The Future of AI: From Prompt Engineering to AGI ft. Richard Socher

29:39Isn't that the same way humans learn? I mean, we are really, you know, 80 % nurture and 20 % nature because we're a product of our environments. You know, psychology has kind of proven that most of the time we're driven by the elements of our learned experiences and what society does. So it's kind of the same thing. We learn language because we parrot other people and we try and interpret it how we use it. You know, if you have a baby this, it's somewhat similar. There's definitely, there are a lot of similarities. I think the biggest difference is in the desire to stay alive and stay in the gene pool, right?

30:22That humans have naturally, genetically built in, right? And the people that don't usually disappear from the gene pool and then that problem goes away or a problem, but whatever. And so, like, humans have this desire to stay alive, but then within that desire, they can choose all kinds of different objectives and goals, right? You can say, I want to work on science. You can decide to be a hedonist and just have a lot of fun as much as possible. You can decide you want to work, go to space. I mean, there are all kinds of things. Like, going to space is, like, you know, a very interesting way of exploring the staying alive objectives, right?

31:03saying, oh, what if this whole planet goes away? And then we want to stay alive on another planet for humanity, right? So there's different explorations of how we stay alive. Some folks focus on just the right here, right now, there are people starving. Other people say, well, we also need to make sure we keep improving as humanity. You keep saving the planet. There's all kinds of complex objective functions that I don't see AI right now being able to sort of come up with that are completely outside of what's called the hypercube or the convex, even the convex hull of all the things that's seen online.

31:42Although, yeah, and part of this is because they're purposely restricted from having memory. So they don't build their own memory of every conversation that they have and therefore they can't build on learning as a human would do from a baby to a adolescent. It's memory plus learning that does some of that. that you're 100 % right. Memory is something we're also working on actually at you.com quite a bit. And we have this personalization. If you say, I have three kids, I love hiking, I live near Palo Alto or something like that, then you can basically infuse that back into the conversation. But it's really, really hard.

32:22We've seen some competition try to do it and they all kind of turn it off because you say, oh, I love hiking. And then a month later you ask, who's the French president? And then the answer is the president of the French Hiking Association is this, that. You're like, ah, that's so bad. Like, it doesn't even answer my base depressions. And because it's like overly focused on that personalization. Turns out it's personalization done really well is really, really hard. And so just like with a lot of other things, you know, future is already here on you.com, but it's not equally distributed. I think you're going to see more folks copying that personalization feature in the future as well and trying and working on it and making it better.

33:02And so, yes, memories be hard. I think it's also problematic because you don't quite know when to train the model based on a conversation and just updates its weights versus when a fact can be mentioned only once, but by the right person that you trust a lot. And hence, you can completely store it to permanent memory forever. And then how do you merge this fuzzy memory of a large language model with very specific database memory? right where you just say look the numbers of the smp 500 were like this you don't need to like have a fuzzy sense maybe you want to have a fuzzy sense on top of the very specific numbers but one of the magical ways that humanity has improved is through writing and through uh like being able to use external memory also and how fast is the rate of improvement and what is the constraints within this is it computing electricity is it the models just keep going keep scaling with more compute how are you seeing the thing and then obviously it's moving to a different substrate as well whether it's biological compute or others how do you see the evolution of of the improvements wow yeah great question um so i think we're going to see a few sort of s curves right um and in some cases we're at the very beginning of an s-curve and we're astronomically far away from reaching uh the the flat period uh the flat sort of phase of of an improvement in other cases we're quite far um and so again like this a computer vision example is a good one just standard object classification is not that hard um navigating in three environments We're making a ton of progress, and I think we'll get there.

34:56I think humanoid robots will get there in a few years. And then it's a hardware question of how fast can you have them move without being too dangerous to people, and there's a bunch of interesting trade-offs in that. I think in terms of knowledge, we're just at the very, very beginning bit of knowledge discovery and what AI can do. and we need to eventually, I think, have better hardware, more hard drives, a better way to store and access all of that data. And so those are sort of pragmatic bounds that slow it down, but ultimately you can learn a lot more than what we know, both in the micro and the macro, right?

35:37We think of computer vision for a long time as perception within the electromagnetic frequency spectrum of our eyes, But you can go much lower to try to see all the way into the atomic level. You can go much higher into the macro world and have thousands, millions of sensors that look at all the way to gravitational waves and look at their speed of light cone and try to infuse all of that data and then extract knowledge from it through compression. So there's a lot of interesting questions. I think it would certainly help immensely if we find more energy efficient substrates for AI. Clearly, we're excited enough about the current paradigm.

36:27We're just going to build more nuclear power plants and try to get more energy to feed into GPUs right now. Yeah, we seem to have not finished how much we can get out of a piece of sand, right? You know, we're still, that seems to be scaling, but I'm seeing the biological substrate coming and people are working on it. Because, you know, obviously, if we just look at the human brain, the amount of energy it uses versus GPUs, right, there's a staggering efficiency. And that feels like the market's going to move that way because if it's efficient, it goes there. That's right. It is like, I've looked at a lot of hardware startups.

37:07It's so hard. A lot of hardware startups, I see them, I pitched them over the last couple of years and they say, oh, we're going to be 100 % faster than these NVIDIA GPUs. We're going to be so much more energy efficient because we're building it just for transformers or we're having, you know, like using light and hence the speed of light to communicate between the chips and all these different ideas. And then you're like, yeah, but by the time you can really get this out of R &D, really have a bunch of fabs in the world that produce it at mass scale and get it into the cloud providers so that I can easily access it and then have all the software stack that also is optimized for this new compute substrate.

37:48By that time, NVIDIA is also 100x faster than NVIDIA is today. And so it's been very hard. In the last five, 10 years, a lot of folks have tried. No one has been able to get even close. even some of the big folks like Intel are not able to catch up despite being much, much larger in the past. So it's very hard, but I agree with you. There should be more done. I think quantum computing could be a path to have just insanely more speed. Again, we need to change the whole software stack on top of this new computing substrate. I think biological substrates are very interesting, very finicky right now.

38:25It's hard to keep biological matter alive. and then properly connect it to a digital world, but also very promising. Even earlier, I would say, than quantum computing, though. It's very complicated. How I look at this space, and you live it and breathe it, is it's so fucking fast. There's so much going on, and so many businesses or ideas will fail because there's so many people sprinting. You do VC as well. How do you allocate capital to this? I find this one of the hardest things I've ever seen, because as you rightly say, somebody's got a great technological breakthrough that 10x is NVIDIA, but NVIDIA themselves have infinite money and infinite ability and the right relationship with TSMC to get the stuff built.

39:16How on earth do you allocate capital in this space? Yeah, so it's non-trivial. We've seen a lot of folks try and fail. Our fund is doing really, really well right now. We're over 3x TVP in fund one and fund two we just started this year, but it's already seeing some markups as well. I think there are a couple of ways you look at it. One is you need to have, ideally, in the early days of this field, strong AI nativeness and AI expertise and then also deep industry insights. I think those two combined is usually a perfect match and what we're very excited about investing in. We personally stay away from some of these mega rounds.

40:02There's some companies that basically raise at a unicorn level. And what that often means is that you combine seed stage risk, where most companies die, actually before they amount to much of anything with late stage returns. Because if you raise at a billion dollar valuation, even in a successful case, you become worth a$10 billion company. It's only a 10x and you need a few winners worth a thousand X in VC world to make it work, have the power law work in your favor and so on. So the expected value of those kinds of investments as an investor is not great as really suboptimal. So I'm not saying none of those will ever work out, right?

40:46It's just that it's seed stage risk. It's very, very hard. So that's one thing. We look at a mix of first principles. Where's the world going? Where do you have data? You know, any job right now that isn't even getting digitized is very safe from AI. No one's going to automate plumbing anytime soon, right? Because no plumber is using a bunch of robotic hands, has a camera, has 3D scanners around it as they crawl underneath your house to fix a pipe. And because no data is being collected, there's zero progress towards automating plumbing in the world. Now, if you're working in radiology where everything is digitized, you get a digital scan in.

41:27they can observe exactly how you go through a 3D like MRI or CT scan, how you change the contrast, and then you mark up certain things. So you label it for the AI and then you get an output. That is a perfect use case where more and more of that workflow can get automated with AI. Same as a lot of digital knowledge work that we're seeing in U.com. And you're just making yourself more efficient and eventually you can just manage the AI to do these things for you. So I think, again, very different speeds of rate of change in different industries. But eventually, almost any industry will get disrupted in the next couple of decades.

42:05At some point, if your plumber makes more than your marketer in your knowledge work company, then there's enough economic incentive to say, let's put some cameras on the plumbers. Let's put some robots next to them. Let's have them guide the robot to do the work and then eventually collect that data and then automate it. And then the plumbing company can just send out a bunch of robots and do the work. So when you're looking at the investment opportunities, are we still at the deep tech as the place to invest or the applications layer or both? Great question. I sometimes wonder if some of the foundational models are a little bit like telcos in the sense that telcos unlock a huge amount of value.

42:52right like it's incredible you can't build an uber without having an internet connection on your phone but the telco didn't make all the money from uber right and so i think there are a lot of uh lm providers and then meta facebook meta comes around open sources a model now that open sourcing of the frontier model it's actually really strong just evaporated hundreds of millions if not billions of dollars of value of knowledge for some of these foundational model companies for how to train a model from scratch. And when that value is now diffused and coming into the world, that's amazing for the world, but it adds additional pressure for the foundational model companies to just be cheaper and cheaper and cheaper.

43:41And eventually, really, it's going to be thinner and thinner margins sitting on top of NBDI GPUs and cloud providers. They're just utilities in the end. It's become so ubiquitous that they become utilities. That's right. And so we're now talking about some of these models getting to the level of a PhD student, which is immensely exciting for humanity's progress. But the corollary here is also that most jobs don't require a PhD. And you can already, with the current technology, as long as a job is fully digitized and data is being collected for it, you can already do more and more automation on it.

44:21Now, I think we continue to need to have foundational model companies that do interesting novel things. I don't know if that's just building another really large LN, but I think there are other foundational models that I described earlier and used for science where there's much less progress. And so I think that's one aspect. And then indeed, to answer your question, we believe the application layer is a much easier space in the sense of so much low-hanging fruit, being the first to take AI, understand it well enough, understand its limitations and its capabilities and opportunities, and then apply it to a particular industry.

45:06In biology, especially, there's just immense amounts of progress that we're seeing on not just proteins, but drug development, drug testing, and drug formulation tools, like how much and how should you actually administer a certain medication. And there's so many opportunities in AI for healthcare, chemistry, material science, like building out new kinds of materials, new touch of batteries, new touch of solar cells. There's just an incredible amount of exciting opportunities in, you might call broadly, application layer. Do you think that this is the death of SaaS, the software is eating the world idea by Marc Andreessen?

45:48it feels that software itself is going to be replaced by this, which is not, it's not SaaS in itself. It's a whole different thing. And everything becomes copyable, right? You essentially can upload a website at basic level now, give it to an AI, it will come back and rebuild it for you. Brand new in a minute. So yes and no. So like AI will rebuild the website for you in terms of the HTML or something, right? But it won't have all the data. I won't have all the logic behind, like, what happens if I click on this? And if you look at Slack and just like, when will Slack send you a notification? It's an insanely complex, like, 50-step process where it checks on a lot of things.

46:31You can't just, like, look at Slack and be like, oh, I'll just copy that entirety and have a really good experience, right? So I think we will see software that is custom built, right? Like anyone can be like, I have this use case where in my house is the Tesla batteries of this and the thermostat says that. Like, I want to build this new app that is really just for my house. I think that will be possible. Right. You can have personalized app development, but it's also not going to be that easy to just replace an entire product. Now, I do think the bar is getting higher and higher to build really high quality products.

47:07And it is actually right now we're in this funny state where it's so easy to build a quick prototype where they say, and we've actually seen this now. We've had companies say, oh, we built Vue.com. Like, we don't need you guys. We built it. Like, here, take a look. I'd show you a question, and it gives you an answer. And then a few months later, they say, well, we realize that the answer is like 60 % of the time correct. And the citations don't really work. And so we don't see adoption. And like 3 % of the people in the company use this chat should be key thing or use our own thing. And then they start using you.com.

47:47They say, wow, it can be 95 % accurate. Wow, the citations are going to actually be trusted. Wow, it's dealing with all the change management. And then they end up becoming customers of you.com. So it is like this sort of proof of concept vibes where you just like look at it. You're like, oh, wow, this is amazing. I'm done. Versus like you actually get something in production right now. There's still a major, major gap. Now, I'm sure over time, AI will get better and better at coding. It's one of those beautiful examples, again, where you can simulate actually quite a lot. You can simulate code, obviously, in a computer and then run it and then get feedback and then you can iterate.

48:21So AI for programming is a major opportunity and it will get easier and easier over time. The question I want to ask you, because you used to work at Salesforce, is what is Mark Benioff talking about when he's talking about the millions of agents? What is that vision that's coming? I don't want to speak for him, but I think... No, but what in your interpretation of enterprise scale agents in something like that environment, what does that mean for people? Because don't forget what you know versus what the general public knows. It's so disconnected, right? People don't understand the scale of what is happening and how big this all is.

49:01100%, yeah. Let me give you a story. We actually launched with a company. We'll talk about them soon. It's a big cybersecurity company. We launched u.com, and you can build agents on u.com. We used to call them modes or assistants a year before people called things agents, and then realized, again, the future was already there, just not equally distributed. Now we call them agents because they are essentially what we now interpret as agents. What are agents? Generally, agents are just neural sequence models, like large language models in chatGPT. But instead of predicting only the next word, they can also predict what's a good next action to take.

49:40What's the next button to click? What's the best form to fill out, the drop-down menu, the app to use, and all of that, right? And so as you include actions in the digital environment in these neural sequence models to try to predict what's the good next step, they become agents. And so we've launched agents last year where they can decide to search, they can decide to program, they can decide to run that code and then merge all of this. They can decide how to visualize the data versus just text. And so we've had these agents for a while. What we've seen is that companies will get a couple hundred seats, like maybe 100, 200 seats from you.com.

50:22Once we do a workshop with them where we actually go through all the examples of what they can do, it goes from 100 to 200 to like 700 seats. Yeah, everyone's limited by what they don't know how to deal. They don't know how to use it. And even they have to ask ChatGPT. They get scared. Exactly. This goes back to this idea that we're all becoming managers, but it's not intuitive for a lot of people how to manage in the AI. Right. And so when you, like, I'll give you an example, right? Like the marketing team is like, oh, maybe I'll like ask it to rephrase this thing, but that's not a big use case.

50:58And we ask them, oh, what do you do? And they're like, well, every like first of the month, we get a new feature release from the product and engineering team. They send us this big document with all the new features that come out. So what we then have to do is we have to go on the web. We have to compare these new features to the competition. Then we distill them, summarize them. And then we write out like three LinkedIn messages, three tweets, and two email campaigns for the two industries that we're responsible for. And we're like, let's describe that to an AI. And now let's drag and drop that document into that custom agent.

51:37And then boom. It's just like it goes through all those steps. It goes on the web. It does the thing. It summarizes it. It writes the messages. and they're like holy shit this is like six plus hours every monday like first monday of the month but the hard thing is it's the inertia of actually doing that step of teaching it right that's the bit that everybody gets stuck at as you said the managing of the ai because like with a new member of staff you have to train it and you have to train it not in complex technological stuff you just need to tell it all the steps and most people aren't that discipline as a radiologist you will be because it's a medical procedure you've got certain rules and criteria marketing people less so accountants yes lawyers somewhere in the middle that's exactly right yeah it's also interesting when you look at lawyers and uh if you tell a lawyer that gets paid by the hour to say hey you can just be twice as efficient in doing your work they're like i don't i don't really care because I get paid this amount per hour, whether I do twice the work or half the work, I get paid this amount of hours until you actually get fired.

52:51And until you get fired, it's fine to do as little work as possible. You just need to do enough to not get fired. Now, of course, if you want to get promoted and so on, it's like different incentives, right? But hourly waged workers probably love AI efficiency increases the least. It's people that are entrepreneurial material people want to excel at their work people who are partially owners of their company which is easier for startups right all our employees have equity in the startup so they are all partial owners they love those efficiency gains from ai and even like for legal it's complicated in the sense that their company internal lawyers they just have a ton of work and they get paid not by the hour they get paid just to help the company not die and not get sued and all of that right So they have infinite work.

53:41They would love to get some help because they will still need them in the company. But if you're just an external hourly paid legal worker or a lawyer, you don't necessarily love 20%, 30 % efficiency increases. You don't. No, because you're not driven by productivity. You're not driven by productivity. You've still got your 60 hours a week that you work of which you can bill for. And you don't give a shit how productive you are as long as you bill that number of hours. The moment productivity comes in the equation, I kind of do double the amount of work and get paid twice as much. Okay, then it becomes a no-brainer in using this because the productivity from this stuff is very, very real.

54:22Exactly. Exactly. One of the things I'd love to hear your thoughts on is what are you seeing with the disruption of education? Oh, boy, yeah. We actually work with quite a few educational organizations. I'm excited about some announcements that we're working on right now with a large university and university system. Education is going to massively change, right? I think generative AI, the sort of rule I came up with is generative AI is immensely useful if it takes you a long time to create a work product, some kind of artifact. but it is very quick to verify that that artifact is right. Now, for example, illustrations.

55:11You can very quickly create an illustration. It would take you a very long time if you did it manually, but an AI can do it quickly, and you can look at it in a very short amount of time and say, that it looks great, and you're done. So that's a perfect use case for Gen AI. Now, in language, it's actually different. If an AI writes you a 50-page document and they're no easy-to-verify citations and it takes you forever to make sure everything is correct in that document, it becomes less and less useful than just writing it yourself. And that's why at u.com we push so much to citations. And actually when you use research mode and you click on a citation, it sends it directly to where it found that fact.

55:51right and that way you can very quickly verify that this fact is actually true because based on the source and so on and you see it directly marked in the browser scroll down like this is where i found that fact so um what that means is that we need to teach kids how to use these tools i think to say like oh we should just outlaw them education makes no sense right it's just saying don't use a calculator you've got to always do the mental arithmetic in your head it's like why right but you also need kids to still be able to think creatively think on their feet be able to hold a conversation and so what that means is you're probably going to eventually have to test more stuff in class right like test a conversation can you argue can you debate a thing and at some level you need to have the facts memorized because it's hard to be like in the middle of the conversations like let me try to find quickly an example when i try to convince who like AI agents work great for marketing, right?

56:49Like I just did. Let me try to quickly find an example for AI agents for marketing. And then you wait for it and you like get the answer. And then you say, like, it doesn't work, right? You need to, if you want to creatively think through a space in science, especially, right? Chemistry, biology, there's a ton of knowledge. If you don't have the knowledge, it's hard for you to be creative and identify boundaries of that knowledge and how to push it forward and all of that. So we do still need to test kits for a lot of things. some of the testing will have to be in the class with no internet. But a lot of times we need to teach them to have good discernment, good judgment for an AI product versus just creating the product, the knowledge product yourself and scratch.

57:33The skills of being human, social skills, the things that we lose in an online world become more important to a child when they have access to infinite knowledge like everybody has access to water that's basically what's happening here infinite knowledge of the scale of water so okay we don't need to now go to the well every day and find our water we don't need to accumulate knowledge in the same way but we still have to interact with humans you know and interact in the world around us and i guess that is what we end up getting taught more more than the retention of knowledge like we don't need to retain the ability to do maths in our head anymore because we use computers or calculators.

58:13That's right. And what that means, fortunately, is that we can operate at higher and higher levels of abstraction, right? Like that's sort of the magic of civilization and humanity is that you don't need to understand how this toaster really works. Like if you drop most people in the jungle and you say, build me a toaster that actually runs, like no one can do it, right? Like you need to have electricity. You need to understand how materials you need to, and there's so many things you can need to understand. But we can build these levels of abstraction. And what that means for education is and even computer science, we see this and most beautifully work out well in computer science.

58:48You don't need to work in assembler. You don't need to understand the very bits and bytes of a CPU to now build a whole website with five lines of code. And soon you can build a much more complicated website with five lines of English. right uh but if then there's something off slightly and you're like well there's a bug and android and this version and so on and you it's still useful to know how to fix it right and so i think the levels of abstraction in education need to keep up with that understand help people under and students understand the basics uh so they can then uh really creatively put it all together.

59:31When I look at all of this, I don't understand how the economic machine works any longer. Because, you know, I look at GDP growth as driven by population growth, productivity growth, and let's say debt growth. Population growth is now going to be infinite because of AI and robots, infinite productive units, and productivity goes higher. I don't know how the economic machine is even recognizable. Even the study of economics is basically a study of human supply and demand, and it all kind of disappears. We actually built an AI economist when I was working at Salesforce. We had this really exciting paper that really hasn't had its moment in the sun yet because economics is an extremely slow-moving field, and you can't really prove to economists that this is a better model.

1:00:20But you can use AI also to model economics. So what we did is we You built a two-level reinforcement learning problem where you had AI agents that just tried to build and maximize their own utility. They want to build houses. They want to collect resources. They learned to block other agents off of those resources so they can have them all to themselves. And then so on. They'd maximize their own utility. And then you had another agent that set taxes and subsidies in order to optimize a specific objective function. In this case, the objective function was to maximize productivity of the overall economy multiplied with equality.

1:00:58And so if someone was able to completely block off a resource and they get monopoly power, it would get much more heavily taxed. And the folks that can't get access anymore to that resource get some subsidies. And it can learn basically much more optimal taxation strategies than anything we have right now in the world. I wish economists looked at this paper a little bit more carefully. It was unfortunately rejected at some major journals by some weird like ethics philosopher like person. It was like it was very unfortunate that paper has so much potential for humanity. But the field of economics is mostly like run based on vibes and not math.

1:01:41And so I think, one, the future is already here. It's just not equally distributed, right? There are still people who don't have access to water, right, in your example. There's, like, fights over water. Like, you know, there's a big dam denial. And, like, Egypt's, like, scared that they lose a lot of their water source and their lifeline, all of this, right? And so there's, like, even for the most basic things, it's not going to happen all overnight. I think the companies and countries that embrace this change are going to first slowly and then more and more quickly move ahead and become more and more efficient.

1:02:22And then, indeed, I think you're right. At some point, the partnership of labor for income is going to change. Or knowledge for income, right? That's the other big one. That's even bigger. knowledge, yeah, knowledge, work, and yeah, everything. And so I think in a weird way, Europe is well-situated and well-positioned to protect people from the downsides of AI, but unfortunately isn't well-positioned to actually get the additional efficiency increases because they over-regulate everything, they don't invest enough in AI and so on. The US is very well-positioned to use the upsides. They have amazing technology in Silicon Valley.

1:03:05a lot of people want to have this constructive optimism to make the technology get better and build amazing things but then they have very little infrastructure uh in their social market economy to like support people whose jobs are going to change like education is really expensive you definitely don't want to go to college multiple times because people are already like a huge debt after going to college once so like you know if college isn't free uh which it is in germany like You don't want to go there multiple times and learn new things. The unemployment benefits are not great. The health care is tied to your employer.

1:03:39So there are all these things that Germany is well positioned to help its populists and its people to prevent the downsides. But yeah, I wonder what the Pareto optimal boundary here is in terms of just the legal and welfare systems to still encourage a lot of hard work the way the U.S. does and having that entrepreneurial mindset. versus just trying to say, hey, let's just not rock the boat so much, just things are so nice and we get so many social benefits already. It's going to be complicated. I think short term, I'm a little bit worried. Long term, I'm extremely optimistic. The long term arc of humanity is very positive.

1:04:19Yeah. Also, I think that with your understanding about how AI can help economics, it's the same for governments, the mass allocation of global resources, you know country level resources there's no reason an ai could not be a lot better at government the system of government much more efficient and the same with running corporations an ai probably does a much better job at the majority of tasks within a corporation um than humans do or it will be in a few years time you know five ten years time that's right yeah eventually eventually i think i can be very very good at a lot of different things i think for now, of course, it can already be a good advisor.

1:05:02Now, the truth is that there's so much identity politics right now, right? And people say, oh, if you vote for me, I'm going to give you these tax credits. If you vote for me, I'm going to make your life easier. If you vote for me, I'm going to prevent AI from happening because you're only going to vote for me if I promise you to keep your job. And so I'll promise you to keep your job and then you can't use AI supposedly as a politician or like improve it and make sure it works right and it's it's complicated it's a messy very messy process and they're just humans don't always want more efficiency right and there i think ultimately different societies will choose different paths right and personally i love like the future i love like automating all the boring repetitive things uh away but some people get a kick out of gardening and just doing that kind of work, right?

1:05:53And so they want to continue doing it. And at some point, we're going to see, you know, like what we already see right now, which is like there's some islands in Greece, some beautiful islands in Italy. They're not all thinking about efficiency every day. They're fishing, they're enjoying the sun, the ocean, and their lives. And they don't want AI to automate all of that enjoyment because they're already enjoying it us. And so, and I think the countries again, and companies that are embracing it, they will just become more and more wealthy and pull ahead more and more in productivity. And then there is more and more inequality between the countries that can embrace AI and do embrace it and the ones that don't.

1:06:34Yeah, it feels that humanity is going to go through a big fork. And there's going to be the two different ideologies. There's the decelerationists and the accelerationists, who will end up merging with the machines in whatever format that means, and the others will opt out. And it's kind of like the Amish opt out of modern contemporary society. I think we will see that, whether that force, that creates a new human, much like we had Neanderthals and Homo sapiens living together for 50 ,000 years, and then one disappeared. You know, it's such a big thing that's happening. People just can't get their heads around it.

1:07:12So it's just fascinating. Thank you for your time for allowing me to just think through some of this stuff because none of us really knows. It's happening faster than humans can even imagine. We're not very good with exponentials and we're not very good with Reed's law, which is like Metcalfe's law squared, which is what this is doing. So, you know, it's just kind of strap in and go for the ride. But listen, thank you for what you're doing. Thank you for joining us. It's been super interesting. Thank you. It was a fun conversation. So I think an incredible conversation to give us yet another layer of understanding of what is happening.

1:07:47Now, what is interesting is within AI is there's not a consensus of what is happening. You know, some people don't think there's consciousness developing. Other people do. There's a lot of things all happening at the same time. The rise of agents, we're seeing that that's overlapping into crypto world. So we really have to keep our eye on all of this. And it's one of the things that we do at Real Vision. we really try and follow this. I mean, I have a whole research service called The Exponentialist that is just dedicated to this, this six years, how we can make the most of it. But Real Vision's got your back.

1:08:21So if you haven't signed up, go across to realvision.com. You can use our AI tools. You can see what our community is talking about and meet thought leaders in the space. It'll make a massive difference and it's free. I'll see you on the platform. If you liked this episode, I'd love for you to head over to realvision.com forward slash join for a free membership. Start your journey today to unfuck your future. Just one click away.

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