This Week in Startups - Sora Disappoints, ChatGPT Pro Tested, Inference Time Reasoning & More with Sunny Madra | E2062

Episode Date: December 18, 2024

Timestamps: (0:00) Jason and Sunny kick off the show (1:26) Discussing Groq's recent developments and Middle East tech inspiration (3:51) Global business challenges and Jason's 2025 announcements (7:53) Gemini app demo and ChatGPT 4 comparison (9:57) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist (11:24) Evaluating ChatGPT 4.0 Pro limitations and robo-taxi fleet costs (12:46) Differences between ChatGPT models and future improvements (19:57) OpenPhone - Get 20% off your first six months at https://www.openphone.com/twist⁠ (20:58) LLM focus shift and robo taxi cost model experimentation (23:01) Importance of prompt engineering in AI tools adoption (25:31) Workplace AI adoption challenges and generational tech differences (27:22) New productivity hacks and Gemini app's growth (30:27) Zendesk - Get six months free at https://www.zendesk.com/twist (34:06) Startup support programs and Google Gemini's research capabilities (37:08) AI model performance evaluation and simplification (45:02) Meta's Llama 3.370b launch and AI industry impact (46:37) Infrastructure costs, competitive landscape, and AI-generated content evolution (55:44) Trust and bias in language models, news analysis startup ideas * Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp * Mentioned on the show: Check out Sora here: https://sora.com/ Check out Kling here: https://klingai.com/ Check out Groq: https://groq.com/ * Follow Sunny: X: https://x.com/sundeep LinkedIn: https://www.linkedin.com/in/sundeepm * Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis * Thank you to our partners: (9:57) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist (19:57) OpenPhone - Get 20% off your first six months at https://www.openphone.com/twist⁠ (30:27) Zendesk - Get six months free at https://www.zendesk.com/twist * Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland * Check out Jason’s suite of newsletters: https://substack.com/@calacanis * Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com * Subscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

I think Open AI is going to lose their lawsuit. I'm saying it right now. I'm predicting it here. I think it's going to be an injunction against Open AI, and they're going to have to settle for billions. You heard it right. I think it will be the largest copyright infringement case in history. I think it will be a billion dollar settlement with the New York Times,

and other people are going to join it. If you are a content creator and you feel your stuff is stolen. Billion dollar, okay. I think it's going to be a three-coma settlement. I honestly do or judgment. Trace commas. This week in start. Startups is brought to you by Lemon.io.

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program and get Zendesk products free for six months. Visit Zendesk.com slash twist today to get started. All right, everybody, welcome back to this week in startups. I am so excited because the prodigal son, my boy, my bestie, Sande Maudra is back. You know, we were on such a tear every week. You were coming in. You were doing all these great AI demos.

We were placing bets. It was completely degenerate and we loved it. And then you got a little busy. GROC has been surging. You raised a bunch of money. We saw that in the press. Yum, yum. I got a little tasty poo of your company definitive intelligence, which was, of course,

bought by GROC. GROC, of course, is the inference chips that Tramoth invested in, I guess, eight, nine, ten years ago and funded. They bought your company, and here we are. Give us the latest on GROC, and then let's get right to our demos. Yeah, no, I mean, look, apologies, I miss doing it with you, but, you know, one of my resolutions for next year is make sure we're doing it every week. But it's been busy for us, you know, post-financing, we've been doing a lot of

lot of deals, you know, spent a lot of time in the Middle East, met some of your friends out there as well, J-Cal. Oh, great. Yes. Really inspired by what's happening out there, to be honest. Why are you inspired? Explain to people what's so inspiring about what's happening in Saudi, UAE, you know, and Kuwait, Doha, Bahrain, Oman, you know, there's just like a lot, Israel, a lot of going on there. Everywhere. Well, you know, if I could distill it down to kind of three things, which I find really inspiring, let's just start with a lot of young folks. Right? Yes.

You know, they have a lot of young folks. And that makes for, you know, a population that has a lot of energy, right? You probably even see that being in Austin, right? And so... Big time in Austin. That's one of the things I love about it. Yeah, get that young energy. That's number one.

Got it. Two, you know, they have a kind of a core business, let's call that the oil business. And they can use that to basically elevate themselves into the next industry for the country, right? Any industry. They could participate in... any industry. When you have that kind of a chip stack, you can sit at any poker table. You're invited to every game. And look, they've made a distinct choice to make a big bet at the, you know, AI poker table, right? And they're doing that across the region. And then, you know,

strategically, the area is central to about, you know, within a thousand kilometers, there's four billion people, right? Within 600 miles, right? There's four billion people. Because you have India is a hop skips. You got all of Africa. You got all of your. all going into the Dubai airport or the Saudi-Iraud airport. Exactly. And, you know, you've spent time out there. There's a really good energy, right? They don't deal with a lot of the stuff that, you know, we've had here, which is changing

now, good to a bunch of our friends. You're referring to woke nonsense, regulation, and an outright hatred of capitalism. Yes. And, you know, they want to win and they're making it happen. And they love America, which is also great, too. I think it's incredible. I think it's well said, you know, it's kind of the equivalent of if the United States was sitting there and you had but one rival, China. Yeah.

And China, you really can't participate in that poker game. You're invited, we were invited for 20 years to that poker game. And I'm like, you know what? Can't play poker anymore. And it's like, well, whose fault is that? Why'd the game break? It's like, I don't know. And whoever the host was broke the game.

The game broke. somebody stiff the game. I don't know why the game broke. But we can't play with the Chinese anymore. Our government is stopping it. Their government is stopping it. And you can't trust the game in China. If they're going to rug pull you and take every education startup, which Xi Jinping did,

and say, you know what, education is owned by the state. All your investments go to zero. That's kind of like, you know, authoritarian behavior that makes people not trust investing in a region. Everything we do, based on some level of trust. Now, Europe is in decline. You very rarely see a company break out there, the Nordics, Berlin, sometimes London. You do have exceptions, and I know people are trying over there. But let's face it, it's a giant retirement community, and that's why we all love going there.

Period, full stop. It's like Epcot Center. Great summer vibes. So you, great summer vibes. Love it. But doing business there is hard. So then all of a sudden, this region emerges. And, you know, it's really charming to see a group of people who are like, hey, the way

you've done things in your democracy in the West has gotten the best results. Objectively, with the King Abdullah scholarships and all these great scholarships, they gave people in the 80s, 90s, 2000s, I understand. They just sent all their kids to America and to Europe to get educated. They came back. Now you got all these 30, 40, 50 year olds, Gen Xers, millennials, Gen Z, who have crazy degrees, perfect English. You would think they grew up in Jersey or Boston or something by their accents because they've spent as much time in America and in Boston going to Harvard, MIT and, you know, NYU, whatever, then they did there. This is all like an incredible setup to they want to do business with us. That is the right side of history, which is another reason

to love doing business there. I see it. And you going there, me going there, Brad Gersoner going there, all of us participating there is exciting because they want to build. Okay, great. But I also think, if you think about the larger planet, it would be very nice for India. Middle East region, which is obviously a lot of different cultures. Yeah. And the West. Africa. Europe.

Africa too. Yeah. And Africa too. But, you know, Africa is a frontier market, which is, it's emerging and, you know, there's various levels of stability and investment. So a great market. But it's just interesting that the people who are writing the checks go build are aligned. And they're aligned against Russia, China, you know, and maybe authoritarian country.

So it's a really beautiful thing that I think's happening. It's easy to criticize it. There's a lot of issues. but we'll leave those aside for now. And I'm just excited that you're spending time there as well. Yeah, we've got to get out there together. And so I had dinner with your friend, Omar, once as well. It was great.

We'll do it. We'll do it. I'm going to announce something in 2025 in the region. Oh, yeah. So I'm going to be there. Okay. Yeah, it's going to be exciting for the audience of this show. But in 2025, there'll be an announcement, two different announcements, two different

regions, two different projects. And so it'll be quite nice. And that means I'll be there twice a year. I'll be there twice a year. I'll be a little advisor on there because I'll be there quite a bit. You know, the reason I want to do it too is I want to expose my family to it, my daughters. I want them to see what's going on in this region because if you're not in America, if you're coming from another country, people are deciding, do I want to be in Riyadh, New York,

the Valley, Austin, Miami, Doa, Dubai, Dubai, Abu Dhabi. Yeah. This is the destination for smart people in the world. So it's super exciting. Okay. Let's get to demos. You haven't been here in a while. there's been stuff dropping on my head like you wouldn't believe.

And I am so impressed with Gemini. I have the Gemini app on my phone. I'm just going to put it out right now. Have you been playing with the Gemini 1.5 and the deep research? I mean, I use it all the time. It's kind of part of my workflow. It is at parody with chat GPT4. In my experience as a user, I'm finding it has access to some data.

images, flights, other things that I'm starting to see get pulled in from the Google suite of services. What's your general, since we haven't talked since 1.5 and the 2.0 and all this deep research has come out. Just generally, and do we have any of those demos lined up? Yeah, we do. So we were actually going to do something and maybe we can expand it. So, you know, I don't know if you've done 4-0 pro yet, but I have 4-0. That's a $200 a month product from opening. Exactly. I haven't ordered yet. Here's the reason I haven't ordered it. they don't allow you to upgrade individuals in your enterprise plan to it. I got to like, it's a mess.

Like just, hey, somebody clipped us and sent it to Sam Walthman's team. Just like what I'm in, I have a paid enterprise account, I have a personal account. I want to pay you $200 a month and there's no upgrade button. So what's the deal? Yeah. Well, there's an upgrade from your individual account, but I don't know how to do it across the enterprise account. That's the thing. So I had kind of a similar issue.

So I had to do it on my personal account. So one of the things I was going to say, J-Cal, is, and we can maybe do like a multifaceted a test here. Why don't you pull up your 4-0? I'll pull up 4-0-pro and you have a very standard prompt that you come up with. You know, the one when you are doing like the kind of the Uber analysis.

So let's both put them in at the same time. And let's compare the results. How about we do that as like kind of our first thing? That would be an interesting one. I'd have to go find it. Let's do, okay. Okay, here we go. Okay. So send that to me in the chat and then I'll drop it

in pro and then we'll do a comparison between what pro to $200 a month, which is meant to be your PhD level, versus your regular maybe undergraduate level. All right, founders, are you tired of doing all your own software development? Do you need help, but you can't afford all this time it takes to find great talent? Are you dreading the endless interviews and email chains just to find somebody great? It takes six months. It takes a year.

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and Twist listeners get 15% off the first four weeks. Stop burning money. Higher developers, smarter and faster at lemon.io slash twist. So here we go. I am opening up my chat sheet of T window. Yeah, I'm using 01 Pro. Okay, so I'm going to use 01. Yeah.

Not Pro 1. Okay, not pro. Yeah, because I don't have it. Here we go. All right, I just did it. And you can see mine. It's going through this, you know, thinking, assessing. saying, you're seeing I have the screenshirt up here, right? It's kind of going through.

I see that. On the right side, you're seeing how it's basically working through this problem. Right. It says it's gathering data. I'm working through a hypothesis on U.S. daily car trips pegged at $1.1 billion. Seems like a logical starting point. Gives a little color commentary. We're finding trip estimates. I'm pulling data from the NHTS, 2017, estimated 940 million trips daily, assessing technologies, influence, calculating robo-toxy fleets, etc., etc., etc. So you see it's doing some logic. It took my prompt, and just to get people an idea of what the prompt was, was build a detailed model, estimating the cost of creating a Robotoxy fleet for all car trips in the USA, including Waymo Cruz as a case study, the model should account for, total car trips in

the USA, Uber and Lyft trips, fleet efficiency, fleet operations, required fleet size, public transit, fleet costs, induced demand, and the output should be provide a comprehensive model with data backed up assumptions and link sources that estimates the total cost of building a Robotxy fleet in the entire U.S., yada, yada. Yeah. So this is kind of like a crazy, insane thing that I'm asking it to do, right? And is yours done? So mine is still working on it, right?

Yeah, mine has been done for a while, yeah. And it just did a very quick one here, and we'll see. Yeah. It took 22 seconds to do mine. I can go ahead and share my screen and I'll show you where my dad. I'll stop my share, yep, because mine is still working on it. And this is where we're going to assess the difference between having a $200 a month. Yeah.

So here it is. You see mine. And the output was, below is a modeled estimate for scaling a robotoxy fleet cover all U.S. trips plus 20% of public transportation trips with a 20% induced demand factor. What induced demand factor, if people don't know, is more people will take more rides, right? So people take 20% more rides because they're cheap and they're available. Key data sources, total U.S. daily trips in 2017, Uber Lyft trips, company filings, SEC filings. It tells me, Public Transit ridership, American Public Transportation Association, Robotax, the Operation Fleet Assumption. So, U.S. car trips, 0.95 billion a day. U.S. car trips, 350 billion a year, which makes a billion a day. U.S. and Lyft trips, 4.5 billion a year, just over 1% of the total trips. Robotaxy trips per vehicle per day. I put it at 20 to 30 trips a day, assuming 25 a day. Maybe it's more. I don't know if Tesla can do more with their fast charging and, and fast turnaround to clean it. I assumed five days off for each car for maintenance

of 360 days on the road per car. Pretty aggressive, I think. Trips recorded. Yeah, you say there's all my calculations. And it says here, with an induced fleet size, you would have around 422 billion rides. With induced demand,

you would need 46.9 billion vehicles. Now, that doesn't count P. So, you know, this estimate probably needs, needs to be doubled to handle peak demand, right? Like people leaving the Warriors game or something. Anyway, you're going to need $5 trillion at $100,000 of vehicle. You're going to need $1.5 trillion at $30,000 a vehicle to replace the entire fleet in the U.S. This is not to just do a ride-sharing.

This is to replace all rides in the U.S. Nobody has a car of route itself driving. What did you get? And, you know, this is a good test, right? You know, you've got these projects. Yours did it in a few seconds cost $20 a month. Mine cost $200 a month. 10 X of value?

Give me 10 X of value. Okay. So first of all, I think it's very important to see here. It thought for two minutes and 42 seconds, right? Which is incredible, right? I mean, just let's level set for a second. All of this incredible works happening either in your case in a few seconds, in our case, you know, two minutes and 42 seconds here in my case.

All right. First of all, I think it goes below as a step-by-step illustrative model with clear stated assumptions and references and calculations. So it's a lot more organized in yours, I think, J-Cal, right? So it goes, total car trips in the U.S. So basically, you know, walks through it and its simplicity gets to a billion car trips a day. Same answer, basically. Yep. Yep. Okay. Annual car trips gets to 365 billion, right? Yep. It says, maintain more conservative. It actually does this interesting thing saying, hey, we'll go to 340 as slightly lower, still representative based on the Nitsa data that it has,

right. Then it tries to calculate Uber and Lyft trips annually. It pulls those from the S-1 filings from 2018 and 2018 of Lyft. Pretty interesting, right? I like the reference here. I don't think yours had that, right? It did not. And it, yes. Here, it actually did the time for context. It's given this context section, 5 billion rides from where Lyft versus 340 billion tall rides. The ride share currently represents one to 1.5% demands, which is a calculation I just did. So it's like you're thinking a little bit more. Yeah. So it's more like a Jay-Gus. then it's like fleet efficiency, right? So it kind of walks through this.

So it's assumption is, hey, each fully autonomous robo taxi can do 20 to 30 trips a day. We'll take a midpoint. I like how it does that. Average trip duration, 15 to 30 minutes, some downtime between rides, high utilization scenarios, right? Okay. It's kind of all the stuff that you were rattling off on your own, right? Then it's got fleet operation constraints, right? Which is, you know, six hours a day for charging, cleaning maintenance, five days a year for

for overall major maintenance shutdowns. So basically it gets, say, you know, 360 days a year it can be utilized. And it gets to daily utilization. And, you know, again, I like this breakdown, right, which is 25 trips per day, 360 days, 9,000 trips per year. So for strict with the numbers, basically we get 8,975, but we'll use 9,000 as a rounded figure. I like how it's kind of humanizing that bit, too, making it easier for us to handle.

And then it's like required fleet size to handle 100% of the U.S. car trips, right? So just going through all the math, $30,40 billion trips, 9,000 a year, requires 37.8 million Robotaxies. What did yours get to on that one, Jake? I think it said something like 46, because it's taking into account the induced traffic, which I put into the instructions, and it put in picking up some public transport. We had the same instruction, so it should have that. Same instructions.

Yeah. Now it's like, it adds the public transport capturing 20% with FSD cars, so it starts to run through the logic here. and it basically, again, still at 38.2 million rides. Now your fleet cost, it starts to calculate it. And then just for, you know, the induced demand, because you and I were talking about this, it's well known that you have these induced demands. So let's say 20% increase in total trips, you know, because of widespread capacity. And summary, after all that, yeah, the summary is like 340 billion trips, 25 a day.

I got to tell you, it's not that. different. It's not 10 times better. It feels a little more polished. I wonder if it's our instruction set. So I think it would be good to have somebody from OpenAI on the pod who worked on this project. So let's just send a little note to them. The results here are not different. The results here are 90% the same. There are some formatting. And so the UX, I think, is determining the value of LLMs right now. It doesn't feel to me like the core LLMs are getting much better because I don't think, I think they've got as much data as they're going to get. or as, you know, 80 or 90% as much data as we're going to get.

So then it becomes the interface. Then it becomes the instructions and how it interprets what you want and how it gets to know you in the personalization. That's my feeling of the gains that are happening now. Formatting like Canvas, you know, this product. Artifacts, yep. The pro. Yeah.

Agents eventually. I think we're now in the fit, finish, and polish of the language models. and the language models themselves, are they starting to plateau because they've run out of data? This is something I saw Ilya was saying, hey, we stole all this data.

He didn't say steal, but let's call it what it is. We stole all this data. We got all this data. Some of it's public. Some of it's not. Okay, whatever it is, it is. Putting that aside,

I think that they're now stuck. Founders know that every missed call is a missed opportunity. Customers don't want to wait. They will call someone else if you don't pick up. But if you use open phone, you're never going to miss another customer call. And guess what? It's super affordable and easy to use. People used to spend tens of thousands, hundreds of thousands of dollars putting in a corporate phone system. And now for just $15 a month, Open Phone will give you a business phone line and complete control of your destiny. Want to know who's answering customer calls and how they were handled? Open Phone can do incredible things like sync with HubSpot and give you AI powered call summaries. automated responses to ensure you don't miss a single ring, well, that's all built into open phone.

And if you've got an existing phone number, they will let you port it over at no extra charge. Get 20% off your first six months. What an amazing offer at openphone.com slash twist. That's O-P-E-H-O-N-E dot com slash twist for 20% off for six months. The LLMs feel stuck to me, and now it's about inference, interface,

how it interprets instructions, how it personalizes you, and then proprietary data sources like Reddit, Twitter, Google Flights data, which comes from other databases, you know, the data, the deeper data and the instruction set and the interface.

Am I right or am I wrong? It's a great summary, Jekyll, and it's interesting that, you know, we do this experiment on something that costs 10 times more. I would feel,

you know, if I had to use the output, the output of the $200 one gives me a little bit more background on things. But, you know, what you've actually done in the way you prompt is that you've convinced, or you've been able to, not convinced, but you've been able to direct the model that's not as powerful to do what the more powerful model is doing. Right.

So maybe if we just said, build a model of what it would cost to replace all U.S. rides with robo-taxies with as many details as possible. Okay. And I'm going to give you the same one to do. That is just a sentence now. Now let's see if it does anything close to what I did. Because you're right, when I did this, I kept a notepad open on the side. I kept notion on the side. And I was putting in my architecture of how I would solve the problem,

which is how many rides are there? How many public transit rides are there? I introduced those three topics. I let it know that I wanted induced in there for 20%. I let it know I wanted to know Uber and Lyft's percentage. I wanted to know just in the U.S. So I did give it a framework. Yeah, this is really interesting. It came to, without my instructions,

an annual operating cost of $150 billion a year and total initial deployment cost of $2.8 trillion. Yeah. Wow. So this came up with a totally different number and it didn't explain it very well, but it's getting them as citations, I think. But see, what you've done, Jay Cowls,

what you've shown our listeners and our watchers is that if you are willing to put the time in to create better prompts, and I think this is important for the industry, you can basically get what is the equivalent of 10 times more expense on a model just by putting the time. And, you know, it was good.

I think this is a really, really good example. I'm running mine now. These ones take a bit longer to run, so we'll just let it run as we talk through it. Look, it just found the daily passenger trips. He got that right. For trips, Robotaxy a day, it picked 20 and it got it from a citation from Littman 2019. So I guess somebody had done a paper at some point that said 20 is the right number. We came up with the number 25 or 30.

The reason we came up with that number is we gave it. We want six days off a year and we want six hours off a day to charge. So we gave it like, hey, five days or six days of maintenance a year. which seems, you know, like if you got to take tires or it gets a fender bend or it needs to be repaint, who knows what could go wrong on these cars for maintenance and then charging, a certain amount of charging. And it came up with 55 million times 45,000. So it used the cost estimates of, I think, a Robotaxi as opposed to a Waymo. Pretty fascinating. Charging infrastructure included 300 billion in charging infrastructure, which it doesn't need to include, I don't think, but maybe.

Actually, maybe you do need to do that because if there were that many, you would. would need much more charge. You do actually need to include that because you would be charging every single car all day long. That's actually a really good point. Maintenance operation, software, $100 billion a year, McKinsey and Company, Rand Corporation 2018, Insurance Regulatory. So people have been working on this data. So it just did it better. It just did citations. It didn't kind of use my framework. What did you get? Is it still doing yours? Mine's still running. It's probably going to take another minute here, my guess is. But like... So anyway, I don't think it's worth it. I think we learned something here. But I'm going to

buy it anyway. Because for $2,400 a year, versus $240 a year in business to spend an incremental $2,000 for an average salary in corporate America of, let's say, I don't know, $80,000. I'm including people who make $40 and people make $150, but we'll pick $75,000. You know, for $2,500, you got to ask yourself, does an employee, an information employee get 3% more efficient with one of these products pretty clearly, yes. Oh, yeah. But they have to use it. And this is the thing that is making me a little mental. I can't get people to use it. I know if people are using it or not. I can't get people to use it. This is a habit.

I think the world's going to bifurcate between people who use this as their default all day long and people who don't. And it's going to be really sneaky. Can I give you a hack there? Please. This reminds me of the time. I would say it was like just past the mid-90s when the internet was just making its way into the corporate world. There was a set of people, which is, you know, our vintage, which were dying to use the internet. And there was a set of people that would not use the internet.

And in fact, they didn't trust it. And so, and what was it? We were just younger and we were more kind of tuned to technology. Yeah, we're tech savvy, had more energy, more fascinated. No kids. My suggestion to you is, as the summer is coming up, either you do it with interns, hire two to three people that are sub-20, J-Cal. And I will tell you.

10020. Everything they do, they use open AI because, you know, what is the one thing that you got through experience? You got mentorship. You got to work through that. So how they account for that is by using these AI tools. And so as long as you have someone with good energy. So imagine the 18 or 17 year old version of yourself. Oh, yeah. I'm super keen, but I don't know a lot of these things. Oh, how am I going to go figure it out? I'm going to figure it out. Yeah. I mean, you're not allowed to hire by age in the United States here, right? unless you were like casting for a movie, I think you could actually do it there. Well, maybe not age. You'd have to be doing it by look. So, but there is generational differences. So while you can't hire for age, if you do have entry level jobs, it generally will skew younger because the salary is, is it what, a 10, 20 years. Like, as internships or something. You could do internships for college credit.

Yeah. And yeah. There is a distinct difference between how young people use these tools and older people and getting older people to use them. It requires sometimes a change a baby. a change in behavior which requires pushing. And so here's what I did. It's called New Tab Override. It's by Sorin, hence shall.

And what it does is when you load a new page in your Firefox browser, and this will work on Brahe, Firefox and Braves is the same. It lets you put in an option of a URL. And then you see where it says focus here? Set focus to the web page instead of the address bar. This is a clearly, this is a very important thing to do. What this does is when you open a new tab, it puts you in the URL bar. Here, it puts you in the URL bar. in the search box if you do this.

So when you start typing, you don't have to hit tab or click your mouse to get in. Super clear. If you work for me, this needs to be on your computer. I will randomly pull it up during a Zoom meeting and say, do a new tab. I mean, I do this kind of stuff. And does that make me crazy? That I spot check? I say pull this up.

So if you work for me, be prepared. Yes, chef. Because I want you to. Yes, chef. Yes, chef. And you know what? We're all chef in this analogy. I always tell everybody, we like a good yes chef.

just acknowledging that we're making progress here. All right. Okay. Mine finished. Let's quickly wrap this one up. Okay. Mine finished. Again, this was this time, it was three minutes and 18 seconds.

So the prompt was shorter. Mm-hmm. Okay, this did a better job. Yes. Your premise is correct. You can get out of a standard model, in the more expensive model, it's doing the sub-prompting, isn't it for you? Yes.

It's all. Correct. Yes. What do you call this prompt generation feature where I can give it less prompting, but get more prompting because it's doing the prompting form. So I think the- Is there a term in the industry for- Inference time reasoning. So what it's doing is it during-inference time reasoning. ITR. ITER. Yeah. Okay, so it's iterating. So we're going to use that. We create an industry term. Inference time. Reasoning. Reasoning. Got it. So when you do the inference,

that's the query. Yes. It's doing at that time, a reasoning as opposed to doing the reasoning when the LLM was built on a bunch of H-100s and the model-scale. The main difference is like, say, when we started on this adventure of, you know, chat GPT, and I'll just go to the summary here, but when we started on this adventure of chat GPT, the minute the first token is predicted, every other token is already determined. And so it's just going through, you know, sort of what's going to happen. What happens in this inference time reasoning, it has the ability to kind of stop part way through, and then work and say, let me go and let me do an offshoot on some of these things,

bring those answers back into my main line, right? It's a oversimplification, but that's sort of what's happening there. And so it's not a just a standard prediction of tokens, which is what we saw in the original iterations. That's why we're seeing it. But what we've shown here, and I tend to agree, if you prompt engineer with more sophisticated prompts, you don't have to do as much inference time reasoning, and you can get very similar results. And so, fascinating here that it did come to the same $2.7 trillion number that you were at and the same operating cost. Hey, startups. When you're a business, you got to treat your customers right. Unreasonable

hospitality is the standard today. But you're going to need tools. You're going to need a platform to help you do this. And that platform is the Zendesk suite. The Zendes suite is going to give your startup all the tools you need to deliver exceptional customer experiences so you can build stronger relationships without growing your headcount. That's key, right? Every dollar matters. You got to control headcount. You got to control spend. So use the tool that Shopify, Squarespace, Uber, and Instacart all rely on. It's called Zendesk. Let's take a look at another customer, Unity. Very famous company, they saved $1.3 million with Zendesk automations and self-service, and they saw an 83% increase in their first response time. These companies love Zendesk because it's so

easy to set up and it scales with you as you grow. They'll also give you all the metrics to make your reporting easy, keeping you and your business agile and investor ready. And that's because you're there esteemed customer. And they've created the Zendes for startups program just for you, where you get unlimited access to all the Zendesst products, expert insights, all the best practices, and entry into their amazing community of founders, all at no cost for the first six months. That's right. They want to support you. Zendes.com slash twist. Get ready to scale with the best in customer support with six months free. Nothing to lose. It's really cool when you compare this to what Gemini is doing. So I guess it the same prompt here. Build a model of what it would cost to

replace all US rides with robotaxis as many details as possible. And it says here, cost of it, and it says what it's going to do. And I didn't put it. these details in here, but it says, in the ITR, inference time reasoning, it said, build a model that it says with as many details as possible by, find the total number of rights taken in the U.S. annually, find the average cost per ride of each model of transportation, find the estimate. So it's actually come up with its own reasoning. Analyze reports, create a report, ready in a few minutes, start the research, and it's doing it right now. Feel free to leave this chat, as you know, I'll let you know what's done. And it researched 69 web pages. Look at that. 69. Not 420. And look at all these.

It's showing you its work. This is why I think this is a better product right now for me. I like to see what it's doing, you know? And it's analyzing all the results here. I guess we're about a minute into it. This is Gemini Advanced 1.5 Pro with deep research. And if you don't have the Gemini app, the Gemini app does not have deep research in it yet. It does have the other features.

It's as good as ChatGPT's app. Gemini and Google have reached parity in my mind with it. Now, did you see some talk about the Gemini API and that API, the Gemini API is gaining steam on everybody? Is that true? Are people, developers using it? I saw a post for that today. I think directionally, it's correct. Like, definitely, there's been huge amount of growth on, you know, Javanai. I'm not sure. I think the tweet I saw was from Open Router or something like that, where they said it's, you know, greater than 50%. That may be, you know, open routers view of it. I still think, you know, it's probably not

50%, but happy to be proven wrong there. If Google folks want to come out and share it was high as 50%, which, yeah. So, you know, which by the way, if you want $350,000 in Google credits, you can get them at get startup credits.com. Get startup credits.com. Okay. You know, I have all these startups meet with us. 20,000 people apply for launch. You go to launch.co to apply for funding from our firm, join our programs, etc. After they apply, is it the idea you did with GCP?

Well, GCP did it for All In Summit, and they did it for this week in startups, and they did it for Accelerator. We also have Oracle provides credits, Azure Microsoft Azure provides credits, and DigitalOcean provides credits to our startups. The only one who does it is AWS. They have like a rack rate thing,

but AWS is not very supportive of anybody but Y Combinator. They've got like a weird thing. They also don't buy ads or whatever, so, which is, but, you know, AWS is great. I don't have any hard feelings towards them. Yeah. But they're not supportive in that way. They kind of picked YC, and I think YC is very sharp elbowed sometimes.

So they're like, we're team YC, we're not team everybody else. Okay, that's fine. I have half the number of applications of YC right now. And next year I'm going to match them. All right. So here we go. We've got this done. And look at this.

It did a nice thing. Total rides in the U.S. annually. It estimated, it got bus rides. It included bus trips. Well, I didn't ask you it to do that. based on the National Survey, Americans make approximately 1.1 billion trips a day, 411 billion trips annually or about 1,500 trips per person. Wow, it added that. That's pretty interesting. And then it has here, look at this, it built a table. Motive transfer, car, bus, train, freight. So included all of those in estimated rides. Average course for a ride and it put the amount, wow. Estimated cost of manufacturing and deploying, early estimates up to 400,000, maybe that's in billions or something. Tesla is projecting $25,000. He got that right for their robotaxie. Buy-Septive. WAMO-180. Wow, that's interesting. Deployment costs, estimated cost of

maintaining. And put that in without saying, estimated operating costs per mile, projected, average distance travel per... I mean, this is incredible. Total cost. Yeah. Cost per robotaxy ride, manufacturing cost deployment costs. Yeah. And it just figured that out. Wow. This is better. Let's be honest. No, it is. And the superpower is that open in docs on the top right. Well, I mean, that is, I think, if I want to, I can just open this up in Google Docs. It creates a document for you.

Yeah. Which is your next step. And now you're starting to see this. Now, if I save this document in the future, it's going to know that I did that document. And it's going to be able to use your documents in your email. So if I was emailing with, I don't know, somebody running a Robotaxie or I had 10 friends who had shares in Tesla, Uber, or whatever. and we had conversations.

I wonder if on my side, it's going to take that into account and say, hey, in your email, in your Gmail, there was a conversation with Dara or this analyst that Warby Parker, Warburne Pinkus, and they helped you do that, and they gave you some data there. Do you want me to include that data? So this could get very interesting, very quick, folks. I believe Google is the sleeping giant. Grock also doing a very good job. The other Grock, yeah, so we'll get into. Okay, let's do a couple more demos here. Well, just quickly, I want to close out on that statement.

So let's do one thing. We've got to get back to our grading. O1 Pro versus O1. And then let's grade 1.5 with deep research. So three grades. I'm going to give a B plus to Pro. I felt like it did a really good job. Yeah, and I would pay for it.

And I would give an A to the new 1.5 from Gemini. With deep research. With deep research, I'm giving it an A. only because I believe the output of both of those with ITR, I feel for the average, I'm creating it on my feeling and what I think it will do for the people who work for me.

The people who are not putting in the deep, thoughtful prompts, they're going to, I think, get a lot more out of Gemini 1.5 with deep research or 01 pro, the $200 a month. Now, Google, it's $20 a month.

So I give it an A plus on a value basis. On a value basis. On a value basis, I'd be like A plus and a B. So there'd be a big gap there. I'm saying in a corporate America, this pricing does not matter for the value. It doesn't matter because you're spending more on people's parking. So just throw this shit in the garbage. Sorry.

And let's just talk about how much it will impact my employees who use it, my team members, who use it, my founders, who we invested in and partner with. I believe B and A. A. I'm not giving pluses and minuses today. Yeah. So my interpretation is I actually, I'm going to give them both A's. And what I really like about what Open AI has done is it is giving the reasoning process along the way, which I think is very powerful for people that are using this in a work context versus when it's just spit out at you. So I liked how it was sharing its reasoning along the way. So I'm, I, I kind of lean towards that a little bit. Which deep research does as well. Gemini's advanced 1.5 pro with deep research.

I got to talk to Sergei and the team over there. When you're naming these things, it's Gemini. That's the product. Yes. It has a version. Nobody cares about the versioning. Just it's Gemini. Don't say advanced. Don't say 1.5. Just call it Gemini and then have Gemini with deep research and abstract out the version numbers for nerds. But I think this is too confusing for consumers, right? It's getting even worse. Like if I look at my menu here, which I'm sure you have the same choices. I have 1.5 Pro, 1.5, 1.5, Flash.

1.5 Pro would deep research 2.0 Flash experimental and 2.0 experimental advance. Yeah, you know, this is, okay, Google's premise was, here's the box. You type them what you want, you search, or you say I'm feeling lucky. I'm feeling lucky is the sniper shot takes you right to the thing that was cute and fun but there was just a search box so here for Gemini

I think it should just either do a quick search whatever the best search is or you should have deep research and then if you want to you have somewhere where you can kind of tweak the model but it's just too confusing

for consumers okay so we make your progress here one last thing on this one one last thing on this one is if you could only use one which one would you pick I'm going to stick with Open AI Pro

I'm going to stick with Gemini deep research because I think Google has access to data that open AI does it and I believe the gap is going to grow. Okay. That's, we'll come back to that one. I don't know if I bet on that, but I'm just using it. Now, one last thing on this one. Okay, okay. How about this? How about this? You have two high school interns for the summer. I don't do internships unless it's friends of the firm. Okay. I do it as a favor bank. You know why? Because in those 10 weeks, they take up all your time and resources and you train them and then they're gone. But they're they're supposed to use these tools. That's what I think it is for the rest of the team. I honestly, I would just rather hire. I just prefer to hire people at school. I'm going to University of Texas. Shout out to Jay Hartzel, president of UT. I went to UT, and I am so impressed by the UT graduates. I went to a game, Longhorns, whatever that is, go Longhorns. And I am all in on YouTube. We went to a football game. I went to a football game. A pigskin. Well, I mean, 100,000 people. I was on the field.

Man, it was awesome. But more awesome, there's 55,000 students at UT, and they're smart. And, like, I think the top one or two percent there are like Ivy Leaguers, but they're blue-collar Ivy Leaguers. With one thing, I did see this thing, and it was, there's UT at Austin. Then there's also University of Austin. UT is the public Austin. Yeah, of course. Yeah.

Big, giant school. They're funded because they have, my understanding is they have land and under the land. they found oil. So they are super funded in Texas. If you're a Texas resident, UT's like eight, nine or ten thousand a year. You can get in and out of UT for 40 dime skis, which is half the price of a private school in the Bay Area for one year.

Because it's 60 or 70 and you got to give a 10K donation or else they admonish you and give you a hard time. For sure. So one year of private school in the Bay Area or New York, Dalton, whatever this nonsense is. an entire degree from Utah college education University of Austin

now if you're at a state they charge you a rack rate and so they're a little bit higher but 80% or 90% of the people go to UT are in state this is amazing okay now let's go over to

University of Austin Joe Lonsdale in a group of these you know kind of free will and awesome free thinking libertarian-ish Republican types on the right, but I would say maybe there would be considered moderates

you know, like I think Barrie's probably a moderate. Yeah, you know, classically, like she probably would have voted for a Clinton Democrat or, you know, Mitt Romney as much as she would vote for a Trump or whatever. So, if she did vote for Trump, I don't know if she did. Putting that aside, they just had their first class. It's like 50 students or something. It's a startup school. They bought some university, so it's accredited,

and they want to teach from first principles, take all the woke out. Okay, got it. But I mean, I'll be honest. And UT doesn't have like a woke movement there. Like when they had the protests or whatever. It started and ended pretty quick. But a long way of saying, you know, I've been thinking about, you know, I have this founder university and I'm going to bring it in person, I think, in the next cohort is my plan in Texas and have in Austin.

In Austin. And I want to get a space. This is the big announcement, you know, as part of what I'm doing there. And so I'm trying to figure out if I do that with a university or if I just do it in a space or if I do remote and in person if I do it every day for an hour a day or if I do it two hours a week and get a co-working space. It's a lot on my plate right now, but I'm trying to figure out how I can have my own university, the founder university, teaching how to be a founder. And that's why I spent so much time getting founded at university and giving people instead of they pay, instead of paying

tuition, we give 25K to the top 10% of students to start their company at a $1 million dollar valuation for 2.5%, which is a good deal for us. Most people argue it's a great. great deal. It's kind of like the Y-combinator accelerator, but we expect only one out of three of those to pull through and get another round of funding. So we're taking high, high, high, massively high-risk bets. So if you were to net it out, if two out of three don't even make it to the next round of funding, it's really like 75 pay at $3 million for 2.5% because you're taking into account how many people would wash out and just not make it a year two. I really enjoy that kind of part of the job and seeing a lot of good stuff. What else? Let's go lightning round. Let's continue on the path. I do

want to give a shout out to a couple other things along the way. So lightning round for the next few minutes here. Okay. Just some of, some of these are not demos, but they're important things to call out for recently meta launched Lama 3.370B. And what I wanted to call out on Lama 3.370B is just in terms of how fast things are iterating. And we won't do a demo with this because, you know, I think it's easy to call it here. But if you look at a comparison against Gemini Pro 1.5, which you were just playing with, with deep research, right? You can see that it is starting to make really good inroads against its previous competitors. In a benchmark test. In benchmark tests, exactly. But this is a relatively small model. So let's not count meta out here. That's all I'm trying to share here.

Oh, no. Met is doing a mitzv for the industry by going open source and not trying to make money on this and they're letting other people use it. There were some weird caps, like you couldn't have 100 million users or something. But I think because of Zarkisberg is going based, and he looks at this like the open compute platform. He knows he's got a network effect. He'll defend it. Everybody can use his language models. And he is going to be the backstop against Sam Altman's closed AI.

Which is so paradoxical, by the way. And what I do want to call out here, right? This column right here is Lama 370B. This is Gemini's GPD 40. And look at the pricing down here. You're talking 10 cents for a million tokens. I put 40 cents for a million tokens. output, your $1.30 and $5 and $2.50 and $10. So it is getting there in being comparable,

but from a price perspective, crushing, which is something that, you know, Zuck and META have always been incredible at. Well, they are obviously investing heavily in this. What do you estimate these platforms are losing providing services at this pricing? Or are they breaking even? What are they doing? Do you have any insight in? to their infrastructure costs and how much they might be losing, making, or breaking even you. I fundamentally believe if you are, and look, I'm a bit skewed here, but if you're not building your own infrastructure, including chips from scratch, it's very hard to be competitive because

you do have to pay an 80% margin to Nvidia along the way, right? And so fundamentally, I do not believe that anybody is losing a ton of money on these things, but they're not making a lot because the big chunk of the margin is being taken out by Nvidia along the way. So this is the challenge for the industry and this is why Nvidia could be a short or could have topped out here

because people are now realizing there's an 80% margin there, which means they're going to have to compress that margin and lower their pricing to compete with Amazon, Apple, gross. Yeah, I mean, everybody's providing inference ships, etc.,

at greater and greater prices. Do you make custom ones with people or do you only make your own? No, our chip runs all models. So, you know, folks come to us and we run them. And so that's kind of where I think things are going to start to net out is that you see that price difference there. Let's not ignore that. Let's make sure everyone keeps watching that because I do think.

Keep watching. I do think there's pricing and there's capabilities and those things are kind of starting to go in some interesting directions. Fantastic. Let's keep speed rounding here. Okay. So next one, let's do SORA, because you had brought. that one up. Sure. And so have you, have you tried SORA yet, Jacob? I haven't tried it, but I've looked at

the demos. I see the demos. Yeah. So basically, you know, we've got it here. The generations you take a while, but like what you can see here is, you know, they've made it available. They've made a very clean UI. They've given you the capabilities to do anywhere from five, 10 seconds, right? And we can do a couple of different variations. And so... None of it looks real. The interesting thing is... It all looks fake. I was going to tell you that. So my overall observation is Kling, which we reviewed before, which is one of the Chinese-based ones, it looks the most realistic. And I fundamentally believe that's the case because it's trained, I think, on a bunch of proprietary

copyrighted data. And because it's done that, it's able to do it. Now, my understanding is a lot of the folks, a lot of the training data that's provided to these is being generated via game engines. And game engines are good, but do you get a feeling? this feels a little bit game engine. This feels like if stock imagery and a video game had a baby. So we have nailed it.

If you look, when you see this stuff, it looks like CGI done, you know, in the Czech Republic or Poland, Eastern European country, South Korea, done by somebody doing like a corporate video or something. In other words, it looks professional, but not in. industry leading like Disney or George Lucas or, you know, uh, JJ Abrams would, would accept. Yeah. So J.J. Abrams, George Lucas, Spielberg, Gorsese, you know, anybody making a, you know, secession TV show, nobody would accept any of this.

It's all 60% of what they would accept, 70%. So this would be great for them to storyboard and to be able to show, hey, here's what it looks like, but it's not good enough for prime time. It feels like it's a couple of years off, if ever, because. because, you know, while the Chinese have no problem stealing Disney's archive and doing this, and they'll have models out there that will let you do anything you want with the Marvel characters, the Disney character soon, I think Marvel should release a model in partnership with one of these companies, and for your Disney Plus subscription, you can create Disney character models,

and you can make short videos, and there only exist inside the Disney app, and you can send them to friends. this would be a killer feature. This is one of the Chinese ones. I was going to play. It's like a two-minute video, but this one, I want to get your reaction to this one. Okay, I'm watching it.

Okay, that looks like stock there. That looks real. That looks pretty real. Yeah. Like, almost like they took a George Clooney film, like a professionally shot George Clooney film in Italy, you know, the Italian job or something. Yeah, this looks like they took,

they stole from Hollywood to get this effect. I think. Yeah, so... Does it look closer? It is distinctly closer in that example To a Hollywood film than a stock photography library. Yeah, yeah. Well, this shows you, and I think we didn't bring this up,

but the OpenEEEye whistleblower, who apparently committed suicide or was whack? There's a lot at stake here. I mean, I know I sound like a conspiracy theorist, but they're... I think Open Eye is going to lose their lawsuit. I'm saying it right now, I'm predicting it here. I think it's going to be an injunction. against Open AI, and they're going to have to settle for billions. You heard it right.

I think it will be the largest copyright infringement case in history. I think it will be a billion dollar settlement with the New York Times and other people are going to join it. If you are a content creator and you feel your stuff is stolen. Billion dollar, okay. I think it's going to be a three-comas settlement. I honestly do, or judgment. Trace commas.

Trace commas. Well, listen, this is not unprecedented in the world. things like this can happen. We have seen records be broken when there is serious damage. And so, and I think it's going to, the reason,

you know, this tragedy that occurred, you can look it up, folks, is a 26-year-old whistleblower inside of Open AI who apparently is unalived and we don't know why.

And he was a key lynchpin in this testimony. I'm not saying, I think it was murdered by an opening eye employee, obviously. But this is really weird looking, very weird looking.

like many weird things occurring in the world. These things I always thought were weird, and then everything that's happened in the last six months, and I'm like, you know what, anything's possible now. Anything is possible. I mean, listen, if people who don't like Putin,

you know, fall out of windows at an alarming rate statistically, you know, who's to say it couldn't happen here, right? I mean, I'm going to be so arrogant to say there couldn't be somebody. Yeah. Oh, look,

very sorry for the family of the gentleman. Hopefully they figure out what happens. there. But quick grade on Sora. I mean, I give Sora still a B. Could be much better. I'm just sticking on my B there. I didn't, I'm not like, I don't know what the use case for these things is.

Okay. I feel like you're not there yet. You're not there yet. Like 2.5 or whatever that was where you were like, or like, remember Gmail could guess the fifth word. Yeah. And you're like, okay. Okay.

All right. But the jump we saw today from. The Chinese. You know, no, no, the jump in the reasoning Oh, the reasons are you. Guess the third or fourth word? Yes. Would you like to?

Yeah. You know, it says have dinner or whatever. Yeah. That guessing game that it was doing five years ago in Gmail, leading to Gemini with deep research, that jump occurred in five years. Boom. If that happens with this,

in five years we'll be sitting here going, make us a soprano's episode, and then we're going to watch it in my movie theater in Tahoe, this, you know, make us a lost episode, you know? Yeah. That'd be great. That'd be fun. Well, no, I mean, it's going to happen.

And there's no reason your favorite character in the Star Wars series, you couldn't direct a film or your daughters couldn't, you know, or your kids couldn't work together to say, you know, we always do like a little, what do you call it, like a, you know, like a talent show, you know, at holidays, like a little talent show with the kids. So if we do a little talent show, it's going to be like, hey, I directed this film about a show come from Star Wars, right? Yeah, yeah, yeah.

It's coming. So I do that all the time. I do Seinfeld episodes in a modern, with a modern scenario. They're always kind of fun to do. Try that in your thing of choice. George starts having chat GPT, do all of his communication. He's just like, I'm so bad with women. I'm just going to, I've always worked.

He's using it at work. Just, yeah, he just had it do what he thought this historical. historical figure would say in certain situations. So instead of doing the opposite, Costanza, he does whatever Stalin would say, whatever, you know, like some lunatic would say. He says, make me, like, make me into Einstein and myself. And all my responses should be like Bob Dylan and Einstein. Yeah. All right. Two more really quickly. And then we're done. We get, you gave a grade there. Okay. This I think is interesting for folks because this comes up quite a bit. And, you know, I had this, like live example. I actually tweeted about it. So I think it's fascinating. So I used in this case

perplexity and I said, hey, why did Syria fall now versus in previous years? Who are the rebels and what parties are supporting them? And to be honest, you know, I actually wanted to know what happened. It was purely curious. And I think it did a good job in terms of explaining what happened in terms of real time information. Then I did the same thing in GROC, right? The XGROC, XAI GROC. Yes. And so even though GROK, GR okay, And so even though these folks have access to real-time information, which is great, and I think there's a huge advantage for Jera OK, there is, you know, the folks that are connecting out to real-time sources like perplexity, I find. And so my task for you here, Jason, is as things are unfolding, because I know you're always having to look up stuff either for episodes of This Weekend startups or all in, really try this in both perplexity for real-time events and in, in I find Grock is really doing a good job of catching the zeitgeist on X, but I do worry about all the anonymous accounts and the anonymous accounts at scale and their bias. You know, like, I don't want King Coa, the anonymous million person account that Sacks is always retweeting and is in love with Sacks.

Like, I get it. It's obviously some right wing person in their mom's basement, or it could be a Russian, or it could be any other chaotic actor. And they've got a million followers. Like, that stuff for me. is really dangerous. Like, an at-scale account with a million followers, that's getting paid by X, right, that has revenue sharing on, but we don't know who it is. Now, I get you want to protect people, and I get it's a pseudonym so you can look at their account, and there's 20 of these accounts that have now hit prominence. Half of them have a real name. Half of them don't. So with the real names, at least you know, like, this is a 25-year-old who's just a fanboy of, you know, whatever, the left, the right, in between this pod, that pod, Joe Rogan, you know, Rachel Matt, or whatever it is.

The bias is clear. It's these anonymous accounts that are hitting scale that have thousands of other anonymous accounts. And this is where like the data at Reddit, the data at Twitter gives you an advantage in that it's consumer driven and it's fast and furious. But now you're going to have an incentive. And I've never heard anybody say this. So I'm sure floated here. there is a big incentive now for a foreign actor or spam accounts to come in and use 100 paid accounts. If I had 100 paid accounts, be on Twitter and Reddit and Hacker News.

And I start building these pseudonyms up and I start having conversations with myself on these platforms. Not only am I getting the value of influencing people in real time, I'm getting the value of influencing the language model. Now let's let that sink in. So a foreign actor, you know, you'd think about like, you know, somebody who has an axe to grind from the Middle East or an axe to grind from China or North Korea, whatever it is, and any political bias, you could send a hundred really smart people. You could have literally 100 people where, yeah, you could have 10 smart people getting paid $100,000 a year in a war room for a million dollars a year with 50 different accounts each, rotating them. them, you know, with a turret display, and then feeding the LLMs, their biases. And now they're going to in real time, because I think OpenAI has a real time deal with Reddit and Reddit has Reddit answers. Just using Reddit as an example, you know, are you going to be able to trust the

data on Reddit? I would trust it before LLMs existed, but I don't know if I'm going to trust it after LLMs existed. Well, and that's kind of almost like why you need LLMs, OJCal, right? Because you need LLMs to look at not just those hundred accounts, but maybe a thousand, right? And look at more, because that's the advantage of the era that we're going into is that you can have sort of a infinite number of parallel agents looking at the data and then aggregating it all, right? And then trying to get to the ground source of truth. I think it's like there are dozens of accounts that reach true influence on these platforms, I think, like it's low hundreds.

So I don't think it's that difficult to shape reality. To shape the message. Shape the message. Just look at Wikipedia. my Wikipedia page, other Wikipedia page, there is a small number of people who are really influential under 100 who are the super editors of Wikipedia

and some of them get paid covertly and by PR firms and there's like a whole griff going on there in the back end, just like the review systems on Amazon and other places. I don't know that these LLMs

are going to figure out who the bad actors are and who are great content creators who slowly introduce bias. It's a good opportunity for a startup though. imagine to do that, like, you know, to look at all that and analyze that and look at real news sources and that's an interesting idea.

All right. Okay, well, maybe we cut that out here and we make our own influence startup. Maybe we should leave this out and you and I should fund a startup to do covert intelligence operations for corporations and individuals. We could start our own little CIA. Interesting idea. Yeah. All right.

This has been another amazing episode of. this week. Fun to be back, J-Cal. It's great to have you back. Let's come back. I mean, I think every two weeks is the cadence that you need because you've got a lot going on. So I think maybe we could even be monthly for now, but you got to lock in because people love to get some deep madras take on everything. And you're on the inside.

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