No Priors Ep. 54 | With Sarah Guo & Elad Gil

Key Takeaways

today I no priors we're having a special episode of Sarah and me just talking hello Sarah how are you hey alot what's going on I see you a lot not much good to see it let's talk about models what's going on in the model world yeah um I guess there's a lot of hand models that are emerging so I was thinking of maybe trying to do that eventually it's almost as good of a business as as investing I know right um yeah so there's been a lot that's happened in the model world uh recently obviously Google launched Gemini which I think had a few interesting characteristics both in terms of uh performance but also the huge context window right it was Million token context window uh companies like magic I think in the past have actually put out like a 5 million token context window model and things like that but it's really exciting to see that and I think for certain application areas like biology longer context Windows actually seem to be quite important and so for example if you're doing your protein folding model and you have a short context window you're often actually not encapsulating much of the protein right the average uh protein is I think something like 300 amino acids long at least in the human genome but there are things that are dramatically larger than that and so you just can't capture it in some of the context window is being used for biological models and so I do think this is going to be one of those areas that will end up being more important than people think at least in the short run um but Gemini 1.5 seems to have some really interesting performance characteristics there's obviously Sora from open AI which was um the video model that uh you know is is beautiful to watch you know there's other model companies like Pika and others that I think are doing exciting things as well and then um mrr or liiz launched uh Le chat which is really the name of the product Le big model Le big model Le Big Mac I believe they call it mistol large yes yes lar M large they launched that and the thing that's really really impressed me about mrst is the velocity of shipping it's incredible impressive they went from basically starting the company to almost GPT 4 level in less than a year right n months y it's amazing and they have uh you know small performant models they have the Big Mac or you know the large model they have chat they have multiple languages it's just it's very impressive execution so and then I think the other thing that they just launched or announced was that deal with Microsoft where you know they're they're now being licensed onto Azure and so I I think the main models in Azure now are open AI llama mstr and then some of the Microsoft models so again that's striking as well so uh just very impressive progress by that company so far I think the design space for what you actually want from Models is certainly going to include state-of-the-art capability and mrr is very much going up after that and they've said so but I I I think like from the beginning the company has talked about um efficiency uh and latency and the ability to serve different use cases with that and um and also you know being long-term proponents of retrieval right like one of the big debates in the research world right now I don't know how much of it is a debate but people are talking about it I'm on one side of this is that um like Rag and retrieval is dead with sufficient context and uh curious what you think here but I'm I'm more of the belief that it just opens up the set of trade-offs you can make between um retrieval more sophisticated retrieval and model reasoning by having a larger context window versus saying like we don't need any um ability to uh work with a specific data set versus just retrain or um stuff something into context yeah we're going to have both in my opinion the other thing I think that's very underd discussed and this could lead into agent stuff but I'd like to um also spend a little bit time on Gemini before I move to agents is if you look at a lot of the optim that are done for um areas where you had uh human related sort of reasoning or other components pre- llm based reasoning uh a lot of it was happening at infer time right so when you were doing when you're trying to build a better poker AI a lot of what you did was um you know certain types of tre searches or other things when you hit inference time right you built the model but at inference it did a lot of extra work and I think that's also a little bit underd discussed in terms of probably a lot of what's going to happen in the future particularly we get into agents and reasoning is stuff that's happening at that point of inference and then it's used to sort of Fe back over and um sort of continuously train or retrain a model over time because I think that's the other piece of it is you know from a model perspective you uh spin up a giant Data Center and you spend $100 million over 12 months overall between all the different works that you do and everything to launch your next model and then you have a file and then you use that file sry for the next year as you train the next model versus saying you're going to do some sort of continuous upgrading or training and so all these things are going to shift over time I think it's early in the technology cycle and so all these things are going to happen um you know one of the companies that has a lot of capabilities to do interesting things over time of course is Google so I'm a little bit curious if Gemini has changed your opinion of sort of the AI model race and what role Google plays in the future you know has it not changed your mind much I think the question on like whether or not uh Google has the ability to do the research work to have a competitive product uh has been answered right Gemini is a very impressive model I think the um the capabilities that they have internally that they haven't released yet around um additional like function calling and multimodality are also really really impressive and so the questions around Google are less about do they have like they have all these extraordinary advantages and you're you're the exg googler like I want to hear your opinion but they have the distribution they have the C the consumer Behavior they have all the data on like what the search behavior is they have the data on what queries are valuable and which they would peel away and turn into like an answer um uh they know how to build like advertising auction systems and they have a great research team and enough gpus and um and the model capabilities you think it was Progressive enough though do I think the models are progressive enough yeah one might actually ask if they're perhaps a little too far in that direction right um and and so I I think like the question is actually can they steer Google to like focus on being competitive versus the many other demands from their employee base um and like different missions that are not you know brokering the world's information and like market cap yeah it's interesting because um the launch of 1.5 has made me more bullish on Google uh and I I was always actually quite positive on them right like um I think I read a blog post a year a year and a half ago basically about the model world and one of the things that I mentioned at the time was I felt like Google was kind of a sleeping giant and once once it awoke you know um it could really make enormous progress quickly and just as mrr's executed from scratch as a startup which is extremely hard to do right you're literally building everything from the ground up um although obviously there's open source to support you and all these other things but fundamentally you're just building an entire company it's pretty amazing right um uh Google has uh really accelerated its efforts and it's had a series of launches over the last two three months that have been quite impressive in terms of the Velocity from cold start to to having things that are externally accessible and they have all the resources that one would need in order to do extremely well in AI right they have the compute they have unique proprietary data as well as all the data from the web all the data from YouTube um they have specialized data that you could potentially opt into like you know all your emails and your Google Docs and you know they have this immense Corpus of really valuable information um and then they have amazing talent and so really I think the the thing that was um lacking until recently was the will and it seems like now because of the competitive Dynamic the will has been reborn right um and so it it really feels to me like they are going to make really big strides going forward and um you know it's always possible that the velocity only increases from here for them if think about the domains in which these um General llms are still not as capable I mean it's every domain but um in particular not as capable as we want um like two of the areas one you already mentioned um uh that I I'm I'm excited about include like biology and then robotics right so maybe let talk about that for a second as a as a task for example if you ask chat GPT to design a DNA sequence that can express crisper cast 9 it can't do that yet right and if we think about cell design protein design protein optimization a lot of these um are areas where you have researchers showing like really exciting progress in use of Transformers and diffusion models to um get to much better predictions for for example um drug Discovery and um Target identification so I think you know I've seen a number of companies in this area of better understanding of biology that really feels like a different type of reasoning a different type of data set and as you said even um like specific context window constraints and so I think that's an interesting one and then on the um I don't know if you wanted to mention the robotic side or if that's something you've been looking at too the robotic stuff seems super interesting it's a little bit earlier on than some of the other models and and part dat to data constraints but it seems like there's pretty reasonable ways to generate some of that data now so so um it seems like the you know in general I wouldn't be surprised if 2024 and 2025 is the year of proliferation of models um where we're going to start to see an expansion in terms of the different types that are covered you know chemistry Material Sciences etc etc robotics will be part of that biology will be part of that maybe physics and math um I think maybe the last thing that is happening from a model perspective is I think the last few weeks have seen um a lot of different sort of agent companies get up and running and um I think that's been an a really interesting wave and some of them again are taking very different approaches from the traditional let's just build a giant llm and they're looking at things like alphago or some of the game Centric uh work that have been done in the past you know how do you build a better Pro poker paper how do you build diplomacy how do you build go and there you have a very strong notion of acting sequential based on changing information you have some forms of what's known as selfplay you know you you have the machine play itself a billion times ago and it learns new patterns based on that you have really interesting approaches in euristic and algorithms at time of inference versus training and so I think that that purpose of knowledge is about to hit the world in the context of new products and it'll take time for those products to emerge you know six months 12 months a year but um it does feel like that's another wave this coming where you're taking a fundamentally uh different approach to re uh that involves reinforcement learning but is just different in terms of how you think about what you're actually doing in architecting and what you're influencing and all the rest so that that's the one other area on the model side that I think is very exciting one thing that I've seen um here is that people are getting much smarter about agents as part of systems versus expecting to um uh simply like construct an agent and have it work with compounding failure across a bunch of tasks right uh in a gen General environment across any type of software right and so if it is operating in an environment that supports reinforcement well like a game environment or even a a web application environment um but one that is constrained to particular tasks or working agents working in domains that better support a sampling and validation like code generation like I'm really excited about that and I I feel like I've begun to see the glimpse of some of those things work whereas a very real question you could have asked in Q3 Q4 of last year would be like does is any of this stuff useful right is it anything and I think now it's like it is yeah yeah people went too broad to early versus just saying I'm just going to focus on a handful of targeted use cases or domains and I'm going to figure out how do you create feedback loops in those domains so I can actually train effect effectively and so you know the very ear early versions of this even predating this llm wave was um you know hey we're going to have a browser plugin and it'll watch everything you do and then it'll do everything you do which is a very different from Problem from saying hey we're going to make RPA better we're going to make code better we're going to make customer support better we're going to make you know XYZ thing better um so I think the targeted approach makes a lot of sense yeah and I think some of the teams working on this have also they've actually experimented with Post train in environments where you can pay for um for uh human feedback data right and if you do that then you actually understand like the um the distribution of data you need the scale of data you might pay for and that's very exciting because it turns it like the agent problem from one that is um like open-ended untenable to just like how much is it going to cost to make a particular task work and I'm massive oversimplifying here but that is a very different proposition when scoped than like as you described the initial set of Fay into agents which is like you know we'll try to do anything yeah that makes sense I think we'll still get there but there is um like rapid success on this front Nvidia everybody's talking about earnings what do you make of it I think earning money is an excellent idea how about you I think Jensen understands this better than everybody else I think one thing that people have been talking about is whether or not this was a like short-term phenomenon right like if um there was only so much demand and once the supply chain caught up a little bit um there would be less insane growth and I think now people are pretty confident especially hearing Jensen's comment that they expect to continue to be Supply constrainted through the rest of the year demand is just like much much much larger than I think most people expect on the capex side um and I think it's like worth understanding the upgrade cycle that drives that right because there's this huge efficiency incentive to upgrade from A1 100s to h100s to h200 to b100s I was talking to one of my portfolio companies that's buying in the tens of thousands of GPU size and is skipping to b100s because they described it as like free money in terms of training efficiency it's funny when somebody describes spending hundreds of millions of dollars as free money but free money in terms of training efficiency if you can actually get access to a cluster of a certain size and so if if others feel that way it it is um wild how much this expand like expands the the server Market yeah it's probably a good time to run a hedge fund I think in general um one thing that's a little bit under discussed is a lot of the emphasis on startups and startup rounds and know look the startup race 100 million or whatever and the reality is a lot of the spend is the big hyperscalers and then other clouds that are building out right now and then I think the other thing is that if you were to look at at least Enterprise adoption of AI it's still really really really early days and despite that if you look at Microsoft Azure Revenue in the last quarter they mentioned that um Revenue grew by 5% from AI related products which if I'm doing the math right if it's a $25 billion a quarter uh Azure sort of um Revenue then that means they're adding something like one one and a half billion a quarter in new spend due to AI right so that's five or six billion annualized and so um you know one thing that is a little bit uh uh perhaps not talked about is there's a lot more stuff coming and over the next two years three years Etc as Enterprises really adopt this at scale we should anticipate as well that um you know the need for compute will continue to grow so it's really interesting to see but this replacement cycle you're talking about the massive spend by big Tac on um llms because they're driving most of the spend on llms because they're they're the big rounds right the big rounds aren't Venture capitalists investing billions of dollars it's the big tech companies it's Amazon and Google and Microsoft and and uh Salesforce in Nvidia actually right um and then there's the Enterprise adoption which is still TBD so yeah there's a lot going on on this point if you look back um a month you know AI years are like dog years so a year to The Meta earnings be at the end of January did you did you see this article uh that David Khan wrote at Sequoia the 200 like ai's 200 billion dollar question yeah was this where he basically said based on the spend if you think of the ROI you need then you need to generate hundreds of billions of dollars in return yeah in order to justify all the yeah all the spend that you had yeah yeah very succin summary and um I was like okay okay yeah that is the question and I feel like The Meta earnings beat was the like one day answer to that question right so to your point they're one of the large Spenders um uh they said they're going to spend 30 to 37 billion doll on uh capex in 2024 driven by like AI driven by servers right um Mark has this great like quote where he's talking about 600k uh h100 equivalent units of compute and saying like there's no room for other people but the response to all of the investment that has um in in capex for um training and inference at meta over the prior years has been like a huge earnings beat from better targeting leading to better conversion better recommendations leading to better engagement better advertising tools leading a better Roi um as well as like the cost controls that the rest of the industry is doing and so they have had this one day I thought it was really nice that the number was exactly this too they this one day ad of 197 billion of market cap biggest single session ad before Nvidia I forget where Nvidia ended up Landing after their beat uh but like that's the answer right like you know 197 billion um of increase in Enterprise Value on 25 30 billion of capex like you should keep doing it yeah yeah it's kind of amazing it's kind of a related question I remember Yuri Milner showed me this chart which basically he looked at the aggregate increase in startup market cap and the aggregate increase of what at the time was like Fang market cap and obviously now there's like the magnificent seven or eight or whatever it is um and so if you looked at the top tech companies of the time they added like I don't remember it was five or 10 times the market cap of all the startup ecosystem combined during the same period of time and to some extent You could argue we're going into the same thing at least in the short run for AI and we still haven't seen the monster AI companies emerge from scratch and in dly those will exist um but at least for the next few years it seems like where we're going to see that really huge market cap incremental ad um maybe companies like openi and some of the model companies but also it seems like increasingly it's just going to be existing companies adding huge amounts of uh uh revenue and earnings and um compute and everything else along the way so it's back to like maybe the right thing to do right now is just start a hedge fund I think that also begs a question of um how to think about like all of the other companies like Tech and not in terms of um amount of impact from AI actually think it would be like a really fun lens to run a HED from um uh with because you can take a you can take a very long-term view of something that feels very secular just classify companies this way and long short like take that strategy as the only lens um because like I I do think that there are a number of services companies that are um squarely in the sites of things that you will be able to significantly automate and the only question is which of these management teams is going to have the um investment capability technical Talent guts conviction to invest the way Mark did through you know people were really mad about the cap expend for a few years at right and I think the answer is mostly especially some of these Services firms like um maybe they partnered to get there but they mostly will not make the transition I think the other thing it isn't really discussed is the impact it's already having on some businesses so obviously service now had like a blowout quarter and part due to AI so we're starting to see a little bit of Enterprise adoption um one of the folks from Clara posted uh today that they built an AI assistant that's powered by openai that in its first four weeks handled 2.3 million in customer service chats for them and so I ended up handling 2third of all their customer service inquiries it was onar with humans in terms of customer satisfaction it was higher accuracy so it led to a 25% reduction in repeat queries um customers resolved their errands in 2 minutes versus 11 minutes it's live 24/7 in over 23 markets communicating in over 35 languages and it performed the equivalent job of 700 full-time agents and so BAS basically Clara in you know a few months or a year or however long it took him to build this built this customer service chat product and it replace 700 people's worth and they say that at this point they have something like 3,000 full-time agents and so it cut the agents needed to BU about 25% right and so uh it's this really interesting post from clarner where they announced this and then one of the things they announc is part of that is you know longer term Society needs to think about what this means for Society uh because this technology seems to be so good for certain human level tasks and this is back to that point of AI adoption in the Enterprise is just starting but how many years is it before every Enterprise realizes that they can cut customer support dramatically at least for certain types of products just through just through adding you know simple apps you know and so uh I think that's the other thing that is kind of happening in the background that isn't talked about that much but you know is already starting to really show its face in in pretty interesting way yeah well I I do think you're going to get this accelerated adoption that goes use Case by use case right where like in in any Market you have early adopters that build it in house or go get these Solutions and are willing to take the risk when you don't actually know like what the impact will be how well it will work but as soon as one payments company does that and it's a better experience for the customer or it has real like impact on operating cost I think like you switch very quickly over to the entire sector being like we have to adopt it in order to be competitive on both fronts yeah yeah this stuff tends to happen slowly and then suddenly all at once and I think we're in the slowly phase right now and um I actually had my team go and take um uh Global Services and look at that right and so if you look at uh spend on software in the US right now it's about half trillion dollar in software spend a year if you look at uh human Services just payroll for things where gen can probably impact things it's 3 and a half to 5 trillion so if you convert just 10% of that spend into AI Revenue you've effectively recreated the entire US market software industry in market cap right and so these are huge trends that are coming and you can kind of Imagine vertical by vertical what are those things going to be and then you can ask is it going to be built as internal tools for companies is it going to be uh new company that emerges that serves these things or is it going to be an incumbent who figures it out and adds it and so this sort of customer support chatbot thing you know you would have thought that there's a company doing this for everyone and it looks like in this case they're um they just did it internally or in-house uh but you could also Imagine an existing company like a zendesk or somebody adopting to this and the real question is which of those three scenarios is going to happen at least from a startup perspective but from a technology wave perspective this is massive right and you can build in the feedback loops really easily for this type of product right because you can have the customer rated or thumbs up thumbs down at the end of a session Etc so you have a really good sort of um rhf or some sort of training set for it as well so it it's a it's a product that should get better and better and better over time as you use it yeah I think one of the things that is an indicator of uh like where that Services spend might be that gets externalized is actually like the big tech companies actually have you know they're tech companies but they have broader businesses than um I think sometimes they're given credit for right like Facebook meta interacts with smbs as advertisers if you look at anybody who has this like large Commerce um type customer base so as you just mentioned Clara or Square or meta or Shopify like they've all done this now and it's working right and and so I I think the fact that these are the companies that have the technical teams that are capable of doing it in house is a nice indicator for like well if it's that effective everybody else should too and the question is I think not every segment of customer like retailers with um enough of a technical team to build an e-commerce presence may not build this themselves then it's a more likely uh scenario that either an incumbent or a new company be it Sierra or something else ends up owning that customer service segment yeah 100% yeah we we have a long list internally of like the companies that I think should exist in the space right because there's there's so many obvious ones and very few companies exist for most of them if any companies and so I think it's it's back to this idea that there are these human capital waves happening in Ai and the very first wave we saw was researchers and they built the early model companies and they built some of the early applications like perplexity and Harvey and all these things were actually started by people who were working on models initially and they were just Clos as the technology so they knew to do and then the second wave of human capital was like infra people because it was the second closest to llms and then the third wave of course is going to end up being application Builders but many of them were not aware that any of this stuff was important until chat GPT came out 15 months ago and they're just starting to show up right it takes some 9 months stick with their job and a few months to figure out what to do and find a co-founder and a few months to build prototype and so we haven't seen anything yet really in the app wve you know all the apps or many of the apps so far were started by people who are very close to the research community and then it's kind of permeated into other areas with with some growing really fast right there's like half a dozen medical scribing apps that all seem to be growing at a pretty good Pace or there's um a few other application areas where it seems like there's a number of people working but then there's lots and lots of spaces where it seems like nobody's doing anything which is which is kind of weird honestly yeah there's a joke that the foundation model companies um are here to replace all the jobs but they don't understand what any of the jobs are and I think there's like a little a little bit of Truth in the sort of exposure to uh what happens in you know a a broad range of companies in terms of functions and Outsource services and so I think that is the opportunity right like now it's a race for people who are just great Engineers smart about a domain to go experiment On The Fringe of that and I I still think there's opportunities around like you and I have talked about um the uh domain areas where you might want specific models or verticalized companies still and we should we should talk about that but I uh I my team and I just gave a presentation at this AI in production conference about how if 2023 was the year of infrastructure like 24 is the year we begin to see applications so I think we're pretty aligned there I do want to ask you like one thing before we move away from all of the earnings stuff which is um the most obvious Place somebody's already making money is either like Cloud providers inference providers or um just Nvidia as a chipmaker what would it take to compete to have like a second source with Nvidia I think there's a few different approaches right I mean uh fundamentally if you look at what people claim as a defensibility in part of Nvidia it's a mix of Chip performance Cuda and interconnect um you know Nvidia bought uh melanic back in 2019 it was an Israeli company uh to basically provide the interconnect side I think that was like a $5 billion acquisition so was quite large relative to n's market cap at the time um and then obviously Cuda has been developed over many years uh uh and then obviously they they've iterated really well on these sort of different generations of chips um so minimally you at least need the you need some form of silicon this performant and then you need to make sure that it's actually able you're you're able to um use it effectively and then you're able to scale it which is sort of the interconnect side um and there's the incumbent side of it right AMD is obviously working on this in trying to Etc and then there's the um the startup side of it where we've seen things like rock uh merge where they have very fast inference for open source models as well as language models which is pretty striking you have sras which has taken a fundamentally different approach to the chip side as well so you know there's a few startups that I think have some interesting early hardware and there's some new companies like ads that have talked publicly about how they're really focused on Transformer base models and architectures for chips that they're building so um there will be this potential wave of second sourcing over time but uh you know in general if you look at many of the most advanced chip markets historically at least there's tended to be a winner or I should say a leader and then there's been there's tended to be a second place party and that was you know during the microprocessor world that was Intel and then AMD was number two and um you know in uh mile it kind of morphed a little bit right you had Qualcomm and arm doing different things but both quite successfully but I think Qualcomm was always at least for a period of time with the bigger company although arm is much larger now I should actually check that in terms of market cap yeah Qualcomm is 176 billion and then arm is 140 billion so they're pretty close actually now um there used to be a pretty big disparity uh between the two in part that's because arm is being used now in sort of broader ways uh so you know you you kind of tend to see these Market structures and semiconductors where there's a leader and then a second place and um I think part of that is traditional mors law chip generation related stuff I don't know how that will hold up or how that'll more of an AI I don't know if you have an opinion on that yeah the um you know the way Jensen has described advancements in chip performance tend to be more um uh memory management and new techniques versus just like transistors fitting um on a particular die size and I think somebody else said Nvidia called it Jensen's law of like ability to get performance from full system but the the only thing I'd add to your description of um competitiveness here is also like manufacturing even for these fabulous chip design companies is a big deal right like so you got to do what you said design something better including interconnect design an entire like build an entire software ecosystem C has been around since 2006 but after that you have to go get capacity at tsmc right and then you need to get yield up and then you need to all to be competitive in terms of pricing I think the desire like the economic pressure given to trillion of market cap and more demand than Nvidia can support is higher than ever but I think the moat is actually really really deep and so um when I think about like what could be what could be enough to go disrupt that I've seen I'm sure you've seen many of these companies but I've seen a few different um approaches it could be a um chip and system designed for like specifically very much around latency um it but the other thing that you said right like something for example optimized two Transformers as an architecture you're taking a bet around how much stability there is around a particular architectural approach and I think that's felt like a um a quite good uh bet for a while now but for the first time in a long time there is some interest in in things like State space models with companies like cartisia and um some some Alternatives right um if you're a really big company with your own use case right if you're meta or you're Google and you all you you know either have like the entire ad system recommendation serving spam Etc um or all that like search and your own cloud then you don't need to make everything work on the software ecosystem side you just need to make one application work and you know these companies also acquired teams in but that's how you end up with like tpus and trainum and all that uh but I I would love to meet companies in this area and still haven't haven't seen something that's gotten me um over the edge even in a place that is so obviously economically fertile yeah I think one thing you pointed out which was interesting to expand a little bit on is tsmc and the whole F Faba semiconductor world where you're basically you know uh Outsourcing the development or the manufacturing of the chip to a handful of players tsmc being the bigger but there's the biggest but there's there's a there's one or two others that are big enough to at least handle some volume and you know there's been this push to try and repatriate semiconductor manufacturing to the US and has run into all sorts of obstacles that are pretty avoidable environmental reviews that go on endlessly or other things that have prevented people actually starting to build these things that take many years to build um and it's been interesting to watch that in Japan they're starting to actually have really interesting uh development of Fabs specifically for this purpose and so I'm increasingly wondering whether Japan emerges as sort of a Second Source location and part of geopolitically hedge Taiwan um but I think that's something also to kind of watch in terms of where are you actually seeing Fabs go out and how do you think about that geographic distribution but also why is the US in some sense getting in its own way for something that has pretty broad-based atretic importance on multiple levels you know including National Security ones so if you listen to the um tsmc CEO about this he talks as much about um uh about like the human capital and the culture cultural elements of human capital required to make a place like tsmc work as the capex spend right and the um the access to equipment and the need to actually build the Fab um I think that's pretty interesting because like you know we we can invest a great deal um but it's it's very hard to change culture and so I I I do think that there's um there's one version of like maybe you have Fabs in um Japan or Mexico or southeast Asia or a um uh like a just a broader Global supply chain for chip production or maybe you have robots making chips yeah I mean that's all true but the flip side of it is Intel has manufactured chips in the US for a long time TI did historically right but Intel still does so I don't think there's a complete lack of human capital obviously it's concentrated in part in Taiwan and secondary extend in um Korea right now but I I do think there's there's the capab ability to do it and I think again we're there there are other things that are getting in the way I think even before that can you can you even break round on the plant maybe step one right we should start with the basics and then we can deal with culture when we actually have a Fab yeah well and I'm I'm I'm uh I guess very willing to believe that these companies and industries didn't exist in the place they places they do without like great leaders for tmcmc or otherwise and so like maybe it's not a solvable problem like I'd be curious if you believe in the Intel Fab business that they're um uh trying to push and push to other customers now but it to me it's not binary it's like of course we can like make chips in America the question is can we make them without the um turn and with the yield and cost to make them competitive but maybe it's so important like you don't need them to be competitive for some period of time yeah and also my point is um we're already doing that for Intel right Intel's fat business isn't in the US not the not the The Fabulous TMCC sty business just making their own chips they've been doing it for decades in the US it's been fine it's been high yield you know yeah it's been it's been fine but it's also been um behind in terms of uh process Technologies right but maybe maybe that's not a human capital issue then maybe it's other other issues that Intel yeah it seems like it's a other issue yeah I think my general take on the H Market is the more I learn the less I know in Ai and it's the opposite of every other field I've ever been in US the more you learn about something yeah usually the more you learn about something the more you can create sort of these straight line hypotheses or you know what you know kind of compounds and it's static and I feel in the AI world like every week there's like so many new things that your entire world model shifts in like a fun way yes it's it's uh fun to be exhausted but I think um you know there there's just so much going on in the pace of in it really feels like you know that that early slope into the you know the exponent that is a singularity or however you want to phrase it but it really feels like this uh self-reinforcing loop of new stuff and honestly a lot of it was kind of held back in the larger tech companies and now it's kind of flourishing externally and that's creating competitive pressure on the larger companies and the larger companies are reacting and that's spawning more startups and it's just this really interesting virtuous cycle uh and to some extent the big tech companies are help fuel it all by then funding the companies that are working at very late stages with huge rounds and they're funding a lot of the compute in the industry in a way that's you know at least in order of magnitude maybe two orders of magnitude more than what the Venture Community is doing and so it's this really interesting brous cycle of startups come out that accelerates big Tech doing stuff that causes some people to leave big Tech to do some interesting things externally they then get funded by big Tech and that accelerates both themselves and big Tech and you have this kind of interesting cycle happening right now so it's very exciting days yeah I drew um I drew slide that has like as you might hope like a bunch of reinforcing Cycles it's very fancy and the one I would add to that is like what we started talking about which is when something begins to work if it is actually valuable like the Clara thing that you describe like at some point if it's valuable and it moves the needle in the business you have to do it like as a part of the competitor set and so I think like we started with this like narrative drift thing where um you know CEOs would say that they're going to do AI because like the markets believed that was the future and it was very generic and you see that show up in the spending numbers or at least the expectations around spend right I was looking at this um survey from one of the investment banks that says like Fortune 1,000 um uh it budgets go to 5 to 8% this year instead of 3 to 5% generally and it's all because of AI like that's pretty big right that's like two x and like if that's true then that's also part of the reinforcement cycle here because if the companies start to work then they get to continue building these products BCS you know or investors like us will will keep keep trying so I think it's pretty exciting yeah it's rlpa F yeah rlpa rules right off the tongue reinforcement learning through product adoption feedback you're welcome well I'm just gonna plug that in to Chachi BT and have it write the paper but um I will be sponsoring author if you'll be first author yeah I'll see if I include you academic violence find us on Twitter at no prior pod subscribe to our YouTube channel if you want to see our faces follow the show on Apple podcasts Spotify or wherever you listen that way you get a new episode every week and sign up for emails or find transcripts for every episode at no- pri.com