SPEAKER_00: hey everybody hey everybody we all know the ai space is moving at a blistering pace right now so we decided to do a little ad hoc miniseries on it we're calling it innovators in ai very creative every week i'm going to interview a founder in the ai space here on this week in startups and every founder that joins is going to be building something awesome that is the criteria we don't want just pontification we want to actually see real products and today we have the ceo of copy ai paul yakubian we have a great conversation about the insane pace of ai as i mentioned and building products for a specific niche in this case writing copy ai right they do writing on top of various llms and he explains how he's dealing with the fact that hey he started i think with gpt two or three then they went 3.5 four five is coming out bard is coming out from google so many different llms out there how do you actually build a product that people are willing to pay for when these core platforms are moving so fast that they're going to absorb the innovations of people building on top of them almost in real time this is a major issue for founders who are building in gpt or in ai with open ai's products so it's going to be a great and insightful episode

SPEAKER_04: please stick with us this week in startups is brought to you by mercury where innovation meets peace of mind now more than ever startups need a safe place to put their cash mercury offers a simple way to manage bank risk and protect every dollar with up to five million dollars in fdic insurance and a money market fund visit mercury.com to apply in minutes the microsoft for startups founders hub helps all founders build a better startup at a lower cost from day one startups get up to 150 000 in azure credits access to open ai apis free dev tools like github technical advisory access to mentors and experts and so much more there is no funding requirement and it only takes minutes to join sign up today at aka.ms this week in startups and mayfair helps venture-backed companies protect and grow their cash automatically diversify where you hold your cash increase your fdic insurance coverage and earn up to 4.35 percent yield on deposits go to get mayfair.com twist to get started

David Friedberg: today all right everybody next up on the program is a company that's been doing generative ai specifically in copy and words for a couple of years i met paul a couple years ago when he was raising money for copy.ai my bestie david sacks from craft ventures is an investor in the company and i

SPEAKER_09: remember meeting you paul and paul's last name is yukubian or yukubian right that's right that's right

SPEAKER_10: yeah um you i remember were working on a couple of experiments when we met one of them was like doing

David Friedberg: taglines for using that's right i don't know if you were using chat gpt at that point but when did you first become aware of chat gpt and or generative ai for a copy good question so i started using gpt2

SPEAKER_14: in the end of 2019 and we were using it to create startup ideas so we were it was insanely creative but it was really bad at telling the truth because it wasn't trained on as much data as gpt3 was and it was immediately clear that the creative power was where the value driver would come from with the generative ai models and i thought it'd be seven years away from being commercially viable because it would just make stuff up and you had to really help like constrain the outputs of it to to drive value from it um so i was creating science fictional products and just like had this explosion of ideas

David Friedberg: what was the interface like for that chat gpt2 at the time was it all like a dev tool because i remember it was a lot of startups were trying to get access to it uh yeah gpt wasn't really available

SPEAKER_13: it was actually open source so you somebody had to set up the model host it and then build the ui for

SPEAKER_14: it and the the simplest ui was really just a text box where you type something in and then just click a button and auto complete it for you and so i was using that but i wanted a lot of ideas and so i kept hitting the button and then deleting what it printed out and then hit it again and i was like a search interface would make way more sense like a uax interface for brainstorming and so i thought it would be about seven years before the model would actually be good enough because only about 10 percent of the results even made sense at that time with gpt2 and then fast forward till uh july of 2020 which is only like six months later and they launched gpt3 just a hundred times the size of gpt2

SPEAKER_28: and that all the numbers flipped so all of a sudden most of the time it was making sense what was coming

SPEAKER_09: out ah and so when you were doing 2.0 this is really interesting to look at the history of it i'm sure

David Friedberg: people are going to study this rapid pace that this has gone on you there was no web-based service there's no cloud-based service you had to download it and then did you have to provide a model or was there

SPEAKER_32: already some training in it based on some model yeah they had pre-trained it and they had open

SPEAKER_14: source the model so somebody else ended up hosting it and providing that interface got it and so that's

SPEAKER_36: what i was using it were they upfront about what was in the model or did they just say hey we trained

David Friedberg: it on some stuff um or were they explicit hey we downloaded the wikipedia hey we did quora hey we did reddit were they at that time because that's something that i've been trying to get my head around

SPEAKER_09: and hopefully you can educate us in the audience as to what what exactly how do you know what's been

SPEAKER_14: it's been trained on yeah they they i think in the paper listed out a number of the sources one is a common crawl data set it's just a huge huge file that's open source you can download it and you can use that to train the model wikipedia was a big a big component common crawl as in a common crawl of

SPEAKER_09: open web so just a bunch of web pages that are in an open source search engine of sorts that's right

SPEAKER_42: they got reddit and i don't think they got quora at that time i think yeah probably had a paywall or

SPEAKER_43: something or a registration wall so they couldn't get in there right i think since then they have

SPEAKER_44: they have probably gotten access somehow to quartz data set so you see 3.0 and it goes from one in

David Friedberg: ten times to getting it right to one in ten times getting it wrong you said another way nine in ten times dude at least making sense like it wasn't completely gobbledygook gibberish exactly

SPEAKER_14: yeah so the first the first thing that came to my mind i was like this is the next big wave in tech because all that you have all these new use cases that are unlocked and we would i thought we would see you know immediately i tweeted this out i thought it was a phase change of the internet so this is the first time the internet can be trained and synthesized into a model and then talk back to you directly right so that's a profound change in the amount of information that you have access to at any any moment in time and given that you're kind of able to talk to the internet i thought that conversational interfaces would be the most relevant way to access that information that's in inside the models so we built built a slack bot and we asked uh open ai if we could launch it and their safety team said no you can't launch that it's too dangerous and so we had to go back to the trying board and we built um built the an app that could just summarize whatever you threw at it so we launched that we got a a mention in the uh wall street journal um for that and then we realized you know people that's like a sometimes use case i mean that is a component of of a skill that they

SPEAKER_44: models have but that's not something that people will subscribe for and pay us money every month

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David Friedberg: a lay person what it's actually doing so you have a paragraph let's say you have a i don't know a movie review from the new york times it's a thousand words about some you know really detailed movie like tar right academy award uh uh oscar nominated best picture but what does it do what is the what is the um what is the model actually doing when it is summarizing so there there are different techniques

SPEAKER_14: for summarization for these models um you know based on neural networks the neural net is learning skills so it is it has learned skills that you would have otherwise needed specialty natural language processing algorithms to do and that's what really what sets it apart so when you had 176 billion parameters in the model and they're structured into layers these layers get really good at doing certain things and for the summarization tasks it can learn you know what summarization means like what does the word mean to summarize something it can pick that up from the corpus that it's trained on so that all the attacks that it's trained on and then that is a task it can do it can do pretty well which which to me i was like okay now this one model we can use to do all of these natural language processing tasks without needing a bunch of nlp engineers on the team it's literally one api call and i can just prompt it in natural language and tell it what i want and i want to give that back to me so the the first first that was a major unlock because it suddenly made the process of building an mvp a software mvp that uses natural language um technology um made that really easy so you could build a prototype and launch it and so when we built our next uh prototype it was called taglines.ai it was just a tagline generator that took 48 hours from beginning to end to build the entire thing and we launched it 48 hours after saying okay maybe people would buy you know maybe marketers would buy words if we can generate them and so within the first week we had validated that not only would you have professional copywriters using these tools but you'd have marketers and freelancers and small business owners students so that was a huge validation of the market size and the tam potential because it was such a wide user base and then we also validated that people would use a tagline generator to do like email subject lines headers for websites um just all kind of like powerpoint slide you know titles um and really validated that the number of use cases were really wide for that as well and then threw up like a stripe paywall and then validated people would sign up for a subscription so we started charging three dollars a month we got some subscribers raise it to six and then raise it to ten and we continued to get subscribers coming in so at that point we validated one that it was commercially viable that you had a wide market and a wide set of use cases right and that makes

David Friedberg: sense there are copy editors in the world you discovered this is working nine out of ten times and if you can make them more efficient for ten dollars a month if they the average copywriter i think freelance probably gets paid 50 bucks an hour 40 bucks an hour 60 bucks an hour something in that range i mean it's not even one hour of their time so that's always one of those great tools for founders is to say what does this person get paid an hour okay this attorney gets paid a thousand dollars an

SPEAKER_67: hour save them one hour a month they'll pay you a thousand dollars or they'll be okay with it right

SPEAKER_14: exactly so when when we went through that exercise it was really clear that from all the way from a student to a professional would find value in these ai models and then the thing that we learned from going from two to three was that if you throw more data at it it's going to get even better and

SPEAKER_37: better performing these tasks at an expert level or beyond so now when we get to gpt4 to get back to

David Friedberg: this sort of these jumps there was three i think there was 3.5 and four in terms of major releases you're working directly with the open ai team you have some insider access to it i understand so you

SPEAKER_32: were kind of in touch with them and and seeing what was coming what was coming between three and 3.5 and

SPEAKER_13: 3.5 and four yeah so for 3.5 they ended up tuning it to human feedback and so what what that would do

SPEAKER_14: is it would allow you to to kind of describe the intent that you had and you wouldn't have to prompt it with examples as much so when we first built our tools each tool he had to give it examples of what good looks like and he had to make sure that even the examples are very diverse when they ended up fine-tuning it on human feedback they kind of programmed that in across a wide variety of use cases and so that enabled the chat gpt experience where you can just tell it what you want and it'll figure out and actually create that content that you want uh and so when you saw

David Friedberg: three 3.54 maybe you could describe for the audience yeah the the step functions there you said before hey you know it's right one out of ten times then it's right nine out of ten times now let's go to 3.5 and

SPEAKER_42: 4. 3.5 yeah it was awesome it was like a a big a big leap and a lot of the complexity and prompting it kind of went away a lot of um even the use cases for more fine tuning of models so um you'll

SPEAKER_14: see a lot of startups talk about how valuable their their fine-tuned models are like oh we have proprietary models well at the end of the day not there aren't that many models that are really

SPEAKER_79: relevant for for you to have fine-tuned what a model is again to a layperson and what's an incredible

SPEAKER_15: example of that where you're restricting it and building a model that maybe results in better output

SPEAKER_14: yeah so the these are foundational models so these are built to be general purpose that that means they can do a lot of things at you know a pretty pretty solid rate what they figured out was that the general purpose model could actually outperform specialized models if they kind of made them bigger um and trained it on more data and that is nuts that's not so i don't know if you've seen some of the gpt4 metrics but they they said oh well it passed the bar exam it passed like these all these exams right it's getting better across every single category so let's pause on that for a second

SPEAKER_10: yeah you used to build in the industry what was called narrow ai yes narrow learning so hey we're going to learn how to beat um you know humans at poker chess go you know pick a video game fortnite

David Friedberg: and we'll really just work in that narrow subject area now this more general model um a little confusing to use the word general because it's loaded because there's general ai as a concept which is it's thinking like a human but their average or their default model maybe is a better word for it their default model is better at doing the lstat or the sats or the bar exam than a model that was

SPEAKER_15: trained just to do the bar exam that's what we're they're learning with form that's right

SPEAKER_87: and you seem to say hey that's insane why is that insane why is that insane uh it really does

SPEAKER_14: level the playing field if you have access to those the gpt4 model so you don't need a whole team of ai engineers you don't need a whole team of machine learning engineers you can actually build you can just go straight into building the application and yeah keep going i was going to say something that i think larger companies are running into issues around is they've kind of delegated the like hey ai team go figure out this generative ai stuff but the ai team you know if the ai team comes back and says you know what we can't beat gpt three and a half we can't beat gpt four they'll get fired right

David Friedberg: right that's fascinating so if i was working inside of i don't know amazon let's just say walmart and i

SPEAKER_96: said hey our internal team is a great example amazon's a great example yeah i'm in amazon i want to

David Friedberg: optimize amazon prime members to show them better reviews and show them better q a so i take the q a section on an amazon page and the review section and we'll summarize it and make it better for users if gpt4 can do that better than the internal team why is there even an internal team yeah that's pretty

SPEAKER_14: wild depends on the use case so if you looked at like the iphone voicemail system you know try to transcribe a voicemail message they had i don't know how many thousand ai engineers at apple and then open ai had a team of i think two or three people that figured out how to how to complete the um voice to text task and they ended up launching a model called whisper and they open sourced it they

SPEAKER_44: literally gave away the technology and it dramatically outperformed all of the existing technologies all

SPEAKER_00: of it all right everybody our friends from microsoft are here tom davis a senior director at microsoft

SPEAKER_102: for startups and your former founder you are here today to talk to us about the giant leaps that microsoft has made in the ai space what does this mean for startups i see a ton of different tools i've been playing with chat gpt4 i have a paid account but i'm also seeing things happen with github

SPEAKER_104: absolutely so uh the work that we've been doing with open ai over the last few years has really set ourselves up with a foundation around uh we've built this sort of ai supercomputer from the ground up and we've been looked at everything from gpu configurations to networking and things and really what we're now able to do is sort of allow startups to access all of this innovation through our founders hub so we've been building this to do something at scale so startups can now build their own ai applications and and build out and train llms as well and this has really helped us to become a far better cloud for ai broadly and being able to drive that down to the startup ecosystem is uh

SPEAKER_102: fantastic it's open to everybody there's no funding requirement you don't have to be anointed by a

SPEAKER_107: vc five minutes to sign up you get six figures of benefits azure credits github open apis which

SPEAKER_102: everybody's really having fun playing with and so much more so go ahead and sign up right now aka.ms slash this week in startups aka.ms slash this week in startups thanks so much tom thank you so

SPEAKER_10: as we're looking at this yeah this is almost like the snake is eating its own tail kind of situation

David Friedberg: we're all going you know uh copywriters you can name your company's copy ai you got 12 million dollars in reoccurring revenue and your premise which turned out to be absolutely correct is hey this thing is advancing fast copywriters are going to use this tool to be willing to pay for it it's going to make them bionic make them superhuman um won't replace them but might replace the bottom third that are terrible at their jobs or make the bottom third great at their jobs or good at their jobs was that your central tenant um about this because the replacing of a job or elimination of the job in other words i'm the developer or i'm the ceo i'll just ask chat gpt for copy and i don't need a copy editor uh what is your belief now let's say copy editing your wheelhouse is copy editing going to go away and the ceo or the sales team just does the cop ask chat pt to do it or will there be bionic

SPEAKER_14: copywriters who use this as a starting point i think before you know before we launched copy i had

SPEAKER_13: no idea what copywriters even did so it was it was never about copywriting it was that was basically the first use case that i thought would be relatively translatable against the models but the thing that

SPEAKER_14: really motivates me and my co-founder is the idea that people have create like a high degree of creativity especially kids yeah they're super creative i've got two daughters that are extremely

SPEAKER_42: creative and then they we pre-train them through school right just like these models are now pre-trained

SPEAKER_14: on the internet and all that pre-training kind of stamps out the creativity and by the time they graduate they're very tracked and the things that they want to do you know so it's like oh i'm going to be an attorney or i'm going to be an accountant or i'm going to be a software engineer and i think that that whole process is going to come to an end here because gpt4 can do all of the liberal arts better than any

SPEAKER_42: expert could right and now that's a tool that's in your pocket which means we don't need to train people on a lot of these very technical tasks that we used to because we've now trained computers to do

SPEAKER_14: that repeatedly so there will be a very um you know a challenging transition for a lot of industries a lot of institutions like higher education education in general and we're going to figure out okay what

SPEAKER_42: what's the best use of of a person's time well that that usually comes down to the individual and what we what i'm seeing you know the whole my whole hope here is that people can be way more

SPEAKER_14: creative because each time they are creative they have an outlet to actually build something they

SPEAKER_110: can actually get things done so a non-copywriter a non-writer somebody's just not good at words

David Friedberg: can now go in there and be good at words so you take the exactly 80 of people who are not good at that now they can be good at it i may not be good at drawing or illustrating but i can pop up stable diffusion or any number of tools and i could be a good illustrator so now you've got the 99 of people who have no illustration skill can't draw up or not and they can be creative so it's not that that everybody's job goes away it's that everybody gets good at everybody's job yes that's an

SPEAKER_120: interesting way to look at it right and that's that's incredibly powerful especially if when you're

SPEAKER_14: looking at entrepreneurship so there if you've ever tried to create your own website for example that takes a lot of time and it can be very stressful for somebody that's trying to get something off the ground that is now you know going to be pretty much instantaneous like oh i need a website boom i've got one you know i need i need a presentation boom you know i can just generate a whole like my whole deck um and so and then work from there so you're starting on third base yeah

SPEAKER_59: yeah and so which happens now you can go on squarespace pick a template and now you'd be able

David Friedberg: to a future version of squarespace will be hey make me a website and show me five different designs i need one that's funkier i need one that's a little more avant-garde um now the challenge the challenge is

SPEAKER_14: what kind of business are you going to create that's going to actually be able to compete against

David Friedberg: what the ai models can do yeah well and to your point the ai developers who are making this are literally working themselves out of a job the better they do the less they're needed because the general model can do better than the specific model so at least in that case the narrow specific models

SPEAKER_35: are being subsumed into the general models yes yeah you're seeing that and then you're also seeing it um in the software itself so i'll give you kind of a run through here so yeah software is why is

SPEAKER_09: software valuable jason uh because it makes people more efficient which means it reduces the cost of any

SPEAKER_14: good or service right and and what like why software is valuable is because it's reusable so your

SPEAKER_13: investment to build a part of it right it's a reusability so we're now heading into a world where

SPEAKER_14: software can be generated on the fly right and it's almost the cost of creating new software is going to

SPEAKER_28: zero right that means that the returns on investing in that software should also go to zero which causes

SPEAKER_14: some problems in the venture capital community i'm an angel investor yeah you know you're an angel investor um so we really this whole thing is going to get transformed from a technology standpoint fantastic i

SPEAKER_10: mean if you can build a company with two people instead of 10 or 10 people instead of 30 well that means you need less investment and the earliest investors are the only ones who get to

SPEAKER_143: a seat at the table i'm fine with that i'm fine with people who do series b and c is getting blocked from having an opportunity to invest because the company's too profitable is that sort of your

SPEAKER_146: premise it takes less money to run this just like cloud computing drop the cost of it yeah so one it

SPEAKER_13: should expand the number of people that can build things which should be good overall for entrepreneurship

SPEAKER_14: uh two like you said on the growth stages that's that's going to increasingly make i take a little bit less sense and you've seen you saw yc shut down the continuity fund which is their growth stage fund uh this week and and so in that and then when we look at the ai landscape a lot of the repeatable tasks are in the sales and marketing space so like personalization now can be done on a one-to-one basis um at scale even things like querying databases you know that's something that people had to do on their own so you'd have to figure out well how do i write the right search query to get the right results maybe on linkedin um so what what you're going to see happen and this uh you know chat is a great example of this we'll express intent right which is what our query is when we type into chat and then the ai will like figure out what it needs to go do to get us back what we're looking for and so that that is a world of ai agents where we're letting the ai go and do things for us and that world looks very different as well so ai doesn't need an interface to go grab data right it

SPEAKER_44: can just query apis directly and go get the information it needs if it's allowed to if it has

SPEAKER_14: access to those right exactly so i had a tweet about this i said the biggest by the end of the year is my

SPEAKER_13: prediction the biggest search engine on the planet won't be google it'll be bing and it won't be

SPEAKER_141: because people are using it it's because the bots are using it so so many bots are doing queries that it's just going to increase the size of the corpus and the learning model the corpus the corpus uh

SPEAKER_14: kind of grows when you publish things and so there are going to be you know agent flows and bot flows that go and create more content and new content um but for the on the retrieval side that a lot of those queries

SPEAKER_44: are going to get sucked in to you know powering some result right and then that result gets re-indexed

SPEAKER_10: it gets re-indexed and now people are speculating it's going to pollute the uh search indexes to

David Friedberg: the point of absurdity somebody like yourself or some group of people offshore uh just like they created content farms they could literally just start creating they could create an ai right now with chat gpt that registers 100 new domains a day makes 100 different recipes and then articles about those recipes and then does the same thing every day until there are so many recipe sites that the internet is flooded with garbage and nobody knows which one actually works that's the fear that's

SPEAKER_156: the negative yeah google kind of knows google knows well they have they have the original index they have

SPEAKER_42: yeah and they they can detect ai generated content overall they're how do they do that how do they

SPEAKER_158: generate how do they know it's um chat gpt generated there are signatures in the outputs that that allow

SPEAKER_14: you to calculate um it's a perplexity score sometimes and that's that's an actual measurement of how likely the next word is is to follow the previous one got it so it's too perfect

SPEAKER_163: it can't be perfect and you can you can like make it not be as perfect just by giving it certain

David Friedberg: instructions and say mix it up a little bit i think this is how they they caught the chess cheater kid was he was doing the book move yes he was and the book move is accurate yeah how often does somebody do the book move and i play in the chess.com thing and it will tell you like this is the book move this is the you know game theory optimal move basically and if everybody's doing game theory optimal so now you have to put something in there randomized to not do game theory optimal so maybe at some point it figures that out you know it's interesting though they said when the digital camera came out uh that this would change hollywood forever that digital filmmakers and wayne wang did a digital film um and bennett miller had done a digital film on this is all on the vx 1000 i believe was the name of the sony camera 71 yeah it became super popular in the late 90s in new york and all these and skateboarding and skateboarding yes well skateboarders use it right yeah and it was like this thing is so good that when you blow it up and you put it on a projector you can't kind of tell or most audiences can't tell a cinematographer obviously and then there were filters in you know different pieces of software that would make it look even better anyway the point was because you don't have to worry about film stock developing it the cost just got sucked out of this you could record forever even a bad actor could just keep taking doing different takes you would get there and it didn't actually change the world i mean it did create youtube it did create a lot of other content so maybe in that

SPEAKER_09: extent it did but it didn't change film so what's different about that argument than your argument

SPEAKER_42: for um you know ai replacing everything well the iphone won right iphone won the camera world it

SPEAKER_14: destroyed it it also won music so my my like base case is that apple wins probably this too to a large degree one because the phones are powerful enough and the models are going to shrink small enough that they can fit on a phone and so even some of these image generation models they can fit now on the phone text gen and same thing on a long enough time frame they will fit on the phone and um i think that you know that's always crazy but then people just get used to it they're like oh yeah iphone 20 will have whatever it is native well just like laptops now have wi-fi built in there was

SPEAKER_59: a time you had to add bluetooth with a dongle or wi-fi with a dongle or a pcm cia card whatever that

SPEAKER_09: was called you know you have different card slots on your laptop to put in different peripherals now it's all on a chip somewhere and you have to do anything for it exactly i have one more point here

SPEAKER_14: so my kids my kids are eight and five and they just think computers can generate images just totally natural to them you know and in the um i think it's adults and people that have struggled to use computers their adult lives it's like this is these are the people that have you know a challenge like accepting that ai is going to power computers to do these task force but kids and kids are like

SPEAKER_183: yeah of course of course yeah of course it's going to yeah i mean it's just like well it's so obvious

SPEAKER_13: that a computer could do it to them that it's not even special or interesting you know if you're

David Friedberg: of the age where you remember the internet uh happening in broadband um and there was a moment in time where i was on a college campus in the late 80s and i had seen higher speed internet speeds bitnet and arpanet and people only had dial-up so i was using dial-up at home but i was like you know this this can get a lot faster there are faster systems in this and i had installed token ring and banyan vines and then eventually ethernet in local area networks and like you know this thing's going to be able to carry images and pictures and sound and eventually video and so it's kind of the

SPEAKER_10: same thing like you your limitation right now that you're like oh it can produce interesting copy that's sometimes good sometimes bad okay images and once in a while a three second looped video i mean you're gonna be able to talk to this thing and just make your own star wars story and

David Friedberg: it would be indistinguishable than i don't know the original series or whatever 10 years ago series without any polish on it i mean that basically is it doesn't take much of a jump actually only takes a

SPEAKER_35: huge jump if you haven't watched rapid change right well i think in the next year you'll see it um

SPEAKER_13: probably just take over business in general so we know it can generate business ideas we know it can create websites from an idea we know it can figure out who the core persona is for that service we know

SPEAKER_14: it can access apis that would retrieve right who the target contacts are we know it can write

SPEAKER_189: personalized emails to them right so it's gonna come up with an idea you make your sas startup and then it starts selling it for you it's all in the same one big sequence of prompts yeah that's all it is

SPEAKER_42: so that's all that is here today it's just a matter of infrastructure and that's something we're working

David Friedberg: on right now so what i mean what you've described means your own business becomes commodified so are you saying that your own business is gonna or you're all or are you saying your business will be commodified

SPEAKER_14: every 24 months and you'll just have to extend it no i'd say like most businesses are not commoditized by that loop you know most businesses have some kind of like real world thing happening let's say like jp morgan right that's not you can't really replicate jp morgan with that kind of system but there

SPEAKER_35: are a lot of one services that can be replicated like specialist consulting a lot of that stuff can be

SPEAKER_28: replicated a lot of software companies can be replicated yeah i mean you're talking about

SPEAKER_09: regulation i mean you could i mean somebody's gonna put ai and web three point web three together

SPEAKER_199: in crypto and be like make me a crypto that does x y and z they're trying to i mean they're trying to

David Friedberg: shut you know shut all that down with um regulation regulation the the crypto choke point yes if you f around too much you will find out what the government is capable of when you're a founder

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SPEAKER_10: that's get mayfair.com twist okay so show me copy ai and what it's doing today because you and i were talking at south by uh and i was like huh a lot of the stuff you showed me is now built into chat gpt4 what does that do for your business yeah so i'll give you that same question which is hey you know the

David Friedberg: stuff you were talking about two years ago to your credit i literally put everybody in my company

SPEAKER_10: i said everybody has homework this weekend we're a finance company we all work six seven days a week if you don't like it don't work at a finance company um and i said just spend an hour or two

David Friedberg: playing with uh chat gpt and um tell me on monday how it helps you do your job faster better or what

SPEAKER_10: did they come back with uh the sales team started doing prospecting in it and other people started doing marketing plans in it and i haven't gone through everybody's responses but uh the my team

SPEAKER_09: on this podcast has been doing podcast notes with it and that works really well which we thought was

David Friedberg: a goof 60 days ago when chat gpt 3.5 came out i was like just ask you what the revenue of this you know netflix was the last five years and it was wrong and then chat gpt4 comes out it's like okay it's right so it's starting to things that we even noticed three months ago four months ago were wrong so you could and then we asked it to take you know um we will take a zoom recording of a meeting with a founder transcribe it and this past week we took the transcripts which now zoom is doing and then we had chat gpt4 summarize it and summarize it in three different lengths so now in our notion instance not only do we have the video with the interview with the founder recorded we have the transcript and then i guess two or three versions of it so i could read the short version of it and i was

SPEAKER_09: like it's pretty accurate i mean i wouldn't rely on it to make an investment decision but certainly would be interesting to read before you meet with the founder what about gpt5 you have to assume gpt5

SPEAKER_10: will be extremely less error prone and you will just trust it just like gps the first gps remember people ran off the side of roads be like turn left and somebody would wind up in a cornfield like it said turn left and it's like exactly dummy you still have to look just also like autopilot it's like it's gonna stop at this red light unless like there's a christmas tree

David Friedberg: at the red light you know confusing it um but it feels like you can trust it i think trust is going to be the main issue is can i trust this by default yes i trust it more than a human and i trust self

SPEAKER_30: driving on a highway more than i trust a human today yes yeah yeah and and we're we went through the

SPEAKER_14: that valley where the quality was lower than people can produce and that was like the gpt3 valley and that's where a lot of people are saying oh well the content's terrible and like it's awful quality and it's just going to pollute the internet that's a very short duration until you basically can replicate all the steps that a person would go through to write a high quality article just replicate that at

SPEAKER_42: scale and i think one of the one of the key um one of the key points that even chat gpt3 is not

SPEAKER_14: it's kind of missing right now is the scalability component so in order for you to get value out of it you have to manually query it you know so your marketing team has to say okay we're going to go do this or your sales team is manually configuring it the the area of opportunity for you know and in our kind of viewpoint of the future is really scaled fully automated um workflows so you give the ai uh you kind of tell it what you want to do or or over time just give it access to your systems and then it will go and configure itself and it will try to improve your business for you autonomously so you can't really get there from chat and so but part of the back end infrastructure is very similar because in the world of autonomous business process agents that is like a an agent building platform and so you do need to say okay i want to do this here's here that here's the list of tools you have access to go do it now instead of reporting back to me report back to yourself and then figure out how you can improve from there right so what you want to do is is build these um you know extend the data flows that you have coming off of your current software stack so you have a newsletter right yep what do you do with the information uh the data that comes out of the newsletter

SPEAKER_114: performance nothing don't care right so is there value there sure right and it's and it usually

SPEAKER_14: comes down to you like the team just doesn't have enough time to do everything right it's an after

SPEAKER_10: thought to to think about and to coach yourself there's no person on there so you could say every

David Friedberg: time the newsletter comes out at the end of the week tell us which stories got the most clicks and then what are stories like those that we can do more of boom and that's just set every week and

SPEAKER_230: it just sends them an agent so it's like google alerts or magic leap had this idea that you'd have

SPEAKER_10: agents out there doing things for you but if you take a google alert which is on your last name or your company name there's a google alert that can do more advanced things yeah exactly and and even the

SPEAKER_14: newsletter maybe you have like very high value contacts in your in your newsletter database sure and you're like hey i actually want to personalize theirs so i want to take this content and just literally personalize it down like one-to-one to them because i want every single message they get

SPEAKER_42: from us to be extremely high quality oh interesting right so those are systems that are going to be you're going to be able to build we're building out um we have pilot enterprise partners that we're working

SPEAKER_14: with now that are deploying some of these workflows into their existing business processes fascinating the the thing that compounds is once we set up a workflow um let's call it like the newsletter use

SPEAKER_236: case most newsletters would need it and so that is the thing that we're going to copy that'll be a

SPEAKER_59: template in your library like notion has templates or any other platform has templates and yeah that

SPEAKER_10: gets super interesting in a creative we did uh just as an example here's a couple of you know when we do

David Friedberg: when the when our researchers are going to do a google search like i'm doing a live show and they're like hey when did netflix launch when did it pass 100 million subs when did disney plus launch when it allows do 100 subs it's now getting that right it wasn't getting that right previously but here's examples of chat gpt doing that um and it does seem like bard from google which i've been playing with has more recent information so in your mind how far behind is bard do you think in reality

SPEAKER_42: um probably the the measure that matters is the user base for the chat programs i think that would that would be the the difference maker there that ultimately will determine who wins so then google does

SPEAKER_09: win because even if open ai has tens of millions of users google has billions across chrome android

SPEAKER_42: youtube etc so google wins it's i wouldn't count anybody out i wouldn't count google out i mean google has they have all the data sets that matter yep so they should be able to go faster and have the users and have the browsers they have to really get their vision together about what they're building

SPEAKER_14: and for them you know their goal was to make um you know all human knowledge accessible and useful that was like their mission statement and search does that right it does that along one dimension which is you can search for something so it's like i'm gonna pull information out and they show you the list of links which is great however the the idea that it's making it useful i think they've they're failing to deliver on that function the output that you get back that and then also just think about how much value is not sitting on the internet that you don't know about that would create value for you right and until now we've never had a way like a technology that could take all of that information and directly apply it and make your thing more valuable whatever it is that you've built and have been working on right so even for for you you know you're the media company content company you're trying to read a lot of stuff and figure out what's important what's valuable and they're

SPEAKER_199: and i'm sure in the past call it four weeks you see more launches than you can even keep up with

David Friedberg: it's crazy right i mean the number of people launching verticalized ai like if i had a nickel for every company that's going to respond to sales emails or customer support emails in a better fashion

SPEAKER_14: i mean it's in the hundreds now yeah right and and the thing we ran into we were trying to build verticalized solutions and we've we realized that it's it's just uh basically the same back-end infrastructure that you need to do any of them and so rather than a comp a large let's say bank trying to evaluate 3 000 verticalized solution providers they'll they'll end up opting just for a platform and you've seen this happen over and over again so segment was a cdp layer customer day layer that they didn't have to build snowflake was a huge like data you know data warehouse in the cloud they didn't have to build that service now one of the

SPEAKER_42: originals right just scaled so hard and they were very use case agnostic and that's that's very appealing

SPEAKER_28: for large companies because they're always trying to reduce the number of of tools that they have at

SPEAKER_30: the company level yeah for sure so that's microsoft office perfect example yeah bundling of stuff so show us your product and where you're at with it and what people are using the most and and specifically

David Friedberg: how does it differ from chad gpt4 because as i said my premise was or my question to you was was like does this thing eventually collide or are you just in an arms race where they're building this

SPEAKER_265: sort of web-based service and then they need people like you to build stuff on top of it

SPEAKER_14: yeah it works both ways i think there's plenty of room in in this you know in these categories this

SPEAKER_28: is our chat product we've connected it to the internet let's ask it so this you know svb got sold today

SPEAKER_14: yep and so this would be you know a real-time query that we can run

SPEAKER_13: get on it um you know most people we're using like our i'll show you these tools here

SPEAKER_14: second so over time all the latency drops and the cost drop and so for the uh for this result you know

SPEAKER_276: you're getting the actual articles right and the citations you're surfing the open web so you have

SPEAKER_19: google news or some news feeds fed into here using chat gpt4s like hey go check the web we can do anything

SPEAKER_147: we could do any of it we want so you can hit like you can do it in real time you can do like a search api

SPEAKER_14: that you can add add to the back end you can use the bing api um that stuff doesn't really matter too much like you do any anything you want and then you can bring all those results back in and for for this one you know it answered the question really fast yeah right and when gbt3 launched it would make something up it would just literally make up like the wrong answer and be very

SPEAKER_39: confident in it yeah right so what's the word for that is that what they mean when they say it's delusional or it's hallucination hallucination yeah it thinks it's right it has no idea yeah okay so this

SPEAKER_13: is impressive what next so what's happening in the back end is actually where the secret sauce gets

SPEAKER_14: built so when you ins you know when a user comes into chat and they ask a question the you know in the google world it would just hit up its index so the index would say okay what's the closest set of results that i should show to this user based on that query in the generative world you can build these agents so you can go try to solve the problem and the more tools you give that agent the ai agent the better it can solve the problem so it is choosing now what route to take so these are not

SPEAKER_10: pre-programmed steps that you build into it so so it's saying i could go to wikipedia i could go to google news or bing news or i could search the open web i could do a web crawl it could do excuse me any number of things i search twitter search reddit or quora and it somehow figures out you know what this news service is better or is it blending the new service with the web search it's going to

SPEAKER_42: do a little bit it can do whatever it wants yeah so so this is a fundamentally a new way of building

SPEAKER_14: backends to software the other thing that you know ours can do is it can recommend new steps so it can add you know we can figure out what tools it wants and we can give it access to more tools so in some cases people wanted access to like linkedin data right so we can go get a link an api that queries linkedin profiles and returns that and it's like a json format which is like a data format and then now we can take that information and do something with it that's relevant to that user this is going to make

SPEAKER_10: these large data sets um invaluable and if you're going to use them you're going to need to get a licensing fee from linkedin reddit quora maybe you could talk about what's going on in the back channel

David Friedberg: because you're probably facing this yourself with those discussions i understand there's like multiple lawsuits about to drop on open ai for using various platforms data without explicit permission and once you start charging for stuff and you take 10 billion from microsoft you can no longer be like oh it's an academic non-profit even non-profits like i never saw the way back machine the internet

SPEAKER_09: archive lost its copyright case that's a non-profit so this is clearly unfair use um now i don't know if it breaks the fair use we'll have like a there'll be a big debate coming up but what's the back channel right now about who gets access to quora reddit twitter pick a data set um and how will that

SPEAKER_265: hash out in your mind what's the fair route there that's a good question i think over time you know

SPEAKER_14: the like i mean licensing tends to get figured out over time music industry they figured it out even in the image generation side now i was at south by southwest and at the shutterstock booth and they had an ai generated image product and even them even though you're using ai to generate it they would find the images that went into the generation and they will pay a royalty to the copyright holder

SPEAKER_45: of that of those images yeah getting images suing stable diffusion right now uh yeah a bunch of open source developers are suing microsoft's github copilot so these those are those are the to me those

SPEAKER_14: were like very they should get sued at least um the the code base i think i would at least want to see like how those arguments play out so if you open source your code someone grabs it trains a model and it can it is replicating your code verbatim and even in some cases like the you know the script the documentation script that was personalized to you i mean that's very traceable right yeah and that's a reason not to put citations in it why wouldn't you right so that that same thing happened on the initial stable diffusion data set so they did you know grab they grabbed images and you could see like getty and the watermark that was right so we we kind of avoided doing image gen i know other platforms wanted to launch that as products but if uh you know any of these image generators they'll produce copyright and content and the way the copyright laws work is if you are the one that generated it on your servers and you're selling it like that's a you're gonna get deemed for that yeah and that's

SPEAKER_59: it's a derivative product that you created now if i were to load uh just like on my browser i can take

David Friedberg: my browser i can save a web page on my desktop that's fair use um and i can even edit the webpage on my desktop it's when i choose to put it out in commerce that your copyright gets uh you know has problems but if i were to if i were to download the original chat gpt open source and it's no longer

SPEAKER_304: open source is this like chat gpt4 is not open source now when did it stop being open source gpt3 was

SPEAKER_14: not open source so three and a half which powered chat not open source four not open sourced and

SPEAKER_305: it's unlikely that opening i will open source is there what's the number one competing product

David Friedberg: and how far behind are they because it would it seems the world needs an open source version of this and if i would have run the open source version on my cluster of servers or you know my desktop my phone and pointed out a data set that i had access to well that's for my own personal use

SPEAKER_14: uh nobody can stop me right you can do that and even the stable diffusion models they've they've shrunk them down they can fit on your my macbook or your phone so no you know no kind of copyright issue there but if you try to monetize that you do run into it in terms of open source um open ai is still state of the art um for their for their core foundational models um they the advantage actually is not on the training side it's it's the computational intensity side so it's how they optimize their hardware how they optimize the training of the models that's where you build a really compounding competitive advantage because the bottleneck across the world right now is gpu capacity capacity capacity and availability so nvidia has back orders like crazy for their h100 gpu and no one can explain what the h100 is it's a really expensive gpu so like a graphical you know processing unit and is you know it's what powers video games it does a lot of um kind of matrix math and that's what you know these neural nets really need that at at scale and anytime a big company wants to launch an ai-powered product that's just more compute that's needed to serve that up and um i think most people will have run into like outages chat gpt goes down or open ai goes down

SPEAKER_44: it's a function of like the limitations of you're just not having enough of these gpus

SPEAKER_70: uh in their server farm and getting overlooked but isn't the tensor stuff all open source hardware as well so other people should be able to build these and compete over time no um nvidia has you know they

SPEAKER_14: make custom modified chips for for folks and they competed pretty hard google did have the the tpus the tensor processing units but those have not really performed for for the applications in the the same way for other companies this is not my area of expertise no i i mean i'm hearing people talk about

David Friedberg: the shortage now and it does seem like it's an opportunity for you know massive competition again

SPEAKER_35: open source and another technology it's just the heart you know that hardware is made in you know

SPEAKER_14: like tsmc taiwan semiconductor and that's a whole geopolitical risk too and um it's pretty amazing

SPEAKER_10: that we've been talking about this invasion of taiwan now for you know close to a decade but that has gotten more and more heated the last couple years and that um increased tension seems to parallel the

SPEAKER_14: development of ai and uh covet yeah i don't know that's that is like you know taiwan built itself on the back of its semiconductor industry because that was the thing that would get the most protection from the us yeah over time and that was a good bet that was a good call on that but i think even tsmc is saying hey maybe we need to diversify a little bit and start opening up fabs in the us

SPEAKER_265: oh and they are yeah india everywhere yep it's absolutely fantastic so show me your product before

David Friedberg: we wrap up here and run out of time uh any other pieces you want to show any other like things you're super proud of i mean it's super impressive um and i understand now the difference uh yours is doing a lot more inputs in the way it's kind of like zapier or if this then that kind of is built into it natively so it can go find more sources dynamically yeah it can go do that and then i think you know over

SPEAKER_14: time and this is what i was getting to about software in general you know you can you bought software for your team before right sure and then what happens if the team doesn't use the software

SPEAKER_59: to its fullest potential we unsubscribe at some point hopefully somebody remembers they put it on a credit card but i cancel our cards every year where i have the cards that you can set the limit on

SPEAKER_211: where like i just say okay take all the cards down to zero and let's watch our phones ring off the hook

SPEAKER_14: as sas vendors are like hey your card's not working that that definitely works and one of the the big the big shift here is when you move away from software and towards agents the agents know how

SPEAKER_28: to use these tools better than people do and so they're going to extract all the value out of these

SPEAKER_14: tools and that it could be apis it could be really anything and you're seeing it across the board

SPEAKER_28: all these little point solutions but imagine wrapping all those point solutions into a generalized platform

SPEAKER_09: that's that's what we're building make sense yeah this is uh pretty crazy well listen you got a team

SPEAKER_327: of 30 or 40 over there 35 we're really lean yeah i mean uh you raise some a decent amount of cash you

David Friedberg: got a decent amount of revenue coming in are you finding your clients are this is you're you're too far ahead of what they need or they're just trying to get understand this or now with chat gpt 3 5 and 4

SPEAKER_211: they're all like oh my god we have to catch up i know we paid for this we need to now expand it and we need to understand this because our bosses are breathing down our uh throats it it's a mix so yeah

SPEAKER_14: there there's an ai maturity model gartner published this model in 2019 and they said hey look we're going to start by companies kind of being interested in different you know potential with ai yeah the i've taken that and adapted their maturity model and basically that phase one is like the end users are going to start bringing in different ai tools into their day-to-day workflows so that would be like people using chat gpt using our chat you know that's also powered by chat gpt or um you know using copywriting tools i saw canva adobe you know everybody's got a notion everybody's got an ai feature kind of a wizard yeah which which was you know i think it took two years and probably the

SPEAKER_28: 3.5 model was really the point where those companies would say okay we can we feel okay

SPEAKER_14: trusting this and putting this out there for our customers um so that's really phase one is are your is your team using it the second piece would be is your team able to actually scale its usage of ai and that's not something that chat gpt is going to really help your team do but if you wanted to make you know repeatable processes um build you know build actual components of your business so focusing on the system rather than the manual tasks automation so that's where you know we built into that that's kind of phase two and then phase three is when you connect you begin to connect enough of the workflows together so that the data feedback loop starts to really run like a flywheel and at that point now more and more of your business is going into like autonomous mode and as you as you go into autonomous mode people have to actually build that system out and it it's not the ai team but it's actually the business users and so we're process people the business leaders even the functional people a lot of people want want to actually build these systems out and so you are seeing kind of two groups emerge one they're excited about you know building more efficient systems being able to do more projects than their you know their team could have ever done before and they're the ones that are really ai native and that's who we build for and then you have folks that are really hesitant to adopt any of the systems and tooling and ultimately those companies are not going to be competitive kind of in the 12 to 18 month time frame they're going to become rapidly uncompetitive and then pretty much at every company the ceo is like hey ai is here like what's our ai plan what's our ai strategy right and so then there's a really big gap between the end user tool and the ceo yeah right and so where we found success with our platform is selling the vision of the platform to the like ceo c-suite and then the leaders of all those groups because they say okay well can you can you do this use case can you do this use case can you it's like yes yes yes because it's all basically just stacking these blocks together and um that'll cover that i mean that could be a 10-year time frame where that really gets adopted or it could be like three years five years it really depends on um the level one of interest and then two it's like how fast is the underlying technology really going to improve um right now the bottleneck is people don't even know what's possible with ai yet so there is some education

SPEAKER_10: that's the key and that's why i just told everybody start playing with it and it's kind of like mobile

David Friedberg: you know one of the first steps people did in mobile was or even cloud was just give everybody a mobile phone so everybody had flip phones and they're like just get everybody on your team a mobile phone get everybody on your team an ipad if you want to build ipad apps they got at least have ipad if you want to see what 5g and broadband is like just buy all your executives broadband at home and get them laptops and show them how it works right they need to really start soaking it and playing with it and and

SPEAKER_139: then the use cases will come out and it's like anything else some people will go too slow

SPEAKER_345: like microsoft did with mobile and they'll give mobile this one they did not they didn't miss this

David Friedberg: one i mean this is like the makeup for missing mobile right you think about how badly they miss mobile and now how well they hit this one they're like we're not going to miss this one we'll pay 10 billion and then you look at like mark zuckerberg with arvr or maybe to a lesser extent apple who has a device coming maybe they just also made huge bets and those huge bets are just not

SPEAKER_66: going to hit the mark so it's uh one of the great things about technology is how dynamic it is all

David Friedberg: right listen paul you're in you're amazing uh really great overview for people who don't know about it and if you're in the business of running a business i think that's as plainly as you can say copy ai

SPEAKER_139: might be able to help you be more efficient so go check it out we have free plans there's a free plan goes to go play with it until your free plan uh you use up all your credits and then uh you go

SPEAKER_09: or what do you do multiplayer mode is where you have to pay we're doing uh right now it's still

SPEAKER_35: credits because it you know yeah it's a little pricey for us oh it is pricey yeah you have to

SPEAKER_312: yeah i mean i thought open ai dropped the price like 100 or 90 or something they did they that saved

SPEAKER_325: that saved us pretty good yeah because that was it was going to be quite expensive and they'll do that

SPEAKER_14: again i guess i bet right yeah over time it's going to go to zero and so then you'll have the scale the volumes will really start to to work through the system and that's another reason that i wouldn't want to focus on the end user point solutions because you won't have enough volume over time to really propel a business yeah it's incredible to think about you know something to think about because there's so it's so easy to create companies and you're seeing this ai hype race right but then the durability of a lot of the point solutions is really going to be questionable i think well i

SPEAKER_188: also think you have to also want and have the motivation and when human motivation this is a very

SPEAKER_10: hard reality for most people especially woke people or people who want to believe the world is fair in some way like it really does break their brain when you're like okay now solve for motivation because i can take any course from mit or harvard or stanford online for free right now and so the world's unfair and those ivy league schools have now given all their information out for free

SPEAKER_139: or the vx 100 you can buy a used one and make a movie and so hollywood's holding you back or you could get three or four of your friends together and make a movie this weekend or a short film for free

David Friedberg: what's holding you back and that's what i learned over time is that human creativity is all there but motivation that fire in your belly the desire to go do something sometimes the unhealthy obsession and desire to go do something that's one that we still uh as much abundance as we have can't seem to

SPEAKER_304: solve for we can't make people motivated to go change the world some people are and some people aren't

SPEAKER_13: my my oldest daughter i asked her like what do you want to do you know when you grow up and she's like well i want to want to breed dogs and i want to do art i'm like sweet i said you can do that now

SPEAKER_307: like you don't need to go to college yeah you can do a dog reader and make art and that sounds like a

SPEAKER_274: pretty dope she said she said i'll breed dogs and then i'll sell the dogs and i use the money to buy more

SPEAKER_371: art supplies and i'll do more art yeah and buy more dogs right so i think if if i could have one wish it

SPEAKER_14: would be that when we do go update the curriculums of schools yeah that the only thing that we instill is like that creativity and getting things done that you care about and if you drive that passion forward everyone will be successful because you'll have unlimited tools to make things happen there's

David Friedberg: literally in a school of um academia so there's montessori and then there's something called reggio and reggio that one of the core concepts is uh reggio amelia is figure out what the child is motivated by and then have the curriculum taught through that in your case art supplies and art and puppies uh and and dog breeding so you could teach biology obviously through dog breeding and you can teach math and you teach science through the science of oil painting or sculpture and you know the physics of it you can teach anything through a lens in which somebody's you know if somebody's into skiing you could teach them physics of skiing and they would learn all the physics concepts but they would be super motivated and then teaching them physics abstractly they're not motivated turns out every

SPEAKER_09: kid could learn physics or pick a topic if you just put it in the right wrapper um but yeah the

SPEAKER_374: education system is broken i just we should just ask copy ai right now how to fix the education system

SPEAKER_14: i'm sure it has a great answer well you can do custom you know custom learning plans for people

SPEAKER_28: people already building those those systems out and even like linking to the right youtube video to

SPEAKER_59: watch that's awesome right yeah if it nails that it's incredible yeah at some point it'll be like

SPEAKER_379: hey where what is this person stuck on you know nobody's gonna do an assessment of somebody like they're you know like some kids lost in life they're like oh this kid's lost like okay let's

SPEAKER_10: figure out some assessment tool and then it's like okay here's what would motivate this kid uh watch these five videos and it's like watch this gladiator video watch this like skiing video watch this puppy video and then listen to this song and it's like it's gonna unlock their brain and

SPEAKER_382: unstuck them that'd be awesome that'd be pretty pretty great all right listen we'll see everybody next

SPEAKER_240: time on this week in startups bye bye everybody