E167: Nvidia smashes earnings (again), Google's Woke AI disaster, Groq's LPU breakthrough & more

27:55I mean, it's been since 2016. So again, proving what you guys have said many times and what I've tried to live out, which is just you just got to keep grinding. 90 % of the battle is just staying alive in business and having oxygen to keep trying things. And then eventually if you get lucky, which I think we did, things can really break in your favor. So this weekend, you know, I've been tweeting out a lot of technical information about why I think this is such a big deal. But yeah, the moment came this weekend, combination of hacker news and some other places. And essentially, we had no customers two months ago.

28:33I'll just be honest. And between Sunday and Tuesday, we've just overwhelmed. And I think like the last count was we had 3000 unique customers come and try to consume our resources from every important Fortune 500 all the way down to developers. And so I think we're very fortunate. I think the team has a lot of hard work to do. So it couldn't mean nothing, but it has the potential to be something very disruptive. So what is it that people are glomming on to? You have to understand that like at the very highest level of AI, you have to view it as two distinct problems. One problem is called training, which is where you take a model, and you take all of the data that you think will help train it, and you do that.

29:21You train the model. You learn all over all of this information. But the second part of the AI problem is what's called inference, inference, which is what you and I see every day as a consumer. So we go to a website like chat GPT or Gemini. We ask a question and it gives us a really useful answer. And those are two very different kinds of compute challenges. The first one is about brute force and power, right? If you can imagine like what you need are tons and tons of machines, tons and tons of like very high quality networking and an enormous amount of power in a data center. so that you can just run those things for months.

30:01I think Elon publishes very transparently. For example, how long it trains to train his GROC with a K, right, model, and it's in the months. Inference is something very different, which is all about speed and cost. What you need to be in order to answer a question for a consumer in a compelling way is super, super cheap and super, super fast. And we've talked about why that is important. And the GROC with the Q, chips, turns out to be extremely fast and extremely cheap. And so look, time will tell how big this company can get. But if you tie it together with what Jensen said on the earnings call, and you now see developers stress testing us and finding that we are meaningfully, meaningfully faster and cheaper than any Nvidia solution, there's the potential here.

30:53to be really disruptive. And we're a meager unicorn, right? Our last valuation was like a billion something versus Nvidia, which is now like a two trillion dollar company. So there's a lot of market cap for GROC to gain by just being able to produce these things at scale, which could be just an enormous outcome for us. So time will tell, but a really important moment in the company and very exciting. Can I just observe, like off topic, how an overnight success can take eight years? And yeah, no, I was thinking the same line. It's a seven year overnight success in the making. There's this class of businesses that I think are unappreciated in a post -internet era, where you have to do a bunch of things right before you can get any one thing to work.

31:44And these complicated businesses where you have to stack either different things together that need to click together in a stack or you need to iterate on each step until the whole system works end to end can sometimes take a very long time to build and the term that's often used for these types of businesses is deep tech. And they fall out of favor because in an internet era and in a software era, you can find product, market, fit, and make revenue and then make profits very quickly. And so a lot of entrepreneurs select into that type of business instead of selecting into this type of business Where the probability of failure is very high you have several low probability things that you have to get right in a row And if you do it's gonna take eight years and a lot of money and then all of a sudden the thing takes off like a rocket ship You've got a huge advantage you've got a huge moat.

32:34It's hard for anyone to catch up and this thing can really spin out on its own. I do think Elon is very unique in his ability to deliver success in these types of businesses. Tesla needed to get a lot of things right in the row. SpaceX needed to get a lot of things right in the row. All of these require a series of complicated steps or a set of complicated technologies that need to click together and work together. But the hardest things often output the highest value. And if you can actually make the commitment on these types of businesses and get all the pieces to click together, there's an extraordinary opportunity to build modes and to take huge amounts of market value.

33:13And I think that there's an element of this that's been lost in Silicon Valley over the last couple of decades as the fast money in the internet era has kind of prioritized other investments ahead of this. But I'm really hopeful that these sorts of chip technologies, SpaceX, and biotech, we see a lot of this. These sorts of things can kind of become more in favor because the advantage as these businesses work seems to realize hundreds of billions and sometimes trillions of dollars of market value and be incredibly transformative for humanity. So I don't know. I just think it's an observation I wanted to make about the greatness of these businesses when they work out.

33:48Well, I mean, OpenA, I was kind of like that for a while. Totally. I mean, it was this wacky non -profit that was just grinding on an AI research problem flex six years, and then it finally worked and got productized into JetVPT. Totally. But your right SpaceX was kind of like that. I mean, the big money maker at SpaceX is Starlink, which is the satellite network, it's basically broadband from space. And it's on its way to handling, I think, a meaningful percentage of all internet traffic. But think about all the things you have to get to to get that working. First, you have to create a rocket.

34:21it, that's hard enough. Then you had to get to reusability. Then you had to create the whole satellite network. So at least three hard things in a row. Well, I need to get consumers to adopt it. I mean, you know, don't forget the final step. Yeah. We had no idea where the market was. Like early on, it started in my office. And so, Jonathan and I would be kind of always trying to figure out what is the initial go to market. And I remember I emailed Elon in at that period when they were still trying to figure out whether they were going to go with Lidar or not. And we thought, wow, maybe we could sell Tesla the chips.

34:55But then Tesla brought in this team just to talk to us about what the design goals were. And basically said, no, in kind way, but they said no. Then we thought, OK, maybe it's like for high frequency traders, right? Because those folks want to have all kinds of edges. And if we have these big models, maybe we can accelerate their decision making, big -and -measure revenue, that didn't work out. Then it was like, you know, we tried to sell to three letter agencies. That didn't really work out. Our original version was really focused on image classification and convolutional neural nets. Like ResNet, that didn't work out.

35:32We ran headfirst into the fact that Nvidia has this compiler product called CUDA, and we had to build a high class compiler that you could take any model without any modifications. All these things to your point are just points where you can just very easily give up. And then there's like, we run out of money. So then you write money in a note, right? Because everybody wants to punt on valuation when nothing's working. You tried six -piece, you had markets, you couldn't land above. You have to make a decision to just keep going if you believe it's right and if you believe you are right. And that requires shutting out.

36:08We talked about this in the Moss example last week, but it just requires shutting out the noise because it's so hard to believe in yourself. It's so hard to keep funding these things. It's so hard to go into part and meetings and defend a company. And then you just have a moment and you just feel, I don't know, I feel very vindicated, but then I feel very scared because Jonathan still hasn't landed it. You know what I mean? You mentioned all those boats landing and trying to try to, there's missteps, but 3 ,000 people signed up. Who are they? Are they developers now and they're going to figure out the applications?

36:41Yeah, I think that back to the original point. My thought today is that AI is more about proofs of concept and toy apps and nothing real. I don't think there's anything real that's inside of an enterprise that is so meaningfully disruptive that it's going to get broadly licensed to other enterprises. I'm not saying we won't get there, but I'm saying we haven't yet seen that Cambrian moment of monetization. We've seen the Cambrian moment of innovation. And so that gap has still yet to be crossed. And I think the reason that you can't cross it is that today, these are in an unusable state. The results are not good enough.

37:20They are toy apps that are too slow, that require too much infrastructure and cost. So the potential is for us to enable that monetization leap forward. And so yeah, they're going to be developers of all sizes and the people that came are literally companies of all sizes. I saw some of the names of the big companies and they are the who's who of the S &P 500. How do you guys reconcile this deep tech high outcome opportunity that everyone here has seen and been a part of as an investor participant in versus the more D -risk faster time to market. And, you know, Jamoth in particular, like in the past, we've talked about some of these deep tech projects like Fusion and so on.

38:07And you've highlighted, well, it's just not there yet. It's not fundable. What's the distinction between a deep tech investment opportunity that is fundable and that you keep grinding at? That has this huge outcome. What makes the one like Fusion? That's not fundable. It's a phenomenal hit question. Great question. My answer is I have a very simple filter, which is that I don't want to debate the laws of physics when I fund a company. So with Jonathan, when we were initially trying to figure out how to size it, I think my initial check was like $7 to $10 million or something. And the whole goal was to get to an initial tape out of a design.

38:45We were not inventing anything new with respect to physics. We were on a very old process technology, I think we're still on 14 nanometer. We were on 14 nanometer eight years ago. So we weren't pushing those boundaries. All we were doing was trying to build a compiler in a chip that made sense in a very specific construct to solve a well -defined bounded problem. So that is a technical challenge, but it's not one of physics. When I've been pitched all the fusion companies, for example, there are fuel sources that require you to make a leap of physics. Where in order to generate a certain fuel source, you either have to go and harvest that on the moon or in a different planet that is not Earth or you have to create some fundamentally different way of creating this highly unique material.

39:30That is why those kinds of problems to me are poor risk and building a chip is good risk. It doesn't mean you're going to be successful in building a chip but the risks are bounded to not of fundamental physics. They're bounded to go to market and technical usefulness and I think that that removes an order of magnitude risk in the outcome. So, you know, there's still like a bunch of things that have to be right in a row to make it work, but yeah, doesn't mean it's going to work. All I'm saying is I don't want it to fail because we built a reactor and we realized, hold on, to get heavy hydrogen, I got to go to the moon.

40:05Right. And Jay, how do you, how do you, you don't, you invested in a real estate? We had done a couple. But like, yeah, so maybe you guys can highlight how you thought about deep tech opportunities versus what you probably do. You can use something really difficult like this every 50 investments or so because most to the entrepreneur is coming to us because we're seed investors or pre -seed investors, they would be going to a biotech investor or a hardware investor who specializes in that, not to us. But once in a while we need a founder we really like. And so, ContraLine was one. We were introduced to somebody who's doing this really interesting contraception for men where they put a gel into your Vaz deference and you as a man can take control of your reproduction.

40:47You basically, it's So it's not a bisectomy. It's just a gel that goes in there and blocks it. And this company is now doing human trials and doing fantastic. But this took forever to get to this point. And then you guys, some of you are also investors in Cafe X, which we love the founder and this company should have died like during COVID and making a robotic coffee bar when he started, you know, seven, eight years ago was incredibly hard. He had to build the hardware, he had to build a brand, he had to do locations, he had to do software. And now he's selling these machines and people are buying them and the two in San Francisco at SFO are Making like I think they the two of them make a million dollars a year and it's the highest per square footage Of any store in an airport and so we've just been grinding and grinding and you got to find a founder who's willing to make it their lives Work in these kind of situations, but you start to think about the degree of difficulty hardware software where retail mobile apps, I mean, it just gets crazy how hard these businesses are.

41:50As opposed to I'm building a SaaS company. I build software. I sell it to somebody to solve their SaaS problems. It's like, it's very one dimensional, right? It's pretty straightforward. These businesses typically have five components. Yeah, and Saks, you've been an investor in SpaceX, but you don't make those sorts of investments regularly that craft. Is that fair? Yeah, I have an Elon exception. I got it. It's about the founder. say. Say.

42:18We're in our portfolio allocation. We say this much early stage, this much late stage, this much Elon. Elon accent. Yeah. I mean, you have to be so dogged to want to take something like this on because the good stuff happens like you're saying freeberg, you're 7, 8, 9, 10. As opposed to like a consumer product, I mean, for me, the one extra dozen by year three or four, the only app that took a really long time, people don't know this, but Twitter actually took a long time to catch on. It was kind of cruising for two or three years and then South by Southwest happened, Ashden Kutcher got on it, Hobama got on it.

42:50I think network effect, I think network effect businesses are different because that's all about getting your seat of your network. What I'm talking about is the technical coordination of lots of technically difficult tasks that not to sync up. It's like getting a master lock with like 10 digits and you got to figure out the combination of all 10 digits and once they're all correct, then the lock opens. And prior to that, if anyone number is off, the lock does an open. And I think these technically difficult businesses are some of the, and they are the hardest and they do require the most dogged personalities to persist and to realize an outcome from.

43:25But the truth is that if you get them, the moat is extraordinary. And they're usually going to create extraordinary leverage and value. And you know, I think from a portfolio allocation perspective, if you as an investor want to have some diversification in your portfolio, this is not going to be the predominance of your portfolio, but some percentage of your portfolio should go to this sort of business because if it works, boom, you know, this can be the big 10X, 100X, 1000X. Two stories about that. One of the Vs early VCs and you want to stole the story publicly. Wanted you want to not make the roadster, not make the model S, just make drive trains and the electric components for other car companies.

44:00Can you imagine how the world would have changed? and then, totally. A very high profile VC came to me and said, okay, I'll do the series A for Uber. I'll preemptively do it, but you got to tell Travis to stop running Uber as a consumer app. I want him to sell the software to cab companies. So make it a SaaS company. And I said, well, you know, the cab companies are kind of the problem. They're taking all the margin, that kind of disrupting them. And they're like, yeah, yeah, I would just think there's thousands of cab companies, they would pay you tens of thousands of dollars a year for this software.

44:35And you can get a little piece of the action. I never brought that investor to Travis. I was like, wow, that's really interesting inside. Sometimes the VCs work against it. I have a very poor track record of working with other investors. Whoa, self -reflection. I do deals myself. I size them myself. And it's because a lot of them have to live within the political dynamics of their fund. And so I think Jason, what you're probably saw in that example, which is exactly why doing things and splitting deals will never generate great outcomes in my opinion, is that you take on all the baggage and the dysfunction of these other partnerships.

45:17And so if you really want it to go and disrupt transportation, you need one person who can be a trigger puller and who doesn't have to answer to anybody, I find. That's why I think, for example, when you look at how successful Vinote has been over decade after decade after decade, when Vinote decides that's the decision. And I think there's something very powerful in that. There are a bunch of deals that I've done that when they've worked out were not really because they were consensus and they had to get supported and scaffolded at periods where if I wasn't able to ram them through myself because it was my organization, I think we would have been in a very different place.

46:00So I think, I think like for entrepreneurs, it's so difficult for them to find people that believe it's so much better to find one person and just get enough money and then not syndicate. Because I think you have to realize that you are bringing on and compounding your risk, the one that Freebrook talked about, with the risk of all the other partnership dynamics that you bring on. So if you don't internalize that, you may have five or six folks that come into an A or a B, but you're inheriting five or six partnership. Yeah. Partnerships and functions. Yeah. Yeah. Can you just explain really quickly for the audience since they heard about GPUs and Nvidia, but they may not know what an LPU is.

46:42What's the difference there? To them. A GPU, the best way to think about it is, so if you contrast a CPU with a GPU, So CPU was the workhorse of all of computing. And when when Jensen started Nvidia, what he realized was there were specific tasks where a CPU failed quite brilliantly at. And so he's like, well, we're going to make a chip that works in all these failure modes for a CPU. So a CPU is very good at taking one instruction in, acting on it, and then spitting out one answer effectively. And so it's a very serial kind of a factory. if you think about the CPU. So if you want to build a factory that can process instead of one thing at a time, 10 things or 100 things, what is they had to find a workload that was well suited and they found graphics.

47:33And what they convinced PC manufacturers back in the day was, look, have the CPU be the brain? It'll do 90 % of the work. But for very specific use cases like graphics and video games, you don't want to do serial computation. You want to do parallel computation and we are the best at that. And it turned out that that was a genius insight. And so the business for many years was gaming and graphics. But what happened about 10 years ago was what we also started to realize was the math that's required and the processing that's required in AI models actually looked very similar to how you would process imagery from a game.

48:16And so he was allowed to figure out by building this thing called CUDA, which is the compiler that sits on the chip, how he could now go and tell people that wanted to experiment with AI. Hey, you know that chip that we had made for graphics? Guess what? It also is amazing at doing all of these very small mathematical calculations that you need for your AI model. And that turned out to be true. So the next leap forward was what Jonathan saw, which was hold on a second. If you look at the chip itself, that GPU substantially has not changed since 1999 in the way that it thinks about problem solving.

48:54It has all this very expensive memory, blah, blah, so he was like, let's just throw all that out the window. We'll make small little brains and we'll connect those little brains together and we'll have this very clever software that schedules it and optimizes it. So basically take the chip and make it much, much smaller and cheaper and then make many of them and connect them together. That was Jonathan's insight. And it turns out for large language models, that's a huge stroke of luck because it is exactly how LLMs can be hyper optimized to work. So that's kind of been the evolution from CPU to GPU to now LPU.

49:31And we'll see how big this thing can get, but it's quite novel. Well, congratulations on it all. And it was a very big week for Google, not in a great way. They had a massive PR mess with their Gemini, which refused to generate pictures of I'm reading this correctly, of white people. Here's a quick refresher on what Google is doing in AI. Gemini is now Google's brand name for their AI, main language model. You can think of that like open AI's GPT. Bard was the original name of their chatbot. They had duet AI, which was Google's sidekick in the Google suite earlier this month, Google rebranded everything to Gemini.

50:09So Gemini is now the model. It's the chatbot and it's a sidekick and they launched a $20 month subscription called Google one AI premium only four words way to go. This includes access to the best model Gemini Ultra, which is on par with GPT -4 according to them and generally in the marketplace. But early this week, users on X started noticing that Gemini would not generate images of white people, even when prompted. People were prompting it for images of historical figures that were generally white and getting Kind of weird results. I asked Google Gemini to generate images of the founding fathers.

50:44It seems to think George Washington was black Certainly here a supporter of the founding fathers of America as you can see it is putting this Asian guy I just made a great mashup. And yeah, there was like countless images that got created generate images of the American revolutionary. Sure, here are images featuring diverse American revolution as an insert of the word diverse. Sex, I'm not sure if you watch this controversy on X, I know you spend a little bit of time on that social network. I noticed you're active once in a while. Did you log in this week and see any of this, Bruh -Hah? Sure, it's all over X right now.

51:23I mean, look, this Gemini rollout was a joke. I mean, it's ridiculous. The AI is incapable of giving you accurate answers because it's been so programmed with diversity and inclusion. And it inserts these words diverse and inclusive, even in answers where you haven't asked for that. You haven't prompted it for that. So I think Google is now like yank back the product release. I think they're scrambling now because it's been So embarrassing for them. But it's actually like, is it, how does this not get Q8? Like, I don't understand how... Yeah, I had the red team not catch this. Yeah. Well, or anybody, or isn't there a product review with senior executives before this thing goes out that says, okay, folks, here it is, have that, try it, we're really proud of our work.

52:09And then they say, well, on a second, is this actually accurate? Shouldn't it be accurate? You guys remember when chat GPT launched and there was a lot of criticism about Google and Google's failure to launch. And a lot of the observation was that Google was afraid to fail or afraid to make mistakes, and therefore they were too conservative. And as you know, in the last year or two, a year and a half, there's been a strong effort at Google to try and change the culture and move fast and push product out the door more quickly. And the criticism is now why Google has historically been conservative.

52:48than I realize we can talk about this particular problem in a minute. But it's ironic to me that the Google is too slow to launch criticism has now revealed that Google's result of actually launching quickly can cause more damage than good. But Google did not launch quickly. Well, I will say one other thing. It seems to me ironic because I think that what they've done is they've launched more quickly than they otherwise would have. and they've put more guardrails in place that backfired. And those guardrails ended up being more damaging. But what are the guardrails? What's the guardrail here? So this is Google's AI principles.

53:26The first one is to be socially beneficial. The second one is to avoid creating or reinforcing unfair bias. So much of the effort that goes into tuning and waiting the models at Gemini has been to try and avoid stereotypes types from persisting in the output that the model generates. Where is telling the truth? Telling the truth exactly. That's exactly what I'm saying. Changing society is our second branch. Oh, we like to. Steer society. No, I think socially beneficial is a political objective because it depends on how you perceive what a benefit is. Avoiding bias is political. Be built and tested for safety.

54:05Doesn't have to be political, but I think the meaning of safety has now changed to be political. By the way, safety with respect to AI used to mean that we're going to prevent some sort of AI superintelligence from revolving and taking over the human array. So that's what it used to mean. Safety now means protecting users from seeing the truth. I feel like they might feel unsafe. Or, you know, somebody else defines as a violation of safety for them to see something truthful. So the first three objectives or values here are all extremely political. I think any AI product for it to be worth assault has to start.

54:38They can have any, I think that these values are actually reasonable. That's their decision. They should be allowed to have it. But the first base order principle of every AI product should be that it is accurate and right. Correct. Yeah. Yeah. Why not? Focus on the area of that. Look, the values that Google lays out may be okay in theory, but in practice, they're very vague and open -turn interpretation. And so therefore the people running Google AI are smuggling in their preferences and their biases. And those biases are extremely liberal. And if you look at X right now, there are tweets going viral from members of the Google AI team that reinforces this idea where they're talking about, you know, white privileges, real, and, you know, recognize your bias at all levels and promoting a very left -wing narrative.

55:29So, you know, this idea that Gemini turned out this way by accident or because they rushed it out, I don't really believe that. I believe that what happened is Gemini accurately reflects the biases that people created it. Now, I think what's going to happen now is in light of this, the reaction to the rollout is, do I think they're going to get rid of the bias? No, they're going to make it more subtle. That is what I think is disturbing about it. I mean, they should have this moment where they change their values to make truth the number one value like Samantha's saying, but I don't think that's gonna happen.

56:04I think they're just simply going to they're gonna dial down the bias to be less obvious You know who the big winner is gonna be in all this to math is gonna be open source like because people are just not gonna Want a model that has all this baked in weird bias right? I want something that's open source and It seems like the open source community would be able to grind on this to get to truth, right? So I think one of the big changes that Google's had to face is that the business has to move away from an information retrieval business where they index the open internet's data and then allow access to that data through a search results page to being an information interpretation service.

56:40These are very different products. The information interpretation service requires aggregating all this information and then choosing how to answer questions versus just giving you results of other people's data that sits out on the internet. I'll give you an example. If you type in IQ test by race on chat GPT or Gemini, it will refuse to answer the question. Ask it 100 ways and it says, well, I don't want to reinforce stereotypes. IQ tests are inherently biased. IQ tests aren't done correctly. I just want the data. I want to know what data is out there. You type in into Google, first search result and the one box result gives you exactly what you're looking for.

57:18Here's the IQ test results by race. Then, yes, there's all these disclaimers at the bottom. The challenge is that Google's interpretation engine and ChatGPT's interpretation engine, which is effectively this AI model that they've built of all this data, has allowed them to create a tunable interface. The intention that they have is of valid intention, which is to eliminate stereotypes and bias in race. However, the thing that some people might say stereotypical, other people might just say is typical. that what is a stereotype may actually just be some data and I just want the results. And there may be stereotypes implied from that data, but I wanna make that interpretation myself.

57:59And so I think the only way that a company like Google or others that are trying to create a general purpose, knowledge, Q &A type service are gonna be successful is if they enable some degree of personalization where the values and the choice about whether or not I want to decide if something is stereotypical or typical or whether something is data or biased should be my choice to make. If they don't allow this, eventually everyone will come across some search results or some output that they will say doesn't meet their objectives. And at the end of the day, this is just a consumer product. If the consumer doesn't get what they're looking for, they're going to stop using it.

58:37And eventually everyone will find something that they don't want or that they're not expecting and they're going to say, I don't want to use this product anymore. And so it is actually an opportunity for many models to proliferate for open source to win. Can I say something else? Yeah. When you have a model and you're going through the process of putting the fit and finish on it before you release it in the wild. An element of making a model good is this thing called reinforcement learning, right through human feedback. Yep. You create what's called a reward model, right? that you reward good answers and you're punitive against bad answers.

59:14So somewhere along the way, people were sitting and they had to make an explicit decision. And I think this is where sex is coming from. That answering this question is verboten. You're not allowed to ask this question in their view of the world. And I think that that's what's troubling because how is anybody to know what question is askable or not askable at any given point in time? If you actually search for the race and ethnicity question inside of just Google proper, the first thing that comes up is a Wikipedia link that actually says that there are more variations within races than across races.

59:51So it seems to me that you could have actually answered it by just summarizing the Wikipedia article in a non -offensive way that was still legitimate and that's available to everybody else using a product. And so there is an explicit judgment. Too many of these judgments, I think, will make this product very poor quality and consumers will just go to the thing that tells it the truth. I think you have to tell the truth. You cannot lie and you cannot put your own filter on what you think the truth is. Otherwise, these products are just really worth it. Yeah, and I'm more concerned about the answers that are just flat out wrong driven by some sort of bias then I am about questions where they just won't give you an answer.

1:00:34If they just won't give you an answer, well, there's a certain bias in terms of what they won't answer, but at least you know you're not being misled. But in questions where they actually give you the wrong answer because of a bias, that's even worse. You should be allowed to choose, right? I actually disagree with you're framing their free burger, making it sound like we live in this totally really a really relativized world where it's all just user choice. And everyone's going to choose their bias and their subjectivity. I actually think that there is a baseline of truth. And the model should aspire to give you that.

1:01:08And it's not up to the user to decide whether the photo of George Washington is going to be white or black. I mean, there's just an answer to that. And I think Google should do their job. I mean, the question you have to ask, I think, is not whether Google is going through an existential moment. I think clearly is. As business is changing in a very fundamental way, I think the question is whether they're too woke to function. I mean, are they actually able to meet this challenge given how woke and biased, what a model culture, their company evidently is? And they used to be able to just hide the bias by the ranking and who they downranked.

1:01:50So they did the panda update, they did all these updates and they would, if they didn't like a source, they could just move it down. If they did like a source, they could move it up. Yeah. And they could just say, hey, it's the algorithm, but they were never forced to share how the algorithm ranked results. The results. And so, you know, if you had a different opinion, you're just weren't going to get it on a Google search result page, but they could just point to the algorithm and say, yeah, the algorithm does it. I just sent you guys. I think this is a hallucination, but Nick, you can throw it up there.

1:02:17We can get sex as a reaction. Wow. Wow. This is nutty, right? But look, it's the ideology that's driving this. The tip off is when you say it's important to acknowledge race is a social construct, not a biological reality. It's George Washington white or black. That's a whole school of thought called social constructivism, which is basically this. It's like Marxism applied to categories of race and gender. So Google has now built this into their AI model. And it started over. The question, yeah, you almost have to start over again. This is to work to function. Fire every minute. Yeah, I think you can really see observation with those search rankings because what I'm afraid of is that what Google will do is not change the underlying ideology that this AI model has been trained with.

1:03:05But rather, they'll dial it down to the point where they're harder to call out. And so the EG will just be more subtle. Now, I've already noticed that in Google search results, Google is carrying water for either the official narrative or the woke narrative, whatever you want to call it on so many search results. Here's an idea, like they should just have the ability to talk to their Google chatbot, Gemini, and then have a button that says, turn off like these concepts, right? Like I just want the raw answered do not filter me. It's not program that way. I mean you're talking about something very deep.

1:03:41Sex, what do you do if you're the CEO of Google? Fire myself? No, seriously, you're the CEO of Google, you're your task. Let's say your friend Elon buys Google and he says, Sex, will you please just run this for your for me? What do you do? Well, I saw what Elon did. What are he went in a fire 85 % of the employees? Yeah, I mean that, but you know, a program actually had an interesting tweet about this where he said that one of the reasons why these ideologies take over companies is that I mean they're clearly non -performance enhancing right they clearly hurt the forms of the company it's not just Google we saw this with Disney we saw it with Bud Light Coinbase Coinbase was the other way no no but they had a group of people there who were causing chaos and yeah exactly so in any event we know this does not help the forms of a company so the extent to which what's these ideologies?

1:04:33Well, permeated company is based on how much from monopoly they are. So, so here, yeah, the ridiculous images generated by Gemini Arn and anomaly, they're a self -portrait Google's bureaucratic corporate culture. The bigger your cash cow, the worse your culture can get without driving you out of business. That's my point. So they've had a long time to get really bad because there were no consequences to this. You can dress place. At this point, the whole company is infected with this ideology and I think it's to be very, very hard to change. Because look, these people can't even see their own bias.

1:05:03Well, I think that there's a notion that people need to have something to believe in. They need to have a connection to a mission. And clearly, there's a North Star in the mission of this, I would call it information interpretation business that they're now walled. The mission of hijack, dude. The mission of hijack. That's what I'm saying. The original question was to organize all the world's information. Then now they're doing, now they're suppressing information that they don't like. Indexed the world's information period. the end. That's the end of the document. Well, and to make it do it universally accessible and useful was kind of the end of the statement.

1:05:34Yes. My real point is maybe there's a different mission that needs to be articulated by leadership and that that mission, the troops can get behind and the troops can redirect their energy in a way that doesn't feel counter to the current contention, but can perhaps be directionally offsetting of the current direction so that they can kind of move away from this socially effective deciding between stereotypes and typical data and actually moving towards a mission that allows excessive value. You know what, I would do something completely different. I would do a company meeting and I would put the company mission on the screen, the one that you just said about not only organizing all of the world's information but also making it useful and retrieval.

1:06:16Accessible and useful. Accessible and useful. This mission has always been our mission and you don't get to change it because of your personal bias and ideology. And we are going to re -dedicate ourselves to the original mission of this company, which is still just as valid as it has always been. But now we have to adapt to new user needs and new technology. I completely agree with what SAC said times a billion trillion and I'll tell you what AI at its core is about probabilities. Okay. And so the company that can shrink probabilities into to being as deterministic as possible. So where this is the right answer, we'll win.

1:06:56Okay? Where there's no probability of it being wrong because humans don't want to deal with these kinds of idiotic error modes. It's not right. It makes it a potentially great product horrible and unusable. So I agree with Saks. You have to make people say, guess what guys, not only are we not changing the mission, we're doubling down and we're gonna make this is so much of a thing, we're going to go and, for example, like what Google did with Reddit. We're now going to spend $60 billion a year licensing training data. We're going to scale this up by 1 ,000 fold. And we are going to spend all of this money to get all of the training data in the world.

1:07:34And we are going to be the truth tellers in this new world of AI. So when everybody else hallucinates, you can trust Google to tell you the truth. That is a $10 trillion company. Right. And one of the things that someone pulled me from Google that as an example, so to avoid the race point There's a lot of data on the internet about flat earthers people saying that the earth is flat There's tons of websites. There's tons of content. There's tons of information. Carrierving. So if you just train a model on the data that's on the internet The model will interpret some percentage chance that the world is flat So the tuning aspect that happens within model development Chimoff is to try and say, you know what, that flat Earth notion is false.

1:08:18It's factually inaccurate. Therefore all of these data sources need to be excluded from the output in the model. And the challenge then is, do you decide that IQ by race is a fair measure of intelligence of a race? And if Google's tuning model then, or tuning team then says, you know what, there are reasons to believe that this model isn't correct, sorry, this IQ test isn't a correct way to measure intelligence. That's where the sort of interpretation arises that allows you to go from the flat earth isn't correct to the maybe IQ test results aren't correct as well. And how do you make that judgment?

1:08:51What are the systems and principles you need to put in place as an organization to make that judgment to go to zero or one? Right? It becomes super difficult. I have a good tagline for them now to help people find the truth. Yeah. Just help people find the truth. I mean, it's a good, it's aspiration. They should just help people find the truth as quick as they can. But this is, yeah, I did not envy Sundar. It's going to be hard. Yeah. What would you do, Freiburg? I would be really clear on the output of these models to people and allow them to tune the models in a way that they're not being tuned today.

1:09:29I will have the model respond with a question back to me saying, do you want the data or do you want me to tell you about stereotypes and IQ tests. And I'm going to say I want the data, and then I want to get the data. And the alternative is, so the model needs to be informed about where it should explore my preferences as a user, rather than just make an assumption about what's the morally correct set of weightings to apply to everyone, and apply the same principle to everyone. And so I think that's really where the change needs to happen. So let me ask you a question, SACs. I'll bring Alex Jones into the conversation.

1:10:01If it indexed all of Alex Jones' crazy conspiracy theories, but three or four of them turn out to be actually correct, and it gives those back as answers. How would you handle that? I'm not sure I see the relevance of it. If someone asks what is Alex Jones think about something, the model can give that answer accurately. The question is whether you're going to respond accurately to someone requesting information about Alex Jones. I think that's the, I think that's the, the knowledge situation. Well, I always think you are like, it says, hey, I have a question about this assassination that occurred.

1:10:33And let's just say Alex Jones had something that was totally crackpot. Yeah, maybe he has moments of brilliance and he figured something out, but maybe he's got something that's totally crackpot. He admittedly deals in conspiracy theory. That's kind of the purpose of the show. What if somebody asks about that and that it indexes his answer and presents it as fact? Well, how would you index Alex Jones, I'm asking you? How would you index his information? The better AI models are providing citations now and links. Proplexity actually does a really nice job with this. Citations are important, yeah.

1:11:02and they will give you the pro and con arguments on a given topic. So I think it's not necessary for the model to be overly certain or prescriptive about the truth when the truth comes down to a series of arguments. It just needs to accurately reflect the state of play, basically the arguments foreign against. But when something is a question of fact that's not really disputed, it shouldn't turn that into some sort of super subjective question, like the one that's math to show. So I just something everyone should get the same answer. I mean, I think my decision on whether I choose to believe one person or value one person's opinion over another should become part of this process that allows me to have an output.

1:11:43And the models can support this, by the way. I think customization is part of this, but I think it's a cop out with respect to the problem that Google is having with Gemini right now. Gemini, what would you do if they made you chairman dictator of Google? I'd shrink the workforce meaningfully. 50 percent. Yeah, 50, 60 percent. And I would use all of the incremental savings and I would make it very clear to the internet that I would pay top dollar for training data. So if you had a proprietary source of information that you thought was unique, that's sort of what I'm calling this tack 2 .0 world.

1:12:23And I think it's just building on top of what Google did with Reddit, which I think is very clever. But I would spend a hundred billion dollars a year licensing data and then I would present the truth and I would Try to make consumers understand that AI is a probabilistic source of software meaning its probabilities It's guesses some of those guesses are extremely accurate But some of those guesses will hallucinate and Google is spending hundreds of billions of dollars a year to make sure or that the answers you get have the least number of errors possible and that it is defensible truth. And I think that that could create a ginormous company.

1:13:03This is the best one yet. I just asked Gemini is Trump being persecuted by the deep state. And it gave me the answer. Elections are a complex topic with fast changing information to make sure you have the latest and most accurate information. Try Google search. That's not a horrible answer for something like that. Yeah, that's a good answer, actually. No, I don't know, but I'm not that it's just like, hey, we don't want to keep We don't want to see it like this whole system is totally broken, but I do think that there's a waiting solution to fixing this right now And then there's a couple tweaks to fix it.

1:13:32I just think the authority at which these LLM speak is ridiculous like they speak as if they are absolutely 100 % certain that this is the crisp Perfect answer or in this case that you want this lecture on IQs etc. When let's all remember I'm sure presented with citations. Let's all remember what internet search was like in 1996 and think about what it was like in 2000 and now in 2020s. I mean, I think we're like in the 1996 era of LLMs and in a couple of months, the pace things are changing. I think we're all gonna kind of be looking at these days and looking at these pods and being like, man, remember how crazy those things were at the beginning and how bad they were.

1:14:13What if they have all been a dystopian way? I mean, have you seen like Mark Andresan's tweets about this? He thinks the positive work is not better. I actually think to your point, Google could be going down the wrong path here in a way that they will lose users and lose consumers and Someone else will be there eagerly to sweep up with a better product I don't think that the market is gonna fail us on this one unless of course this regulatory capture moment Is realized and these fed step in and start regulating AI models and all the nonsense that's being proposed Freeberg, aren't you worried that like aren't you worried that somebody with an agenda and a balance sheet could now basically gobble up all kinds of training data that make all models crappy and then they basically put their layer of interpretation on critical information for people.

1:14:56If the output sucks and it's incorrect, people will find that there is open people out there. No, you can, you can lie. They're, they may not be, for example, look at what happened with Gemini today. Like they put out, they put out these stupid images and we all piled on. We're in V zero. What I'm saying is there's a state where let's just say the truth is actually on Twitter or actually let's use a better example. The truth is actually in Reddit and nowhere else. But that answer and that truth in Reddit can't get out because one company has licensed it, owns it and can effectively suppress it or change it.

1:15:26Yeah, I'm not sure there's going to be a monopoly. I think that's a real risk. I know if I think the open internet has enough data that there isn't going to be a monopoly on information by someone spending money for content from third parties. I think that there's enough in the open internet to give our all give us all kind of, you know, the security that we're not going to be monopolized away into some disinformation age. That's what I love about the open internet. It is really interesting. I just asked it a couple of times to just just just to list the legal cases against Trump, the legal cases against Hunter Biden, the legal cases against President Biden, and it will not just list them.

1:16:00It just ponds on that. It's really fascinating. Then chat GBT is like, yes, here are the six cases, perfectly summarized with it looks like, you know, beautiful citations of all the criminal activity, Trump's been involved in. Ask the question about Biden's criminal activity. Let's see if it's really. I'm joking. No, I'm serious. Ask if, you know, that's where you start to see. Gemini would be by neither. I think they've just decided they're just not going to do it. They won't do Biden. They won't touch it. It's obviously broken and they don't want more egg on their face. So they're just like, go back to our other product.

1:16:34Look, I can understand that part of it. If there's some issues that are so hot and contested, you refer people to search. Because the advantage of search is you get 20 blue links. The rankings probably are biased, but you can find what you're looking for. Whereas AI, you're kind of given one answer, right? So if you can't do an accurate answer that's going to satisfy enough people, maybe you do kick him to search. But again, my objection to all of this comes back to simple, truthful answers that are not disputed by anybody, being distorted. I don't want to lose focus on that being the real issue.

1:17:11The real subject is what Jamaf put on the screen there where it couldn't answer some questions about George Washington. Okay, everybody. We're going to go by chopper. We're going to go by chopper. We need to chopper. We have our world correspondent general David Sacks in the field. We're dropping him off now. David Sacks in the helicopter. Go ahead, Chad, I get all those going on in the Ukraine on the front. What's happening in the war is that the Russians just took this city of Deyaka, which basically totally refutes the whole stalemate narrative as I've been saying for a while. It's not stalemate.

1:17:46The Russians are winning. But the really interesting tidbit of news that just came out on the last day or so is that apparently the situation in Moldova is boiling over. there's this area of Moldova, which is a Russian enclave called Transnistria. And officials there are meeting in the next week to supposedly ask to be annexed by Russia. And so it's possible that they may hold some sort of referendum. They're one of these like breakaway provinces. So it's kind of like Transnistria and Moldova is kind of like the Donbass wasn't Ukraine or South Asetia and Georgia. They're ethnically Russian. They would like to be part of Russia, but when the Polesuit Union fell apart, they found themselves kind of stranded inside these other countries.

1:18:35And what's happened because of the Ukraine war is Moldova is right on the border with Ukraine. Well, Russia is in the process of annexing that territory now that's part of Ukraine. So now, Nostria is right there and could directly make a play to try and join Russia. Why do I think this is a big deal because if something like this happens it could really expand the Ukraine war The West is going to use this as evidence that Putin wants to invade multiple countries and invade you know a bunch of countries in Europe And this could lead to a major escalation of war. All right everybody Thanks so much tuning into the all in podcast episode 167 for the rainman David Sacks the chairman dictator from our probably and in Freeberg I am world's greatest.

1:19:22Love you boys, Angel Investor, or whatever. See you next time, bye bye.

1:19:48Besties are gone, go for it. That's my dog thinking I wish you're driving. Sit down. Oh man, my ham is the actual meat. We should all just get a room and just have one big hug or two because they're all just useless. It's like this sexual tension that we just need to release our house. What, your beef? What, your beer of beef? Beef, what? We need to get my cheese already. I'm going all in.

1:20:20Chop 5 I'm doing all Yin