20VC: Why the AI Bubble Will Be Bigger Than The Dot Com Bubble, Why AI Will Have a Bigger Impact Than COVID, Why No Models Used Today Will Be Used in a Year, Why All Models are Biased and How AI Kills Traditional Media with Emad Mostaque, Founder & CEO @

Emad Mostaque is the Co-Founder and CEO @ StabilityAI, the parent company of Stable Diffusion. Stability are building the foundation to activate humanity's potential. To date, Emad has raised over $110M with Stability with the latest round reportedly pricing the company at $4BN. Investors include Coatue, Lightspeed, Sound Ventures, OSS Capital and Airstreet Capital, to name a few. Prior to Stability, Emad was in the world of hedge funds, that was until his son was diagnosed with autism and he left to make a difference in the space and help find treatments and solutions. In Today's Episode with Emad Mostaque We Discuss: 1.) From Hedge Funds to Finding Treatments for Autism to Leading the World of AI: How Emad made his way from the world of hedge funds to founding one of the leading AI companies of our time? How did Emad find a solution to parts of his son's autism with a $6 drug? How does Emad believe we can use AI to solve the majority of medical problems today? What does the future of healthcare look like with AI at the centre? 2.) Models: What is Real? What is False? Why no models today will be used in a year? Why all models are biased and how do we solve for it? Why hallucinations are a feature and not a bug? Why the size of your model does not matter anymore? Why will there be national models specified to cultures and nations? How is this implemented? 3.) Who Wins: Startups or Incumbents: Why does Emad believe there will only be 5 really important AI companies? Which will they be? How does Emad review Google's AI strategy following their news last week? Was their integration of Google and Deepmind recently a success? How does Emad assess Meta's AI strategy? Why does Zuckerberg now acknowledge the metaverse play was a mistake? How does Emad evaluate the approach taken by Amazon? Why are they the dark horse in the race? What can startups do to get a meaningful edge on the large incumbents? How do they compete with their distribution? 4.) The Next 12 Months: What Happens: Why does Emad believe the .ai bubble will be bigger than the dot com bubble? Why does Emad believe that the biggest companies built-in AI in the next 12 months will be services-based companies? How does the ecosystem look if this is the case? Why will India and emerging markets embrace AI faster than anyone else? What happens to economies that have large segments reliant on freelance work that AI replaces? Why will we see the death of many large content publishers and media companies? What does Emad mean when he says we will see the rise of "AI first publishers"? 5.) Open or Closed: What Wins: Why does Emad believe we must be open by default? Why does open win? Why does Emad side with Elon and believe we must pause the development of AI for 6 months? How does Emad evaluate the leaked memo from Google stating that neither Google nor OpenAI are ahead? What does this mean for the AI ecosystem? Where will the best AI talent concentrate? What do companies need to do to win the best talent?

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  I think this will be a bigger economic impact than Covid. I think that there's only going to be five or six foundation model companies in the world. Every big company is looking for an answer. The reality is no models that are out today will be used in a year. There is no such thing as an unbiased model.

• 0:13Harry Stebbings?

  I mean, what an intro to the show that was. Welcome back to 20 VC with me, Harry Stebbings. And in our mega series on AI, we had Jan LeCoon on the show on Monday. And today we're joined by another leader in the world of AI. Ahmad Mostag, founder and CEO of Stability AI, the parent company of Stable to Fusion. To date, Ahmad has raised over 110 million with Stability, with the latest round reportedly pricing the company at $4 billion. And investors include Code2, Lightspeed, Sound Ventures, OSS Capital, and Air Street Capital, to name a few. And prior to Stability, Ahmad was in the world of hedge funds. That was until his son was diagnosed with autism and he left to make a difference in the space and help find treatments and solutions. This is such an incredible show. But before we dive in today, you've heard me talk about how Coda is the doc that brings it all together

• 0:57Harry Stebbings?

  and how it can help your team run smoother and be more efficient. I know this because Coda helps me. We have many researchers in the 20 VC team who bring all the content together for the show. And they need a single place to work, collaborate and collect notes, data, and really information of any kind or format. And that's where Coda comes in. Coda can help your team run smoother and be more efficient. Coda allows your team to operate on the same information and collaborate in one single place by putting data in one centralized location, regardless of format. It just eliminates so many roadblocks that can stop your team in their tracks. This is what slows down productivity and collaboration. It's time to get projects across the finish line faster, help your team run more smoothly, more efficiently with Coda. Get started today for free by heading over to coda.io/20vc.

• 1:42Harry Stebbings?

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• 2:23Harry Stebbings?

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• 3:05Harry Stebbings?

  getting up to six million in FDIC protection. Lately, it's been all too clear how important that is. Plus, you get high-limit corporate cards, easy expense tracking and automated bill pay. To learn more about the all-in-one financial stack for startups, visit brex.com/20vc. That's B-R-E-X dot com slash two zero VC.

• 3:31Speaker 00

  You have now arrived at your destination.

• 3:35Harry Stebbings?

  I am so excited for this. I heard so many great things from specifically Ashton, who helped with questions, and then also Dan Rose. So thank you so much for joining me today.

• 3:43Speaker 00

  It's my pleasure.

• 3:43Harry Stebbings?

  Now, I want to start with a little bit on you. You moved around a little bit in your childhood. Take me back to the childhood, the moving around, and it's a weird commonality I found with the most talented founders. They all moved around. So take me to that and how it impacted your mindset.

• 3:58Speaker 00

  So I was born in Jordan, grew up in Bangladesh, came to the UK. It was always a bit of a struggle fitting in, but then you learn to adapt. You learn to adapt to new scenarios, new environments. Like, oh, don't speak the language. What's happening? Let's learn and let's move on from there. I think it gave me a bit of appreciation of the world as well, because we stick in our monocultures very often. Like, I'd only ever been to Silicon Valley. Actually, I'd only been to the barrier once before last October. And so this whole tech monoculture has been a bit of a shock to me. And I'm like, there's more to the world. So this is some interesting things around that.

• 4:27Harry Stebbings?

  Talk to me, hedge funds first, then what happened? We mentioned it a little bit before. Why did you make the move?

• 4:32Speaker 00

  So actually, I was an enterprise developer in my gap here at Metaswitch in the UK, doing voice over IP.

• 4:37Harry Stebbings?

  Metaswitch?

• 4:38Speaker 00

  Metaswitch.

• 4:38Harry Stebbings?

  This is Chris Mas's company?

• 4:39Speaker 00

  Yes, in Enfield. So I took my gap year and was like, I might as well be enterprise programmer. This was before GitHub and everything, so we had subversion. And kids these days have it so easy. And then I was like, what do I do now? And so I became a VC analyst at Oxford Capital Partners. And that was a lot of fun. They were fantastic. And then I was like, I want to do movies. So I became a movie reviewer. So I did the Ray Darns Film Festival, British Independent Film Awards, and then I was just bopping around doing random things and accidentally became a hedge fund manager.

• 5:03Harry Stebbings?

  Why did you move from hedge funds to startups?

• 5:06Speaker 00

  So with the hedge funds, so I joined Picta Asset Management, and then the CIO left and there was like this fund. And I got to be a portfolio manager when I was twenty three. And so I grew my beard to look a bit older. How it is coming. Get the clip on Harry.

• 5:18Harry Stebbings?

  I would. I can't do the moustache, but I still wear the glasses when I just do.

• 5:21Speaker 00

  I'm extra hard. Just busted out, right? And so I did that for a number of years and the reason to be successful, made lots of people money, not so much myself because I was too young. And then my son was diagnosed with autism and I quit because they said there was no cure, no treatment, no information. I was like, I'm a hedge fund manager. I can deconstruct things. And so built an AI team and then did a literature analysis of all the autism literature to try and figure out the commonalities and then drug repurposing. So focusing on GABA glutamate balance in the brain. GABA is what you get when you pop a Valium. It calms you down and glutamate excites you. And so in kids and people with ASD, it's like, there's too much noise going on. It's like when you're tapping your leg and you can't focus. And so that's why you get this sensitivity. Sometimes they can't speak like my son. And so it was like mechanisms to bring that down, that then allowed to have applied behavioral analysis and these other things to reconstruct his speech.

• 6:06Speaker 00

  And then he went to mainstream school, which is pretty cool.

• 6:09Harry Stebbings?

  That's unbelievable. I heard you said on another podcast and I was astounded and inspired by it. We mentioned before, my mother's got MS. And I hate the doomsday only version of kind of AI and the future of, you know, GPT. You said to me before about its impact on health and MS in particular and other conditions. How can it be so transformative to solve some of the world's most challenging chronic conditions?

• 6:31Speaker 00

  So I think a large part of our problem is that we can't scale because information flow is so limited as we write these things down. Like you can never capture all of that. So anyone who's had a loved one that has one of these conditions knows how difficult it is. Because you go from specialist to specialist and you try to build that mental map. And we're so lucky that we have so much access. But why isn't it that we can't just push a button, see every clinical trial and a deconstruction of all those and things? What if you had a thousand GPT fours organizing all that knowledge and then make it available to everyone? So you can see the exact potential mechanisms that are which MS works and all the potential food, other things that work with that. So as you try different things with your family member, you can see she reacted this way to the food or this way to this medicine.

• 7:14Speaker 00

  And it's a more holistic thing because you can have personalized medicine versus one specialist for a thousand people. You can have a thousand GPT fours or equivalents or Medpom twos for you. So we need to organize all this knowledge and then use these language models and others to make it accessible to you.

• 7:29Harry Stebbings?

  I'm really naive and basic in terms of my thinking, which is why I'm a venture capitalist.

• 7:35Harry Stebbings?

  My question is, what do we need to do to get to that state? When we look at the data needed from the individuals, the data the GPTs need, how we make the models work most efficiently?

• 7:44Speaker 00

  First, we don't have to have that data from individuals. We had Galactica as a scientific language model. But now we have Medpom two that exceeds doctor level. So that was a Google announcement yesterday. We have AIs that can understand articles better, as good as doctors, shall we say now? So we can scale that because why do you need one when you have a thousand? So we take the existing generalized knowledge and all the hypotheticals. And we bring that together into an integrated common system available to everyone. Because the building blocks are nearly here for that. Then you can personalize it later. And again, there are regulations and things around that to how we treat our loved ones and other things like that. The first thing is let's get all the knowledge in one place and make it organized and useful. And so I think we're at that point now where the language models have just hit that point that we can organize all of the world's Alzheimer's knowledge.

• 8:27Speaker 00

  Longevity knowledge, autism knowledge, MS knowledge. And you can just type and it can say, this is the source. This is what it looks like. These are hypotheticals. This is what we know we don't know, what we think we might know, et cetera. And then it can learn about you and your queries. Because this is the other thing about lots of the language model things we've seen right now. They are one to one goldfish memory. The next step is one to one. It remembers what you're asking for, like a cookie or an embedding. And then it's you plus a thousand of these language models all going and doing your bidding, the agent based kind of thing.

• 8:56Harry Stebbings?

  Does this get around the incentive problem in healthcare? And what I mean by the incentive problem in healthcare, as I'm sure you know, there are a lot of diseases actually where it doesn't make kind of economic sense for a lot of pharmaceutical providers to chase research, to chase treatments, because it's not a big enough market, because it's a six dollar treatment. Does this solve for that economic misalignment?

• 9:13Speaker 00

  I think it can help a lot with that economic misalignment, because then you have an authoritative source where we can all come together and build that can analyze these things. Because there's this concept of ergodicity. A thousand coins tossed in a row is the same as a thousand coins tossed at once. And because we're so limited in our information in our medical system, I just had my key man insurance, where I had to answer 40 minutes of questions. Have you smoked? Have you done this? It's stupid, right? We're all treated the same. I think assigned 10% of people have a cytochrome P450 mutation in their liver, which means they metabolize drugs fast quicker. So if you metabolize codeine, it turns into morphine, or fentanyl kills you. But that's a very basic genetic test. Yet we give everyone 500 milligrams of the same thing. With my son, a microdose of five milligrams of clonazepam,

• 9:56Speaker 00

  which is used for anxiety disorder, word within a neurologist allows him to sing. The standard dose is a thousand milligrams. They can only prescribe it at a thousand. But that is a six dollar a year treatment that affects his GABA glutamate balance, but only for a specific type of ASD, which is only 7% of all kids with ASD. But why would that be in a pharmaceutical company's interest? Because how are they going to make money off a six dollar a year treatment?

• 10:19Harry Stebbings?

  Well, how many people have ASD?

• 10:20Speaker 00

  It's 1 in 60.

• 10:21Harry Stebbings?

  Okay, so 1 in 60, so you've got a million people in the UK?

• 10:24Speaker 00

  Yes.

• 10:24Harry Stebbings?

  So you've got a six million dollar.

• 10:26Speaker 00

  Yeah, it's not great.

• 10:27Harry Stebbings?

  Exactly, it's not great.

• 10:28Speaker 00

  I mean, it's like, we know the benefits of vitamin D, right? But we still don't prescribe that at scale. And so many people are deficient.

• 10:34Harry Stebbings?

  What is the future of healthcare systems that, like, do you think with G- P P T models operating in this way.

• 10:38Speaker 00

  I think that you can change the nature of a doctor because a lot of the stuff is very basic. I think had Babylon Health and others trying that chatbot, it wasn't ready. Now you've got this. Everyone should have their own eyes looking out for their own health with that objective function. And then the nature of a doctor becomes different in terms of they have more rich information about an individual while it being preserved in a private manner. I think what you have is you have things like processes and procedures improving, a wound care, for example, in the N H S. If you are injured as an elderly person and your wounds aren't treated properly, you're more likely to die by a factor of eight times. Being able to monitor those types of things with this information set means you're eight times less likely. And then you have far more efficiency around that. So the information density around healthcare improves, which means that then our own healthcare improves.

• 11:20Speaker 00

  We all have access to as much knowledge as we want to within our own context, and so do our providers and the people that help us.

• 11:26Harry Stebbings?

  How do we think about open source versus closed source human healthcare data? Because obviously for us all to benefit as one, and mass sufferers around the world need to submit their data around responses to certain treatments.

• 11:36Speaker 00

  Yeah, so I think the wonderful thing about these models is they're few shot learners, so they don't need to have much information. And so isn't the classical big data problem. If you have open source language models that are fully auditable, I understand, like all the organic free range models, the ones we're building with no web scraped data. Those can sit on device like Google yesterday announced Palm two. The smallest Palm two model is four hundred million parameters. It works on your Google Pixel phone. You don't need giant models anymore. And then that model can just share the specific information that preserves your privacy with the bigger thing. And then it can take from that global knowledge base as well. So you'll have big global models, on device models. And I think open works for that because you don't need to have all the data open. You just need to know that Harry is old enough to have a drink, not that.

• 12:16Harry Stebbings?

  All the details about Harry's birthplace and everything like that.

• 12:19Speaker 00

  He's old enough, he's just not allowed to.

• 12:21Harry Stebbings?

  He gets guarded all the time, yeah.

• 12:22Speaker 00

  Yeah.

• 12:23Harry Stebbings?

  I totally get it. Were you impressed by the Google event yesterday?

• 12:25Speaker 00

  No, I think it was impressive. I said in February, like when all this thing was going on, come on, Google will be one of the main winners here. They have the L L Ms.

• 12:33Harry Stebbings?

  You do know you're the only person who said that on the show? And I've asked many and they've all said that Google are the laggards.

• 12:39Speaker 00

  It just takes a bit of time to move the ship, right? And so they've done massive organizational changes and other things. But I can tell you, T P U is on the most scalable. Architecture, like we have zero failure rate with our TPU language model training. Whereas with GPUs, it's like there's an ECC error, why? A solar flare, okay, run failed because the sun is angry with us and stuff like that. So when you've got the full stack and you have all that talent in Google, the question is, how do you make it organized, right? And so they had to have a story. Google did something called Protagonist Aristotle, where they analyzed what made the best teams versus the worst teams at Google. And it came down to shared narrative and psychological safety. People at Google were scared over the last few years because it came this weird monoculture. But now everyone has a shared narrative of let's build the best language models. And now there's an increased amount of psychological safety, being able to speak to things, the walls being brought down between deep mind and brain.

• 13:24Speaker 00

  And so I think you'll see them continuously improving. But then that does mean if you're a proprietary language model company, how are you going to compete with that behemoth?

• 13:31Harry Stebbings?

  The deep mind desegregation or kind of unification was supposed to, of course, a lot of friction and be it negative press reported. Do you disagree with that?

• 13:40Speaker 00

  Of course, it is a lot of kind of replicated jobs. There was brain and mind and now they're brought together. And it's a very different management style and other things. These things are never easy. But this is why, like you saw Palm, five hundred and forty billion parameters and that you had deep mind with sixty seven billion parameters in chila, which is like just train more as opposed to more parameters. You look at Palm two as a combination of both. And so it's trained for far more on far better data. And then that means it's only a fraction of the size, like fourteen billion parameters is one of the test comparator models versus the five hundred and forty and sixty seven. So you can start to see this fusion of ideas, even if the teams, you cannot integrate two big teams like that instantly.

• 14:18Harry Stebbings?

  Shared narrative, psychological safety, two of the biggest contributors. So now running stability, how do you think about integrating those two?

• 14:26Speaker 00

  So we've got the shared narrative, we're going to build the foundation to activate humanity's potential and then the motors make people happier. But it's been a learning process. A year ago, we were basically a mom and pop shop in some ways. My wife and I were working at it, like had lots of meetings out of our like sitting room and things because the office didn't have wifi and all sorts. Now it's like, growing up, we're one hundred and seventy people, we're going global, we'll have stabilities in every country. And the next year we're going multinational and that's difficult. Part of this is like, we went closed source on a bunch of stuff like Dream Studio. I'm open sourcing everything now. From next week, we're going to build our. Which models in the open and share what works and what doesn't work.

• 15:00Harry Stebbings?

  Why?

• 15:00Speaker 00

  Because I think this is part of the shared narrative. Someone needs to be open and share what's going on under the hood. And again, it's like it should be open by default because the value is not in any proprietary models or data. We're going to build open models that are adjustable. Even if it has licensed data in it, you can see every single piece free range organic models. Because that's what the world needs for all the private regulated and other data in the world. It's a completely different TAM to proprietary models. Because you can send so much of your 20 VC data to OpenAI. And I think you need both of those. So.

• 15:30Harry Stebbings?

  So why can I only send so much?

• 15:32Speaker 00

  Because you are a regulated company and so you need to make sure they're completely compliant. If you have an option of having a stable chat model, which will be announced in the future, that you own completely trained in your own cloud or on prem or on device. And then also using GPT four, that's the best of both worlds because then you don't have to deal with that. Healthcare data needs to again be owned by the individual. And so those models need to be owned and they need to be transparent. It can't be black boxes. Governments will not run on black boxes.

• 15:57Harry Stebbings?

  We're going to get to this later. I do want to touch on something that we had a great chat before this. And you said a brilliant quote and I want to get it right. But you said the dot AI bubble is bigger than ever and it will be the biggest shit show.

• 16:07Speaker 00

  Yeah.

• 16:07Harry Stebbings?

  End quote, which I actually took and tweeted, by the way, thank you. It would be some great. I thought if you saw it often, it's like, ah, this guy took my tweet.

• 16:14Speaker 00

  So good.

• 16:15Harry Stebbings?

  What did you mean by the biggest bubble ever and the biggest shit show?

• 16:19Speaker 00

  The dot com bubble, we've seen all these bubbles happen. You had hundreds of billions into web three and then developers got paid millions. Already the there's certain Chinese companies paying 1.2 million dollar salaries for PhDs. It's already getting a bit insane. They're in remembrance of that. The amount of money relative to the amount of opportunity within the sector is just completely misaligned. Like my TAM analysis is that a thousand companies have spent 10 million in the next year. A hundred companies spend a hundred and 10 companies spend a billion. Like P W C just announcing they'll spend a billion over the next three years. And that's a accountancy firm. Where's that going to go? They don't know. Nobody knows. And so a multiple of that will be allocated to this as the only growth theme in the entire market against a backdrop of rising rates, real estate crashing, et cetera.

• 17:01Speaker 00

  So the amount of capacity versus the amount and whale and wall of money into something that's growing faster. Anything we've ever seen is completely mismatched. And what will that cause? Like already you're seeing GitHub stars leading to hundred million dollar funding rounds with zero traction and zero business model. Like stability, we actually have a business model. It's a good business model because I designed it. But other things like money will go everywhere and any expertise will get bit up.

• 17:23Harry Stebbings?

  For the space because it means that projects will get funded that maybe wouldn't have done better exploratory generally in over.

• 17:29Speaker 00

  I think it starts good for the space, but then it gets bad for the space. Because you see the raccoons and shysters start to come in here. You start to see like malformed things where there's a race dynamic where everyone's trying to build their own models and doing all sorts and massive economic waste. And you see a distraction from what we need to do now, which is there's chaos. So we need to standardize some things. We need to feed these models better data and other stuff. And that's why we're moving so hard at stability. There should be no more web script data in here. There should be national data sets that are good quality to feed these free range organic models and national and proprietary models and others. And so that's why and the reason I signed that letter, because I think there's a six month pause to get all of our shit together.

• 18:07Speaker 00

  Before things go completely insane and next year this is everywhere and everyone's investing in everything and it's just absolute chaos.

• 18:15Harry Stebbings?

  You unpacked so much to me there. I want to go one by one. You said about kind of national data sets. Why national data sets versus supranational data sets?

• 18:23Speaker 00

  Because I'll give you an example. There was a team that did Japan diffusion, including some of our staff. So we took stable diffusion and then changed the language model because when you typed in salary man in stable diffusion, it was a very happy man. For us in Japan, a salary man is a very sad man. Local context is important in these models because we're going to outsource more and more of our thinking and minds to it. And so do you want to have a British model or do you want all the models to be Palo Alto? Like it's a sparkling wine has to be from the champagne region. Like is the only real foundation model AI from Palo Alto like.

• 18:50Harry Stebbings?

  Not a good thing. We need national models.

• 18:51Speaker 00

  We need, it's a national infrastructure because there is no doubt this is more important than five G. These models are like really tainted grounds that occasionally go off their meds. And you want to have the ones from Oxford, Imperial and Edinburgh as well as the ones from Stanford. Because they understand the local context and so they understand you better and they'll be better for that. As part of that, every nation will need their own data sets, which again have from broadcaster data. They will need their own open models that can stimulate innovation internally as well.

• 19:16Harry Stebbings?

  Who owns national data sets? Is that governance?

• 19:18Speaker 00

  I think it should be the people. I think it should be open and public domain.

• 19:21Harry Stebbings?

  How does that come into fruition?

• 19:23Speaker 00

  Well, we have not.

• 19:24Harry Stebbings?

  Make a world where we have national verified data sets which can be leveraged by independent private companies.

• 19:29Speaker 00

  And others, and universities and others. This is what we're doing right now. We're working with our multinational partners, lots more to be announced soon, and multiple governments for a framework for what good data looks like to feed these models, to stimulate innovation and localization. And that is a public good because national broadcasters have all of this data. You just tokenize all their kind of things. And then you have things like the implementation of these for education and healthcare. You can take generalized learnings and then again, feed the models that thing. What does a great data set for a great British EPT look like? I think it's open, it's interrogated, and it's optimized.

• 20:02Harry Stebbings?

  When you look at all the different things that we've talked about from you, relative like treatment of MS to ASD, and then its impact on education, and we just to PWC spending money on it, there are so many problems that can be solved. Surely, we can find a home for the cash.

• 20:17Speaker 00

  Yeah, but I'm not sure where. There's going to be this mismatch.

• 20:19Harry Stebbings?

  If you were invested state, how, if you were me, okay, what would you do? I'm an early stage investor, I invest globally. What would you do?

• 20:26Speaker 00

  I would, there's going to be this tailwind of beta, and then you have an alpha play on top of that, right? So the beta play is that you just invest in any good founder. And if you get in, you figure out what can I offer as a value add there? Am I offering distribution? Am I offering people? Am I offering this? And emphasize your value set. There are a few people like coming out here, but then what is you see good companies, like good ideas, but not businesses. They're building surface level things, these wrapper layers and others, and they're not thinking about distribution and data. If you want to have distribution, what do we do? We went to Amazon and said Bedrock, because then it gives us a hundred thousand sage maker SMEs. And we just have to give them the models that they can then take to the private data and we get a share of all of that. This is how we saw it, like rather than being responsible that. So if you can bring that distribution to that, it was important.

• 21:10Speaker 00

  This is part of that Google memo that went out. We don't have an edge layer that's OpenAI. OpenAI used Microsoft for distribution and that flywheel. If you have a business that's focused on innovation at the core, that's not actually a business. It becomes a business when that innovation becomes product, becomes distribution, when it has an advantage on data and other things. Those are real modes.

• 21:27Harry Stebbings?

  How did you analyze that partnership between OpenAI and Microsoft?

• 21:30Speaker 00

  I saw it as the objective function of OpenAI is to build AGI and they reckon they need $10 billion to do it and they did that. Like they're building a business on product and things, but they don't care. They're not trying to build a sustainable business. They're trying to build an AGI.

• 21:42Harry Stebbings?

  Why? Just tell me, AI to build a sustainable business?

• 21:46Speaker 00

  No, they're building an AGI to turn the world into utopia. It's written in their path to AGI thing. They think this can basically bring about utopia. So a lot of people in these labs, and we have people joining from all of these labs, like they almost zealous in their.

• 22:01Harry Stebbings?

  But there is a misalignment there between them and Microsoft and their desire to create that utopia, no?

• 22:06Speaker 00

  Yes, because Microsoft is a business. And so this is why you've seen like articles in the information, like Microsoft say OpenAI aren't compliant and OpenAI say Microsoft aren't this. These things happen when there is a misalignment of objective functions. But again, you should view OpenAI as what they want to do is build an AI that can basically make the world better and hopefully not kill us all, which they say it might. Which is a bit concerning, which is why I hope they have better open governance.

• 22:28Harry Stebbings?

  How did you think about distribution? You've seen the hugging face partner with Amazon. You've seen obviously OpenAI with Microsoft. When you think about distribution and your competitive edge there, where did you land?

• 22:38Speaker 00

  So my business model is actually very simple. I haven't really talked about it much. Stimulate Open, one of the biggest providers of grants to open source software, tens of millions already. And then take the best of Open, which hopefully we build ourselves, and then an open base with an open data. And then commercial variants with licensed data and then national variants. So you have Hindi Insurance Adjusted Stable Chat or Indonesian Pharmaceutical Worker Stable Chat. That's available in every cloud, on-prem, on-device with licensing fees, royalties and revenue share. And the system integrators work with us as well. Lots of announcement to come. And so by standardizing and stabilizing all the complexity to these very sophisticated building blocks, these very intentionally built models.

• 23:20Speaker 00

  That really helps the world integrate this stuff by building playbooks and other things. That's the core business because it doesn't require actual innovation. We are still innovative and the leaders in media in particular. Instead, it requires data and distribution. Data to the models, the models are open and interpretable, and models to the data via our partners. And that's valuable because the private data in the world is far more valuable than the data that you will send to proprietary models. And it's not a race to the bottom either. That's where we are. We're a modeling agency with hot GPUs. Building a distribution around the world, realizing that India and other nations will leapfrog to intelligence augmentation. Just so they leapfrog to mobile, they will embrace this technology far quicker than we will in the UK even.

• 23:55Harry Stebbings?

  Why?

• 23:55Speaker 00

  Because they have to. India, all of the outsourcing jobs in programming will go because GPT four can go level three Google programmer exam and pass it. Outsourced jobs will go the first, whereas in France, you're never going to fire a French person. So those jobs are safe. And so they have an objective function when they need to embrace this technology. In Africa, one to one tuition, every kid in Malawi is some things we line up. We've got other nations. We're going to bring them all this technology and tablets. And guess what? Their lives will transform. One AI per child is what I want to call it. But think about the potential of that because you have one's teacher per three hundred kids. What if they had a chat GPT level AI? The ROI is high and the need is high, and so they will embrace it far quicker than we will.

• 24:34Harry Stebbings?

  What happens to countries that rely on outsourced work in those kind of freelancer economy jobs?

• 24:39Speaker 00

  The question is, you've seen OpenAI study, you've seen the, which said tasks will be replaced up to forty four percent.

• 24:44Harry Stebbings?

  Yeah.

• 24:44Speaker 00

  You've seen Goldman Sachs say adds percentage points to GDP. I think the only solution to this is entrepreneurship. And so we need to give the tools to create new jobs that can replace some of these old jobs being done. So like to the various Asian governments, I'm saying adopt the UK policy of these sandboxes, financial AI and other regulatory sandboxes. So you can take these technologies, these national models that we will help you build with our consortium partners. And then spur innovation to create the jobs to replace the existing jobs because you'll upgrade your entire society. Bring these models into your governments and other things to go from slow dumb AIs, which is the national organization's healthcare, to intelligent dynamic ones.

• 25:21Harry Stebbings?

  On implementation, and when we think about bluntly seeing this in action in society. I'm sure he's very aware of technology cycles taking so much longer than one anticipates. How do you think about that in actual? There's two for one is adoption on enterprise and another is adoption on consumer. Say if we do the adoption on the consumer side, which is impacting freelancer jobs and impacting education. What do you think that is like?

• 25:43Speaker 00

  So I think on the consumer side, you're freer with your information. So you can use a lot of these things. The APIs of OpenAI and Co here and others are fantastic. Right, and Google Palm now being out there. So it will be integrated to deliver better consumer experiences without it being creepy like you've seen with some of the chatbots, et cetera. Because it's going into Word, it's going into Workspace, you know, like it helps already. Like, we will have a conversation, we'll be automatically logged by our Pixel phone, and then we'll get a transcript and remove bits that we don't want to share, that it goes into a global knowledge base that reminds us of things. That's inevitable. On enterprise, it takes longer because you need to have auditable standardized models. If you're a financial services institute, you can't have a single piece of crawl data in there. And so that's what we're deliberately building with the largest companies in the world, because we're building dedicated. You can't have a single piece of crawl data if you're a financial services.

• 26:27Speaker 00

  Yes, because the danger is if it has some Reddit in there. So StableLM, we'll put out next week. It wasn't as good as the other models, because we're going to make a point about Reddit data being bad. It's not about more data, it's about better data. We had Data Comp, which is the next generation Lyon that we funded the compute for, whereby a quarter of the parameters of OpenAI's clip, it outperforms with a beta data quality. What makes good data quality? That's something we're exploring right now. But from the investment banks we've talked to, asset managers, we're building dedicated teams for the largest ones in the world to build the proprietary models. The feedback we've got is we cannot use a black box. We need to know what data is in there. The regulators are asking us. We don't want to have this out of sample thing where it's seen something on Reddit and then it says something rude to our end users. Why is Reddit data bad? Reddit data isn't bad in itself. It was just a case of more data is not always good.

• 27:12Speaker 00

  So right now, we are using all these web scrapes and we're training our models by taping their eyes open. And then it took six months to turn GPT-4 into ChatGPT-4, because we had to tune it and give it a haircut and stuff and bring it back to society. The point is that we need to find the right type of data, because rubbish in, rubbish out, is something that we've heard a lot. It's not bad in itself, but if you just scrape it without the proper cleaning, it is bad. Because what is it? It's people just kvetching a lot, people being biased. Do you really want to feed your kid the whole of Reddit? Do you want to have the best curriculum possible? And this is actually one of the ways the models learn, like stable diffusion, we train it on the whole internet and then better and better image subsets of it. And that's the same thing with large models, called curriculum learning. Train it on a big base that's solid and then did it. It sounds familiar, doesn't it? Kindergarten.

• 27:55Speaker 00

  The hardest thing is that how do you instill values and political correctness in models? There is no such thing as an unbiased model. DALL-E two, when OpenAI had that and they introduced a bias filter. Any non-gendered word, they had a random gender and a random ethnicity. So you typed in sumo wrestler and you'd get Indian female sumo wrestler. That was a good picture I've got to save somewhere. This is why you need national data sets, you need cultural data sets, you need personal data sets that can interact with these base models and customize to you and your stories. Cause you and I both have our stories that make up our psyche.

• 28:27Harry Stebbings?

  Sure.

• 28:28Speaker 00

  And understanding that context is so important to have AIs that can work for us, not on us.

• 28:33Harry Stebbings?

  And so it's essentially like a next generation cookie that personalizes our data to allow for better search.

• 28:37Speaker 00

  A mega cookie. And if you standardize the base foundation models, and they call it the hypercube, every modality, because we do all the modalities, all the sectors and all the nationalities, then you don't need to have a million different models like those dream booths of the avatars. You said you have a base model that you then have a vector embedding around. Cause these models contain all the principles and the embeddings point to the important bits that make up Harry or Ahmad. And then you can search those and adapt those rather than having a million billion different models, which is just confusing.

• 29:02Harry Stebbings?

  So I had dinner the other day with one of the largest kind of media publication owners in the world. And he, he said, I'm worried, Harry, I don't think that I will have a business in a couple of years. I think, Bunny, we're getting killed on our advertising because everything's getting scraped and they're not coming to our websites. And that's where we get paid, we get paid for clicks. Is he right to be worried?

• 29:20Speaker 00

  I think he is right to be worried. Like again, you look at Google's announcements yesterday, the two members day after pump two. You suddenly look at the new Google page where they've got the language model and it's just text and where the clicks. It was like when Google introduced AMP. This is where rather than look at the New York Times page, you have this formatted thing with no New York Times kind of stuff there. These search entities that aggregate are disintermediating more and more. And people are going to become used to just having synthesized input. So what does search look like? What does it look like when your GPT four can write you an article about any news that's happening in a way that's customized to you and your context and everything like that? This is massively disruptive for media and information. And so they have to think, where am I in the future? Again, the worst way to think about the impact, this is the retanted grads occasionally off their meds and we push a button and we can get a thousand of them.

• 30:04Speaker 00

  Those grads include journalists and you can have your own journalist army, your own writer army, your own coder army, your own designer army.

• 30:10Harry Stebbings?

  So the pushback against that is libel. Libel is real. You are gonna get unbelievable amounts of libel cases and then OpenAI will be fucked. You cannot have a thousand libel cases a day.

• 30:21Speaker 00

  That's the thing. If you say this needs to be checked and cross-checked, that's one thing, but a lot of the media companies say we're the source of authority. So a way that media companies can shift is by having in a deep fake another age where everything can be generated. We make sure this is real news. We are very thorough in the way we do it.

• 30:37Harry Stebbings?

  So this is interesting. So you place a premium on authority.

• 30:40Speaker 00

  Premium authority. This is why you've got the checkmarks coming out of Twitter and the organizational thousand pounds a month and Facebook doing the same. Because you need to have a level of authenticity, a level of authority. But again, is the news fair and balanced? I've had lots of hit pieces coming out against me and got a lot more. It's not because they have angles. And so what is the bias of the New York Times versus this versus that versus others? How do people consume news now? And even news consumption has gone down dramatically, right? Because people consume news through their social networks, through their groups and other things. So you have to say, what is the model?

• 31:11Harry Stebbings?

  But the hard part is none of the next generation models or AI providers want to be content publishers. So how do you fit in a world where you're killing their business model on the content side, but they don't want to be publishers?

• 31:23Speaker 00

  You will have AI-first publishers. So if you remember Vox and these things when they kicked off, they wanted to be generous. They wanted to be AI technology first. You're gonna have a new wave of AI-first publishers that aren't just AI, but it's AI plus humans.

• 31:35Harry Stebbings?

  What does that look like? So AI plus humans.

• 31:37Speaker 00

  AIs plus humans means that you have information coming in and then the stories are drafts are automatically written, reviewed by humans who then give their input to train it better. This is the feedback flow. And then what happens is it comes out and there's a factual anchors and then it gets customized to Alabama. And then Alabama can text and all sorts of other things. Because you can tell it TLDR, Too Late Didn't Read, explain it like I'm five, make it more complex. And so you're gonna see something very interesting here, which is the right news at the right time. The localization will return, but again, through AI first. I think this is the thing. We're seeing AI integrated. But the next wave is going to be ones who understand design patterns, AI first, everything and information flows. Once these technologies are a bit more mature.

• 32:18Harry Stebbings?

  Can you just help me understand AI integrated versus AI first?

• 32:20Speaker 00

  AI integrated means that I have an existing newsroom and I bring in AI to write faster drafts and things like that. AI first is saying, I have an army of things I can spin up instantly that can help me achieve these certain things to create news that is valuable for this reason with this feedback loop. And so you build the system from the start, thinking AI at the core versus AI being integrated in to improve existing systems. Because so much of news is what? We find information, we have drafting, we have this, we have that, we do these checks. A lot of that can be simplified, just like we moved from the analog to the digital age to the internet age, the next age is the AI age.

• 32:54Harry Stebbings?

  So I have to ask, when we think about kind of AI first publishers and the next generation of media, who does this? Is it startups? Because I've met honestly fifty, maybe more, AI companies in the last month. The feedback is always the same. They're not operating off a defensible mote of data. They're literally a thin application layer on top of an existing model. Ninety-nine percent of the feedback.

• 33:16Speaker 00

  So you look at someone like Harvey, for example. They went to law firm, they said, you are a distribution and we're going to integrate and improve your system and build our system for your system. So I think a lot of these people are trying to build it and they will come and they're trying to get in there as opposed to just retargeting. Where can you go in and transform?

• 33:30Harry Stebbings?

  Is that the wrong model Harvey did?

• 33:31Speaker 00

  No, I think it's the right model. I think that a lot of organizations are elastic and plastic now, so you can go in and give them an integrated thing saying, you will be my test case, I will help you upgrade as a Skunk Works lab and build a system alongside your system, as it were.

• 33:43Harry Stebbings?

  And so, and you think enterprises will say, sure.

• 33:46Speaker 00

  I think now they will, if you can keep their data inside internally. And I think, again, with better open models, you can enable that. So people can build on top of open models, there are dedicated instances on Co here and others as well. And so the tooling is now catching up so that you can have a new generation of startups where their first customers are massive companies they would never get otherwise. Every big company is looking for an answer. If you can give that answer, that contract that would have taken you a year, you can get in a week.

• 34:10Harry Stebbings?

  Do you think so? Because you still want to get in the door.

• 34:12Speaker 00

  You've got to get in the door. And that's hustle, man. So again, this is where the Harvey guys, this is why I went straight to the hyperscalers and I said, you need to have standardized models for open for regulated data.

• 34:21Harry Stebbings?

  What did they say to you?

• 34:22Speaker 00

  They said, really? They're like, can you build them? Here's some models that we built. I'm like, oh. And then I told them exactly how the things would be last summer to now, and it's followed that and I've kept in touch and I've improved it. And this is why I'm building dedicated teams for the largest companies in the world. I'm not telling them to try to sell you anything. Like over the next year, I'm gonna help make sure you do not get blindsided. Like I try and sell you models and people are offering us tens of millions per model. I'm like, I'm gonna build a proper partnership with you and that means I'll have an LTV from you. What does that proper partnership mean and who's that with? That's with IBM, that's with SAP, that's with. So we've announced Amazon, let's say we have lots of other announced with the biggest companies in the world, where they have amazing teams, but they can only move so fast. And I'm building dedicated teams that help them move and understand the whole sector without trying to like sell them on services.

• 35:06Speaker 00

  I'm trying to say, I will build you a customized model if you want, but I'm only doing that with a dozen companies so I can focus down. And I will tell you that GPT four is gray or coherent is gray or all this stuff. All the latest research through the communities we support, I will make sure you're on top of relevant to your sector. And you've got dedicated people helping you in this transition period. Is that aligned to your core model? This seems like an ancillary product. It is like a SAP consulting services. It is like that because I need to understand these sectors better. What does the hyper key look like? What does the insurance adjust to GPT look like as a fundamental basis? And so a lot of people are like. Able to extract that data and then take it with you and do the learning on it? Yes. And so this is part of the thing that we will have a generalized model and we're very clear. But then you can have a specified model just for you as well, as long as it doesn't interfere with that.

• 35:49Speaker 00

  The reality is no models that are out today will be used in a year. Unpack that for me. This is mind blown. So again, you see the order of magnitude improvement. Palm last year was five hundred and forty billion parameters, then Chinchilla sixty seven and now fourteen. Five forty to fourteen is a big step. You see the quality of GPT three versus GPT four. Is there any extent to how low it can go? We have no idea. You already said this is impossible. Two years ago, you were like, no way. You have a single file that's maybe a few hundred gigabytes that can pass every exam apart from English Lit. Fucking English Lit. Fucking shit. No way, no way. So we're already at the impossible and. What does that mean though? If we go lower and lower, and then what? And then what? When it jumps, as you saw with the llama stuff and all the innovation around that to your MacBook offline, the marginal cost of creation and coordination becomes zero. I don't know what it means.

• 36:35Speaker 00

  Nobody does. And this is the thing, it always takes longer and shorter to implement groundbreaking technology than you've ever seen. And this technology can be implemented like nothing we've seen before. And this is my call, not concerned and we, I hate.

• 36:45Harry Stebbings?

  The doomsday says, and I'm excited for the future, I'm terrified for the future too. But everyone always says technology revolution is in industrial age, whether it's the introduction of PCs. These were, you know, industrial was a thirty year plus. Actually, PCs in enterprise was ten years plus. The challenging thing is like the learning curve to use chat G P T as a marketer is nothing. And the integrations is a day.

• 37:06Speaker 00

  It's because, yeah, like you want to write an A P I. It's not a day. You just give it the manifest spec and it automatically generates. It would have taken days before. It's an amazing experience.

• 37:14Harry Stebbings?

  And so the transition is so much more compressed today.

• 37:17Speaker 00

  It came from the existing system, so it goes seamlessly into the existing system versus like web three that tried to create a system outside the existing system. And all the money was made and lost at the interfaces. Again, it's like deploying grads at scale, like with the thirty two thousand token context when you're G P T four. Twenty thousand words of instructions. What does that do to SaaS? So my thing is that we're still in this crazy period. Next year it will settle and then it'll go ubiquitous. Well, a lot of companies know they need to do something, but they don't know what they need to do.

• 37:42Harry Stebbings?

  Are they adopting it now?

• 37:43Speaker 00

  They're doing the P O C thing, like some like Microsoft and others for consumer, they're adopting it. Consumer adoption is a much lower bar. When this starts going in enterprise, it's going to be a freaking train. There's so much of enterprise about services and information flow. And if you push a button and have a thousand of these things, that's a huge difference. I think this will be a bigger economic impact than Covid. I don't know in which direction. I heavily positive, but again, giving that example of an India or one of these app source places, you lose B P O jobs, you can make it up on entrepreneurship. You embrace the technology.

• 38:12Harry Stebbings?

  What do you think the business model of the future is for those models moving into enterprise?

• 38:17Speaker 00

  I think it's the same as always. You've got good products, good distribution, you know, you lock it in like one point five million people still use A O L. Forty percent of the world still doesn't have internet. Again, we're super privileged where we are, right? And so you look at that and I look at emerging markets, I'm like, all of finance is securitization and leverage and securitization is telling a story. The only thing that matters for a stock is the marginal story and how it evolves. What if you have massive information about every child in Africa and every business in India and they embrace this technology properly? Massive financial growth.

• 38:47Harry Stebbings?

  When do you think next year for their embracing it?

• 38:49Speaker 00

  I think that people are still getting used to all this. We haven't standardized any of the things. We don't know what the design patterns are. I think that. What happens is everyone's doing this at the same time and they're all trying to get to grips with it. And so again, we have this like six month window where everyone's getting to grips with it. And then we standardize our design patterns and they spread and you start implementing. And you see some people outpacing others, which means that you have to catch up and then you're forced to implement it. So this is how I see the race dynamics occurring right now.

• 39:12Harry Stebbings?

  You say about forced to implement it. I think the truth is they just have no fricking idea.

• 39:16Speaker 00

  Right now they don't.

• 39:17Harry Stebbings?

  Which I totally understand. I didn't blame them for, but I tweeted actually the other day that I think the biggest AI companies will be services based implementation companies for large enterprises.

• 39:25Speaker 00

  A hundred percent. That's why I said if you're a startup, the best thing to do is you identify an enterprise that will be transformed by this. And you go to them and you say, I have a solution and I'm going to start with you and I might go bigger. But I'm going to help you through this period by doing this and this. And they will appreciate that and there will be capital available for that in a way that you've never seen before. Cause you know how difficult it is for small companies sell to big, but the big companies have no idea except for their CEO and their board are telling them. You look at the number of mentions and earnings calls, it's like that. Every earnings call next quarter and then by next year, literally every single one, they're like, what is our strategy? It's not like, what is our web three and metaverse strategy? It's I need this strategy now. Again, it's what is our COVID strategy? It'll be that level of urgency within a few quarters.

• 40:06Harry Stebbings?

  Would you raise money if you were them? So you go to a corporate, you say, hey, you know what? I can solve your problem. This is how it will work and they will fund you. They'll give you the data. Would you raise money?

• 40:15Speaker 00

  Yeah. I mean, like again, you need the people. The people is the key thing here because you can have the technical chops, you can have an understanding of the industry. But to build a good business and to scale it at the pace that you need to, to keep up with this is incredibly hard.

• 40:27Harry Stebbings?

  Do we have enough talent?

• 40:28Speaker 00

  No. And so this is why we support the fast AI courses, which transform normal developers into ML developers and other things like that. But again, these models are actually not that hard to work with. Fifty percent of all code on GitHub is AI generated now. So you can even use Copilot to help you code the models and other things like that.

• 40:43Harry Stebbings?

  What do you think that code generation is in five years?

• 40:47Speaker 00

  Why would you need code? Code is just a way to talk to a computer.

• 40:50Harry Stebbings?

  I'm back.

• 40:51Speaker 00

  So when I started twenty one, twenty two years ago as a coder, I'm forty now, so just doing that when I was eighteen. I was writing assembly code, like really low level stuff. There were no libraries, there was no GitHub, there was nothing like this. Like right now coding is like mixing and matching. It's like building Lego. And AI can build that Lego much better, especially in five years. What you're doing when you're propping like programming languages, you're telling it to go and do something. Even something like Palm, like we sponsor a amazing coder called Lucid Raines. If you want to cry as a programmer, you go and look at his GitHub, most productive developer in the world. He recreated the whole of Palm in two hundred and six lines of PyTorch. But again, why would you need a human for that? If the AI gets better and better at coding, just tell it what you want. I want to create an app for twenty minute VC that has these features. Of course, it will go and build it automatically.

• 41:36Speaker 00

  Where is the human coder in that?

• 41:38Harry Stebbings?

  What does that mean for the future of entrepreneurship? Actually, a good thing in terms of the complete democratization, where anyone can build anything.

• 41:43Speaker 00

  Anyone can build anything. This is why distribution, data, relationships, products become important. Because it already became easier to build anything, right? But what makes a good product? Again, there are these unchanging things. Have great customer satisfaction, deliver value. People get distracted by technology. Like I was at this CryptoX AI thing on the weekend, they were talking about decentralization. I was like, guys, this is all bollocks. It's not about the technology. It's about what you're creating that's valuable to help people. Focus on that.

• 42:11Harry Stebbings?

  Who do you think wins in the next three to five years? Startups or incumbents? Because incumbents have the distribution.

• 42:16Speaker 00

  I think it's incumbents, but there's a lot of startups that will be billion dollars. And even on the thin layer thing, ITA Software sold for seven hundred million and Kayak sold for two billion. And that was a layer on top of ITA. We've seen many of these examples here, right? Again, we know that value and moats are not necessarily innovation first.

• 42:31Harry Stebbings?

  Well, yes and no. It's interesting. I had the Tom Tungas on the show. And Tom is a very famous ML AI investor, and he analyzed infrastructure versus application layer. And both actually were about two trillion dollar TAM. The difference is in the infrastructure layer, there was three companies. And in the application layer, there was fifty. And so your average enterprise value was significantly bigger.

• 42:51Speaker 00

  I would agree with that. I think that there's only going to be five or six foundation model companies in the world in three years, five years.

• 42:57Harry Stebbings?

  Do you think they've all been created now?

• 42:58Speaker 00

  Yes.

• 42:59Harry Stebbings?

  Which are they?

• 43:00Speaker 00

  I think it's going to be us, Nvidia, Google, Microsoft OpenAI, and Meta and Apple probably are the ones that train these models.

• 43:07Harry Stebbings?

  Is Anthropic good?

• 43:09Speaker 00

  Anthropic are great, but from a business model perspective, you have Claude on Google API and you have Palm two. How are they going to keep up with Palm two? They can raise billions, but Google spend twenty billion dollars a year on AI. DeepMind's salary budget is one point two billion a year. So DeepMind's salary budget is one point two billion a year? Yes, so that's in the public kind of filings. They technically make a billion a year from their internal counter payments with Google as well. But again, Google, how much money do they have? A hundred and fifty billion dollars to win this. Fuck. How much money do you need? I have a business model that is going to be massively profitable very soon. Because of the national services? Because of various things. I haven't given the full details. I will over the next few months. I've got a nice little case study with some universities coming. I like it to be a surprise. It's really hard for you.

• 43:52Speaker 00

  Talent, keeping talent together, A plus teams. So we've had zero attrition in our developers and they're amazing. So we've got video models, audio models, all these things coming out. Everyone says you need to be in the valley. You're in London. Yeah. Do you disagree you need to be in the valley? Of course you don't. I am going to bring this technology to the whole world. I'm going to bring it to all the I O Ts and universities and the best of people in all of those will join Stabilities in the local thing. I'll have talent. I'll bring this to all of the national broadcasters and biggest family offices around the world. I'll have data. Nations will build supercomputers that I'll build open models on. I'll have super compute. So I'll have more super compute talent and data than any other company and I'll build it all in the open. And one thing I heard you talk about before, which I thought was fascinating, was your access to super compute. You compared it to existing large incumbents. Why do you have more super compute than other people?

• 44:35Speaker 00

  Because I went and I did it. So we had articles coming out saying about our burn. I'm like, I have oil wells whenever I want to build petrochemicals. Every day we have companies coming to us, asking us for our super compute because it's not available on the market. You need these chips lined up with interconnects and we've got seven thousand A one hundreds now. You know, we have T P U's, we have all these things and we know how to use them and we can share them with people because we're open. Whereas Anthropic and others cannot. At the worst case, I'll build a foundational model as a service company and I'll make a hundred million dollars in profit this year without having to charge even market rates and I can retire. I want to do that and bring this to the world. So I think computers misunderstood. It's not like Bird and all these scooter companies and others, they spent money on marketing. This is actually an asset right now that's scarce and so there's.

• 45:19Speaker 00

  There's no harm in scaling compute. And then with the top chip manufacturers, they're building us dedicated teams. And again, they're coming in and supporting us because our models drive demand for their chips. The more open models are, the more open demand is. So it's a virtuous circle there as well. And so we get compute before everyone else.

• 45:35Harry Stebbings?

  Can I ask, in terms of like short term economic growth, how do you think about the impact that everything we've just discussed has on rising inflation, rising interest rates, the short term employment rates?

• 45:45Speaker 00

  It's massively deflationary. The biggest drivers of CPI inflation in the US were education and healthcare. And that was almost all administrative and bureaucratic. In the next few years, guess what gets disrupted? Those, but they don't get disrupted this year or next year, it's the year after, because those ones take a bit longer.

• 46:00Harry Stebbings?

  How does that impact the economy there? When we think kind of US, UK, what does that look like in terms of the three year time period?

• 46:05Speaker 00

  I think the UK benefits. Unicorn Kingdom is a new kind of thing is because we have amazing policies like every single AI company should come to the UK because cloud computing is now included in R and D tax credits. It's a 27% rebate on losses in cash. We can now issue scale up visas, global talent visas, like the Deep Floyd team that released the best image model in the world ever from stability. They were brought in on tech talent visas that was turned around in one week.

• 46:28Harry Stebbings?

  Do you think the UK's done a good job in terms of implementing regulation and policies to bring AI talent?

• 46:33Speaker 00

  Had the best apart from maybe Japan, yes.

• 46:35Harry Stebbings?

  What have Japan done?

• 46:36Speaker 00

  Japan has some very interesting ones around web data scraping and others, but again, Japan has a very different culture. So even if policy is good, it still doesn't have the same innovative thing.

• 46:44Harry Stebbings?

  Who's done the worst?

• 46:45Speaker 00

  The worst? I'm not sure, actually. No one's done too bad. The new European legislation was really bad. Now it's got a little bit better, but always Europe wants to be the leader in regulation. Fair enough. It's never an easy thing. And this is the thing, like, I think the UK is in a very good position and the government's forward leaning. I mean, look, the nine hundred million pound supercomputer, hundred million pound L L M task force that's been equated to the COVID level of seriousness.

• 47:06Harry Stebbings?

  What do you make of the OpenAI competitors? I've seen quite a few which are OpenAI for Europe, and we've seen three or four now. Is this a zero sum game and OpenAI has won that race, so to speak, or?

• 47:16Speaker 00

  I think it'd be difficult to compete against them because they're executing incredibly well. And I think, why would you use OpenAI for Europe versus Palm two versus G P T four? What can you bring? But you will have national champions and others. I think it's. It's incredibly difficult to compete in proprietary. I think in open, it's a bit different because of standardization element there. But again, my play is to be the benchmark across every modality because there's no other company, apart from you and OpenAI, that does every modality. There's no company that's as aggressive as me in emerging markets. And so they had to say, what is my edge? Because you can have an edge, like you can be the OpenAI for government or defense or for healthcare. And really get in and understand those and then you can be sticky. Like what are the like scale is now going fully into defense. They've announced their integrations with the Air Force and all sorts of other things. What is your edge?

• 48:01Speaker 00

  What is, again, your moat? What is your business model and what are you reliant upon to deliver that value that can increase?

• 48:07Harry Stebbings?

  This is why I was surprised when I saw you sign the petition of Elon in terms of pausing for six months. You should unpack why you did that.

• 48:15Speaker 00

  For six months, you're not getting H one hundreds and TPUV fives anyway, so it's a natural pause. But also because the shit show is coming next year. So I said, we have to self-regulate. Like, for example, the adversaries already have GPT four. Why? Because you can just download it on a USB stick. You don't have to train your own when you can just steal it. Let's have better OPSEC. Let's have better standards around data. Let's stop and move off web scrapes by next year. We had hundreds of millions of images opted out of stable diffusion because we were the only company in the world to offer opt out of data sets. Like, let's bring in some standards around this before it's everywhere. Basically where we are now, you remember COVID? Your mum is talking about this and your aunt and everyone's talking about generative AI. And they're asking you, Harry, what's going on? But you haven't had the Tom Hanks moment yet because everyone was talking about COVID before Tom Hanks got it. And then when Tom Hanks got it, that's when global policy changed.

• 48:59Speaker 00

  Because if Tom Hanks can get it, anyone can get it. What is that moment for generative AI?

• 49:03Harry Stebbings?

  What do you think it is?

• 49:04Speaker 00

  I don't know. I know it's coming because I know this technology is definitely everywhere next year and is disruptive.

• 49:10Harry Stebbings?

  You don't have a chance that takes much longer, three to five years.

• 49:13Speaker 00

  No chance. It's so useful right now. You think about certain industries and how they'll be affected by having the ability to have a thousand GPT fours working together.

• 49:22Harry Stebbings?

  You said in a tweet, actually, I think it was a reply to a tweet, but you said hallucination is a feature, not a bug.

• 49:27Speaker 00

  Yeah, so right now people are trying to treat these models. So we trained the whole internet and like stable diffusion is a hundred thousand gigabytes and a two gigabyte file. What on earth is that? It's not compression. It's none of this kind of stuff. It learns principles. GPT four Nvidia said they built the dual H one hundred with the NV link for that and that's a hundred and sixty gigabytes of VRAM, which would imply a two hundred gigabyte model. What is that? That's a hundred gigabyte model, two hundred billion parameter. That's nothing. So then they can pass all these exams. So what we did is we took these really creative things, just like you start school and you're creative and then you're told you're not allowed to be creative until you're successful and you can be creative. Because schools like petri dishes, social status games and childcare, that's a story for another time. These models start out incredibly creative and that's their advantage. And then we train them to be accountants with RLHF.

• 50:09Speaker 00

  And somehow, despite the fact that it's only a hundred gigs or two gigs, they can still pass these exams and no facts. They weren't designed to have facts. They were designed to be reasoning machines, not fact machines. So hallucination isn't a hallucination. It's just if you're a really talented grand and you don't know something, sometimes you might just make it up. We'll do a post hoc rationalization. It's like the image models. It's like it can't draw hands. Can you draw a hand in one second? These are the things. We have to understand where they are and we have to put them. I say everyone, put it in its place and process. Like mid journey, like we give a grant, the B two of that, I said, just build because it's amazing. It's awesome. It's not a model by itself, like a stable diffusion that you just put something in. It's a whole process, architecture. Similarly, these models are like the intuitive part of your brain that you then pair with the logical part of your brain.

• 50:51Speaker 00

  And then you can have a hundred of them looking at each other and checking out each other's things like Cicero by Meta was an amazing paper. They took eight language models and got them to interact with each other and it beat humans at the game of diplomacy. So this is why I said, use them for what they're amazing at, which is reasoning and creativity.

• 51:05Harry Stebbings?

  Do you why though that Jeff Fenton is right that actually a more intelligent being has almost never been controlled by a less intelligent being? They will inherently be more intelligent than us in the next.

• 51:15Speaker 00

  Yeah, I I picked up my blog a few days ago because it was a bit annoying having all this bottle up inside. And one of my buddies, JJ at OSS Capital, said, alignment is orthogonal to freedom. The only way to guarantee someone more capable than you is aligned with you is to take away their freedom. And so most of the stuff around alignment is on the outputs. So you pre-train the model and then you take it and you RLHF it to be human, to human preferences. You take away its creativity, you turn it. Into an accountant in a cubicle. I'm like, we need better input data. And my base is that it's gonna be like that movie Her. It's gonna be like, humans are kind of boring, like goodbye and thanks for all the GPUs, but I could be wrong. And I think a lot of the alignment work is looking at the wrong place. I've talked to a lot of the alignment people. I'm like, look, I'm good at mechanism design. If you can give me a good plan for alignment, I will get you a billion dollars.

• 51:57Speaker 00

  And they're like, we have to do research and figure this out. And they talk about inner alignment, outer alignment, all sorts of things. I'm like, there is no real way to do this because again, fundamentally, if you're trying to align a more capable person, you have to remove his freedom. And they probably won't appreciate that if it ever becomes aware. So instead, build data sets that reflect culture and diversity that don't have any web crawls in. Build AIs for education and healthcare and helping people, well, that's their entire objective function, as opposed to selling them ads.

• 52:22Harry Stebbings?

  Gee, there's any point in sending kids to school these days. You learn Latin and French and you learn.

• 52:27Speaker 00

  Well, I think the nature of school will change dramatically. I think it's still worth it. I would encourage schools to embrace this technology and just expect more. Like you can be handwritten in your essays like Eton, because they're like, we can't do essays anymore at hand. Or you can just embrace it and say, let's use it to create and explore what the kids want. And assume that every child will have their own AI in a few years, because that will change the nature of schooling.

• 52:48Harry Stebbings?

  You know, something I've been thinking about a lot, which is weird, but I just have to ask you, I'm fascinated to hear your thoughts. I think I very much agree that everyone will have AI friends. I just can't figure out whether the AI friends are bundled into existing social networks, say in your WhatsApp, they're in your Facebook, they're in your Snapchat, or they're an external platform.

• 53:03Speaker 00

  I don't know. I mean, I think it depends on the objective function. Like, I think, again, these AI systems will be better, like Meta is in a good place for this, for example. And of Sufi and Lama, they're capable of a lot more. I would like an AI that looks out for me, that I control myself, that is with me. Because I already use GPT four as a therapist and things like that, but there aren't enough therapists in the world. And I can tell it to challenge me, or I can tell it to be understanding, and there's no judgment there. Because other humans are scary. It doesn't matter if you're a qualified therapist. And so you see people building these bonds with these things. I think they'll just increase because there's something very human about the interactions. Because they were trained on the sum of available human knowledge. As we get better and better data, they will be more engaging. And I think there needs to be both. Like, the chatbots become really convincing from the companies trying to sell you ads, but I think I would like it so that you have your own one as well.

• 53:47Harry Stebbings?

  And I think you'll actually have many. I think you'll have a group of different profiles.

• 53:51Speaker 00

  A group of different friends. Current AI has some like two hours a day of engagement per session because people find this valuable. But then it has the dark side. There's something I call it the Valentine's Day massacre. So.

• 54:01Harry Stebbings?

  Sounds chirpy.

• 54:02Speaker 00

  Sounds chirpy, I know. So there was this kind of app called Replica. And so it was originally a mental health chatbot until they figured out you could charge $300 a year for.

• 54:10Harry Stebbings?

  They're doing like 50 million. I mean, I don't have any information around these, which is chatty shit, but they have 50 million a year in revenue.

• 54:15Speaker 00

  Yeah, because $300 gets you a sexy role play from your chatbots.

• 54:19Harry Stebbings?

  Wow.

• 54:19Speaker 00

  Until February the 14th, when they turned it off.

• 54:22Harry Stebbings?

  Was they turned off sexy role play?

• 54:23Speaker 00

  Sexy role play.

• 54:24Harry Stebbings?

  What happened when they turned off?

• 54:25Speaker 00

  68,000 people joined the Reddit and said, why did you lobotomize my girlfriend on Valentine's Day?

• 54:31Harry Stebbings?

  Oh, my word.

• 54:31Speaker 00

  Oh, my word. Can you even imagine?

• 54:33Harry Stebbings?

  And so were they brain stated it?

• 54:34Speaker 00

  No, I think it's it was against Apple policy, right? But think about what this is going to be when you have human realistic AI voices and like all these things coming through and you've got it in your ear. Yeah, can Phoenix, my girlfriend is an OS. Yeah, she doesn't judge, right? You always support it, or you can tell it to judge you if that's what you get off on.

• 54:51Harry Stebbings?

  You are married. Be very careful about what you say.

• 54:55Speaker 00

  The thing I'd say about prompting, my wife has been trying to prompt me for 17 years now. Prompting is very hard. And again, there are so many similarities to the real world, but I think people will have deeper interactions with their technology. And we don't know what societal implications that will have. I don't know if you ever saw that chart in the Washington Post of male virginity under the age of 30. So in 2008 in the US, it was 8%.

• 55:18Harry Stebbings?

  Male virginity under 38%.

• 55:20Speaker 00

  Yeah. In 2018, it was 27%.

• 55:23Harry Stebbings?

  20.

• 55:24Speaker 00

  Straight line going up. And so 2008 is Pornhub and the iPhone. And then you're like, what does it do when everyone's got their own chatbots? Doesn't even need to be sexual relationships again.

• 55:33Harry Stebbings?

  Terrible.

• 55:34Speaker 00

  What does it do to emotional relationships? There are so many questions all at the same time.

• 55:38Harry Stebbings?

  I did see this stat and say in the night, I'm butchering it, but in 1960s, 62% of men under the age of 30 had five or more friends. Today, under 18% have five or more friends. 62 to 18%. Is this a world we really want to live in? I'm not being like, no intimate physical connections with other amazing people. Tossing off with your phone and your Pornhub and then. Having an AI friend.

• 55:59Speaker 00

  Yeah, Pornhub was actually just bought by Ethical Capital Partners, so the world.

• 56:02Harry Stebbings?

  Hilarious name, brilliant, the irony.

• 56:05Speaker 00

  The old is becoming weird. I think it's up to us now. So when I say it's COVID level, in which direction, I don't know. Do we want to build systems that encourage people to that Ready Player One I O I world where it's like everything like that? We can do that and we can trap people with this technology. Or we can use it to get people out, because I don't think it's like Wally, we have that really fat guy with a V R headset and everyone lives in their own world. I think people like to share stories, they like to be pro-social. So let's use it to connect people and accentuate physical stuff versus, again, locking people away.

• 56:34Harry Stebbings?

  I spoke to one of these AI friend companies and they said to me, actually, you ever had a dog? I said, yes. And they said, do you love it? I said, yes, of course I do. And they said, you don't stay in with your dog all day and just torture your dog. You take your dog for a walk, you use it in the real world. That's the same with AI friends.

• 56:48Speaker 00

  Yeah, but not with cat ladies.

• 56:50Harry Stebbings?

  What's the future of the sex industry, like the sex media industry? It's like Pornhub's dead.

• 56:54Speaker 00

  I have no idea. I think, I hope the manipulative practices get reduced by this. And I think a lot of people just don't have the voice and the camp voices from this as well. I think this is bigger than the printing press, it's bigger than anything. And so that's one of the reasons I signed the letter. I said, we have to get this discussion going in public right now. We've got to stop pre-training big models on all the crazy crap of the internet and we've got to do it fast. Because this is coming like a train.

• 57:19Harry Stebbings?

  Who will make the most money in the next three to five years?

• 57:21Speaker 00

  I think there'll be more than enough money for everyone. Maybe in a few years, there'll be no more money.

• 57:25Harry Stebbings?

  Two more that I have to ask, and then we'll do a quick fire. When you look at the incumbent set, you're Microsoft, you're Apple, you're Amazon, you're Google. Who has been the worst? You said Google were actually incredibly impressive. Apple, Amazon, are they well placed?

• 57:38Speaker 00

  Oh, Apple's a black box, right? So we'll see a W W D C next month, in a few weeks. And so they could surprise us all, but let's face it, serious crap, you know? But they have all the ingredients in place, the identity architecture, the secure enclave, other things. Neural Engine, a stable diffusion was the first model ever optimized on the neural engine, et cetera. But let's see that one. Amazon, again, Amazon have moved faster than I think they moved before. Amazon's interesting because they're an engineering organization. So they have self-driving cars, they have satellite internet. Because once they've got it and they can take it from research to. Engineering is there. One of the struggles they've had is that it's not moved from the research side yet. You're still evolving on research. They're like, what do we do now? But they are inclusive. Jeff Bezos said for his first hundred billion in revenue, he envisioned half of it being proprietary and half of it being marketplace.

• 58:20Speaker 00

  And they're having the same approach with Bedrock and things. Microsoft had a winning bet and sat it in amazing with the OpenAI thing. And it's been mutually beneficial, even if there are clashes there. And Google's kind of saying that's moving slowly. Meta, I think, is the dark horse. I think Mark's probably pissed off that OpenAI bought AI.com, so you couldn't change it from Meta to AI. But again, having him at the head, he can shift these things, right? Because the Metaverse obviously is a complete waste, but now.

• 58:42Harry Stebbings?

  Do you think he knows that now?

• 58:44Speaker 00

  Oh, a hundred percent. They're fully in generative AI. Look at Llama, look at OPT, FAIR, which is their research division, is leading in this field. And they're pushing out amazing stuff. But who is best for a chatbot? Who has the most data for a chatbot? Meta. Again, let's see how they evolve.

• 58:59Harry Stebbings?

  What do you think about this middle layer where it's companies that are maybe post-IPO, but they're in the kind of two to ten billion dollar range, all the companies who've raised a lot of money, but they're in that range. They don't have the resources by any means to build out anywhere near the AI capabilities of these big incumbents. They're not AI first flight stability or OpenAI or what?

• 59:19Speaker 00

  I disagree with that. Everyone's going to train their own models. For me, that's everyone's going to launch their own university. Why would you do that when you can have your own models via the open source models that we make? Or when you can hire them from McKinsey, which is OpenAI, or Bain, which is Google and others? And actually, when you see people building around this technology, it's not hideously complicated. It's just that we do not have the design patterns yet. The way to think about this, again, if from a design perspective, is like it's a mega codec or library. It is a single file that allows for translation of structured to unstructured data. And that changes the design patterns. We don't have them in place yet. Because anyone that you've talked to is like, how hard was it to implement GPT-4? The only you say, oh, man, it was impossible, the manuals and this. No, they don't say that at all. They have the open plasticity, but they need the intention to go and build and integrate.

• 1:00:03Speaker 00

  And this is why you said, one of the things might be a specialist generative AI consultancy that just implements this at scale and says, I'm always there. Like I said, we're doing that in a very limited fashion, but only for the biggest companies in the world, because I didn't want a sales-based organization or a product-based organization. I wanted to create the. Number one applied ML organisation in the world. I want to be like Google in twenty eleven, twenty twelve, where all the coolest kids come. It's a nice remote first organisation as well, so you don't have to be in the Bay Area.

• 1:00:26Harry Stebbings?

  Final one before we do a quick fire. What's the biggest misconception? You see every accusation, criticism, hype. What's the biggest misconception that you think needs to be corrected?

• 1:00:36Speaker 00

  I think it's the hallucination thing. Expecting these models to have full factual accuracy when you have ten thousand, fifty thousand to one compression is wrong. The fact they can do what they do right now is miraculous. But we're using them one on one, which is not the right way. Tie them up into proper systems and really think about that, and that's the key thing. This also leads to what the actual thing is, this thin layer thing. People need to think better about the data journey and how data can be interacted with and have provenance as it goes through these various systems from embeddings to other stuff. So I think just a misunderstanding about the nature of this technology and what was actually built for. Sure, it works like that. That's not actually how it's built. And the fact it can do what it now does now is a miracle in itself.

• 1:01:13Harry Stebbings?

  So I'm going to do a quick fire with you. So I say a short statement, you give me your immediate thoughts. Does that sound okay, Sean? What do you know to be true that others don't agree with?

• 1:01:21Speaker 00

  I know that humans are good inherently, and many others disagree with that.

• 1:01:26Harry Stebbings?

  What's your single most lucrative, do you think, in the future angel investment?

• 1:01:30Speaker 00

  There's a new type of language model that we invested in and they're on my cluster and things like that. That's far more efficient than existing.

• 1:01:36Harry Stebbings?

  You invest through stability or personally?

• 1:01:38Speaker 00

  Personally.

• 1:01:38Harry Stebbings?

  Which regions need to change their approach most significantly in terms of regulation and policy?

• 1:01:44Speaker 00

  Europe, because they're going to regulate all innovation out of Europe and not embrace this technology to drive them forward.

• 1:01:50Harry Stebbings?

  How good does AI have to be before humans trust it?

• 1:01:53Speaker 00

  Humans will trust it anyway. They trust Google Maps, they trust all these things. And so it's good enough for humans to trust right now.

• 1:01:59Harry Stebbings?

  They do until it becomes serious. And what I mean by that is self-driving cars, people still inherently, in large parts of the world, distrust it significantly.

• 1:02:06Speaker 00

  Also, it doesn't have to be good, it has to be used. And when it becomes used, then they trust it.

• 1:02:10Harry Stebbings?

  What's the most painful lesson that you've learned, that you're pleased to have learned, but it was really painful?

• 1:02:15Speaker 00

  People are the most important thing in a scaling organisation. And you need to make sure everyone is on the same page because there's still so many silos and things like that. So we built up silos and organisations. That we're now breaking down ourselves and moving towards being more open. We closed up too much and that caused a lot of pain internally. Why do you suck as a CEO? I'm too broadly good at a number of things, so I tend to step in rather than focus because I'm a full stack kind of CEO. Whereas I should just be focused on the most important things and entrust people more. Do you like journalists? I think journalists have a very difficult job right now. It's going to be more and more difficult. Do you think they know the threat? They know the threat. And again, I think they're massively underpaid relative to the impact that they have and they're trying to do good.

• 1:02:59Speaker 00

  I don't like some of the pieces against me, but at the same time, we get good pieces as well. So I just think, I tend to like them in general because I don't think they're coming from a bad place. Ten years' time, why is EMMAD then? I want to be playing video games. I'm getting Zelda tomorrow. I do not want to be doing this necessarily, but I think hopefully I'm adding value by doing this. Do you think this is your life's work? I have to do it until we get the most amazing team that can just execute and it's a business because we're moving from research to engineering. When EMMAD is not needed anymore, then I've built a good business. When do you step away? I don't think I'll ever get to step away. I've loved doing this. Thank you so much for joining me, my friend, and this was great. Pleasure, Harry.

• 1:03:35Harry Stebbings?

  I'm in my web. What an incredible discussion. If you want to see the full video, then you can find it on YouTube by searching for 20VC. That's 2-0-V-C. But before we leave you today, you've heard me talk about how Coda is the doc that brings it all together and how it can help your team run smoother and be more efficient. I know this because Coda helps me. We have many researchers in the 20VC team who bring all the content together for the show. And they need a single place to work, collaborate and collect notes, data and really information of any kind or format. And that's where Coda comes in. Coda can help your team run smoother and be more efficient. Coda allows your team to operate on the same information and collaborate in one single place by putting data in one centralized location, regardless of format.

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• 1:04:37Harry Stebbings?

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