Magic.dev CEO Eric Steinberger on making developers bionic | E1744

0:00but people should just take note i mean the fact that you know people who are building this stuff jeffrey hinton the guy who's just a godfather of ai of course but he just he drove this um that he's warning of it and everyone i mean i you know i'm just a tiny fish in the pond the people who've been working on this for decades are worried uh this is different from when people are worried about you know crypto like or when people are worried about whatever else is the thing they're currently worried about right that the people who have been working on us for ages who have dedicated their careers to it, including myself, are saying that this is very much a path with a juncture.

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1:28Large enterprises pose unique challenges for SaaS startups. Unlock customers with unique needs for private and single-tenant hosting without the toil of DIY with Release Delivery. Get your first month free at release.com slash twist. Hey everybody, welcome back to the program. Today we have another great guest. We're doing this like ai series where we're interviewing anybody and everybody who's making interesting stuff builders in fact of the most interesting ai startups today uh we have eric steinberger he runs a company called magic and uh plain english magic is building a colleague inside of a computer basically it's powered by ai and it's going to auto generate code based on a developers inputs welcome to the program eric hey thanks jason thanks for having me all right how many people work at this company uh we're 10 now 10 people where you based uh half of us in sf half of us in vienna in austria okay great and you're in sf i will i'm just moving i'm getting my visa have my my visa appointment in one week well congratulations are you going to be in san francisco are you going to be in the bay area yeah i'm planning to be in sf wow would you have an office there yeah yeah small office you know small team but whereabouts whereabouts in town are you uh not sure yet we're probably going to get a co-working space just to be quick um for the first phase and then eventually the reason i ask is a lot of ai people are saying they're going to be in the city of san francisco where a lot of startups and obviously the big tech companies are no longer going to san francisco explain the decision to be in san francisco as opposed to saying being down in palo alto mountain view cupertino san jose san mateo wherever i don't i mean i have a few friends who are just really good at mal engineers nsf and uh i don't i'm not very familiar with the area uh so i think sf is the place that i've been i've spent some time in berkeley but i you know i think sf is better for what we're doing great awesome uh it does seem like a bunch of ai developers are choosing to be in san francisco which is great for the city since there's like 15 salesforce towers of open space uh so let's do a demo here it's always great to demo but when we do our demos here on this week in startups we sportscast them which means we try to explain what we're seeing on the screen so let's pop up your screen and let's show people we like to show rather than tell and in this case we'll do a little bit of both so if you're just listening we'll describe what we're seeing on the screen uh so eric take it away great thanks jason um so you know after spending about a year in the cave uh building an lm training and inference framework to to build our own code models and scraping a lot of code data um we finally managed to build something that uh we think has a a qualitative edge on on other products in this space namely um you know you're familiar with code completion tools they give you completions here i can like i'm gonna go put a new line here and i'll get a completion um that's standard but so what you're showing on the screen is you're developing in uh python maybe i can't see yeah that's right i'm doing i'm developing writing code um in a python project that here this is like a reinforcement learning code base i used to do research in a few years ago great um i mean and so when you start typing in a command they're like you know sort of this is like it's it looks like copilot on first glance i got like you know um uh the i get completions and um but what's what's really um different here so i should say this model we're just training we're still training it's like sort of an intermediate checkpoint but the one thing i want to demo here is that the key difference you know copilot takes in like say the current file or maybe you know like a few adjacent pieces of information and you're referring to githubs you're referring to github's co-pilot which is the original og auto completion so if you were using gmail as a user and you see it shows you one word and then it started showing two or three words um this is something that developers use and developers have a more constrained possibility set than say people just writing email is that correct so therefore it can maybe give a little bit more code and it knows the existing code base right so copilot doesn't know the existing code base and i think that's the that's the key thing that's exactly what i wanted to cut to it so luce you're right that coders you know a lot of code is predictable right you know sometimes it's just like super clear what the next line is sometimes it's really hard and copilot generally and general like code completion auto completion tools find it easy to complete things that are straightforward and somewhat obvious.

6:17It's quite harder to do hard things, but getting better at those with language models being scaled up. But they have this limit of context size. Even GPT-4, it can fit more, but it's still limited. What we've developed over the last year is a technology that can really look at all of your files. It can see your whole repository. So you can see here, I've opened up a few folders just to show that there are a lot of files. And I can keep going. like the folder depth is pretty substantial. This code base overall is, I don't know the number of lines of characters, but much, much, much, much larger than any of the GPTs could fit into their context window.

6:58But our model does. So for instance, if you were to autocomplete from the start of a file, usually here, if I were to go to the very first position, the model has no idea what I would be about to do unless it sees the repository. It's starting from a blank canvas. So if I take out here, for example, if I take out my name and I want to type something, if I start typing, it knows that I wrote the code base. And how does it know that? Because it goes to other files where I have written this same line. But at the same time, you can do your normal completions. So, you know, as you said, like this is, for example, a more obvious line, right?

7:42Like here, I have something similar above and then it adds this this minus one piece, which is intelligent because it's like player zero player one figures out that I want to access the other player. So it just sort of is because you see, it just autocomplete that that for me. um okay so when you say that what you mean is it's uh you have a string of code above it it recognizes that code your learning model knows and your auto completion model knows the code you wrote above it it assumes now that you're writing about another similar thing and it can then put in gray hey here's what i think you're going to do next it reads your mind and then instead of typing you just hit like the tab key or something to fill that in exactly that works great so just in gmail but this is as if it had read every one of your gmails and it knew that you like to often let's say if you were a vc you might talk about your portfolio companies you might talk about other partners in your firm it would have that information so when you says hey you should talk to my partner susan it fills in a little bit about susan exactly it's it's able to take in the context of what the developer has done and produce code that is similar.

8:54As you said, I think you said this very, you put it very well, it reads your mind and tries to do what you are about to do. And, you know, we can do a few more like this, right? I can write this line of code. And because the function is called do pickle, which in Python just means store a file in a certain format to disk. It just wrote that for me, which is, of course, relatively straightforward, but it's very useful to do that faster. But now the thing that, again, our AI does different from existing ones, we just wrote this function this do pickle function um if i go very like could completely different file um we're here where i use this function and i can even close this right we're not we're not using um the fact that uh you just edited this it's it just sees the the entire repo so if i this should hopefully work this gray text the intelligent suggestion knows it because it's semantically analyzing the code which is if it goes down at three of import statements and so on very specific doesn't always work only works when it's kind of syntactically clear but here our model successfully understands that we have a method called do pickle somewhere in the repository and that's probably what we want to use if you tried this with copilot for instance github copilot or other products um you would find that um because it is limited to the current file and slightly small bits of adjacent information that are only grabbed heuristically as in algorithmically designed and not learned by the model, he would find this difficult.

10:16And so what we find in practice is that as you code, when you write code, you know, you often have a lot of code already in your repository. And our AI is able to reuse your code to use your coding styles to use functions from across your repository in various contexts across the project. And so that's just what that's what we've been working on over the past, past months or past year at this point, and working on a few other really interesting things, but very excited to get like tangible models now and launch very soon. We're giving you a sneak peek here. The model will finish trading relatively soon and then planning to launch that with the same that I just showed you.

10:57When do you think you're going to launch it? Yeah, this month. Great. Probably the most common challenge I hear from founders is related to building. Either they are in technical and are searching for a technical co-founder or they can code but they're just spread to then this is one of the first major obstacles you're going to face and i know how discouraging it can be but there is a solution do you have a great idea but you don't have a technical co-founder well crowdbotics can be your cto as a service boom just like that this means you can focus on building an awesome product and delighting your customers rather than wasting your time on infrastructure planning architecture your compliance and all that boring stuff.

11:38Crowdbotics also offers professional scoping to help you flesh out your project at the MVP stage and beyond. So cut out the hassle and get back to building that perfect product for your delighted customers. When you think Crowdbotics, I want you to think getting your time back to focus on product. Product drives everything in a startup. So let the folks at Crowdbotics show you how it works. Schedule a free scoping session and get your detailed build plan at crowdbotics.com slash twist. That's crowd b o t i c s.com slash twist. And so when you have a company like this, you have to you build your own language model, or do you use say chat GPT is existing language model, which has been trained on a lot of open source products and then do a local version of the repo of this company's code and then merge those things two together or are you building also your own so you're not dependent on chat gpt4 or open ai for that matter yeah uh we built our whole uh language model training and inference stack from scratch um the main reason for that is that we wanted to be able to innovate on the architecture of the model we um do a few things different from the sort of the standard way which is also why it's taken us so long to launch something the way we really look at code gen and the code gen space more broadly is that i think ultimately there will be a user experience that frames the idea of fully automated development in a way that anyone and everyone finds attractive and and useful and reliable enough there'll be some way to talk to your computer to to automatically write code and that ux will replace whatever comes before of course including auto completion because if you don't write code in an editor why would you need auto completion so we're very focused on making sure that what we do in the short term and in the midterm and in the long term is um geared mostly towards the final stage of what this space will look like and last optimized for the immediate we could have built something like what i just showed you within a month of just existing as a company um but if we just took other people's models and you know other people's inference servers and so on and those things would have worked they would have been interesting products um and then would have probably brought in some revenue um but it would have slowed us down on that um so you're starting with a tool for developers so that they can write code faster hopefully they can write code 50 percent twice as fast i don't know what you've what is the benchmark you think you'll be able to achieve this year how you measure it right like you there are there's some pipes of engineering that are um inherently dependent on contextual understanding outside of your code base right you just need to talk to people um i think those numbers are often not like those things are not considered an estimate and there is some coding that is really just boring template writing or we hope that it just does it all for you it really depends on what type of work you do but you know like speed ups and this just take the average group of developers you got a hundred developers in a development team at some mid-size startup what would you expect across the range of developers the average would be each year in terms of gains from this type of technology yours and other people's yeah i think if you measured the time it takes a group of people to build a piece of software um as is with and without the technology which i think would be like the best benchmark to really quantify productivity sure i think it is possible to get um beyond the 2x mark even with you know a few more steps of innovation i think right now this number is below the 2x mark with existing tools like um you know others out there and uh because so you think the current tools are making people 20 30 percent faster i don't want to invent numbers now but the numbers published by various companies are used different metrics and i i think ultimately if you measure it like end-to-end how long does it take you to build a production scale project um time there's just so much work in there that's not writing code and i think that's also something we're really optimizing for it like a lot of code gen companies are like okay let's automate writing unit tests but how much time do you really spend writing unit tests a bit but you know not that's not your when you say you think people will get twice as fast do you think that's gonna that would be like every year they get twice as fast or ultimately they'll be twice as fast how do you what's the time frame in which you think if we were to come up with our own morse law and i forced you to bet the time it takes with ai for the average developer to become twice as efficient all in they can basically finish a project in half the amount of time how many months will the doubling take on average going forward i think the first ai doubling should be done this year if it didn't already is if it didn't already happen for some developers um then that should really be something that happens this year how many months do you think it will take on average yeah i think it'll be much faster than doubling every constant number of months or years at some point there is there's this inflection point where ai is reliable enough to just write code make a pull request you trust that it's right you know the same way i have we have great engineers if one writes a pull request another one reviews it i don't even look at the code right it just goes in um and there'll be a moment when ai writes a pull request and reviews its own pull request and it's as reliable or more reliable than your engineers wow and the difference between that isn't now right but that will come and the difference between sort of things like self-driving cars and that is that a company can take a look at this and like well this is clearly better no lives at risk right let's go do it got it so you think that's it would be faster than more than it could be faster than more's law i think it looks like this got it and so what you're drawing is a slow uh sloping up curve and then a huge giant exponential spike uh yeah no this is i think uh i would agree with your central tenant and the key issue is can you trust the code and right now you can trust the co-pilot because every line uh every suggestion it's making a human decides yes or no but correct me if i'm wrong if enough humans are saying yes or no that's reinforcement learning the model uh will look at a correction that's made if it gives a bad suggestion it says no i don't that's not what i'm looking for in terms of the autocomplete this is what i'm looking for does it take that into account the next time it's going to do an autocomplete so i think reinforcement learning plays a key role in all of this but that the reliability piece is bigger than that um because so this is actually something i love talking about this thanks for bringing it up um no matter how large you make a model if it's not aware of its own actions of in the past in a given interaction um it is just physically unable to remain grounded um in in its history like so to be to be more um concrete i suppose imagine you forgot what you did before we started recording this podcast and i asked you how your day was all you could do is invent something you wouldn't know better right and so a lot of the issues we see with hallucination and truthfulness and reliability, which you could think of as a branch of those things, comes most likely, I think, from the lack of grounding in the models past.

19:04It is sort of the average, like the surface area of the training set with just like a cloud of points, but the surface area doesn't perfectly represent the points. And I think if models were to perfectly represent the points, they would solve a lot of these reliability and hallucination issues. So there are parts to it that aren't RL, but RL plays a key role, and I certainly believe strongly that you need it to get there. But it's so far an open question how you make models as reliable as humans. I think the key there is to get them to say that they're unsure when they are unsure instead of just trying and then acting like they're confident.

19:46All right, everybody. Our friends from Microsoft are here. Tom Davis, a senior director at Microsoft for Startups, and you're a former founder. You are here today to talk to us about the giant leaps that Microsoft has made in the AI space. You've been giving Azure credits to startups, and that's delightful and amazing, but people really want access to the OpenAI API. Yeah, absolutely. So there's two things. First of all, we've got a benefit that we offer our startups. They can get$2 ,500 worth of OpenAI credits so they can get access to the latest and greatest models that open ai are delivering but then they get access through the up to 150 000 worth of credits that we offer through founders hub to leverage the azure open ai service which has a full sla around it so when they want to go into production and really have that reliability that we provide with with the azure sla they can leverage the Azure OpenAI service APIs.

20:46And they can do things like the GPT models with codecs for coding and also for the DALI models as well for images. So it's a full service. It's not just the great APIs that you get and access to the LLMs. They can build out their own LLMs using open source and then they can manage those with our AI tooling services as well. Amazing. Well done. And if anybody wants to sign up for that, do it now while you are in front of your computer aka.ms slash this week in startups aka.ms slash this week in startups well done Microsoft and well done Tom so there's two paths that are occurring here which you alluded to one is you're making developers more efficient more reliable at their job but then there's this other path which is hey can I just talk to the AI as a non-developer maybe I'm a product manager or the CEO founder of a company, but I'm a non developer founder, or I'm just making something small.

21:45And I say, Hey, listen, I want to make an app that does a high interval training. And I want it to be able to track my GPS, log in with my Google login, and then put the results into a Google sheet so that I can see them there. And I can see my sprints and my recovery time. And please put my heart rate in there it could write that code and we would trust it enough and and have it published to the app store or whatever uh to test flight and you wouldn't need a human involved those are the two paths that humanity is on right now correct yes you're choosing to build this tool the auto completion with the civilian the non-developer in mind is that correct is that my understanding of your startup magic i think as a company we're not just one product um we're building towards agi and safe agi in particular the code completion product we built is a very good way for us to have early grounding in a user experience and customer feedback and you know potential early revenue and so on but we're thinking like a lot further than just um just code completion i do believe that in the near term um this this product will be uh the the primary driver of our interaction with the outside world um and i'm i mean you know we we as we're nearing the launch um you know we're talking to uh folks who you know like to use it internally and i should say one of the key differentiators here is that we uh want to offer a much more privacy focused version of this experience in the sense that companies that don't want to run on a public API that don't want to send their code to Microsoft, OpenAI or others, we offer private cloud solutions where enterprises are able to host their own version of our system.

23:46And so we think a lot on this in the early days now, we think a lot on the axis of product differentiation and just early deployment of of our um ai models but ultimately i i would say we are mostly focused on building systems that are much more reliable that you trust with much larger tasks where you'd instead of going and asking for like two lines um you ask for uh you know a day worth of work and uh give it you know three minutes time and then it makes that how close are we to that piece of code i you know we'll see um where we're uh we're working hard um and just generally the industry because right now it seems like doing snippets pretty easy sometimes it's going to pull in you know uh some elements that um you know might be reused over and over again libraries let's say uh but then putting together multiple pieces to to build the app as i described it you know that this magic moment where some startup founder or just some civilian on the weekend wants to build an app and they can just describe what they want and have something come back to them because we see people doing that with images right now we see people doing that with text hey i want to write a blog post uh and i want it to be about how to utilize social media to build the company and it gives you a lot of great ideas and you have to polish it off um people seem to think that those blog posts will be ready in the next year or two to be published without needing polish right so i guess that becomes the issue here is the last 10 of self-driving cars the last 10 of robotic kitchens it just gets really hard so if you had to think of a year when people could do the task i mentioned what year would it be i think it's it's certainly possible that it's further away and that um you know we'll bump into the same 90 10 type problem as you described you know the self-driving car world and many others um i agree that this that there aren't that many applications of technology automation where you don't run into the final reliability step but um ultimately i mean what people are building here is a system that is converging to intelligence greater than those of humans and ultimately i i think it is whether that's good or bad um it's a different question but i do think it is feasible that that happens in the near term um near term being under five years yeah yeah i'd be comfortable saying that um okay because i mean i think that's one of the fascinating things about self-driving let's say we've been two years three years away for about 10 years uh you know in my estimation and listen i use full self-driving it is amazing but you must keep your eyes on the road hands on the wheel because there'll be a left turn or there'll be a bicycle that comes out of nowhere there'll be it's not ready for it to take the steering wheel out um although cruise i guess has some on the road in a very short you know constrained environment uh in san francisco now so that seems to be in your world a developer doesn't have to review the code and we seem to think maybe in the next under five years which we call the midterm i guess in our industry short term would be low number of years midterm be five and long term be 10 to 20 decades we're kind of in that midterm uh area i think based on what i'm hearing from you of i talked to it and it gives me the code back that being said the pace of code being written is just even doubling or you know if it doubles we have like some kind of if we just take more's law every 18 months a developer's productivity doubles that means we're doubling the number of programmers on the planet it's that's pretty amazing does this mean the developer shortage will end does it mean developers will be less expensive and salaries will go down what are your predictions you know as we move out of this towards what you think is going to happen on a societal basis and our industry for that matter yeah um i found it most useful to think of it as two points rather than a line and then draw the line after i think right now we live in a world where um humanity is constrained by human ingenuity and productivity where you need a smart person to come up with something and then you need a lot of smart people to come up with the details and build them and make them for an extended period of time in hindsight when you look at these things like you'd always imagine like okay you could build this so much faster if you had already known all the things that you figured out along the way and if you your body moved a thousand times faster so it is not inconceivable that you could build the things that today cost hundreds of millions of dollars to build for almost nothing if you take out those components.

28:49And in a world where you have very affordable AGI, first of all, you have to make sure that humanity doesn't go extinct because then nothing happens. And that's unfortunately a real risk um but if you get to the path where humanity is fine i think it's almost guaranteed that we will end up in a in a world with virtually infinite ingenuity and productivity across the board and it's very hard to imagine how fast such a world could move even with things things like Moore's Law in the back of my head you know you might imagine Moore's Law every two hours um it's it's just those things if you if you think from very kind of foundational principles it is not inconceivable that if you had all the ideas that it took to make you know Apple's microprocess like Apple's M1 chip and you didn't know exactly what to do and you could just draw the whole thing super fast and you could run like a million copies of yourself that like can communicate at the speed of computers not the speed of you know human brains there are many orders of magnitude in between um at the throughput and the volume the upper cap of this is unimaginably um crazy so i i don't and so now like going to the line um i think it starts with ai assistance it starts with augmentation it starts with things like you're describing as sort of a moore's law of in you know right now i would say the multipliers even though things are flashy it's quite small uh i do think chat should be to chat should be did a great job at uh getting a lot of people's like helping people when they're stuck which usually takes a lot of time right because it can answer questions about things that you might be stuck with um ultimately it will go from that to okay like you just do it for me why am i even doing this and then once you get to the stage where you let it plan what to do and you don't just so if it's you know you no longer just delegate micro tasks but you let it decide what they'll then i i just this is goes beyond anywhere as well um that we could draw up here um so so developer productivity you know like i think you can draw these graphs for the next short while um but the world is just going to be an inherent like a completely different place we're gonna have much much more stuff hopefully much more sustainably um hopefully distribute it in a way that you know there will be still be some people who have more than others but everyone will have much more than the average right now and uh i think that the utopia version of this is is amazing and um just almost gonna feel like a different civilization um to what we're used to i i don't think there's a i i i have so far always failed to draw a line i can draw like this short line and then like a big jump yeah i mean our brains uh have a hard time it took us thousands of years as a species to accept the concept of evolution just that that human beings emerged slowly over time from primates and you know mammals before that and you know etc etc now we're on a timeline that's moving so fast the concept of moore's law moving from 18 months to 18 weeks to 18 days to 18 hours to 18 minutes to 18 seconds it is conceivable like you said whoever the team was that designed the m1 chip then the m2 chip all of that knowledge going into a learning model and some artificial general intelligence it could make instead of a new m2 chip every 18 months m3 coming you know 18 months from now it could make it 18 days or 18 weeks from now and then we could have an m3 and m4 coming every day and then we don't even know what a world with that much processing power would be like or that much software development would be like we can't comprehend what it will learn about things in the real world medicine fusion energy uh transportation logistics you know it's it's unimaginable how quickly this could solve certain problems longevity diseases cancers dna we we We don't know what this speed level will do to these problem sets, correct?

33:06That's your interpretation of this. That's right. I mean, I try to think of what the limit is. And, you know, you can keep going, but I'll take it one step farther. Humans are currently made by humans through a nine-month pregnancy process. so there must be there's clearly a way to piece together a few molecules out of pretty much nowhere um through a biological process to create something as complex and amazing as a human being um if you look at human like at factories that humanity has built for things like computers which are probably the most complex piece of engineering we've ever made or for simple things like i know i have a glass in my hand now right this is probably easy to make um compared to a computer they're they're just like nothing compared to what the human body can make and that's something that was created through evolution not through design now imagine what you can create through design if you had factories that didn't operate at the level of computer complexity but at the level of human body complexity or beyond but now instead of it being designed through an evolutionary process that has all of the flaws you could imagine it's just pieced together of raw out of raw atoms and molecules whatever it is that you want to make the universe is made of atoms not of things and we just see things because that's how we evolved but the max level of craziness that we could get to with this technology is is i yeah i don't know what you can build once you have machines that make things of the it's scary it's really scary um but it's also it seems like you know this is the probably the most important thing for a civilization um in in the existence of that civilization to to build to build a thing that surpasses it like evolution does that evolution creates things that are more fit and by the evolution by evolution's fitness function than the previous thing that were that was there just through survival of the fittest but never ever ever in history has one of those things made a thing that is more fit like never um but we're about to do that and that loop is probably never going to stop as you've alluded to right like Like, if you make an M1, you get more compute, you make an M2.

35:13Like, that thing is real. Like, if you have an AI that's a better ML engineer than I am, like, why would it be better at writing the next version, right? That's real. And, you know, again, that can go horribly wrong. If it goes right, it's just, it's a different symbol. It's an evolutionary timescale. It's an evolutionary scale event. It's not just the technology. It's not like mobile or the internet. Those things are really important at the human timescales. But this is like... there's a you know it's really interesting how you have as somebody on the inside understands this and and this is i think the problem uh we're seeing in the world uh you know when the ancient romans started writing books by hand and then giving them to the nobility right those books took months and months to be handwritten by monks or scribes whoever wrote them now if you were to take that book and transport the monk you know from 400 ad and put them into a modern printing press today take their book press a button and show them 10 000 copies of their book the next day they would not know they would think you were a god yeah they would think you were an alien they would literally think you were zeus and that you took you know uh this book that had been written and took years or months and made 10 000 copies of it they would not understand uh it would be like jesus taking a fish and turning it into two but instead of two it turned into two thousand right like it would be like taking a farmer who was you know making uh you know pulling potatoes from a field and some irish farmer you know pulling potatoes from a field and then showing them a million potatoes going through a factory onto trucks they would how many humans do you have inside that box to do that does it wouldn't it would be hard to comprehend and this could be even more than that analogy and as humans we use analogies to try to understand stuff but that's that's the analogy i i've come to be just hard for somebody to understand modern day book printing or modern day newspaper printing or modern day food processing if you were but a farmer or somebody, you know, handwriting manuscripts.

37:35Developer talent is the most precious resource for B2B startups. You know that. And you want your developers focused on product, not on compliance, right? You want product, you want to ship features for your users. And when you're selling B2B software to large enterprises, well, you're going to need to jump through a ton of security and compliance hoops. You know that. One of those hoops, especially for large companies, is the customers want to host your software on their cloud. And you need to build that out on a per customer basis. It's a lot of work, right? B2B startups constantly face this dilemma.

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38:43You can turn your ideas into apps and deploy those apps quickly and flexibly. Let Release show you the power of release delivery and get your first month free at release.com slash twist. That's right, they got that domain name, release.com. What a great domain name. That's up to$10 ,000 in value at release.com slash twist. I like the analogy. I think maybe one way to look at it is that you move the person that you move somewhere from the Stone Age to the point in time where they could write books. And it's like, okay, what is writing even? And then like 14 seconds later, you move them to the time where you could print 10 ,000 copies and 14 seconds later you move them to the time where there is the internet right but you just keep doing that forever and you can't really ever grasp what's going on that it just keeps getting faster and crazier we are already at the point you know things changing used to take many generations um thousands and thousands of years ago that innovation was so rare um on the time axis that it was normal for a a generation like that many thousands of years ago right it was normal for a generation to just not have to change the paradigm um and even until recently you know the invention of things like the industrial revolution that took forever um if you look at the internet that did not take forever no i mean we take it that did not take forever take a gen xer right yeah and compare what they experienced to what their great great grandparents did in the 1800s right you know they might have experienced the printing press uh hmm nuclear power maybe if they made it to the 50s the machine gun uh trains maybe those were the the biggest things they ever saw oh and the airplane right yeah pretty amazing stack for a hundred year old to live from 1850 to 1950 to have seen they got to witness an airplane a car nuclear bombs and the machine gun now what is gen x experienced the internet dna sequencing uh gps you know i mean it's now ai right like it's a whole different cohort of and it's not over well and it seems like it's going faster like what i just described would be like one item right yeah um yeah exactly oh and self-driving potentially and life extension you know like these things it's just unimaginable what um this could do in the midterm to long term when you saw i don't know if you saw yesterday ibm's like you know what we're pausing hiring about 30 percent of uh new jobs thousands of jobs because we actually think they're going to be these back office roles are just going to be all ai so what's the point of even hiring them now we'll just pause hiring them knowing that ai is coming so quickly now this is ibm that created watson big blue deep blue you know b kasparov like they know what they're talking about uh you know you can make fun of ibm if you want to but they actually have been a pretty storied institution uh yeah and for them to just say what's the point of hiring operations people it's a pretty big statement you know i don't know if they're blaming the ai maybe they're blaming the ai i i don't know about the specific story but i would i would say that generally you know like people say a lot of things pr whatnot but in those actions you really see what people believe uh you know a public company allocating funds one way or another uh is usually associated with their beliefs of where the world is going and um and yesterday you know there was an earnings reported a company that lost revenue because of josh pt and um that these these things are just going to increase um is the company you're talking about and chag is a a student resource for getting homework help to solve problems writing citations you can rent textbooks all of that those study guides exams they said people are just using chat gpt now so students are going to find the most affordable free in fact solution to their problems that's why they will photocopy a book you know or a chapter of a book or share a book or buy used books so they're just going to go down like water going down a hill it's going to find the most optimal route uh yeah chag was the company you're talking about yeah it's crazy for a second yeah um the thanks um yeah you seem to have a positive view of all this well look um i studied game theory for many years this is the field i did research in since i was 15 that's just the thing i kind of i don't know i like decisions humanity is just a large collection of decisions and the incentives and rules around those decisions um and and sort of a lot of you know kind of group behavior is predictable by those things and a lot of limitations on group behavior are predictable by those things so i'm i'm a fan of that um am i optimistic well i let me be clear i think there is a good path um and that path is mind-blowingly amazing it's an evolutionary timescale thing that we are just immeasurably lucky to be experiencing if it happens we we are like maybe the generation people are going to envy forever um you know the last one to experience non-ai life the first one to experience ai life and the whole transition i mean this is probably really the most exciting part of history to ever live in and maybe forever who knows um just in terms of how exciting it is but at the same time you know humans and ants um you leave them alone when you're hiking in the forest and you're kind of like cute but you don't really care and when they're in your kitchen they're just dead like a half a day later because you have some ant poison and then they don't understand what you're doing they don't understand how ant poison works so they just they go there the way ant poison works is they have to put a little thing somewhere and then they go there they carry that ant poison back and all the ants die they didn't even know what happened it just right then uh that's what happens if a dumb species annoys a smart species and if we build ai that is so as much smarter um than we are as we are smarter compared to ants and it doesn't like us or someone tells it to kill us all and there's no way to defend against that which is both a game theory problem and a simple is it even possible similar to cyber defense where it's just not clear how hard attacking versus defense is in some vectors that is real and if it's extremely high variance in that i believe there are ways to do this that lead to the good future and there are ways to do this that lead to everyone dying um i i do i think both are possible um i have a lot of thoughts on the split in the three at that junk track uh there but um i don't know if i'd call it optimistic i'd say this is the most important time and to ever live and i don't know why but glad i have some ability to contribute to hopefully making it go well um well let's hope it's not a zero sum game you know if if this is a you know like a giant 10 000 piece puzzle hopefully the ai and humans are solving this puzzle together and that whoever puts the last piece in uh we both benefit from the puzzle being completed the puzzle being humanity understanding the nature of consciousness the universe the cosmos why we're here what all this is and then having some joy from the experience and you know i don't actually define it as living forever but hitting 52 uh i'd certainly like to double my lifespan and maybe preserve consciousness and understand what the hell the universe is it does feel like this is going to tell us but i love your analogy of hey let's be thoughtful because this thing could grow so you know exponentially a 16 and 18 second moore's law is like lighting a fuse to something that we don't know the chemical nature of and it's literally like you could be lighting a fuse to a giant bucket of kerosene underneath you and not know it and by the time you figure out what happened you're incinerated right like that is kind of the analogy here and i think we have to be thoughtful and the fact that you're recognizing that i think are you for slowing this down is there any guardrail here should people who work on this technology be licensed like doctors are or truck drivers are should people be required if they're using these tools to have you know some uh ownership of what they're building you have any thoughts on when regulation should come to this space if at all yeah i think regulation is is needed now um i think that would be a good thing um the making it i i do think you need to do this work because it's it's ultimately i i do believe that if something is possible it will happen um if you can't live in a world where all of humanity knows that you know how to build a screwdriver and nobody builds a screwdriver it's just not gonna work right someone's gonna hide in a basement and build it and you know with compute getting cheaper training a gbt4 quality model will get cheaper and you know it's it will eventually happen so we need to make sure that it gets developed safely so both that it gets developed and be safely in the same set you need to develop it to develop it safely uh otherwise i just can't i can't see a world where stop it or pause it um and again there is a utopia version that i would certainly love to be in um but yes i do believe you should make it extremely um hard to mess up like you know life is fascinating yeah which is just not much enough what what are the choke points here i mean one i can think of is access to compute power required um people and then maybe the people who do control that compute power i know it sounds silly aws nvidia azure they control the means of deployment that seems to be a place where you could be thoughtful just like people who control the precursors to certain chemicals that are dangerous have some controls uh and for people who are in the no control camp go try to buy a bunch of fertilizer after the oklahoma city bombing if you want to go buy a bunch of fertilizer it's tagged it has secret codes inside the fertilizer they report back to the government when somebody comes in and buys a lot of fertilizer who's not a farmer there are some controls in place because people realize fertilizer bombs can knock a building down it's it's not controversial to think about regulating you know a certain size of a gun a certain caliber of gun you can't buy a 50 caliber i don't believe uh you can't buy 50 caliber certainly machine guns automatic machine guns like that's limited to the military but you can buy you know 38s and 45s so there is a certain caliber under which uh you know some regulations occur how do you think about specific regulations i put one out there which is the deployment uh yeah access to deployment technology If you, I'll be careful here.

50:35So first, I think training is something that is a huge checkpoint because it right now requires a whole lot of compute that you can't hide to really, to train these models who need very specific hardware that only a few companies are building. So if you have those companies, it follows very strict procedures and who gets access and what monitoring should look like. I think disclosure is important to just get an idea of what's even going on. Training is one that's easier to regulate. This starts to change once, you know, what happened with this model called LAMA, where the way it's just, you know, where they kind of half open source, then they leak, then everyone has them.

51:14Once that happened, I think you're in big trouble. Because inference is not that difficult. Even if you, like, so, you know, if I download a GPT-4 on my computer, like, I could figure out how to serve it without anyone noticing regardless of government constraints it might run extremely slowly but frankly who cares so oh my god my completion takes 15 minutes instead of three seconds like what's 15 minutes nothing right now i can run that i could run that on my cpu with hard drive offloading which is fancy terms but the point is like you can run this stuff on any hardware it's just going to be extremely slow so that i don't think i don't think you can particularly stop individual deployment, you can stop rollout, you can stop productization which I think a lot of the LLM companies are doing themselves, which is good from making sure that malicious use cases are not being pursued.

52:10They do that well. But training is the big one and I think for that, that's why I think regulation should come soon. For that to work, it just needs to be done before there are a lot of models like that. And you need to make sure that, as you said, I think licensing of companies and individuals that are in control of that and then the security procedures to ensure that the weights do not, in fact, get distributed to the wide world are important. You know, you bump into many international issues there, like what do you do if other countries don't want to comply with that? Like, do you just slow down as the US?

52:43I mean, I'd understand if the US doesn't want to slow down if other countries don't. So this needs to be a multinational thing, just like with nukes. And there is a long story about how it went with nukes. i don't know how much time we have but basically the the whole like we don't use nukes thing fell through because they couldn't detect um i think it was underground tests um back then because they didn't have a you know measurement capabilities that were good enough uh so if you can't detect that you know people don't trust each other governments don't trust each other and then you get everyone doing it anyway um but you know training i think you know i just bioweapons too i mean yeah yeah we're supposed to not be doing bioweapons research right like i think there's some international treaties on this and it's like is saddam hussein or yeah kim jong-un or you know some bad actor are they going to follow it no and and can you detect it no uh can you maybe get spies to tell you what's going on perhaps can you find some precursors and follow them around or do some lab detection i guess but yeah man brave new world but people should just take note i mean the fact that you know people who are building this stuff jeffrey hinton the guy who's just a godfather of ai of course but he just he drove this um that he's warning of it and everyone i mean i you know i'm just a tiny fish in the pond uh the the the people who've been working on this for decades are worried uh this is different from when people are worried about you know crypto like or when people are worried about whatever else is the thing they're currently worried about right that the people who have been working on this for ages who have dedicated their careers to it including myself are um saying that this is very much a path with a juncture and and and we are we're supposed to be careful here um yeah and you know we have government authorities checking on development of other potentially dangerous technologies and tools and we should just have that here um yeah if you don't know about uh hinton he um he has uh left google with a warning of danger ahead and this is uh a reoccurring theme uh people who uh helped build this saying um this is moving at a pace that's making me uncomfortable thank you so much for sharing what you're doing i wish you great success and uh we'll have you back on the program uh after you launch and you get a you know i don't know a couple of more more's law cycles uh learnings so i'll probably have you back on the show in 18 minutes uh to to explain how this all turned out for humanity uh great having you on the show are i know you raised a bucket load of capital are you hiring and uh yeah who are you looking for to join the team yeah this is a great conversation i was i'll be totally honest i was like wow uh okay this is really simple uh i get it a totally logical idea for a company it's going to be very successful do a great return for investors and then we started talking about the big picture and you turned out to be a wonderfully thoughtful guest and we had some really great moments here of like thoughtfulness just batting back and forth um you know some analogies of where this is going and you really expanded my thinking on it and i'm sure the audience will get some great usage out of your thoughts and your thoughtfulness uh where can people find out more about your company sign up for the beta and then uh maybe join your team I just really appreciate what you just said.

56:16Thank you. I'm glad I can contribute some amount to hopefully, you know, the wider picture of VGI going well beyond me just pressing buttons on my computer, which I certainly like doing and do a lot of. um but you know okay so yes we can find us at magic.dev um dev like you know developer and um joining us yes we're looking for people who care a lot about ai going super well and not going super bad and um who are good at pressing buttons that make computers think uh you know the deal if you if you are one of those people um so just reach out at eric at magic.dev and uh we'd love to hear from you um magic.dev slash join as well to see what you're looking for and waitlist and uh yeah no i'm an open inbox guy uh slash waitlist if you want to use the product um maybe i don't know when this is maybe when this comes out potentially is already used but probably waitlist we'll see all right everybody uh that's eric magic.dev and we'll see you all next time Thank you.