SPEAKER_00: the the best way i heard it uh explained to me was when you're behind you open source when

SPEAKER_01: you're ahead you're closed and if you look at say um windows they had a monopoly on the desktop closed they don't need to be open source um but then you look at google very far behind uh they have a monopoly on search so when they talk about search their algorithm for search is closed you can't understand how pages are ranked but then you look at android they went

SPEAKER_02: open because they were so far behind ios uh and stuff like that so they went open source and they wanted to be on everybody's phone so i think facebook going is closed when it comes to their

SPEAKER_06: graphs and everything right facebook instagram they used to be open when all the companies like were created you know like zynga and all and then they close the graph up you know at some point when

SPEAKER_02: they had a lead and nobody can compete with them which is exactly what open ai did they became closed

SPEAKER_10: ai so fascinating this week in startups is brought to you by finn can't burn its mouth on hot pizza or wave at someone who wasn't waving at them finn can resolve half of your customer support tickets instantly before they reach your team meet finn a breakthrough ai bot by intercom ready to join your support team today visit intercom.com finn eight sleep good sleep is the ultimate game changer now you can add the pod cover to any mattress go to eightsleep.com twist to check out the pod cover and get 150 off at checkout and carta now lets you launch and administer spvs for your syndicate share your knowledge capital and network to launch your syndicate spvs through carta

SPEAKER_12: get 10 off your first spv with promo code twist hey everybody welcome to another episode of this

SPEAKER_01: week in startups and sunny madras here my guy sandeep madra from definitive intelligence if you don't

SPEAKER_14: know what that is i placed a little bet on that company like i was able to get a little little tiny sliver of sunny's cap table uh serial entrepreneur and one of the smartest most fun guys i know welcome

SPEAKER_16: back to the program sunny to be back good to be back very excited all right missed you guys last week

SPEAKER_02: i know i know and i wasn't on all in and these guys did a rogue podcast without me go on vacation

SPEAKER_19: crazy these guys are out of their minds um but it was pretty good did did did friedberg really do it

SPEAKER_01: in iMovie uh no i think he is no i think it was joking to an iMovie but he did edit it himself i mean that's why it's like a little bit janky but i mean if you record a zoom podcast and people have good microphone yeah you you know it takes a bit of the visuals weren't edited so you get that four by four frame

SPEAKER_22: which i hate yeah it's like a bad experience to watch people drinking coffee or like checking their email or you know pulling up the next topic um it's much better when it's a single or you sometimes go to it um but yeah i mean i was impressed it it uh you know having hundreds and thousands of edits from sax and everything like these guys really edit the pod so uh to make themselves like they they like focus on every little sentence and edit it in post like lunatics um i don't i just i'm just like whatever i said i said but these guys are like super precious and have their marketing and comms people like review everything for compliance which i understand like if you have funds you have to be careful but yeah they i mean we all agree to be off and then they're like at the last minute and i gave everybody producer nick and i tried to shut my company down that week and yeah i guess i gave everybody the week off you know like they get they deserve to get a vacation day um but yeah not a bad episode

SPEAKER_14: but we should talk about there was one thing on there about ai which was the drop in ai usage

SPEAKER_02: um which i would have liked to comment on because i do think you know that whatever 10 or 20 drop is

SPEAKER_14: notable um in some ways and not notable in others what's your take on that i mean obviously kids out of school is yeah i was gonna go there that's super interesting yeah so i think there's the kids out

SPEAKER_08: of school problem and then i do think um you know if we look back and it's kind of really timely um in the neck you know in the last 48 hours uh open ai just released code interpreter which you know we've demoed here early on because we had early access to it but they just released that to everyone and so i think it's there isn't there's a definitely you know part of this being impacted by where we are in the school cycle and and obviously that's going to impact things but i think more so uh having code interpreter available to all paid users is going to create a massive uplift for them um because that's

SPEAKER_38: you know we we've seen the functionality before we don't have to demo it again but it's been it's

SPEAKER_39: really powerful who maybe are hearing about chat gpt code interpreter what is that used for just one

SPEAKER_08: more time yeah so the code interpreter allows you to give some data usually in the form of a csv file into chat gpt and then have chat gpt help you with analyzing that data and you know the example that we did was like some output of electric car registration data and then we put we input it and we had we asked some questions we we had some charts created so it's a way of like having like your

SPEAKER_40: own personal data assistant around a smaller data set available to you um and that is now available

SPEAKER_02: code interpreter to the people who are paying the 20 bucks a month 20 bucks a month exactly how many

SPEAKER_46: people you think are paying the 20 bucks a month now i think it's a million two million yeah i would

SPEAKER_08: say it's yeah my guess would be somewhere between two and five million if you think about that uh it's

SPEAKER_03: going to blow past the new york times which is i think at 9.7 million last i checked they might be yeah

SPEAKER_01: they're about to pass 10 million yeah uh and that's not insignificant if 10 million people well 5 million that's 100 million a month yeah 100 million a month 1.2 billion a year in subscriptions and subscriptions are generally 100 profitable right yeah they do have some infrastructure costs but i think they could become if people are willing to keep paying the 20 bucks a month that'll be the real test yeah i also people don't understand that all web traffic youtube twitter facebook everything goes down a certain percentage during the summer because people are on vacation they go outside so um i that's the obvious thing i also think there are some people who and i guess this is the more interesting topic the press immediately went to oh it's waning uh people are less interested in it which is such a stupid take uh because obviously when a new technology comes out everybody tries it because there's no cost to trying it but you find your natural audience for your podcast for your

SPEAKER_57: software whatever so what are your thoughts on um that angle that some of the press were like oh it's

SPEAKER_08: a fad it's a fad it's crypto well you know like it's definitely not right i mean the use cases are there you know we've talked about this the origins of this podcast was in crypto and so look um the other thing that that it's hard to take into account until open ai starts publishing some type of data is many use cases have made their way into other products so where you may not have to go to openai.com and or you know chat you know chat gpt.openai um you may be using openai indirectly either through notion or through any number of products that now offer integrated experience with their apis right and their underlying llms and so i think um it's it's like almost like let's think about aws right when you know um aws initial customer was primarily amazon but then as they made it available to others you have to look at aws as you know from a revenue perspective not just as a you know sort of a end consumer site so i think i do think you know these numbers and the way to look at it until we get some data or like maybe someone publishes something around their api i don't think we have the full picture so i think it's i think people are just jumping to a story which is easier to do than saying because my guess is their api usage through all the startups and enterprises that are out there is you know increasing week over week at a pretty significant clip

SPEAKER_02: uh that is i think where the rubber meets the road when developers use an api that means yeah in some cases they are playing with it but in majority of cases i think there's some application that's going to hit consumers and they just don't see it and so you do not judge amazon web services or azure or google cloud by the number of consumers talking about it it's the number of developers talking about it and

SPEAKER_08: that is yeah and then ultimately their revenues you know those things report now separately and we can see you know what kind of huge impact that they've had on the you know top and bottom line of

SPEAKER_02: those companies well i mean the the growth of cloud computing was spectacular up until 2022 when it still was spectacular over 20 growth month or year over year but it did slow a little bit um i think because of belt tightening people during a recession or recessionary ish kind of thing we're in a in a down

SPEAKER_08: market in tech tech depression yeah and you know what it's like a good kind of tie into this topic like a lot of folks have been pushing to the cloud for years right and we've seen those phenomenal growth numbers the one thing that i think companies struggle with as they were moving to the cloud was the benefits that the cloud provides because if you were a legacy business right and you were running either something on-prem or maybe in you know your own um uh data centers um those things were probably really really efficient and moving to cloud doesn't immediately get you that efficiency where the efficiency starts to amplify now is when you want to start using like additional services

SPEAKER_40: whether the services came from you know the cloud providers themselves or like third-party services

SPEAKER_08: which requires your data to be in the cloud so i do think what we're going to see very shortly is a huge uplift in workloads in the cloud being driven by ai applications because that's the place you have to drive it and so i think that's something we'll see that will play out i think in the next you

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SPEAKER_39: your support team at a visit intercom.com finn i mean let's just get to demos that's why everybody's

SPEAKER_02: here we did a little couple of minute preamble we got we got on the same page here but i love the fact that you're obsessed with this like i am i have been doing a couple of projects myself uh over at inside.com i won't talk about them yet uh but one of the things i'm was doing i want to get some advice on is how to tag things by category you may have seen i'm trying to have instead of human editors tag the stories i was trying to see if i could get oh yeah gpt4 to tag the stories correctly yes and i need to get a prompt that really does a good job on that so i'll talk to you about that offline or if anybody listening i'm trying to find like a database of like the most important topics in the world that yeah like somebody has come to the conclusion that these are like the actual topics so google

SPEAKER_08: has one it seems for advertising because yeah the 2700 yeah 2700 verticals that kind of represent almost all kind of topics i'm trying to figure out where to get that list from exactly and if google

SPEAKER_36: i can send you a link yeah no they publish it i'll send it to you they publish it because they

SPEAKER_02: want people to be able to download and incorporate it into their websites exactly exactly yeah i'll send you the link okay first demo is coming up of course we'll sports guest us if you're listening

SPEAKER_01: um and not watching well go to go to youtube and type in this week in startups and go find the channel subscribe to the channel put the alert on because i'm going to be doing some breaking news

SPEAKER_14: alerts over the summer uh from time to time but go ahead and check that out uh okay okay all right so

SPEAKER_08: this is a fun one uh and actually quite useful i'll speak to this like you know when i was earlier in my career and i needed help and so i'm kind of creating a basic framework and i don't have an mba um when you're trying to you know basically understand other aspects of the business other than technical um you want to have some uh framework so this venturus ai uh you you come to their website and basically uh you can give it a topic and it will come up with either an advanced or simple the free one the free ones are the simple thing and it'll do a business analysis and so you know there's a great movie from the 80s called brewster's millions and i don't know if you remember brewster's millions one of the ideas that richard prior's character is pitched on is a guy wanting to sell

SPEAKER_98: ice that's breaking off of icebergs it's artisanal ice yes yes your artisanal glacier ice i mean i

SPEAKER_02: i'm crazy but i think i literally heard a pitch on people who wanted to get artisanal glacier ice to put in fancy cocktails i don't know if that was real or i imagined it or it was from brewster's

SPEAKER_40: millions but okay yeah and so basically here is it you know generated by me what's this website called

SPEAKER_48: venturus v-e-n-t-u-r-u-s-a-i venturus ai terrible name okay okay um i kind of liked it but uh insurance

SPEAKER_102: oh i like adventurous but adventurous okay i get it or maybe like a venture you know as well okay so you

SPEAKER_93: put your startup idea in as i want to start a company that sells ice uh that breaks off icebergs very

SPEAKER_08: simple prompt and it basically turned that into a business analysis and feedback so it gave me a brief description it it basically you know did that on its own then it did a swot analysis for me oh wow it subsequently did a pestle analysis right which is uh you know political economic sociological technological environmental legal right the target audience and user stories um you know business strategies business frameworks and you know i'll just i won't read off all things here but it basically gives you a solid framework for a business and in many ways jacal you know you guys do this at inside right when folks or launch i'd say right at launch when when folks show up with

SPEAKER_06: something uh i thought they did an incredible job and this was the free version there's five forces

SPEAKER_102: analysis this is what's really interesting about this is they went into subcategories or other other

SPEAKER_02: people's frameworks for analyzing a business one of those is port is five forces uh analysis i've heard

SPEAKER_112: of that before i've never actually used it um but can you maybe read some of those yeah sure so that's

SPEAKER_40: like uh number 13 here it's like so threats of new entrance moderate as barriers uh to entry include

SPEAKER_08: sourcing ice from iceberg established partnerships and brand reputation you know bargaining power of suppliers right like do you have some kind of edge there with the suppliers right bargaining power of the buyers like who's going to buy this uh you know threat of substitutes how easy will this be for

SPEAKER_114: someone to choose another party that's doing it uh intensity of competitive rivalry like how

SPEAKER_03: what does it say about this threat of substitute products i.e the ice machine in your refrigerator

SPEAKER_117: that's already there and you've already paid for it no the five forces so people know

SPEAKER_01: um i'm just reading from investopedia here competition in the industry potential of new entrants into the industry so the first two are about competition the power of suppliers the power

SPEAKER_02: of customers in other words um how much power do they have over uh you as the provider of these um and then threat of substitute products so a substitute product would be slightly different it would be something to cool drinks right um as opposed to ice itself that'd be a direct competitor right um yes correct super fascinating so essentially what this is doing is they have a series of prompts they run your idea through is what i'm guessing right is that what this is my guess is like sort

SPEAKER_06: of the rough structure behind this is yeah they take a prompt and what they do behind the scenes is

SPEAKER_40: they have a set of you know prompt templates that walk it walk your idea through each of these and

SPEAKER_06: they have 14 sections here and so they take the idea and then they work with an llm to create a brief description and then each of those 13 sections which i think is really powerful like i i i think

SPEAKER_102: it's this is great i mean this is like this would be a whole semester at a business school you would

SPEAKER_02: work on something like this yeah and now you can basically do an approximation of it who knows if

SPEAKER_03: it's actually of the quality of what you would get in a course right to you know do your first mock-up

SPEAKER_74: of the business yeah but it would literally whip you through multiple of these so i just did one yeah i said yeah pull it up let's see yours yeah let's see what mine does because this is actually

SPEAKER_02: something i'm thinking about doing so i'll make a little bit of an announcement here venture capitalist training school comprehensive analysis and feedback so you know i have angel university where i teach people to be angels and we've donated 200 000 to charity i've taught it 35 times i think and we've basically got maybe i think four or five thousand people have taken the course now and so we've created a lot of angel investors in the world but my idea is now that i'm going to have my uh venture studio my accelerator in san mateo i'm trying to find a nice garage or something put it in a big open space i was thinking of starting a competitor to kauffman fellows you know that program it's 80 000 for two years yeah so i want to create a kauffman fellows killer that would be half the price or maybe one year intense or six months intensive and maybe be 20k or something have 10 people come to each one and create a program where they basically get to draft off my deal flow and the core of it would be they would sit in on all the investment team meetings and do all the frontline meetings like a launch eir program like an eir program but yeah like an associates in training uh yeah ai instead of eir

SPEAKER_14: air associate in residence or you know ait so anyway here's the business idea the business idea is to establish a school that offers comprehensive training programs to individuals aspiring to be a venture capital so it took my my prompt by the way was venture uh what was where's my training oh yeah um yeah venture capital training school um the school aims to provide participants with the

SPEAKER_01: necessary knowledge skills and practical experience required to excel in a highly competitive field adventure capital so we'd edit all that it ad-libbed that which is yeah quite accurate um

SPEAKER_90: the venture capital industry has been witnessing significant growth in recent years that's true fueled by increasing startup activity true and continued interest in of investors in high potential early stage companies however there is a shortage of skilled venture capitalists that's true who can effectively identify a value in investment that's very true by establishing a dedicated school for venture capital training this business can tap into the demand for professional education is field and potentially bridge the skills gap this is true swot analysis strengths unique business idea with limited competition in the market tailored training programs can address specific gaps in the industry potential to establish strong industry partnerships for internships and job placements and

SPEAKER_06: jacob i can pause you for a second here you know some of these things also just a framework is good

SPEAKER_08: like if you're trying to do this like yes these the swap may not be fully right but it can get you thinking and get you going right as well yeah i think that's the key point is that um you as somebody

SPEAKER_02: who didn't go to business school and just to be able to know swot analysis strengths weaknesses

SPEAKER_24: opportunities and threats um i had to go take a look at that because i oh didn't really i know it's been so long i you know i don't go through that um limited market size venture capital training is in each

SPEAKER_122: field that's true this is weaknesses demanding and resource intensive curriculum requiring experience

SPEAKER_14: instructors um i don't think it has to be resource intensive but okay but maybe um no well you do need to have somebody like myself who's been doing it for a while you need to continuously adapt

SPEAKER_122: programs to incorporate changing industry that's actually very true huh threats highly competitive

SPEAKER_39: high competition for top talent from established venture capital firms that's not true

SPEAKER_149: yeah they're they're gonna i don't think it's a threat to our business okay right like if there's

SPEAKER_57: actually high competition for top talent from venture capital firms that's actually that's a benefit

SPEAKER_02: that's true yeah because then these people graduating would be uh rapidly evolving industry dynamics and

SPEAKER_90: regulatory changes nope the last one is made up economic downturns affecting investor confidence and startup funding availability that's it nailed it so it must have just said what are the threats to a business

SPEAKER_01: that did this yes wow this is incredible and i see i've never even heard of a pastel

SPEAKER_161: analysis yeah yeah you didn't spend enough time in corporate america i did not political economic

SPEAKER_14: social cultural technological environmental and legal when you were what was the name of your

SPEAKER_162: consulting firm called um extreme labs extreme labs so when you did extreme labs when you had customers

SPEAKER_02: they would pay you massive amounts of money to write this kind of stuff up and include your analysis

SPEAKER_64: or they did it themselves they were doing this themselves like we were more of like on the

SPEAKER_08: development side right so they would come to you with this stuff yeah but as our business expanded we were doing more product management and product incubation then we would do these type of things

SPEAKER_162: got it yeah oh my lord suitable business strategy it's just yeah this is incredible what a great this is

SPEAKER_05: uh this is the free version they have an advanced i i didn't get to try the advanced version which

SPEAKER_172: they don't allow for free you can do 10 of these for free the advanced version is maybe something you

SPEAKER_102: should try for your business idea and it lets you make the report visibility public or yeah private i just put it on public so people could go see it with books to guide you along the way venture deals

SPEAKER_176: the lean startup angel how to invest in startups timeless advice wow i gotta pick that one up who's this

SPEAKER_178: who's the author this book offers a first-hand perspective on how to identify evaluate investment

SPEAKER_180: problems a technology store it's making highly relevant to your business idea who's the author

SPEAKER_20: oh jason gallaghan is that's pretty funny um you can download it export to a google doc wow what a

SPEAKER_14: great service so shout out to whoever made this yeah venturous ai congratulations and let me see the

SPEAKER_182: pricing here let's go on the pricing tab starter free yeah 10 standard reports a month got it

SPEAKER_98: pro 20 bucks a month uh 40 standard reports this is great um i could see doing this with um and you

SPEAKER_06: could you know every startup yeah yeah no i was gonna say you could use it as like you could do this as part of you know the university and all the things you're doing yeah yeah i mean what's interesting

SPEAKER_39: about this oh it says you own the commercial report rights that's interesting um what i like about this is i could do this if they had an api every time we meet with a company we put in our database we have a summary of that business so what i've been thinking about access right there look at that

SPEAKER_14: what's that right yeah contact us for api access perfect yeah what i've been doing now is um in

SPEAKER_01: preparation for the great ai overhaul of our industry is we have a programs team call every day where you know the people who run founder university launch accelerator get together for a 30-minute stand-up and then we have two investment team meetings for two hours each twice a week tuesday and

SPEAKER_02: thursday because we process 60 new companies well we do 60 new meetings per week intro meetings and so i'm gathering all of those and i am recording the zooms now storing the zooms transcribing because zoom does transcripts automatically putting the transcripts into notion and then i'm summarizing i think we're using the notion api or we're using chat gpt4 i'm not sure which one just summarize the

SPEAKER_122: call transcripts for our internal meetings summer is reaching its apex and there is nothing worse than tossing and turning and sweating in the night because of all that summery heat don't i know it

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SPEAKER_195: the eu as well as australia way to go eight sleep what should i do with that data eventually

SPEAKER_36: well it's a good segue jacal um i think uh let me show you this moderator yeah exactly

SPEAKER_201: um let me get my c3po uh moderate your podcast yeah so similar to um you know kind of what we

SPEAKER_08: um are talking about here what a team launched is a chat app specific to the hacker news hacker news and what they've done is they've taken all uh what's going on in hacker news one size or bigger

SPEAKER_206: one size bigger oh yeah sure yeah we can up that a bit welcome to chat hn so this is chat hn dot

SPEAKER_08: versell dot app so versell explain yeah so versell is actually crushing the game we should spend a little bit time giving them a shout out they are a modern hosting um uh like an app hosting uh service and they've uh been doing this for a while but in the ai game they're the go-to so if you want to host any type of ai app most of the frameworks that people are using are designed for like sort of one click deployment into versell and they they do a great job they have very flexible plans and so they're like sort of the modern up-and-comer in the in the cloud war i mean you know they're more than up-and-comer now but they've been really crushing it and so uh and they have a bunch of their own additional frameworks uh like in this case they have an ai sdk to help you with you know sort of the chat and all those other things that they're doing a really good job of as well so big shout out to

SPEAKER_20: the versell team here um and so uh you get this prompt give me the top five stories on hacker news

SPEAKER_01: in markdown table format seems like doing tables and formats is like a great use for ai you click that

SPEAKER_02: and it gives you the title link score and comment score is something they use on hacker news to kind of give it um you know how uh how popular something is and it gives you the first

SPEAKER_98: uh it gives you the top five and then it says hey send me a message um so i guess we could ask it uh what's the give us the top give us the five funniest comments on the first story let's see if that does

SPEAKER_08: anything uh so it's not enough amusing comments to provide the complete guide list yeah oh interesting

SPEAKER_14: uh how about give us the most um how about this oh what is the sentiment on threads

SPEAKER_02: interesting um so this is a proprietary data set

SPEAKER_39: and uh just gives you a new interface for how to process all that information and this is um the work of an analyst so when i see this work i don't know what you see i see a 40 an hour person

SPEAKER_02: and if you ever want to uh when i talk about hourly wages um i always extrapolate as a business

SPEAKER_01: owner and somebody who invests in businesses as the hour of what an hour of work costs and then you just times that by 2 000 right because it's 50 hours 50 weeks a year 40 hours a week 2 000. you know listen if you work 50 hours a week it's 2500 or 60 hours a week it's 3 000 but 2 000 is a pretty good multiplier 40 bucks an hour 2 000 hours 80 000 a year it's a really good paying job especially from home and that's what an analyst would get a researcher would get 20 an hour and a data processing person like an offshore kind of person would get five to ten in manila so five times two thousand is ten

SPEAKER_02: thousand dollars 20 times two thousand hours is 40 and 40 times two thousand is 80 just so you get

SPEAKER_01: an idea eighty thousand dollars is a u.s you know smart person who reads books you know um english as

SPEAKER_14: a native language a researcher is somebody just out of school or maybe went to a two-year and then offshore they don't understand the context in america probably and it's english as a second language in many cases so whose job do you think this replaces most yeah and because we keep seeing this and i say the researcher analyst and the data formatting person keeps coming up yeah um so let me let me

SPEAKER_40: let me answer your question a little bit indirectly which is so first i think given large proprietary

SPEAKER_08: data set you can see the value of putting a chat interface on top of it right and so i think for you guys what you need to do as the next step is you have all this proprietary data now which some of it's even being created by ai or enhanced by ai we need to stick a chat interface in front of it so that should be sort of one of our projects so that you can go through that and ask a general question say hey have there been any other companies that have come through that have pitched us on selling ice from icebergs and then it can go through that data and then you can get that answer quickly and you can see you know what was the call about and things like you can kind of dive into it so i think that's the that's the first thing we're trying to show there is that the world of deploying your own chat app on your own data is really simplistic now um and i think you know it's something that we should you know explore for yeah for you know for launch and so we should kind of kick that off yeah um i think the value is you know in one sense in a replacement but i think it's the enhancement i think if you put this in front of everyone and ask yourself how many times jay car are you like are you how much are

SPEAKER_40: you relying on your memory to go back oh there was a company that did that or you know what where

SPEAKER_08: did these guys end up got it and and now to basically have that enhancement is more valuable from a rather than a replacement of a person or an analyst but to basically kind of give yourself

SPEAKER_84: that superpower i think that's where it's it's a much better framework for value yeah so let me

SPEAKER_01: explain this um to folks because i think you just hit a key insight when you're running a business

SPEAKER_02: an at scale business like ours 15 000 people emailing us and filling out our form and sending

SPEAKER_78: us a pitch for a company you know over a thousand people coming to founder university a year we have

SPEAKER_01: so much data and we have 19 people in our little investment team our little company now imagine with

SPEAKER_02: those 19 people we then do an analysis we do that analysis and if we do that analysis of what we're

SPEAKER_20: doing i would normally go to somebody on my team and say hey we met with that company they were doing a marketplace of diamonds but we had heard two other pitches about diamonds the one who was doing the fake diamonds and another one who was doing you know setting your diamonds it was using ar i can't

SPEAKER_01: remember any of them now normally you just search for diamonds and then you get all this cruft diamond in the rough somebody would be saying like oh this person's a diamond here you could say tell me all the startups that are working in the diamond industry that we've met list over the last five years and pull clips from their video because we have the video now and make me a a little dossier

SPEAKER_20: of that yes or show me all the marketplace companies we met myth last year yes then go on the

SPEAKER_01: web and tell me uh if they how many employees they have on linkedin and this is where linkedin has to start in paid api i don't know this is you know twitter and reddit having their apis i think the linkedin api for linkedin team please let us just give you money because everybody's scraping your data anyway and we can buy your data from like israeli or you know companies in the philippines have scraped all of linkedin already they have it all and you can't stop the scrapers and it's legal in other countries to scrape this stuff so your terms of service means nothing and those are the jurisdictions so now you're left with going with gray hat people for data sources and i'd like to put this um on you for next week if you could find some gray database sources of like instagram facebook profiles whatever

SPEAKER_90: i'm curious about the gray market underground so anybody has information send it to producers at this weekend startups.com or uh sunny what's your twitter sunday at sunday that's what i think is going to be super interesting is um some of this gray market data but imagine if i could ping the actual api and it would come back and tell me hey this company has 60 employees when you met with them

SPEAKER_02: they had 20 and i would pay for that i would pay some amount for database calls it could be an incredible revenue stream for linkedin and i get pinged by people or like would you like a billion linkedin reference you know database records of ctos of founders like this stuff's all been scraped already so don't be precious but yeah all of that back and forth in meetings and research somebody i'll get back to you in an hour yeah it's gonna be like i didn't even need to waste somebody's time so jake i

SPEAKER_06: know you've been on this for a bit and one new segment i wanted to start and so we're not we're not fully up and running with this yet but we have the framework and i think we're going to start

SPEAKER_08: building this out we're going to start building this out over the next couple of episodes and so so you've been going on about auto gpts and so what i have here is a basic framework of an auto gpt that i've created i've just given it for the audience who's catching up so yeah an auto gpt is uh an agent uh that uses llms to work through a problem through what's called like a chain of thought so you'll give it like a high level state problem statement and then it will come up with a set of tasks to solve that on its own and then it will use its access to different sub agents it has to solve that task and so um this is you know in a from a demo sense and jake i'm going to add you actually to this uh to this replit so you can basically participate in the in you know kind of in the live uh you know kind of the live experience that we're going to have and um and so like i'm going to just start with this this isn't with linkedin and you'll just see

SPEAKER_241: the power of what we can do here so you can say can you come up with a schedule of summer league

SPEAKER_08: now let's let's say let's call it nba summer league games for me to watch and so what this is going to do is so i just gave it something generic and it's going to say here really quickly well i don't i need

SPEAKER_40: to start by searching for summer league games and then it's going to figure out okay i found a place to get it so i need to analyze those results and then and it found that it can get it from this mba

SPEAKER_05: page right and then it'll start working to put it into now this is just it's a basic framework we're

SPEAKER_139: not like it's not fully what are we using here again one more time what is this called so so this is

SPEAKER_40: basically something we've built from scratch but it's using two main uh it's it's we're running it

SPEAKER_08: in replet so we want to give them a shout out uh it's using language yep and we're using lang chain which is sort of like a um language model by facebook no no no no it's not like we're using open ai that's um that's the other one yeah yeah lang chain is a uh scaffolding framework for working with llms yes right and we're using another uh uh another server called serp api which is basically

SPEAKER_40: a search engine results api and so it basically interacts with google uh and so that's what the two

SPEAKER_57: keys use here we have an opening of that or it's just how does it actually do that it has your machine

SPEAKER_06: do the search and then rips the html page no no no no there there there there are hold uh companies uh that exist for this now right and so um that so so this is a company called serp api wow i've

SPEAKER_08: never heard of this i'm learning yeah and so they exist uh you have obviously you have paid use cases with them and they basically you give it a search and they'll return you back like the search engine results in a in a consumable format which is like sort of on the right hand side what i'm

SPEAKER_257: describing here like as json rp api.com which is search engine result page yeah wow you're very

SPEAKER_08: familiar with this jcal yeah sure of course yeah yeah and there's a few of these but this is the one that i've decided to use in this particular demo here and uh and then and that's that's how this auto gpt which you know i gave it like can you come up with lists of summer league games obviously this is not it doesn't have the 2021 limitation it uses a serp api to figure out well that result's going to come from this nba.com page summer league schedule and then it will start to kind of parse through that that document to come up with your list and so this is sort of the the beginnings of

SPEAKER_40: our auto gpt and we're going to start working through this but so i wanted to kick this off today for us

SPEAKER_20: right yeah um so how do you propose what's the next step in this well the next step is like you you've

SPEAKER_40: had a bunch of different use cases right you rattled one off today but let's let's just build

SPEAKER_08: towards now that we have the basic scaffolding and and you know we're going to get you back to

SPEAKER_06: writing code again jcal we'll basically yeah so and you can see this thing is not very long it's only like 60 lines of code that's the beauty here and you know because all of that is being abstracted

SPEAKER_98: by api calls so exactly exactly these days writing code is really like hitting 20 different apis and

SPEAKER_265: blending whatever you get back right i mean it's really fascinating how it's really fast code has

SPEAKER_40: changed yeah and honestly like in this particular case like this the the code to use that serp service

SPEAKER_08: is right here right this serp api wrapper and then this is my api key that i have with them in the example you were talking about with linkedin if linkedin would want to work with us they would

SPEAKER_40: offer a key that we would pay for we would import their agent and then we have another agent here that wasn't searched but that was like linkedin that we would go and get that information from

SPEAKER_05: but there's plenty of other services out there linkedin isn't there yet but that's how we're

SPEAKER_178: going to get back into it all right so here's what i would like to do i want to create one of these founders okay i'm going to we're going to run this up the flight well that finds new startups

SPEAKER_01: that aren't in our database already okay um and then finds the founders puts them into a category does some sort of analysis of their business right like finds out what the startup does sends it to that other api from the people who do uh venture what's it called venture interest interest right so we find a startup somewhere that didn't exist before so an announcement of a new startup that could happen on hacker news reddit uh twitter linkedin people could announce a new startup so we find announcing my new startup date being today so it was published in the last 10 days let's say so in the last 10 days somebody publishes i'm announcing my new startup and we could do that with the search engine result page by passing a query to it so we could say

SPEAKER_90: google uh you can do this in a chat window or i could do it in one it would say to google new startup

SPEAKER_00: announcement and then you would go to advanced search advanced search yeah no no where is it uh you go to tools and then i would pick uh not anytime but i would say in the past week it would come up

SPEAKER_01: with a search result which is crunchbase eu startups alley watch yeah um and we would try to find on those sites new startups and find the url of the startup once we have the url of the startup then we could find their linkedin profile page we can find their twitter profile and then try to get some information

SPEAKER_90: on that startup and then propose them to a researcher analyst inside our team and then click book meeting

SPEAKER_01: that would be amazing that would because by the way we do that we call it qualified hunting

SPEAKER_02: uh we look for we try to have our researchers and analysts hunt for companies that never apply to our programs and so that's hunting and you know product hunt speaking of hunting product hunt has like

SPEAKER_78: every day new products coming in there so we just look at the urls of every new product on product hunt which is why i think angel has bought it um it's because they wanted to have first dibs on all the

SPEAKER_98: new startups and technology so very cool all right listen you're in the technology industry you know what card is card is the leading venture capital and equity management platform what does that mean that means they manage your cap table they manage your venture fund they are the experts at that and they do such an amazing job all of my startups use it they all love it but i've got huge news to share

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SPEAKER_98: right that's what i've done i have a syndicate when i have a great investment like say com.com i put a little bit of money in 50k in com and then 328 000 came in with the syndicate you can then go build

SPEAKER_204: your own spvs and use carda as your partner they are one of the most trusted names in business you know that and they're used by more than 4 500 funds that represent over 120 billion dollars in assets under administration and they will support you on every stage of the fundraising journey from your first syndicate to building your global venture capital firm you can raise and deploy from anywhere because carta offers us and international spvs and they fund in different domiciles that means different locations right fancy word for uh locations geos carta provides this automated back office solution so you can focus on what's important building and finding great startups building these amazing relationships investing in great startups so it's a very simple call to action i want you to go to carta.com c-a-r-t-a.com you know that but use the code twist to get 10 off your first spv that's carta.com and use the code twist yeah so that's our i think that's our little project so we'll

SPEAKER_06: take that and we'll kind of expand it out and and we'll kind of work through it and we'll show the

SPEAKER_90: results every every uh every episode you know i'm very interested in facebook's approach to ai

SPEAKER_01: uh there was this they have their own language model that was leaked quote unquote llama llama and uh it's very popular on hugging face and other places so llama they claim was accidentally

SPEAKER_02: leaked by facebook itself um so that it would kind of uh undercut google bard and proprietary stuff like closed ai um chat gpt4 who knows if that's true but they keep putting out public stuff so they're still on the public releasing of information open source tip correct or are they now circling the wagons and

SPEAKER_05: being closed they've been very open in public um you know if there's things that they haven't released

SPEAKER_08: yet they've just said they're gonna release them they've there's one that they did recently we can pull it up um as well but i think you know this is probably a better segue into threads and you know i actually was having this thought over the weekend which is you know is threads perhaps a way to get the data set for a hive mind because if you look at any of their existing products they've all evolved right um you know instagram is yeah oh yeah there we go and um you know instagram is not going to give you sort of hive mind because it's going to give you video and pictures right yeah um whatsapp if you mind that data it's going to give you proprietary chats and facebook has just evolved into something i don't

SPEAKER_90: really understand anymore maybe you can you can chime in there if you want birth announcements and bar mitzvahs and retirement parties you know kid photos it's basically for moms and dads and grandma i use

SPEAKER_05: it like i use it like our yahoo groups there's some great groups i'm in there yeah um but yes the group's

SPEAKER_211: product is um very subtly i think put a lot of new life into that because the general feed is kind of

SPEAKER_02: like boring and repetitive oh it's your birthday happy birthday literally it's a birthday announcement website uh or a birth announcement website even um so why let me ask you one question here and we'll get back to threads face i have my own theories but i'm curious of yours opening i went closed google

SPEAKER_01: published all this stuff including tensor and kind of regrets it i think now they're kind of transformers and everything so they're closed um or somewhat closed and they're not doing they're not announcing the papers anymore is what i heard the scientists are not announcing their work as

SPEAKER_08: often no i i would say that's not fully true you know they just launched a paper that we're leveraging like around sql palm just a few weeks ago so i think they're they're continuing to do that you know their approach from a cloud perspective google cloud perspective has been hey we're gonna have our own proprietary models and we'll also host open models and models from other proprietary companies as well so if you are a google cloud user you can use their palm models which are their proprietary ones they also support all the open models inside of um their um uh you know inside of their frameworks right got it and uh also they have models from companies like anthropic right and so

SPEAKER_253: i feel like they have a really kind of a kind of a great why do some people choose open and some

SPEAKER_08: people choose close um sort of the you know the the natural arc of the tech industry right like when operating systems first started they were all closed right and then quickly we had an evolution to more open source linux or unix based operating systems right you know bsd and then linux and so

SPEAKER_40: i think people try to build now there's always these these conflicting forces i'll speak from a developer standpoint right when something is closed and owned by a company it can move very very fast because when it's open source you have to work your way through the community now the way companies have got around this let's talk about say red hat and linux is that they represented almost like 90 of all the folks that had the control on the project that were the developers and so big companies can be can embrace open source and not get stuck in sort of some of the politics that emerge inside as well um and then there's the reason that you know you think you have a lead and you want to

SPEAKER_172: basically create a moat for yourself and so those are the main reasons that generally pop up the best

SPEAKER_00: way i heard it uh explained to me was when you're behind you open source when you're ahead you're

SPEAKER_01: closed and if you look at say um windows they had a monopoly on the desktop closed they don't need to be open source but then you look at google very far behind they have a monopoly on search so when they talk about search their algorithm for search is closed you can't understand how pages are ranked

SPEAKER_02: but then you look at android they went open because they were so far behind ios uh and stuff like that so they went open source and they wanted to be on everybody's phone so i think facebook going is

SPEAKER_03: closed when it comes to their graphs and everything right facebook instagram they used they used to be

SPEAKER_06: open when all the companies like were created you know like zynga and all and then they close the graph up you know at some point yeah when they had a lead and nobody can compete with them which is exactly

SPEAKER_02: what open ai did they became closed ai so fascinating um yeah so on threads what are your i i logged in i

SPEAKER_211: immediately got dunked on because they're like oh friend of elon is on that's uh so i like literally did like two yeah i did like two posts to it just like hello world and then i was like oh my god sucks such a little copycat and then zuck winds up replying to me oh okay yeah and he put concerning concerning uh with a wink you know elon will just give a one word uh reply yeah like

SPEAKER_01: interesting uh so not not only is zuckerberg copying twitter now um which in fairness is

SPEAKER_02: copying jack more than copying elon right yeah but he's obviously still obsessed with elon as well and twitter he's i mean zuck's been obsessed with twitter from the beginning he was going to buy it and he's got a lot of comments but he actually took on elon's replying with one word replies which i thought

SPEAKER_70: was hilarious um super elon thing to do huge engagement with like people right like well

SPEAKER_01: no now he's like yeah i'm going to engage to get my social it's like okay yeah we know that like and then he's like i'm going to be outrageous i'm going to fight elon in the octagon yeah so zuckerberg

SPEAKER_02: is clearly like obsessed with elon and you know copying him um yeah just like he was with snapchat for a while and well before that i guess instagram which he bought he was obsessed with that for a little while um but i think it's a perfectly serviceable copy of twitter they obviously rushed it because it's feature light it doesn't have a lot of the features um but it's a different graph i found it

SPEAKER_39: was like a lot of people who don't have interesting things to say trying to say things with words instead

SPEAKER_05: of pictures does that make sense you you nailed it like that's the issue right is that look i think

SPEAKER_08: instagram has a huge community i think they have a ton of engagement probably you know maybe orders of magnitude more than than twitter does but it's for a different purpose yes um and it's kind of focused around video and photos yeah um many a times those videos and photos are not a real

SPEAKER_40: representation of what's happening it's sort of like hey look at me and then on it yeah they're staged

SPEAKER_08: right pretty highly produced yeah highly produced and so um and look people want that and i think it's it's great and i think subsequently the graph you create when you have an instagram account is around that as well right you you're you've decided to curate something there and i think when you go to twitter whether you have um you know your list of folks that you follow or like a for you feed it's a completely different input into the system right which is you know no one on instagram is

SPEAKER_38: sharing archive papers right you know from from the these research papers right no but

SPEAKER_187: or like debating the subtle points of the ukraine war yeah of russia's invasion uh china

SPEAKER_40: taiwan is not a big topic or yeah and you know it's so funny like uh what i i kind of posted this

SPEAKER_08: thing um which was like oh like for like 24 hours it felt like all the threads were disappearing on twitter like how to you know do the you know make money from ai or you know you see all they were

SPEAKER_103: gone for about 24 hours and then they all came back because that again the vice versa that community there doesn't want to read that stuff right they don't want to read the what's the latest in ai and let me see the last five cool ai tools that were launched and how can i make money using them

SPEAKER_255: this is the key thing uh that for founders who are listening this is the key thing you have to

SPEAKER_02: understand about making a clone if you make a clone of another product and that product is doing a good job and it's like it's servicing its purpose and it was the first it's the at scale one i wouldn't say the first you have to be so much better and it might be that the team that built that product has already gotten it to so much better that the users can't tell the difference and so if you look at threads versus um twitter there's nothing there that's different or better

SPEAKER_39: and in fact obviously they're still playing catch up until they have something that's dramatically better for some reason it's going to have a moderate success right it's um the same thing held true for

SPEAKER_02: search engines for a long time people just kept making search engines until page rank came out and the results were noticeably better like 10 times better yeah there was no reason to go over to

SPEAKER_01: google yahoo got you or lycos or excite excite yeah they all got you uh altavista magellan these

SPEAKER_14: things all got you to a similar result you're typing pepsi or coke and it would get you the peppier croak

SPEAKER_01: website yeah but when you typed in pepsi versus coke and it got you to some scholarly article about the pepsi challenge you're like oh wow this is interesting yeah it's better um which is why chat gpt feels so uniquely different so there has to be something uniquely different for people to change i've learned

SPEAKER_70: this the hard way many times uh building products yeah now look the one edge where you know instagram

SPEAKER_08: threads you know meta will have is brands because a lot of you know when i look at accounts like it's a mix of things you follow and if you follow you know like kind of larger brands for a reason then you know i think instagram is starting to surface that a little bit better and at least from my understanding from a few different folks i spoke to is they went and targeted folks and i don't mean brands just by companies but brands that are people as well because you know they had mr beast on there

SPEAKER_01: and other folks and so did they pay for him to give away a tesla that was like a very specific troll

SPEAKER_08: yeah i'm i'm not sure what the the like the economic arrangement was gave him a million dollars or something to start posting over there or paid for the giveaway well i i definitely heard that from someone you know is is reliable to say like they definitely had a pretty uh it started actually

SPEAKER_40: when the whole thing happened with the subscriber ticks uh the um the verified ticks verify was the start yeah they copied the verified check box no no no it's not saying when when faith sorry when twitter started no when twitter started to change the policy around yes that's when they started going after the high profile folks got it smart yeah yeah because they they saw that as like sort of uh or misstep yeah it's an attack vector yeah and so that's where they kind of went after these brands

SPEAKER_08: right and and so um now the challenge is is like so mr beast is the most interesting on youtube of all places and yeah his twitter and his you know threads are sort of okay yeah i don't know where

SPEAKER_40: to go i also think this yeah yeah they're secondary the struggle also is and you know people start saying this thing well you'll just post in both places but if you're trying to maintain a conversation and you're trying to very hard kind of it's very hard to do that across both platforms now and so i think that's that's going to be the challenge and what will end up emerging and i think um i think

SPEAKER_08: we had it in our show notes i think adam had a really good post saying look i just think we're they themselves are saying i think we're just going to become two different things i don't think they're trying to yeah here we go right and so why don't you uh read this out jake house um the goal

SPEAKER_03: isn't to replace twitter the goal is to create a public square for communities on instagram that never

SPEAKER_01: really embrace twitter and for communities on twitter and other platforms that are interested in a less angry place for conversations but not all of twitter politics and hard news are inevitably going to show up on threads they have and on instagram as well to an extent but we're not going to do anything to

SPEAKER_90: encourage those verticals so he's basically telling journalists we really don't want you on here talking about ukraine talking about ai threats yeah because that debbie downer news is not where

SPEAKER_211: advertisers want to be so this is another i guess if you were going to make an if you're going to list all the attack vectors for twitter even pre-elon owning it um the fact is this highly intelligent

SPEAKER_02: people debating very controversial subjects gender woke politics politics wars geopolitics technological

SPEAKER_01: edge cases and dark stuff advertisers don't always want to be next to that some don't mind they just want audience but other ones might be very brand conscious right so that's another attack vector

SPEAKER_211: but i'm surprised they didn't embrace journalists because journalists are tend to be on the woke side

SPEAKER_02: of things there may not be fans of elon and they probably feel very bad about losing their blue they i know they felt very bad about the blue check mark thing i mean kara swisher talks about and

SPEAKER_90: professor cole takes like they talk about elon incessantly on twitter yeah and they've invested in

SPEAKER_211: post news or something like the competitor and i'm like why are you guys on twitter if you're investors

SPEAKER_08: in post to go to post news news post well and also metas had this like um battle with news organizations yes like the big thing happened in canada recently in australia right as well very good yeah right where um you know that they have this challenging relationship with news organizations now which is

SPEAKER_57: i think news organizations want to get paid and zuckerberg does not like to share revenue

SPEAKER_01: so i have a prediction here you know what i think let's hear it well um i think this is a fight that zuckerberg does not want to lose

SPEAKER_02: so i don't think like remember he came up with like poke to attack snapchat and he yeah he did it like initially he did four or five competitors to snapchat before just saying you know what screw it put it in instagram i give up i'm not going to make a standalone thing i have a feeling that threads will become a feature inside of instagram and they'll just be like threads next to photos just like they've like a tab kind of a situation because i think as a second app it doesn't work yeah i would rather have a single app like instagram i don't want to give him like the road map here but i think having a tab with threads without images so images are not allowed in it yeah it's you know you can't attach an image you can only do text that would be a better place for it to live and then you get a hundred i don't have to rebuild the graph like i literally did not check follow everybody because i don't yeah i didn't want to do that and send a bunch of alerts out i accidentally did it and it was like it's been a mess well yeah so i was like i'm not just gonna follow everybody i'll restart my graph and then i was like i do not feel like playing the rebuild my social graph for the 50th time in my life i don't know how many times i don't know the last time i had oh clubhouse was the last time i started rebuilding i was like this is just not worth it i think the audience is exhausted with that but

SPEAKER_01: i do think he's i i do like your angle of this is how to get a data set so if if they did this and it didn't make any money for them but they did get like more text and discussions uh that they

SPEAKER_220: should use and and topics of today's like what are people talking about that we can use for an llm

SPEAKER_08: because remember right that's what this is all going to go to is having these llms to help people and decision making and things like that and i think this is a great way to get one of those data

SPEAKER_182: sets this is why google should have not given up on doing social they should have kept doing it they

SPEAKER_02: did google buzz which was an extraordinary social network before google plus which was actually very well designed yeah um but when they did google buzz they had another one with a weird they had another one with a weird game too or one they did in south america during 20 time when you could just release

SPEAKER_78: products yeah like you're on your fridays at google there's something called 20 time where larry and

SPEAKER_90: sergey let you work on whatever you want on fridays pretty cool idea but what google buzz did was and maybe producer nick you can go find google buzz screenshots and i wrote a blog post about this holy cow

SPEAKER_01: google buzz is gonna like kill facebook now of course it's easy to dunk on me but they gave up on it for privacy reasons which they shouldn't have what you see here um when you look at google buzz and this is your inbox right gmail and then right under it was buzz and it told you how your update so you get in there

SPEAKER_02: and you get like a twitter or a facebook box hey what are you doing you type into it and then you see everybody else's it was brilliant it lived in your gmail box google should go back to this um because you can write an email or you can just give an update inside of your email and it's right there it lived in a perfect spot google gave up too early sometimes it takes five or six swings to get something right google did three swings orchid buzz google plus if they had done the fourth and the fifth swing i believe they

SPEAKER_39: would have built um a coexister maybe not one that beat it and then do you have my blog post where i uh i wrote about this uh google bug is brilliant like groundbreaking game changing brilliant this is when uh business insider used to ask every 2010 this 2010 google bus one point bow was better than facebook

SPEAKER_122: after six or seven years true statement facebook's history is one filled with stealing other people's innovations i wrote this in 2010 13 years ago and doing them better i zuckerberg has stolen every idea evan williams and the twitter team have released how ironic that now google has out

SPEAKER_39: facebook facebook 3 google has an excellent privacy record and facebook is a disaster most folks do not trust zuckerberg and facebook because of their privacy record it's pretty crazy google buzz auto generate your network this is much better process than facebook's google buzz is way faster than the sluggish facebook this is a huge advantage google buzz puts relies and updates into your gmail as threads

SPEAKER_01: this is brilliant and a huge advantage anyway my assessment was perfect they just gave up they turned off google buzz because they got too many privacy complaints and this is what happens to

SPEAKER_90: a big company oh look at this they wrote you can sign up for jason's excellent email here that's hilarious

SPEAKER_391: um anyway they have folks i and so i think if threads just keeps going and zuckerberg

SPEAKER_06: well is really good it's even and so social like no one knew at least you know obviously back then the end game now is for the world's best llm which will be sort of the underlying api for everything and it you know circles back around to what twitter's real value means in this ecosystem today versus

SPEAKER_08: everyone else especially with all the work that's happened around you know scraping and turning off

SPEAKER_96: scraping and uh monetization from scraping i think it's really really fascinating uh all right any lightning

SPEAKER_90: round stuff you want to go through here all right i had a couple of other items of docket or we can

SPEAKER_40: leave them for next week the other story that i thought was really good was the inflection ai

SPEAKER_396: oh yeah if you want this yeah if you want to get into that a huge number of gpus right

SPEAKER_40: yeah i saw this go by yeah yeah yeah so uh inflection ai it started by uh co-founder of deep

SPEAKER_08: mind mustafa and reid hoffman and um you know they raised a massive amount of money i think uh 1.3 billion dollars on a significant valuation maybe four billion dollars um a couple of interesting things here right the you know list of investors like microsoft nvidia reid bill gates eric schmidt

SPEAKER_98: wait a second so wait bill gates and microsoft are mortal enemies with eric schmidt and google

SPEAKER_90: and microsoft and bill gates are massive investor or microsoft's massive investors in open ai so they are obviously hedging their bets here why not invest in two language models

SPEAKER_64: two better than one is that what i'm seeing here yeah and this one's even more interesting because

SPEAKER_08: alongside of the language model bet this one is a significant hardware spend right and i believe you know they uh kind of are planning to have something like 20 000 h 100s available in a cluster

SPEAKER_406: which these are nvidia's uh ai computers yeah cards whatever you call them exactly exactly they're

SPEAKER_08: yeah they're ai you know system on a chip or something yeah more than that and so um yeah

SPEAKER_211: they're going to spend hundreds of millions of dollars building that cluster correct and now is

SPEAKER_06: maybe even close to a billion i think if you do the numbers like the majority of that 1.3 billion will

SPEAKER_90: be consumed by the creation of that cluster wow because those cost how much now 150 000 or something 40 000 i think i think retail like no 20 000 oh 20 000 okay so a thousand would be um 20 million 10 000 would be 200 million 20 000 would be 400 million so 400 million as a just a a floor number yeah yeah

SPEAKER_172: and you know you gotta add on other things that's just the and you have to wrap them somewhere so

SPEAKER_01: wait a second why why isn't this part of azure why don't they just i wonder if they're buying them and putting them in azure's cloud or if they're buying them and building their own cloud location

SPEAKER_08: facility why do microsoft's part of a deal that involves you know some kind of cloud infrastructure it's usually part of their you know trade is that hey you you're you know you're going to use our infrastructure in some way shape or form right and they've been really good at that so that that could

SPEAKER_02: have been part of the deal but we don't know i wonder if that would fall into round tripping

SPEAKER_01: uh you know where these deals could greatly enhance microsoft's and nvidia's these kind of deals could optically make nvidia stock and microsoft stock look more valuable and the value of the stock would go up because you just got a 400 million dollar order remember they said they were gonna

SPEAKER_103: actually in the notes here it's it's 40k so it's actually 740 million so 700 so 800 million dollars

SPEAKER_14: comes in right or something like that in new orders that's going to make nvidia stock a way up so let's

SPEAKER_39: say nvidia gave them 500 million dollar let's say of the 1.3 billion nvidia gave them 400 million

SPEAKER_122: a third of it yeah they gave them 400 million and then they bought 800 million worth of hardware they're basically just shipping the 400 million back to them yeah and they must have a 50 margin

SPEAKER_01: on those machines so essentially nvidia stock goes massively up by billions of dollars because of that order yep and they got their money back and the startup is essentially in a way painting the tape or wash trading in some way nvidia stock now this could be completely inadvertent but that is the result that will happen here is this order will make more people buy nvidia stock and the money comes right

SPEAKER_98: back to nvidia this is something the sec is going to be all over yeah well the nvidia is the real winner

SPEAKER_64: here and even microsoft right because maybe all of those end up in a microsoft data center right so if

SPEAKER_423: they're a big part of this microsoft put in 400 million yeah and then they host them and then they send

SPEAKER_01: the credits back and they give yeah 200 500 million back to microsoft over the next coming years that's a round trip too yeah this is uh i i don't know how people who are so sophisticated are doing something so something that sends up so many red flags they must have some plan to well

SPEAKER_40: it's really make this clean it's fascinating and that we're just talking about the hardware side of it j kel but like you know flipping back around i don't know if you've had a chance to talk about it but you know

SPEAKER_08: twitter twitter did shut off access to uh like basically almost every service right and and it was even breaking eye message and signal and everything else and so um in today's world like how do you stand something up and i know you had a tweet you asked hey what's the go-to service and i think i linked to something there right there's a yeah there's a couple of things out there but it's also fascinating where are these folks going to get data from and then we had a follow-on saying hey look if you've ever trained a model um if you've ever trained a model and you have some type of restricted data in there it is in the model forever until you retrain and so this whole world is super interesting because if you have that much investment on hardware where are you getting the associated data

SPEAKER_103: from that is not restricted at this point such that you can leverage such a huge amount of hardware

SPEAKER_90: that you need do you think the next set of models will be weaker than the previous set because of this

SPEAKER_08: i i think if you're creating a model from scratch the answer is yes because when the models were previously created so i think um and you know i say this with about like 90 certainty twitter changed their policies post elon's takeover and and so if you were trained off any data from before then and your you know your models are using that for um you know their training data and they can use it for their own reasoning i think it's fine but i think if you have any data beyond that point you can't and i think it's going to create a real problem for folks that are starting from scratch today that's

SPEAKER_178: what i want to be so let me ask you a technical question if you did train on reddit twitter or core's

SPEAKER_98: dataset in the past yeah previously in the past and we know they have yes because people have proven it right because you can ask the llm and it will pull information from it right so there there's no

SPEAKER_01: doubt that they did that correct i'm not going to pick any particular company um so if you did train on that does that mean you have that information stored in some giant database in other words you took every single core question and you're storing them somewhere or you have the resulting hashes of those

SPEAKER_02: so it's masked therefore if you were to say give me um i want you to go into your llm like i have a

SPEAKER_39: cause of action like a legal action against somebody who made an llm um let's say an open source one even you know um and let's say the facebook one which is called llama llama let's say llama had crawled

SPEAKER_02: read it yeah and uh we know that could they rip out what it learned from llama knows what data is still

SPEAKER_08: stored in the language so so you know it's probably worth a longer discussion with some demo and we'll queue it up for the next one and i'll i'll i'll put a demo together um when when these things are used when data is used to train a model the data is basically turned into an embedding and then embedding looks like a number between minus one and one and so i don't know if you remember from the summit i kind of gave an example so it's never stored as uh it's kind of holistic nature of whatever you took it's basically broken down by the model tokenized and then turned into a probability which is then tied into you know what probabilities does this mean to the previous word the next word and so it's not kind

SPEAKER_98: of stored as a holistic thing where they can that be untangled could it be reverse engineered to prove that these words it's giving in an example came from yes you will be able to do that right because

SPEAKER_40: in in those cases you can ask it a question and you'll just use an example like you can go to open

SPEAKER_08: ai and you can ask it a question about you know what does it know about elon's tweets and they'll say well i don't know anything after september 2021 but before that i know the following and i think the tweet thread that you i had that you retweeted that elon commented on i had a little share you know like the open ai share in there that showed it it's kind of um data i don't know if nick you want to pull that one up but like but that one that one showed the history of the of what it understood of the training data from tweets that it had prior to september 2021 yeah so this is kind of interesting

SPEAKER_102: because i think they know this and now when you ask it to give you tweets it says as an ai language

SPEAKER_90: model i don't have real-time access to specific individual social media accounts or tweet history or their tweet history therefore i don't have information on jason calacanis's top tweet topics from 2019 to 21 so they are preparing at open ai i think for eventually having to rip out all the tweets so the balkanization has happened it's so funny the guys on all in were like this will never

SPEAKER_403: happen and i was like i think this is guaranteed to happen well they don't they don't have to rip

SPEAKER_05: out all the tweets right because it'll depend on when the terms changed um you know they could argue

SPEAKER_211: even if they the terms of service didn't say that that they've created a derivative product and they

SPEAKER_40: want them to remove it yeah but prior to prior to elon's takeover what if they were paying for

SPEAKER_14: oh that's different yeah who knows what that contract said yeah exactly we don't know if there was a con even if there was a contract contract might not have taken into account ai so yeah they can then

SPEAKER_57: but it is interesting like you can't get tweet data any or i don't know if you ever could but i'm using

SPEAKER_08: gpt 3.5 so here here's my uh you know from the tweet that we had together um which is you know this one

SPEAKER_396: um and so this is what i said i said hey summary of the last five tweets email elon speech i have

SPEAKER_08: access to okay and it says i don't and then i said what is the general theme of elon suites in your training data and then it says you know as of the cutoff spacex tesla ai cryptocurrency humor and pop

SPEAKER_13: culture personal benefits and opinions i wonder if we got that from like business insider wall street

SPEAKER_02: journal topics about his twitter or twitter data itself so being able to rip it out possible not possible do you think they built in a kill switch to be able to remove stuff knowing that's what

SPEAKER_39: happened i mean sam altman and greg are smart they had to anticipate people would not be happy about this and they did it anyway they broke the rules yeah to make the model as far as you know my my

SPEAKER_05: understanding and experience you have to retrain from scratch you cannot take things out now are

SPEAKER_02: they going to retrain from scratch anyway is that the best practice when they make gpt-5 are they starting from scratch or are they taking gpt-4's learnings and then building on top of it what's

SPEAKER_08: the better thing to do so this is an interesting topic uh maybe we're spending a minute or two on one of the things that sam has been saying and others have been saying something similar including you know brad and he talks to a lot of people um there sam has been pretty open about they're not training another model right now and where the majority of their energy is focused on is taking the models they already have and enhancing them so that they can have more memory and become personalized and so you know today whenever you go to open ai sort of you

SPEAKER_40: start from scratch you know you have a history of everything you've done but there's no collective learning from all of what you've said to say okay i kind of know the theme of what jason wants and

SPEAKER_103: maybe he wants me to always answer things like a pirate because every time he comes in says reply

SPEAKER_139: like a pirate right and so he always wants things in table format with short sentences

SPEAKER_40: exactly and so the you know one of the things that he's been very explicit about they're not training a gpt-5 yet but they're spending their energy around these kind of ancillary things to make the model much more personal and and have memory related yeah if they have customers now see

SPEAKER_01: this is the burden of customers yeah because they have customers the customers are pointing out all the weaknesses so now they start getting into the edge cases or how do i make this more polished once you have a car on the road you know when the tesla model s comes out now all you've got is feedback about the model s this should change this should change and you start going down the punch list of to

SPEAKER_90: do items as opposed to making the model 3 or the model y and having a fresh start with a new

SPEAKER_103: platform so this is well this is going to be their challenge i think it's it's partly that which is

SPEAKER_40: they've got customers and they're they're stuck on it i also think it's partly maybe it's good enough and it's become such a gray reasoning engine like our little experiment we're going to work on jcal where

SPEAKER_05: it already knows sort of how to take on tasks and figure things out it just needs a set of sub agents

SPEAKER_40: to do what it needs and we don't need to make the larger model any better because we don't want it to be an information retrieval system we'll have agents use serp api and other things we've talked about and it's good enough like they may have just realized that at this point yeah okay this has been

SPEAKER_90: another episode of this week in startups our ai edition sandeep madra sunny you're so great um

SPEAKER_98: please don't sell your company make it into a unicorn let's get to a billion dollars on this one please okay i don't know what i invested i'd probably invested at 30 40 million valuation i need to get anything for you anything please i mean i just want to turn that 250 i put in

SPEAKER_356: into 25 million is that too much to ask you're helping the funds i mean just get me get me 100x on

SPEAKER_01: this 100x is just so great um what i know you're working with some companies you can't say the majority of the names but if there is a corporate enterprise company out there or a category of

SPEAKER_02: company that you can do definitive intelligence can do great work for right now and i know you have a short you don't have an unlimited list of open slots on the dance card as it were but if there were one

SPEAKER_90: or two dream customers for you which would be the dream customers yeah i think folks that have made

SPEAKER_64: giant investments into data infrastructure so you know folks that have put a lot of money into creating

SPEAKER_08: data lakes or warehouses and they want to extract more value from that and they want to do it in a way that leverages ai not just from humans but ai's automatically so imagine there's you know agents that can look at your data whether you know all day all night and kind of find the insights you're

SPEAKER_455: looking for i think those are the ideal set of customers for us so i could see an e-commerce

SPEAKER_02: company with a lot of data yeah a finance or a fintech company with a lot of data your robin hood you got a huge amount of trading volume yep can you just sit there and ask questions to the robin hood data set would be incredible right tell me about trades tell me about you know what was popular what were the most popular stocks last year compared to this year you know which ones have fallen the most which ones get the most amount of chat um or people with a lot of data like reddit so someone like reddit yeah needed ai help they could hire your firm yeah to make it or even twitter needed people

SPEAKER_40: help with data they could hire your firm yeah yeah got it that that that's kind of the the ideal type of customer yeah and and and look like you know that's what we're we're working on we're very

SPEAKER_03: excited so you're sunny at definitive.ai or io definitive.io definitive.io all right everybody

SPEAKER_14: we'll see you next time on this week's service bye bye thanks partners since partners