the past. He knows that We have slightly different views on it. and I think they're very humble. I think they're very open minded and I think they're good citizens trying to do the right thing. They're good people and I think they' they're very u open to feedback and iteration I think I agree with you. I would just pushb back ever so slightly. I don't think it suffering It's easy. It's very easy to make someone else suffer. It's very difficult to make someone else feel joy or at least slightly more difficult. and suffering But I would just offer you. I think it's actually the happiness that defines the consciousness. The suffering' almost trivial. I have two young children They're very good at making each other suffer. but it' like almost the easiest thing that they do It's very hard to do other thing. Let me answer you one final question. I just want to come back around Again, a couple weeks ago I was at Google, I saw Dennis Hobas say we are in the foothills of the singularity You've talked a lot here about super intelligence and how it should be built You've talked a lot about your lengthy history talking about. discussing and researching and writing about How super intelligent should be built disagreements with others in the industry.