Building Self-Accelerating AI to Accelerate Science with Mirendil

Transcript

With an AI technology that is helping with AI research, what is the role of everyone else in the company? This is a very disruptive technology. With disruptive technologies, you need to rethink a lot of pieces to kind of enable them to actually grow and flourish and some of these pieces are how you build a company around it. If the business model of the company is I train a big model and charge people for using it. How is this company incentivized to share this technology with everyone else? We've been at the frontier labs for a long time and we know how much work it is to do something and we've been able to do it in maybe 10 times less people and less resources. The jump from Sonic 3.5 to 4.5 has been materially better in terms of where does it get stuck, where does it need oversight? Even the time is reduction in oversight can lead to large amounts of open-spend than just if actual art comes to being challenging. What you guys are doing is almost the next level of cheat code. When the internet gets faster, you don't like recursively get faster internet. Where do you think it all ends up? So, why do you spend trauma park games? One of the oldest ideas in AI is also one of the most ambitious. The idea that intelligent systems could help improve themselves. For decades, that possibility remained largely theoretical, but as AI systems become more capable at coding, research, reasoning, and engineering. The question is becoming increasingly practical. What would it mean to build AI systems that contribute to their own development? And if that becomes possible, where should that capability be directed? Math Bornstein speaks with mere deal co-founders, that amnature-born, and harsh mata about self-accelerating AI, scientific discovery, and why they believe the most important applications of AI may be in advancing science itself. Benham Harsh, welcome to the A6ing Z podcast. It's awesome to have you here. Benham and Harsh were most recently research scientists at Anthropic, were together before at Google, at BlueShift Labs, which Benham you were a founding member of the Thrill Chat. You hear, tell us a little bit about Miran Deal, what you're aiming to accomplish, and tell us where the company came from. A lot of reasons behind building the company comes back from when scaling while was happening at OpenAI and back then I was at BlueShift team, and that was the moment when I realized we are on a verge of evolution, and then everything is about to change. And for me, the most important thing was what are the technologies that would accelerate all areas of science, and what are the main bottlenecks for making that happen. And since then, I've been kind of trying to close, they may close to that path and remain on the shortest path. And known Harsh since then, and kind of trying to be on that path, and more recently, as we've been in Anthropic with talked a lot about, what does it mean to kind of remain on this shortest path, and we felt like the main labs are starting to diverge a little bit from, what does it take to accelerate all areas of science and that led to studying Miran Deal? We think the self accelerating AI is a technology that is disruptive, and at the same time it's important for accelerating science and technology, and we want to focus on building that and making it available. So science is notoriously hard, right? I mean science is science. It's an experimental discipline where you have to run experiments in the real world to understand laws of physics or biology or so forth. Can you guys describe a little bit what do you think is the shortest path for AI, to sort of accelerate science and why? So from last five years, when the models kind of started getting better at some of the primitives of kind of things science, like high school math and college math, and then a little bit of coding, and then now the code models are really good, competitors to start a good really. And this is this all in scope when you say science. By the way, I just want to make sure we're talking about strictly like physical, you know, natural sciences or as a broader sort of thing. So ultimately, what we want to build is AI systems, which in a very broad sense can conduct AI research and engineering itself. And if it has this capability in a very broad sense, it has the right primitives and capabilities which are needed to conduct any science, which is in the realm of digital world or any science, which is in the physical world, but then has a digital component as well. And so what do you think is sort of the path? It may be contrast to what the major labs are doing, because people may be familiar with that at some moment. Yeah, so when you think about what is a scientist or what is a scientist or an engineer do, the main skill they have is get deep, very deep in a domain and build expertise, and they build a very sharp expertise that accumulates over time. And as you kind of become expert in a domain, your expertise is becoming sharper and sharper in something that has tiny bit of volume in the whole set of possible things you could be working on. And getting to that point, like being able to get there is a capability that is needed to advance all areas of science. What does it take to have an AI system that is able to be directed at a direction and making improvements faster and faster and gets eventually to the edge of that area and eventually make progress. And what that view means is that you have to work on what does it take to direct a system towards a particular angle and make fast improvement. And that's the self-icelerating AI technology. So this in principle is very disruptive because this technology allows a model to kind of keep making improvements and keep getting better at a direction. And as you know, when you think about the business model, you can build around this et cetera. It's just very different world where you want to make this available to everyone as opposed to make AI available to everyone. Yeah, so explain this term self-icelerating AI unlike a huge sci-fi fan. So I've always hoped that you know the machines would start improving themselves and take over the universe. But if that doesn't seem to be happening, you know, and I'm not sure if it really is possible or makes sense. So explain what you think of, like how is this actually going to happen and so what does self-icelerating AI really mean? I think I have many different definitions and we have a specific version of it which we are targeting. I feel like the history of our field in some sense also go where we can create like all these self-play loops is already self-isolating AI. Another version of it is if we can put the AI in an unknown domain or an environment can it learn by itself and improve its own capabilities and context that is another version of self-isolating AI. And this is sort of like the RKGI benchmark for instance where it's all dropped into an environment and fast to like understand the roles before optimizing them. Correct. And more like inside like enterprises are general knowledge work. Another example could be like a system which ends inside a company. If an employee kind of like enters a company then they have to gain all this context to function well and can any high system do something like that. Our version of self-isolating AI is can these systems do the work of an AI researcher or an AI engineer like everything from the low level kernels to like in works to conducting research itself and a very high throughput. And we think that that's kind of like a short version of self-exolating AI will lead to a broad version of self-exolating AI which kind of ultimately has the capability to conduct kind of like advanced science and a more broader. And so it sounds like part of what you're saying is that scientists kind of have to become AI people in a way and they're not right going to like do it on their own but like the tool that you're building for them can help them actually share the coming AI people. Yes. So one way we like to think about this is if you think about each areas, each of these important problems that exist in science and technology and what does it take to solve these problems. These are grand challenges that they're not human level problem. These are superhuman level problems. Things that we don't think people can solve and we don't think it's enough to build an AI scientist that would do the job of a scientist. These are superhuman problems. So what does it take to solve these problems? Each of these problems need a pre-adding lab which is a bunch of experts who understand the domain and are excited about kind of attacking these problems directly and then a whole big AI effort just to push AI for that particular problem and the thing that's difficult to do for every other lab is assemble the best frontier AI team and kind of iterate over the AI solution that's best for them and what we want to do is take that part and minimize it, make it small so that you can maybe today if you need 200 people to have a good frontier AI lab and 200 of the best people which is almost impossible to do we want to reduce it to 10, 2, and eventually 1 and eventually you wouldn't need to hire on the AI site so that you can move much faster when it comes to solving the problem because each of these problems have a lot of other constraints to solve like physical elements like getting access to data and all the other bottlenecks that exist so we just want to remove data about the market and this has been sort of a longstanding goal in the machine learning community you know I remember even 10 years ago Google had a product it was sort of like a neural architecture search thing but they sort of claimed like machine learning is now solved right you just run the computer and it finds the right architecture for you what's changed now or what's the state of the industry on self acceleration now I think that's a great example and in some sense it kind of like boils down to the intelligence of the agent of the system which is conducting the search and as you move the intelligence dial higher and higher you are much more computer efficient in getting the results that you want and that has been happening like from that particular point to kind of like an overview of today and it's also happening in a very broad way it's like not just architecture search that you can do but much more like large swaths of like an or software that you can move on its own and it's a very expressive space of things that you can do right now. Can you explain this point about general versus narrow and I'm guessing you know on a reveal all the secrets of the models you're training but I think it's a really important question like like will general language models or general coding models like be able to run these sorts of AI experiments or or is something more focused necessary. So I think the importance about kind of like you know the general kind of like you know sense of and the taste of like these AM models is like if you make like if you kind of like toned down the intelligence dial and like make them focused on like you know something very specific then they were kind of like you know still make very like you know dumb mistakes like you know not wire things in the core properly or like you know they won't have the right kind of mindset of an expert in that particular field. The the jump from like you know sauna 3.5 or 4.0 per cent 4.5 that has been kind of like materially better in terms of like how long can we run an AI system on a particular problem or where does it get stuck where does it need oversight and every deduction and like you know even the timeiest reduction in oversight can lead to like large amounts of token spent and like you know just effective outcome to being generated. And so you were actually adding traffic when a bunch of these models released that you just mentioned and I know you're starting to work on some of these ideas then can you just explain a little bit of the history you know behind behind this you know product you're working on now. Yeah so like you know Benem and I have been like you know excited about this grand challenge that he mentioned about accelerating science with AI and this was five years ago and like you know we have been added for a while and one of these overnight breakthroughs it takes five years to uh yeah exactly we had to start with like in a pretty bad models like you know models which are not good at even high school math like you know auto complete coding and stuff like that so we first build the math specialized models at Gemini and then and work on the reasoning models and then some of the primitive is kind of like started to come together when we join entropic and that serves like the right moment where we can actually be ambitious enough to directly attack the problem of AI being able to conduct its own engineering at research and the biggest jumps kind of like you know like you know Dario calls it like a smooth exponential like you know and scale has been very helpful both in terms of the models the underlying models being better but also conducting the infant style kind of like you know research and engineering at a medieval high torque rig all embarrass you a little bit harsh you told me when you started work on a sit-and-thropic nobody else wanted to work on a few years ago I think they had good reasons and you're meeting the models didn't work yet or or yeah girls so like you know when we joined like it was like it feels long time ago but like you know the state of the art market was on at 37 and we are I think I was too excited in some sense to kind of like jump in and attack this problem and one of the reasons why I joined entropic was like you know Dario had to say like machines of loving grace and we just very inspiring and it really felt like the place where this kind of ambitious work can be done and and that's why I felt the conviction to stock the it is it is funny it's almost easier to use model version numbers as date you know it's like time reference points now not rather than like years months like like I can actually understand like what you're indicating when you say it's not going to be like to money to money to what I'm like yeah no I and I think a lot of people feel that way I guess what what gave you guys the sort of impetus to go out and and you know start mirror and deal you know having been in opposition and you know having done some interesting work in entropic I think so I was you know like co-leading the science team in entropic harsh was kind of inshated that let the automated pre-training project entropic and if you're both like talking about like what does it take to kind of apply this to science and and et science and one of the there are a few observations that kind of made us decide to you know like take this challenge one is this is a very disruptive technology it went with disruptive technologies you need to rethink a lot of pieces to kind of enable them to actually grow and flourish and some of these pieces are how you build a company around it you can't let it's very hard to have a disruptive technology show up in an existing company and and and and flourish because a lot of elements have to be redefined like with an AI technology that is helping with AI research what is the role of everyone else in the company and how do they rethink and it's very hard if the culture is not built for it it's going to be harder and the incentive of the company the business model of the company if the business model of the company is I train a big model and charge people for using it how is this company incentivize to share this technology with everyone else because that's directly letting everyone else train a model which reduces their dependency to the company so these are like funding so fundamental that it doesn't matter who runs the company but you can clearly see that it's in in in in contrast to what what you want to happen and and you know like those observations and seeing that these are like practical limitations with with let us to kind of start the company that is really rethinking all pieces for forward this technology and making it available to accelerate science so I just just because you mentioned that I have to ask about the fable much right where it's you know we've we've guard railed certain important safety you know areas like bio weapons and you know etc and AI research right like one of these things seems a lot less dangerous than the other things um do do you think that um you know like access to sort of cutting edge AI research assistance is going to be um is going to be sort of curtailed or like inaccessible most people if if you know you guys don't succeed or or you know companies like Mary and Jill don't succeed yeah I think um I think uh uh they're a good reasons for um why um you want to be careful about the technology and it's it's very powerful um and the same kind of like can you actually sketch out the the pro case like like you can so for me as sort of a non-exper right to see it's like okay I understand why bio weapons are dangerous why drug you know synthesis is dangerous you know like there's a bunch of things that actually should be filtered out of pools of information generally yeah the the AI training ones stood out to me it's like well this actually seems like a positive good like why would we wish it but but like but since you guys were sort of on the inside but maybe sketch out like the the case for why that does make sense um so there are a few cases like one is like if you um if you think about you know like as as let's say if if let's say speaking of antropic if if you're concerned about other states developing and competing with US then you would be concerned about them using your technology to move faster um and I think that has been like a concern you know like from from um antropic and um the other concern is like let's say for bio weapons like if you're concerned about bio weapons then you would be worried about someone using this technology to build them all that would help them with bio weapons so it's a it's very powerful it can be applied to anything um so there's you know like some merits and being worried about this at the same time the same way as AI came to its distance and it was a powerful technology and you know like you have to have considerations around it but you can't limit the whole technology because it it's very useful it's true for self-isolating it. It's powerful it's gonna it would it's very enabling and what it really requires is rethinking how you should do safety how you should you know think about godrails how you you know when you build a company around it you would be focused on how to get this right as opposed to when you're trying to do a lot of things then you would be looking at the sudden like okay this is too much to much trouble for me and and also it's very hard to separate incentives from from reasons and it's just get melted it's just hard to separate it because you're also like deeply incentivized to you know like to keep your distance with others etc and and you know it doesn't matter who runs the company it's just like when the incentives are set this way it is hard to fix it. No that's that's fair and and suffice it I guess I'm I'm inferring from what you're saying that the deans both the incentives and sort of value structure of of mirandial are designed to to enable access these sorts of technologies rather rather than curtail it. Yeah there's a kind of like a no very large benefit that we can get from offering these this technology and there's also harm and some companies would prefer to kind of like you know throw a very large hammer that okay we block access to everything but because of our focus and our kind of like you know the intent to advance science and we want to date like a sharper approach where we put the energy of like you know making sure that in every use case that we are offering this technology we can make sure that it's kind of like you know being used in the most positive sum and sicker that makes life sense. Do you want to talk a little bit about the products and models that you plan to launch again you know don't need to reveal anything super secret because I know you have a launch yet but it's a great chance just to just to you know tell people they should look forward to. Yeah I think in a in a broad sense are kind of like you know models and kind of like you know product look very similar to existing state-of-the-art models but sharper in in the specific capabilities which are needed to conduct engineering inside like an AI lab or anyone who is really trying to train or serve their models. So the hope would be that it would be really good at kind of like you know everything from low level kernels to like you know libraries like by dot jacks rlfamworks befitting frameworks and and the the product will kind of like amplify these capabilities to make it very very accessible for anyone who is doing this kind of work and like specialized towards that. I want to add maybe up the one one other points related to kind of our approach and and how like how we are thinking about incentives etc so you know like we are the way we are thinking about the product and how we are forming relationship with the rest of the world is how can we enable businesses to start owning more pieces to have don't need for their own AI and I think what we are noticing today is that as a result of creating more dependency to these big AI up there like you're graduating like gradually losing control and losing bigger parts of the business and then becoming weaker or weaker what we want to do is kind of reverse this so that every business every lab would have their own AI optimized for their own workflow and for for their like with their own data with their own infra and this would allow them to have better margins more control and and kind of this is like a much more enabling future and we think it's going to be just a matter of that I think already business have started being worried about this and it can be just a matter of time before they they want to lean into this angle more yeah I mean that I think is a really interesting question it's it's almost the like most important question right now like anything about major technical breakthroughs that have that have like massive economic impact you know one one characteristic of that is that everybody can use them right everybody can build on them and build with them not not just consume them you know you have some monopoly industries like power generation where the capex is so high that everybody can but but you know it's like consuming power is like not very limited right this is like really low level infrastructure versus AI is not right AI is like application level all the way sort of down so this is the thing that we think about a lot on our team it's like how how do we get to a point where where all the major companies start up you know big banks healthcare companies harm companies etc can actually build with us and and and you know that I guess the hope is it's something like what you guys are doing sort of can help us out yeah I think I think it's interesting because you know like let's say with caught code coding went from just being anything related to programmers to like something that everyone can do and now everyone is unable to do a lot more and the future we see is same happening to AI where everyone can go from their wish to have AI do something for them to seeing that happen like their own AI and it's just a matter of having to compute that resources and not matter of like expertise etc so businesses if they want to kind of take an angle they have you know like their own data they have their own processes this should give them naturally a lot of advantage and the thing that they're missing is putting everything together to build the modes and and you know like we think AI is going to be that enabling thing that would kind of give the control back to them is it true that all researchers at Miradil have to submit their traces and and chats to it you don't have to answer that if you don't like to all I can say is that when you set a company with a certain goal you optimize the entire company into and for what do you going for maybe sort of unrelated note like are there any moments that you guys have seen either in the last few months of Miradil or just in general that kind of gave you the chills like oh my god like self-solar acting is actually working I think the kind of like the rate there to which we have made progress technical progress I think that surprised me as well I think we've been able to do a lot with a lot of your resources and and people and like we've been at the frontier labs for for a long time and we know how work it is to do something and we've been able to do it and kind of like like maybe 10 times less people and like in all the resources that that was surprising to me I also maybe a surprise for me is when I thought to candidates and they get surprised that you're like how do you think you can compete with big places with you know like only having you know like I don't know 20 people and I'm like looks like you're not a believer in this. Have you been using like more shit to say that you have this thing now I can help us. You know also start right like you know in many ways start up sort of just a more efficient way to you know resources and people right you know compared compared to sort of diminishing returns at big companies and things like that. I guess part of what I'm getting at is like we have this term self accelerating um you know do you believe in the kind of like real like self acceleration sort of a cursive thing or or like to you is this a tool that that is just going to like bring prosperity you know and and and like these great capabilities to everybody. I think a lot of people have a sci-fi view of it which is like a mall kind of making changes at such a I think one thing that maybe we have been kind of thinking a slightly differently is building AI systems as opposed to like thinking about one AI mall this is where like um you know like an ecosystem of malls and ways of working with each other and I think we think for for civil future maybe you know like I don't know like maybe a few years this ecosystem also includes human and then you're thinking about this entire system as one intelligent being and you're asking this question of how can this system improve itself and so that's like a gradual view of like how do we get there but also systems are much stronger than individual malls like if you think about you know like best AI researcher in the field what can that one person accomplish versus an entire company is built but it's also not obvious how to build an entire company from individual brilliant people it's actually hard thing to do to join like 24 and make your company out of it poor guys or you have to figure out org charts yes so it's actually not not a straightforward problem how to create a system that is like fast moving and and it makes improvement so the way you think about this like there's a system of AI that are not necessarily same model I could be specialized could be larger than my journalist models and then some people and then this system wants to kind of keep getting better um which getting better means this system kind of develops the next AI's that would be part of the next system and you know I can't kind of keep making improvement and that's like a very realistic view of how you know it would evolve into at some point becoming fully autonomous so you're saying in the future and an agent will leave it's 10 million agents swarm and talk to you know the 1,000 agents swarm and say how do you guys get any work done? no I think I think that's yeah no I think I think it's very interesting point you're almost saying that like could because one issue I've always had with this is like somebody has to prompt these things like like they're they're they're not self-directed but you're sort of saying is like the level of abstraction goes up there's almost like a standing like you only need one prompt anymore which is like making improvements to yourself and this like sufficiently large and complex system can kind of like operate continually it's almost like prime directive rather relevant prompting at that point yeah exactly and if you look at kind of like how the models have been developing like it requires less and less oversight and I think this trend is just going to continue and at the end the ultimate prompt is like you know the achieve goals and the system should be able to kind of like learn on its own regard all the problems asked for directions when needed and just as humans would and achieve those goals I mean related to the systems I wanted to kind of throw the status like interesting thought experiment is is you know like when you think about systems there's this other angle of scaling up which you know like we have we have so far scale of models in terms of size and compute etc now we are facing this another access which is the scaling of systems and these systems today they're like systems composed of people and agents so each access is a number of agents very like some of the more people and some of the more models and you know the app different properties but the question is really how do you get a favorable scaling of a system like companies the scale and their productivity go down with scale so people don't have a favorable scaling so you go from you use your company size goes 10x and your productivity using like it's like 1.2 so that's not that's not great and today agents are also not that amazing in terms of like being also actually scale them in a way that you can you can see that productivity of the entire system grows so the companies of the future where you would want to be able to kind of throw compute that problems and kind of scale up agents and see that you let's say when you double number of agents you get to the same goal to each faster that's the question like how do you save time is the the the the the real future like the real thing that the world is fighting for is how to get to a point faster and all the competition between companies and and a lot of these companies are willing to pay 10x more compute to just get to something like one month faster than others and I think that's the the you have to solve this scaling problem and one interesting thing that we know about scaling loss is that if you want to get scaling loss right you have to start a small and get the scaling right before you scale up otherwise things are not going to work so that's me I think of ourselves as like the experiment you're like writing a experiment as small scale thinking about like how can we get favorable scaling and then scale up the system and I mean it's really interesting is it a communication like what like what aspects of the system do you think need to be solved in order in order for the store better oversight a lot of oversight issues I think a lot of resource allocation problems like if you have same compute but you increase number of researchers you can't you know how do you decide which ideas would get prioritized etc so a lot technical this is what really happens yeah yeah so there's a lot of technical problems to solve there that are things so now we're talking interagent politics it's like why did that age didn't get 1000 views I get only two at the end of the day with agents and people incentives are very important yeah and this is orthogonal to the performance of the agent it's okay because I know you've worked a lot on long running agents and and you know like cartridge things sort of narrow sizes of problems and but these things you're talking better actually orthogonal to it's like individual agent performance yeah well one thing I want to highlight is like such a like very like you know interesting and impactful time that we're living in like another like this history is of science and like you know for example physics a whole bunch of really really impactful work happened in 1920s and 30s and then there was like a little bit of blank space and I think we're going to like a similar similar thing in intelligence and it's going to be it's improving at a very fast trade and it's just a like a really interesting time to live do you think this sort of self acceleration is already starting to happen meaning when you look like the latest releases from from the big labs you know is this is the slope getting steeper because of this absolutely I think it has started kind of like you know in some sense and small ways when the coding models kind of like you know became like truly useful and it has picked up by kind of like every generation of kind of like new models and it improves everyone's productivity by itself but that's also kind of like improves the the time to the next breakthrough next model it's this sort of a unique aspect of a eye isn't it that it's like you have these sort of cheat codes it's like coding is kind of a cheat code because a lot of the system is built on code well you guys are doing is almost the next level of a cheat code because like the actual experiments are sort you know what you don't see with like like when the internet gets faster right you don't like recursively get faster internet right you know is so it's it's it's sort of an interesting thing wait where do you think it all ends up so white spectrum of hot comes but our preferred outcome is kind of like you know just generally prosperity especially specifically scientific prosperity in the sense that a lot of the science like it's just unknown at the moment and a lot of the engineering kind of like you know large learning products are also just bottleneck by people being able to design things well and just the intelligence being the bottleneck and we we hope that like some of the progress can translate into just us knowing a lot more about ourselves and the universe. Quest your question is like maybe also one of the other reasons where I just kind of push me to kind of start a company is I think the current picture that's being painted about the impact of AI is not that positive like automating people's job away and you know like that doesn't seem like an exciting future and so you know I'm I am thinking you're trying to in a way focus on building a different one that has more positive outcome for everyone and we think accelerating science is pure good for humanity so that's that's where you could you could decide where to put like you're building something powerful you could decide where to put it at work and I think we we we should feel like we are in charge and we can kind of build a future we want I think um these problems that you know like solving Alzheimer disease being able to predict it much in advance and solving these are like super challenging problems there are so many things that have to be solved on the path of it it would it would show everyone that AI like it should have been used this way from the beginning but somehow we are kind of using focus on like these are the important problems that have to be solved and smart people should be working on these problems so we want to kind of change at least we can choose what we work on and we want to kind of be focused on creating this powerful technology and directed at these problems that are long standing and it will still take a lot of resources it would create jobs because you know like you have to solve all other aspects of it that are not intelligence related bottlenecks are going to move away from intelligence so so it's it's interesting to kind of directed at these problems that are just not about day to day but but solving problems that help everyone um and is it fair to say that that continuing the scale of pre-training is probably not sufficient to solve these kinds of problems you're talking about and like because you're almost talking about generating net new knowledge which which which maybe I guess is hard no matter how much pre-training do yeah and in some sense like I think of like an opportunity training post-training and all the like you know baddams that we have of using computer effectively as like you know tools in my toolbox and at certain points in time like you know some tool is effective and like you know we go for for the fix-up on-of-compute that we have we go for the best tool but what you're trying to build is like is not even just like adding compute but kind of the system of putting like expert people into like into like the system as a as a whole and this is like part of what I'm obsessed I think you're saying yeah so you know like for let's say you know just let do this my experiment of like the way things are going um you know our best hope is that we're going to have models that are going to be as good as our best scientists or maybe become better than the best scientists but how's that going to solve all some of the things for us like it these are problems that are you know like we don't even know if it's possible we don't even know what are the limits of the exists so we really need to move at much higher speed and if you're not thinking that way if you're just thinking about slightly higher building it an AI scientists etc these are this is not like going to solve these problems like Alzheimer disease for example it has so much structure in terms of data that you have to use etc that you cannot even see how in ten years existing models would be able to kind of move things much faster and we decline the speed we are not going to get there in time so we had impatient about solving those problems awesome what was so so thank you thank you so much for working on us and for ever coming on podcast thanks for listening to this episode of the A60Z podcast if you like this episode be sure to like comment subscribe leave us a rating or review and share it with your friends and family or more episodes go to youtube apple podcasts and Spotify follow us on x a 16z and subscribe to our sub stack at a16z dot sub stack dot com thanks again for listening and i'll see you in the next episode as a reminder the content here is for informational purposes only should not be taken as legal business tax or investment advice or be used to evaluate any investment or security and is not directed at any investors or potential investors in any a 16z fund please note that a 16z and its affiliates may also maintain investments in the companies discussed in this podcast for more details including a link to our investments please see a16z dot com forward slash disclosures