This Week in Startups - Demoing the most innovative new ChatGPT features with Sunny Madra and Vinny Lingham | E1732

Episode Date: May 1, 2023

Sunny and Vinny are back to break down ChatGPT's latest innovation: the Code Interpreter. They demonstrate its capabilities with two separate datasets on EVs in America and US Bank Failures (10:58) before discussing how platforms like ChatGPT will revolutionize organizational efficiency (31:19). (0:00) Jason kicks off the show (2:06) ChatGPT's new code interpreter (9:24) OpenPhone - Start your free trial and get 20% off at https://openphone.com/twist (10:58) ChatGPT's Code Interpreter example with EV data (23:50) Coda - Get a $1,000 startup credit at https://coda.io/twist (25:13) The global GPU shortage (28:10) ChatGPT's Code Interpreter example with US Bank Failures  (31:19) How ChatGPT will change the modern-day organization (38:55) Release - Get your first month free at https://release.com/twist (40:27) Getting more efficient with ChatGPTs new updates (47:53) Web browsing with ChatGPT   (56:11) How this technology will enable people (1:04:15) How this has impacted Sunny FOLLOW Sunny: https://twitter.com/sundeep FOLLOW Vinny: https://twitter.com/vinnylingham FOLLOW Jason: https://linktr.ee/calacanis Subscribe to our YouTube to watch all full episodes: https://www.youtube.com/channel/UCkkhmBWfS7pILYIk0izkc3A?sub_confirmation=1 FOUNDERS! Subscribe to the Founder University podcast: https://podcasts.apple.com/au/podcast/founder-university/id1648407190 OTHER LINKS: https://twitter.com/jbrowder1/status/1652387444904583169?s=20

Hey, everybody, welcome back to this week in startups. We're doing another AI roundtable, and this is the best one ever. Vinny and Sonny join me again to demo ChatGPT's new code interpreter. This was just released on Friday. We're playing with it over the weekend, and we're going to play with it here on the show. We take a random couple of CSVs that we grabbed off government websites. We uploaded to ChatGPT, and it takes this and acts like a data scientist, and it starts doing analysis of these documents. It's incredible magic.

Make sure you listen to this episode with your teams. because at your startup, you're probably wasting tens of thousands of dollars that this new tool is going to remove from your expenses. These rapid innovations AI are going to change the world. I've been talking about it multiple times per week here on this week in startups and on the all-in podcast. I think people are going to become 30% more efficient this year. But, but Sonny thinks, I'm wrong. He thinks it's 300% or more. We get into it. I show you a bunch of details. of some GPT stuff I did over the weekend and some stuff I'm doing in Python on a replet. It's going to be a great show. It might even blow your mind. Stick with us.

This weekend startups is brought to you by OpenFone brings your team's business calls, texts, and contacts into one delightful app that works anywhere. Get 20% off your first six months at openphone.com slash twist. Coda is the all-in-one doc for teams. If you've got a stack of niche workflow tools, or if you're buried in docs and spreadsheets, Cota is the dock that brings it all together. Get a $1,000 startup credit by signing up at coda.io slash twist and release. Large enterprises pose unique challenges for SaaS startups.

Unlock customers with unique needs for private and single-tenant hosting without the toil of DIY, with release delivery. Get your first month free at release.com slash twist. Hey everybody, welcome to another episode of this week

and startups with me again, Vinnie Lingam and Sunny Sundip Madra. We were doing a crypto roundtable boys and AI has taken over all of our lives. Crypto still seems like an important technology,

but it does feel like the amount of energy putting into, being put into AI startups, language models is 100x or a thousand X what's happening in crypto. So we'll skate to where the puck is going and continue our discussions about AI here. So this is our weekly AI roundtable. You have ideas for the producers here.

Producers at This Weekend Startups.com. If you see something interesting, say something. Email producers at this week in Startups.com. All right. So let's get right into it. You shared a link with us, Sunny, on the group chat. that some chat GPT users now have access to a code execution or code interpreter plugin. What is this and why is it important?

Yeah, so this is really, really big. And what chat GPT has enabled, Open AI has enabled, is the ability for the interface to run code. and what it's really what's interesting, and you can now input data via like an upload feature. So one of the really cool examples that people are doing this weekend, as was just released on Friday,

just goes to show you the pace, is that you can take a spreadsheet, that spreadsheet can have data in it, you can upload it, and then you can basically have a chat GPT do some basic data science for you. And so it's really, you know, the process to do that

would have been to, you know, either go get a data scientist or write a Python program. And so it does all of this in line. And a very similar way to how we saw the plugins work. We're seeing that now for, you know, running code. And that code interpreter, if you were to just do a Google search right now for, if you do a Google search for chat chapt,

and you go into chat GPT, on the drop down, you see, especially if you're paying the default, which is 3.5 version of chat GPT, GPT4, and then you'll see some other things like GPT 3.5 with browsing, which is in alpha, GPT4 with browsing,

that's an alpha, and then code interpreter, which is marked as alpha. And you see this all in the drop-down menu, and if you happen to have applied to the plugins, which I applied to and I've been using and I got my team on, you'll see plugins alpha.

I think paying for chat GPT the 20 bucks a month. We'll get it there. So is code interpreter available? to everybody, do you know? I think it's only available to those folks that have plugins enabled, which means that they've been allowed into this very limited beta or alpha group that are kind of developer-centric or people that are, you know, real, you know, publishing stuff to the community to help educate everyone.

So it's not widely available yet. Got it. And so an example of this might be what. And this is stuff you might ask a data scientist to do in Google, sheets or Excel previously or to query an SQL database or something? Exactly. That's normally how someone would deal with it. So inside your organization, Vinnie,

people are like, oh, we got this Google sheet. Oh, we exported our Google analytics. Oh, we downloaded some data. We got some, you know, client data. We've got, we exported something from Salesforce or whatever tool we're using. Now the team has to go find somebody smart who is either in the accounting department, the data science department or it just happens to be good at hacking this stuff together

and this is something that civilians the other 80% of people who work at a company just don't know how to do it would be too hard for them to do you have that experience I guess in your startups as well Vinnie yeah I mean it's it's definitely a lot easier to I mean it's the barriers to

using data science right now is coming down by the by the day you know this is where it's democratizing data science. Like, I've got a friend who's a data scientist and, you know, I invested in his company and he's been using data science models for years. And like, it's just, I think it's a game changer for them. I mean, they, some of the data science companies out there right now, they charge ridiculous amounts of money. I mean, we're talking like millions and millions of dollars to do data science for companies. And there's some big businesses out there. I think

one's data dog, I think. And there's a couple of others. You know, and OpenAI and ChatGPT is basically, you know, reduce the ability to do this. You know, SMEs, enterprise individuals can do it. What I think is interesting, though, on a slight deviation here is Google has got access to so much company data to the Google suite. So if you, if you, like, run a startup and you're on Google, Google Drive, you know, Google Docs, Google Sheets, everything. That information is incredibly powerful. So now, Google just need to take BARD and say, would you like to activate BART on your company documents

and then create like, you obviously have to figure out the privacy stuff and, you know, rights. But basically, you have access to the document. That's already been done in an organization, right? Generally speaking, the organization should have set their permission. Well, so just keep this in mind, right? If Bide starts learning across the company, it needs to be able to partition the knowledge and not infer information

that only you have access to. If I'm the HR department and I've got a bunch of documents that only the HR departments are and then somebody in sales does a query, hey, how much do we pay our people internally? And what's their compensation? You don't want that coming up in the results. Exactly. So that is an important permission issue. Yes.

But if you're the CEO, you should have, you know, do you have access to someone? Do you have access to everything or do you have access to? And what about like if J. J.K.L. has got a private dock sheets in there that, No one else actually, are you allowed to see that? Of course. I mean, the organization owns that this is like a fallacy that some employees have that. I agree with you.

I agree with you. If it's personal information, you shouldn't have it on the company's service anyway. I am amazed by that. It should not be, if it's company information, it should be available to your manager, your manager. Right. So that's an important issue to flag. But, you know, just as a fair warning to everybody there who works at a company, everything you say on your email is saved for all attorney,

your documents or Slack for all eternity. Do not expect anything. Phone calls as well. A lot of companies record all calls in coming in. I mean, in some of it's compliance and some of it's just the default. When you leave a company, you assign the documents to the next person or to the CEO.

So if you wrote your diary or your journal in your corporate account, I mean, wake up people. It's 2023. Don't do that because it's going to be indexed. And then somebody's going to be able to pick it up. So, poor initiative flag. Stop using your personal phone for your startup in 2023. You have to stop doing this.

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Open Phone is the number one rated business phone on G2 for customer satisfaction. And Twistners are going to love it. Brian Jagger, he's the co-founder of a startup called Athlete. He tweeted the following. I'm literally cash flow positive from listening to This Week in Startups for Listener Deals. And he explains that he previously got Open Phone money from this incredible discount that they give to this week in startup founders. And he says, I'm not paid to say that. I don't know Jason, pure honest feedback and appreciation.

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So head to Openphone.com slash twist and start your free trial and get 20% off. Do you have an example to show here, Sunny, if people are watching at YouTube.com slash this weekend or on Spotify or the video feed? Let's just open up GPT4 here. And I have something that I was playing with this weekend. I got interesting too that I'll share.

Okay. So I'm going to share here. Give me a second. All right. And we're doing this live because we just got the data set from our producer. Okay, so we're inside of chat GPT here, and we're going to upload this electric vehicle data set.

Ah, and that, when you said send a message, there's a link on the right there, and if you're on the left to send a message, and that's where you upload from? Yeah, right here. There's like a little, like a, there was a, yeah, see this little plus icon, and normally the,

so you can see the first thing. I didn't know that. Is that only for... That is only for the code interpreter. Got it. And so show just so people can see the interface here, because we have never done this, but we just hit a new chat there,

and let me just show people the interface and then just describe that for folks. Okay, hold on. Let's go. You click new chat in the top left. You hit this down arrow, key. Now you can see all the different items, plugins, default, etc.

So you've got a sports guest just a little bit so people see it. And then it gives you a little description of what it is. and how good it is and the sort of internal rating of what it does. But you picked code interpreter. Interpreter, correct. All right.

And then you hit the Flutkey. And now, you know, this is about, I think, a 29 meg file. And so it's going to take, you know, a few seconds, stop load here. I see that, yeah. And so now what it's going to do. And none of us have really seen this file yet, which is fascinating. It is fascinating. I'm, by the way, doing this a lot of,

alongside of you. Yeah. So this is the code. So it's generated this code. This is Python code here. Jay College you were asking about this weekend to read that file. And it's still generating. And it's understanding.

Now you can see here, it's starting to tell us, hey, the data has been rolled it into a data frame. And from the first few rows, we can understand that this is the data. So we're going to let this just let this complete. And I'll tell you the next piece,

which what Vinny was talking about a second ago is like, you know, where you normally have to go get a data scientist. and so to do something like this. And so, and it throws some things up here and it says, okay, so it's done. So then my next question is going to be this.

Well, let's describe what it showed there. It's loaded the data and it says, oh, it looks like the data contains VIN, location, model year, make, vehicle type, MSRP, and Department of Licensing Vehicle ID, some locations, utility,

and some census tracking information. So what producer Nick gave us was the electric vehicle population data. and it figured out what's in there and it's reflecting that back to you in plain English. Correct. It is. And it's saying, hey, I'm ready to do something. It's loaded it. What I'm showing here is the prompt

where it's loaded it into like a Python library called Pandas, which is what a lot of data scientists would use to start analyzing data. So there was a little carrot there that said, showed the work. So after it uploaded it, when it finished work,

it asked you to do that. And fascinating, when it did, For me, it did a different response to the same data, which is really interesting. Like chat GP4 told me the dataset contains information about electric vehicles with each where I represent a specific electric vehicle. The columns in the dataset are as follows, and it did it one through 10. It actually gave me a list of them, which is really like a totally more helpful response. That's very fascinating that we had two different experiences.

And that's sort of the nature of LLMs that can happen. But this next question, which I'm putting down in the prompt, so I'll read to everyone, says, can you conduct whatever visualizations and descriptive analysis you think would help me understand the data? Because I have this producer Nick sent us this file. And so now, let's see what it does in this next phase here. And so what it's starting to tell us is we'll look at the following aspects of the data. Distribution of electric vehicle types, you know, battery electric vehicles versus plug-in electric vehicles. that's dev versus Phev, top 10 most popular electric vehicle makes and models, distribution of the

vehicles by year, geographic summary of the vehicles, and summary statistics of the range and base MSRP. And that's all, it's doing all of that just based on this question, which was can you conduct whatever visualizations and descriptive analysis you think would be helpful to understand this data? And so now it's doing the work to basically do those five things for us. And again, you can imagine that is that you did. a very generic question, which is you asked the CEO question. All right, thanks for the data. Yes.

Data scientist in a meeting. Why do I care? Just get to the point. What did you learn by studying the data? And it's basically just starting with some general ideas here to get you started, and you could pick one to double click on. Yes, correct. And so it's now doing the work.

And what you can see here again, like, you know, yeah. Describe what you're seeing. Remember, imagine people are listening, Sunday. So sportscastings. It gave us five examples of things to look at the data. So the first is the distribution. A chart of the distribution. Yeah, exactly, of a chart that shows us the distribution between battery electric vehicles

and plug-in hybrid electric vehicles. And this is a visualization. It would have taken someone a few minutes to, you know, maybe 30 minutes to generate this chart in PowerPoint. And it's been generated for us automatically. And it shows us that the distribution is, almost five to one here, right? Maybe four to one in terms of there's way more battery electric vehicles than plug in electric vehicles according to the data set that we were given.

Okay. The next chart is we're going to look at the 10 most popular electric vehicle makes. And we see here that Tesla is a clear leader with Nissan at number two, then Chevrolet, then Ford, and we see a visualization of a chart there. Next, we're going to look at not by make, but we're going to look by model. And we can see here that the most popular model is the Model 3, then the Model Y, then the leaf and so forth, if you look at this chart. And then when we look at by year, and obviously, you know, this, we're only partway into 2023, we can see that by year, the distribution of electric vehicles has generally been increasing with a little bit of a slowdown in 2019 and 2020 and a pickup back in 21 and a huge jump back in 2022. and we're only, you know, a quarter,

a little bit more than a quarter away to 2020. That would be my interpretation. But what's interesting here is now that you start to see some of these things, you could actually ask Chad Chapti, why is there a spike? But you could just do that in another window at Chat Chbt4. What's your takeaway here, Vinny, just to bring you in on the conversation? I mean, I'm going to start using this to analyze my wine collection.

Fantastic. You have a CSV upload it. What do you tell me about this? That's exactly what. I'm going to do it. I'm going to go, I'm going to pull it right now and see if I can go, you know, come up with some strange stats, you know, recommend other wines for me. Let's see what it comes up with. Do you have plugins? Go do it. We'll show it on the air if you're comfortable. What's interesting here also is based on the visualization and summary statistics,

here are some key insights from the data. It actually wrote some of these and it said top 10 most popular electric vehicle is 0.3. Tesla Model 3 is the most popular electric vehicle model followed by Nissan Leaf, etc. So you start getting into some really interesting concepts here. And for mine, let me share mine. This would be very interesting to do, if I may.

Oh, did you have another one you wanted to do, Sunny? No, no, no, that's what, you know,

I wanted to just show that capability because that's the new feature the unlock is uploading the data set, which I know you've been thinking about a little bit, J-Cal, because you have a lot of spreadsheets.

I know in your business. I got, can you see my screen now? Okay, great. So I did the same thing. I uploaded the same file, but what you'll see here is that if you're seeing it, remember I said, it gave me just a list of what are the columns. So it gave me the list of columns. And then I asked a slightly

different question, what are the three most interesting trends in this data? And it said, to identify interesting trends in electric fuel population, we need to analyze various aspects of the data set, pretty generic. Let's explore the following three trends. Electric vehicle adoption over time, most popular electric vehicles make some models, distribution of electrical fields like yours. and then it gave me a couple charts. It did a different design style, which is weird. But electric vehicle adoption over time,

instead of using a histogram, it did a line chart for mine. It did the same thing, most popular electric vehicles, and then it did the same thing, the distribution. And it, too, gave me some highlights here. And what I could do here is,

an interesting one. Let's see if this works. Please give me the same analysis, but take out all Tesla models. And if it gets this right, that's like game over, right? Because this is something you might ask. You're like, okay, we know Tesla is running the table on everything, but I don't care that the, I mean, we all know,

Model 3 out sells everything because it's, you know, the greatest, well, Model Y, I think is the greatest car ever made. But those two, but let's just take out all Teslas and see if it does that, right? So now you're starting to be able to do things with data, I mean, this is just stunning what could be done here. I was over the weekend trying to do things here inside of it.

I'll show, well, I can't leave the screen. It's one of the problems with chat GPT4. I think if you leave the screen, it will pause. Yeah, sometimes. Yeah, I guess they're trying to get people to not do this, but all of these little blocking and tackling things will be worked out over time,

like doing multiple queries simultaneously. Like, just for the love of God, Greg and give me a corporate account here. Let me put all my people into chat GP4. Let all of this data be shared in a common repository.

I need multiplayer mode for chat GPT4 and I would pay $200 a person per month. I would pay $4,000 a month, $50,000 a year. Right now I'm paying $20 across everybody in my organization and hopefully everybody in my company

is actually doing this now. If you hear my voice, I've been like tweeting about just, oh wow, here we go. Let's see. Electric vehicles over time without Tesla. That's interesting. And then the models, yeah, wow, it nailed it.

Most popular electric vehicles makes without Tesla models. And you see a very more even distribution in the chart. Nissan, Chevrolet, Ford, BMW are one, two, and three. But it's not as spiky because you're taking out. And then you see here that actually the hybrid, since I guess Tesla doesn't produce a hybrid versus battery electrical becomes much more normalized. So here, peak sales in 2022, it looks like, is. 14,000.

23 is not complete yet, right? So that's why it's, it's, it's off. Last complete year, it was 25,000 over 25, I'm sorry, number of EVVs. Would that be 25 million? What is the left hand here? No, it can't be 25 million. It would be 2.5 million maybe. A million or, yeah, probably that makes more sense.

Yeah, so it's, it is like, yeah, it says 14,000, but it actually means add probably two zero, so 1.4 million. So you're just taking out a lot of vehicles, probably. probably. Yeah, Tesla sold what looks like 500,000. Is that right? No, 50,000. Is this how many in America? I think they sold close to a million. Yeah, this might be U.S. because it was a state, it had state vehicle and other information. What's the time frame? What's the timeframe for this? Like a year, a month? Since 2000. They went back a few years. Yeah. I mean, this is just incredible. I mean, you just see like, we're lifelong technologists. We know

how much time this kind of takes to do this kind of stuff. But imagine you take your website information or your podcast data and then you start slicing and dicing that now. And imagine the work and the number of people it took and the time it took. You, Jason, would want that answer right away.

Where are the listeners from? Which ones, all the different, you know, you're going to go after this, Jason, and download all your data and are going to be uploading it immediately, is my guess. Right. Yeah.

I mean, I mean, it doesn't need to have a developer account for that? Like, what do you need to have to be able to use this? Right now, you have to be, have a developer account,

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I want you to take advantage of it right now because I don't know how long this absurdly generous offer from Coda will exist. Codda.io slash twist for $1,000 in sign up credits right now. There is a wait list for plugins and but this is compute intensive. Jay Golly this goes back to the whole GPU shortage problem, right? This is compute intensive. They gave everyone the 100 million users plus access to this. It would just fry the system. They don't have the capacity for it.

honestly, like, I think we're getting to the point where this is so valuable for organizations that Azure and AWS should just start offering your own, what is it, A100 is the NVIDIA. Amazon's working on it, but it's not that simple just to like spend these things up. It's going to take a couple of years to get. Oh, I mean, just racking them is going to take time, producing them. You have to basically. Jekyll, there's a shortage. There's a chip shortage out there.

I absolutely understand. But what I'm saying eventually was the word I use, Vinny. Eventually, I think organizations are going to start provisioning their own GPUs for this because it's so valuable. And if you told me right now, an A100, you know, cost $10,000, would you like me to sell you one for $20,000 to have it in your organization today to start doing this? I mean, it's a de minimis amount of money compared to the value created. I just asked it another question. And I was like, which states had the most growth in 2021 and 2022? And it said, based on this, the electric vehicles dates 2021.

Here are the two top states with the most growth. Do you want to take a guess? Which states had the most growth? Percentage-wise? Without California? No, it included California and Newark. No, I said percentage growth, though. A percentage of percentage of those.

I said which states had the most growth in 2021 and 22 interpreted that as percentage, not raw numbers. Okay. So it is in fact- Texas? I'll say Texas. Okay, which are, okay, keep going. I'm not going to say that right.

Texas and probably. maybe Florida Maybe Washington Seattle I'd say I'd say yeah like Washington You actually

smart Sorry A number of minutes In F bomb Washington is number one They grew from 18 to 27,000 A growth of 9000

EVs 50% growth And Texas was number two Yeah Actually it got that wrong It says number of EVs In 20213 number in 224 It's Washington state

Data specifically It's in Washington state. Oh, this data set? Yes, this data sets Washington state data from Washingtonstate.gov. Oh, sorry.

Okay, so what we're looking at has nothing to do with by state. Okay, that's why the numbers were low. Okay, great. Yeah, what I'm looking at the CSV though, it does have all kinds of counties and cities.

Like I see San Diego. We just took, by the way, for the folks listening, we just took a random data set the producers found and just uploaded it. So we found this data set. It doesn't have perfect information. and so just understand like the

where this is kind of an interesting use case. Somebody sends you a CSV, you don't know what it is and it starts interpreting it for you. All right. Well, producer Nick, who is an exceptional producer, you hear people talk about producer Nick on All In and Here at This Week in startups. Did a wonderful job producing today.

And he said, explain to the audience what you found and what you did while we were alive on air. Yeah, so I found a website where they have a bunch of CSV files from government data, one of which was the one that you just saw previously, the Washington State EV data. I also found one, which has something to do with the topic that we're covering today, about FDIC bank failures, which was from the actual FDIC.G.gov website, which you could see right here.

Pretty amazing. I uploaded it. It found a formatting error on the CSV file, and I was about to look up how to fix it, and chat, JBT just fixed it itself. Found a unit code error. Okay, yeah, that's common. And then just fixed it, pretty crazy. Perfect.

Then I asked it, what are the most interesting ways to visualize this data? Gave you some examples. I said, okay, do that. And here you go. Okay, so let's take a look here. It said bank closures by year, bank closures by state, top acquiring institutions, fascinating. Heat map of bank closures timeline, heat map of bank closures,

timeline of bank closures. This is fascinating. So let's scroll down here and see what charts it came up with. again, finding errors and fixing them, scroll down. Now, let's proceed with creating visualizations. I'll start with bank closures by year, bank closures by state, type acquiring, top acquiring institutions is fascinating. And obviously we see by year, scroll down 160 or so in the financial crisis, and then it slowly

went down. But what's interesting about that, Vinnie, if you look at it, you notice that the bank closures that started in 2008 peaked in 2010. So it was a full two plus year process of peaking and then trailing off, you're going to have some per year, but it still took, it was basically four years of bank closures. Well, so just remember, so a lot of this was back in the days, we had a, we've had a lot of like the smaller banks being consolidated up. And then they pass the laws on the bigger banks as well. So it's unlikely for us to see the same sort of tail right now because all the small banks have been cleaned up. However, if you look at the latest data and just the amount of money that's in the banking sector has blown up in the past two, three months, out of like five banks, we've had more AUM blow up.

I think in 2023 than in 28 and 9 and 10 and 11. Like the whole banking crisis, in the past three months, we've had more. Like a dollar amount. By dollar amount. Yeah, yeah, yeah. Like Washington Mutual versus Silicon Valley Bank versus First. public, et cetera. Like, the scale is so different right now because these banks are so big.

It's interesting also about what Nick found here is like you can see some of these didn't have requires. They just shut down. Some of them, you know, were acquired by state bank and trust company, first Citizens Bank, Ameris Bank, U.S. Bank, N.A. So just fascinating ways to look at data. If you're listening to this in your organization, there's going to be two possibilities

of what happened. This is what I've been trying to explain to people. Maybe I have to go back. to based Cal and start using all caps on Twitter. But I am finding that 30% of what I do can be done inside of chat BT4 today. I'm finding my producers, and you saw Nick pull up his thing there, and I saw questions in his thing that were questions I was asking during live this week in startup. So when I'm doing the show, the producers are looking up data.

They're using chat GPT4 all day long, and even during shows. So this to me is what I would implore people to try to understand right now. Smart people who are using this are taking, I would say, between 10 and 50% of their job and automating it, and then they're quiet quitting, or they're doing more work and they're going to be more effective in their organizations or their boss is going to figure this out and everybody's going to get more work done. And instead of hiring, people are going to start firing and getting more done. So just think about gains, 30% gains across an organization of, let's take my investment firm, about 20 people. That's the equivalent of having 26 people. So one of two things is either going to happen. If you had 20 people, you're either going to go down to 14 and save that money, or you're going to act like a 26 person organization or something in between.

That's how management thinks. Now, for my team, first doing a great job. I just want you to become 30% more efficient so we don't have to hire more people. but other people are going to look at this, Vinnie, and they're going to take a different approach, which is, okay, we have how many data scientists? Great. Half their requests are not necessary.

They're going to be done by chat GP4. People are not going to need them. So we just get rid of half the data scientists. Now, take a moment to think about what I just said. There's been a competition for data scientists. Some organizations say, how many of these data scientists do we need? Well, I mean, well, I'd say right now, Jekyll, we probably don't have enough on a global basis. So I don't think there's going to be a shortage of data scientists anyway in terms of.

So they may be reallocated, like from companies that have seven down to three, and then those four go elsewhere that's needed. So I think you probably need fewer data scientists per company, but there's still companies out there that's going to need that never thought of having data scientists because they just didn't have the, you know, but I mean, like you still have to pay for the licenses. right for the software that they use, which is like millions of dollars a year. So now the cost of the software has come down dramatically. You still need the people to operate it because some people just need to be focused on this stuff. And a lot of companies that data is in multiple databases and spreadsheets and it's very disparate. You start to build data warehouses that have all information, et cetera.

So it's not as simple as that. Is it not as simple as that? No, I don't think so. I think in a world where everything was highly efficient and everything was run properly, yeah, maybe. But we're so, I mean, the gap, right? now between the haves and the hafnots in day science. I don't know, Sunny.

I might disagree. This weekend, I started learning Python. You already called me sunny right now. Vinny. No, I was going to Sunny. I was going to throw to Sunny. I was going to throw to Sunny. I was going to throw it to Sunny.

Listen, you guys are Sunny and Vinny. You're two of my best friends. The names are different by one letter, Sunny and Vinny. Two letters. Sunny, Vinny? Two letters. Oh, right. Yeah.

Sorry, sorry. I had a long weekend. I had the kids alone. Anyway, I am going to disagree. Vinny and Sonny, I want you to reflect on this. You and I were chatting. We're trying to get together over the weekend to do a little code jam. But kids, whatever, got in the way.

But I started on a Warriors game. Nick's loss, Warriors won. Incredible. Shout out, Steph Curry. Replit is like a coding environment. So I just signed up. And I started taking their Python course. So it's like, oh my God, this takes so much concentration.

I'm never going to be able to do this. Like, this is not going to be my chosen career, but I do want to see how far I can take it because they have a bounties thing on Replit. And I put a bounty up. And then I explained in details, I'd like an order a GPT agent that checks our database of already contacted companies by URL.

So these are startups we've talked to. So we say, hey, com.com and uber.com are in the database ready. We don't need to call them. Then finds new startups on crunch-based products, hunt and LinkedIn and sends them a semi-automated email from one of our researchers introducing our venture fund acceptance criteria. App is able to find a recently updated crunch-based profile within a specific criteria, geography investment stage, and sends an email to that founder.

Pretty simple, right? And I put this up for 27,000 cycles, I guess they call them on Replit. Shout out to the team at Replit that emailed me immediately after I talked about it on the pod. And I put it up for $270. I got four applications. And as you can see here, one person says, Jason, have built this in the past

and building for a few funds. So I'm not the only one thinking like this. We'd love to chat more about you. You can check my GitHub, LinkedIn, for resources. And he's done three bounties. Cribs, Jake Al, I'm a fan of the pods. I've read your book. Dumbuck.

I'm poking around Repleting and see what all the fuss about it. Regarding your bounty, I'd like to help ask you to flesh out your criteria. Yada, yada, yada. I do either of these free. As long as we can take it pretty much time you coaching me on my personal journey.

I don't like taking free stuff. But anyway, my point here, Vinnie, and then I'll go to Sunny, is I am the CEO of the company. I'm the GP, the general partner of the fund. I'm looking at this and I'm like, I wonder how long it is between when I can describe

something to a bounty program and have code sent to me, and then I run it myself, just like I am using chat GPT4, and I feel like I'm on a collision core, Sunny, between using chat GPT4 with plugins and uploading stuff myself, and then working with the developer community to write tiny little scripts for $270 that had a $50 salary or $40 salary or $60 salary

for, let's say, an operations person in our organization, you know, that would take five hours. I can basically take what is 50 hours a week of work in our company two researchers doing 50 hours a week at work, $1,500 a week, maybe, I don't know, $2,000 a week fully baked with benefits, $100,000 a year of work, and I can just automate it for $270. Am I crazy or is this going to change the world? No, I mean, we're 90 days away, J. Cal, we're 90 days away at the pace we're going at right now.

Because, you know, what you put in here is mostly just doable. and it's like I said, we're entering a world where the core framework is being absorbed by open AI and so if you just saw what we did that they're going to open, they're taking their time right now

from a safety perspective that the code interpreter that we were just playing with, J-Cal doesn't reach out to the internet just yet but we know that they have browsing capabilities because there's other plugins that can browse. Yes. As soon as they allow code to go out to the internet, which they've controlled that.

It's not like they don't know how to do it. Then you have that problem solved right inside code interpreter. It's crazy. Because you would describe your problem inside code interpreter and say, here's my spreadsheet, go to crunch pace. And so the same thing you did in the replica, you'll do inside there. Developer talent is the most precious resource for B2B startups.

You know that. And you want your developers focused on product, not on compliance, right? When you're selling B2B software to large enterprises, you need to jump through a ton of security and compliance hoops. And one of those hoops is large customers need you to host your software on their cloud. And you need to build that out on a per customer basis. Think about that. So B2B startup companies constantly face this dilemma.

Do you keep developers focused on infrastructure, which could hurt your problem? product velocity, or do you keep them focused on the product velocity, which would then delay your ability to close large customers? Well, I have a solution for you, and it's called Release Delivery. What Release Delivery does is it automates the creation of Enterprise Class App Delivery for private clouds and single-tenant applications. Basically, this lets you deliver your software seamlessly into any customer environment. This will unlock a ton of revenue potential for you, and Release Delivery will put all the tedious stuff on autopilot for you. So you can turn your ideas into apps and deploy those apps quickly and flexibly into their clouds.

So here's your call to action. Let Release show you the power of release delivery and get your first month free at release.com slash twist. What a domain name. R-E-L-E-A-S-E-A-S-E-D-com slash twist. It's up to $10,000 in value at release.com slash twist. I would agree with Sonny on this. I mean, guys, this is the fifth generation language. Like, we, you know, we never really got to it.

This is natural language programming. Like, everyone's a programmer now. You just need to speak English at this point to be able to do it. And even not even English, other languages as well. Check if you can translate for you. So as long as you can, I mean, if you think about it, like, you know, language is code. You know, like natural language is code. And we just, we had to create this layer where digital, you know, digital, you know,

digital, you know, softening programs and machines could interpret what we're saying accurately and because the human brain is so complex. The language is a very complex thing for us. But machines that we've had to instruct machines based on a very limited number of words, you know, functions that we have that was written. And now it's fully expansive. Like, now you have the entire English vocabulary that you can use and the machine understands what you mean. You can be extremely precise in what you're saying to it as well. Whereas in the past, like, you'd have to write functions to do certain things. It basically now understands every single word in the English dictionary to a very, very deep level, and every single word becomes, you know, effectively like somewhat of a function or a describer or something.

So, like, you know, I posted a tweet, I think yesterday, we'll pull it up, Nick. I think this is a very important point we should, we should probably touch on today and get your views on this. I think that, that, you know, in the next cycle, so we're in a, we're in a bare cycle right now, right? or we're heading to one or whatever we're going to call it. Obviously, we may not be in a recession. I think we are in a recession for what I'm seeing and seeing the signs of a recession already. The next cycle that we go through is either depression or it's a recovery and a boom, right?

So whatever you want to, you know, how do you want to define the next cycle? Regardless, I think we're heading for deflation in a big way. And I think that this will become the number one drive of deflation. I think you're exactly correct. What's going to happen is massive efficiency will come to the company. that get on this early. Then what will, and, you know, the, if you're running a company right now, you should just give everybody the tool, ask them to show you what they did with it.

And if you have 10 people in your department, if seven people use the tool, and three people don't, you should fire the three people who don't use the tool. I know this sounds crazy. But this is exactly what I saw happen in the early 90s. We put PCs on people's desks. Some people literally did not want a PC on. their desk. They wanted their secretary to have the PC.

And those people lasted I think, you know, less than a decade in corporate America. And that was back then when, you know, you got to keep your job for a long time. There wasn't as much turnover for boomers. But there were boomers who were like literally when I was installing computers in the early 90s who were like,

yeah, just I don't want the computer. You can, don't put it on my desk. Put it on this like little cubby over here in my law office. And my assistant will do it. And they never logged in. And those people got phased out. They were relationship people. If you're not using this every day, you're literally a dinosaur. You're literally a dinosaur. That's my belief. So you're exactly correct. This will make every company 30, 40, 50% more efficient. And then what you have to ask yourself is, are there enough problems in

the world that your company addresses for you to solve to generate revenue in a capitalist society? I believe there are decades of problems left. I don't think that this is going to result in a UBI, universal basic income where all the jobs are done. I think humans are going to be creative and find more things to do. But I literally believe efficiency of 5% gains per year for humans, let's say if everybody got, maybe let's say everybody got 10% and every year, every seven years, people doubled their efficiency. I think what we're going to see is everybody's going to become 10% more efficient like a month

or let's say a quarter, which means every seven quarters, every year and nine months, people are going to be twice as efficient. What do you say, Sonny? Well, I think there's a great example, J-Cal, and I've seen it, but Nick, if we can pull it up in terms of efficiency. So this is someone who's working on a do-not-pay plugin. Oh, Josh Browder. He's been on the program. Yeah, yeah.

Oh, there you go. So maybe-J-C-C-C-C-O. This is just, you know, Josh Browder is Bill Browder, who wrote the book Red Notice's son. He's an entrepreneur, and he has Do Not Pay is the name of company. He's been on the podcast. and his whole thing was to help you get out of like reoccurring subscriptions, etc. But he's also addicted to GPT4.

So let's do a reaction thing, JCal. Why don't you read this because you've seen it? So go for it. I haven't seen this. So what did it say here? Okay. So this is, you know, do not pay. It's an app on top of a chat GPT leveraging it.

It goes, ask, how can I help you? He says, find me money. Is it connect, the app says connect your bank account. He connects account. And then it finds the subscription. that this person is paying. It obviously learns that. And then it says,

what do you want to do? Disney Plus. Yeah. Yep. Okay. So let's go to the next spot. Incredible. Okay.

And in this, he says, first using do not pay at Plaid Connection. I had... It scanned about 10,000 bank transactions. So I found $80. $86 leaving his account

every single month and offered to cancel those. Great. let's keep scrolling. Then the bots basically got working, mailing letters in the case of Jims. And it used a USPS API and chatted with the agents to basically start working on the cancellation. And so we can scan through this and we'll maybe drop the link of the notes. But the beauty here is going back to efficiency.

Think about the time and effort. There's one last example. if you can go back there, Nick, where it actually found a bill for a Wi-Fi connection. And it turned around and asked, hey, was that, did the Wi-Fi work properly? When he said no, it drafted a letter to send to, you know, Go-Go, whoever the Wi-Fi company was, asking for a refund for that, for that. And we've all experienced that where we pay for it and it doesn't work or it's bad. Yep.

And basically, yeah. And so very similarly. The negotiation process to cancel that and get a refund. Yeah. And then similarly, it started a negotiation process with Comcast. It's just, that's what I'm saying, Jake, these are apps that are being built on top of the technology. So we are almost where you're talking about. So I said, less than 90 days away from incredible things happening for us, which then aligns the deflationary argument.

It's definitely going to be super deflationary. If you hear my voice, you know, like, and you're not using this. and you're not getting up to speed on it, man. You're not really following how fast this is. I started playing with, I'm giving a speaking gig on Wednesday in Laguna down in the Orange County doing my paid speaking gig thing.

It's a corporate gig, and I'm talking about travel. And so I started testing some, I was like, you know, in this luxury hotel kind of situation, I wouldn't say which one. Okay. But let me share my screen here. So I started using the GPT4 with browsing. Browsing.

I don't know if you play with this, but it doesn't work very well. I had said on All In and Chamoth and Sachs laughed about this that, hey, you're going to need to start citing your sources and then getting permission from them, etc. Or else this thing is going to become gnarly and all these lawsuits have already been filed. But when you hear, I said, what are the major trends in luxury hotel travel? And it started to browse. And I guess it did a search. And it said, Search major trends in luxury hotels,

2023. It found this link from a website, EHL, and then it read the content. It got a bunch of failures. It's not working very well. Their web crawler is terrible, or it's really taxed.

I don't know what's going on. My team today has been playing with the web crawler. But it only found this one, and then it basically just cribbed it. So now you can kind of see what's happening with Chad GPD4. It is cribbing a lot of data and just rewriting it.

And then it does. some thinking on top of an analysis. I want to clarify something. Okay. So in the case when you're without the plugin, you're asking for something, then the cribbing is not occurring. And I think that's a discussion that's happened before.

In this particular case, you're asking chat GPT to go look for something with the browser plugin. So then it will crib. It's two very different use cases that we have to be aware of here. So anyway, this EHL Insights had written this. and you know you can see it basically took what they had on their website

and it summarized it a little bit better and then way down here it gave a citation you see that 12 it gave a little tiny citation and then I said which hotel chains are known for having the best hotel workspaces and then of them offer dedicate work desk and high speed internet over Ethernet connections and it started browsing the web it's actually doing it right now

because it failed so many times but I want to show you another one I did here and this one was a fascinating. I said, what are the major trends in luxury hotels? And it gave me up to September 21, this is without doing web searching, personalization, sustainability, wellness,

authenticated experience, smart technology, blending home, blending work and leisure, unique design and architecture, multi-generational appeal, privacy, and exclusivity partnerships. So I said, which three of these are the most important for maximizing a hotel's loyalty and revenue?

So I'm asking it to think, you know, a bit here. Yep. And it said personalization, smart technology, and authentic experiences. And I was like, huh,

the first two definitely authentic experiences, I don't know if that's actually like culturally immersive activities, genuine connection to the destination. I was like, I don't know, this feels a little woke to me.

I was about to say, I was exactly what took my time. I was about to say that it's woke cheapity. So I was like, please give me 10 examples of how a luxury hotel might personalize a hotel guest's experience. So I just went after the personalization. And this was incredible.

Like, and I don't know if where it's getting all this from, like, is it from its web crawl, you know, but it said pre-arrival communication, customized welcome and amenities like a favorite drink or snack, tailored room setup, like temperature, preferred lighting, curated experiences, personalized dining options, customized spa treatments, dedicated concierge service, flexible room configurations, tailored in room interchial. attainment, personalized turn down service. I said, you know what? Expand that list of 25 ideas. And it just went to town, you know, and customized minibar. I'm like, well, that's a great idea. I've never experienced a customized minibar. I've had that idea before personally.

Personalized fitness and wellness programs. Customized transportation options. Customized bedding and linens. I've heard about that. Actually, Chimov has that at the peninsula, where they have CP pillow cases with his initials on them. So he had talked about that. Pet friendly personalization.

Sent experiences. That's dumb, but interesting. Personalized communication channels. They do that in Vegas. Yeah. Communicate with guests through their preferred channels, such as text, email, or phone. That's actually a really important one.

Everybody's got a different one. So then I was like, okay, well, let's take this to the next level. And I said, let's see if we can set up a scenario where we tell it, you know, to pretend it's something. I said, you are the CEO of a hotel chain. And you're building 100. room hotel, take these top trends and write a three-paragraph, 400-word description of this new hotel, follow that with 10 bullets about what makes this hotel unique.

And it does this. Introducing the premier destination for discerning travelers, our 100-room luxury hotel expert expertly fuses modern technology, personalized experiences, and authentic cultural immersion to create a truly unparalleled, parallel retreat. From the moment's guests arrive, they are welcome into a world of bespoke services and innovative amenities all meticulously designed to cater to the individual preferences

and needs. It was like really like well written, et cetera. And then it gave like, you know, their top 10 bespoke guest experiences, state of the art technology. Yep.

Incredible. I said, rewrite that in half the number of words. And so he did it in half the number of words. So it was a little tighter. And then I said, okay, you're a branding executive

who has been given the description and location on a beach in Southern California and you're being paid to name this hotel. Give us four ideas. Came up with terrible ideas. ideas. SoCal Serenity Retreat, Pacific Sandshaven, Coastal Bliss Retreat, Azure Shoreline Sanctuary. I said, please do that again and come up with one word names.

Microsoft sponsored number four. Exactly. So it came up with Wave Crest, Sunhaven, Tide Song, and Beach. Those are much better. Much better. Like, not terrible. And then I said, give me four more, but none of the names should include Beach, Water, or Wave Concepts. Because I was like that's too obvious. Well, I like Elysian. Yeah. It's good.

It's good. It's to go with that one. Zephora. Elysian. Solsti. Eden Vista. And this is where I left off in this insanity. Yeah.

So, Jacob, can I challenge something that you said? You said 30% more efficient. If you ask someone on your team to do that, that's more than a day of work, including the back and forth with you. I would say an average college educated person,

getting paid, the average national salary for an operations position or an administrative assistant position you know like a non-programming non-sales position is 60,000 a year, 70,000 a year, which if you divide by 2,000

is, you know, something in the range of 30 to $50, right? Yeah, that's 50 hours. I think they would say 50 hours of work to put that presentation together and to get that level of output because you would be starting from zero. You would basically surf the web

for 20 hours. hours. You would write down all your ideas. You would go eat a bunch of bagels and donuts and you'd have a meeting with you. And then you'd say, oh, that's too long. Make it shorter. I don't like these names. Come back. You'd have these. Each time that's a 30 minutes in interaction with you. Yeah, 50 hours of work. I put it out. Times 40 bucks is $2,000. Maybe a hundred hours of work. Yeah. To get this. And then forget about asking him come up with names. You know, that's like a very specific thing. That's an agency. We charge you $20,000 for those four. names at the end, I think.

Yeah. And so it's not 30% more efficient. I think it's 300%. Yeah, I could be wrong. Then I wonder if the gains are sustained. Because these feel like early gains. So now my question back to you, Sonny, is, are these like massive gains,

300% gains for the first year of AI, and then we get to 30% a year? Or is it compounding and 300 turns into $3,000? That's a good question. I hadn't thought about it, but my guess is, you know, this is hard. Well, when the iPhone first came out, right? And even to this day, and we don't get as many Uber's and Airbnbs, but it's still, it's

compounding on itself. Yeah. And we're 10 plus years. I mean, we're 15 years in when I was saying 10, right? Yeah. We're 15 years in and an iPhone still compounds. Crazy. Yeah.

So I think it compounds. This is back to the whole thing with like human beings are really. really bad at being able to see like the compounded growth charts. Like we, you know, exponential growth. When it's sitting right in front of us over the three months or six months, we can't imagine how fast this thing is going to grow. We, you know, our brains are not wired to understand the curve. Yeah.

That's really, yeah, we have an evolutionary, not an exponential mindset. Exactly, exactly. We only understand evolution. And even evolution took thousands of years for humans to accept. The idea that we evolved from primates and primates evolved from, you know, reptiles or whatever. I don't know what the exact forking was. That took thousands of years for us to understand. We have three billion people, three to four billion people who are, I would say,

activated in the global economy. So they have an internet connection, they have access. It's a highly networked place. Think about this, right? Like, a hundred years ago, I mean, the most connected network. of people will be maybe people living in New York or London or like and that's maybe maybe a hundred thousand people you know and that was like a network because it was it was separated by by obviously distance and maybe you know once the telephone came out

yeah and and exactly access and when the telephone came up now you had like a wider connection so you could access people you know over space and time quicker but that you know it took for airplanes it took for airplanes now we've got now we've got this I mean This is like taking the number of people. Like if you had to like work out some sort of, let's just say for example, you said the number was, you know, let's say it's 100 million people 20 years ago squared was the number, right? Now it's, yeah, and then you bring the internet in. That's what it was, right? Now you've got three billion people squared.

Like that number is orders of magnitude more than 100 million squared. It's insane. What's really going to happen here, I think is such a great point. is think about the impact of giving somebody internet access than high speed internet access

now you give them this so for somebody who's a knowledge worker I said oh 30% more efficient and sunny said 3,000 and now imagine you are a person 300% sorry 300% now you're a person in San Paulo

and you just you had low speed access sometimes flaky internet access. Now imagine you get a Starlink connection and you've got 100 megabits down and you get chat CPD4. And instead of you having to figure stuff out,

you start asking you questions like this and you ask it, okay, how do I create a hotel chain? How do I name a hotel? You start asking these questions or how do I code and it starts teaching how code. This is crazy. Like those people are going to experience they're going to be comparable

to somebody who is educated in New York at NYU or in Boston, at Harvard, like the ability to close the gap in knowledge and ability and network is crazy. Just like LinkedIn made it possible for, I get people emailing me from Hong Kong or Australia because they found me on LinkedIn. But yeah, this is, it's hard to comprehend

what happens when a billion people have access to this. So if you take it down to like the biological compute stack of the human being, right, we've got this like a, ability to store data in our brains, and then we have the ability to compute data. And so what's happened over the first, you know, with the internet in the first 20 or 30 years, I'd say, let's say the last 20 or 30 years on the internet, was that we basically all floated, and with mobile as well, we've all floated the storage layer to the internet. So whatever you wanted to know something, you didn't have to remember, remember all these

facts and figures. You got to Wikipedia, you search, you find this information. And we just did the compute on that. That's how we did research. We'd like, you know, gather some facts, take hours and hours. to find the data, and then we go interpret that and see what it produces, and then we'd like apply it in our lives, whether it's business or personal. What open AI and chat GPT and AI in general is doing is basically, you know, the compute function for the human brain is being now,

the same process is happening to that. So now we've got storage on the internet, and now we've got compute on open AI. So the human brain now is not, it's no longer about doing compute. Like, we're not going to sit there, I'm not going to take a spreadsheet and do the graphs and do the analysis and trying to figure out the financials of a company. I'm going to take the company financial, stick it into Open AI and say, okay, this public company, you know, based upon Buffett's methodology, how would you value this if you saw sales growing at 20% faster than the current projections? It would do all the Calcs for me. It would come back and say, yeah, actually, you know, based upon the Buffett's style of investing, this is a great investment. I know it's a really

shudy investment. And that happens in minutes. I can analyze the entire company's financial statements in minutes. So let me just finish the point. So what's really what's really happening with the human brain right now, so we've offloaded storage, we've offloaded, or we're offloading compute. We're starting to. The third thing, which we're not offloading, and we shouldn't, and this is where the debate gets in is decision making, right? Because the, the, these systems are not making decisions for us. Morality, ethics, decision. Exactly.

Exactly. So morality ethics, decision making. And then when you have this like, now it always says this is what it looks like, this company looks like a good investment. Now you make the decision, do I want to deploy my capital in there? Now you can automate that eventually. But that's, you know, and the financial decision is the easy one. But the morality stuff is where we're going to have these conversations.

Let me go to you, Sunny, in a second. But I just want to give a shout out to Kora's Po. and if you can log into it at the web now, it's P-O-E.com, and they have something called Sage, but they also have GTP4, Claude Plus, Claude Instant, Neva's AI,

they've got everything here, and you can create bots. They're really cooking with oil over there. And it said, I asked that what are the major trends in luxury hotels to try to, you know, do the Quora dataset,

and it gave me really great stuff. But what they do is they highlight keywords, which is really interesting. So again, you get technology, local experiences, social responsibility. And then I said, okay, give me 10 specific trends around points two and four. And I sit short here, 10 specific trends around personalization and technology.

Again, the same as I was doing in the other chat GP24 instance. It gave me all these things. And so then I just clicked on smart room systems because I didn't know that smart room systems was a category. So I click smart room systems and it appended, tell me more about to that. and it started explaining, you know, one of the key features, adjuster room's lighting, temperature, all that stuff. And it gives you prompts now.

So it's actually telling you what to ask next. This is really getting interesting. So it's, this is pre-cog. If you watch Minority Report, Sunny, where like knows you're going to commit a crime. It knows what you want to do next. And it kind of gives you the next one.

What are some examples, smart room systems, how they prove, and I can say what are examples and boom, you can just keep Philip Hugh. So now I'm like, you start thinking about the research again, back to your point of like how many hours this would take. We're going to have companies that

were 20 people will be five, you know, or they'll be able to do twice as much. The way I told my team Sunday night and this morning was if you're not using this, like you're falling behind and I said offload as much as you can to these systems and let's meet with twice as many founders. Like let's actually spend more

talking to founders as opposed to researching stuff. All right, let's wrap up here. Any final thoughts, Sonny? We got Vinnie's, I want to get your final thoughts, Sunny. How is this impacting the work you do every day and how you're looking at your entrepreneurial career and running your own company, Sunny? Yeah, I mean, I think we've touched on the major points,

but like for us, we think about enabling this within the enterprise. That's our primary focus, right? So we think that's really important and how do we do that in an efficient way such that enterprise is going to harness this? It's not as straightforward for most enterprises to just go to chat GPT4 just yet, but we're working on that problem alongside it. I think, too, what we have to kind of focus in on is how do you know what it's telling you is accurate?

And I think we saw a few examples of that where we're kind of questioning what it's told us. Where we started today's conversation, we can see if you give it a dataset, it can be very kind of definitive about it. And if not, you have to be careful on what it's telling you and where it's pulling it from. Your example of the crawl was not sort of, you know, using Vinnie's framework of memory and compute. It wasn't doing it. It was kind of doing the cheating thing of humans. And so I think there's a lot of opportunity here. And what everyone should think about is the speed at which you can move in this environment, right?

I think, and the speed forces you to basically use the technology to its maximum capability. You have it, folks. You can run literally 30% faster every week, compounding week after week, if you embrace these tools and you use them. Stop what you're doing. If you hear my voice, this is not a drill. I know, like, in technology, we get really excited and we hype stuff up. You know, mobile, broadband, crypto, everything, VR, AR, we hype stuff up. excited about it. All of that stuff, you know, had, you know, different levels of impact. This is different. This is just very different. And it's compounding at a pace that I think is a self-fulfilling prophecy on the way to AGI. I mean, we're getting to artificial general intelligence. It's so clear. I mean, you're beating the touring test already. Like you're smashing it, beating it around like a dead mouse.

I mean, you can't tell the... If I took this and I put it into a presentation and I gave you that pitch on your luxury hotel, you would think like a bunch of McKinsey people spent three months on it. And not even McKinsey, Jacob, if we can pull up one more thing, I know we're running short on time here.

We won't listen to it, but maybe we can drop it in the notes. But this developer basically built an entire Google Translate, but that works. It takes into account two of these trends. We're talking about this, you know,

This is AI voice treatments. And so what it does is it takes his voice and what he's asking, translates it, and then speaks it in the language that he's looking for. And he's got a link to the program here. It's all open source. This one person basically built an entire Google Translate that speaks out the translated version of what you're asking for in his voice. So I can do this week in startups as in Spanish, but it would be in my voice.

Spanish, it would be in your voice. That's bonkers. Yes. And he built that and all the code is there and it's just incredible. And think about the armies of people this would have, you know, this does take at, you know, the Googles of the world or, you know, meta's of the world. It wouldn't be done. I mean, that's, I've been pitched many years for taking this podcast and now all in and making a German language version or a Spanish language version. And they're like, we hire voice actors to redo your podcast every week.

And for 500 bucks or a thousand bucks, we can make another language version of it. And I'm like, yeah. And they're like, you get sell advertising. I'm like, I don't have the time to do this. It seems like a lot of work. But if I could press a button and take this podcast and put it into 10 languages and then have 10 different websites with it, I would do it. Yeah, for sure. I would do it.

Yeah. And I would pay 50 bucks. I'd pay 50 bucks to do that. I wouldn't pay 500, though. So if somebody wants to take this episode and translate it into Spanish and then use, our voices, I would pay 50 bucks for that and you could do it every week and I'd pay you 50 bucks a week. I mean, that literally might do all, you know, 250 episodes a year, it would only be, it wouldn't be that much money, you know? Yeah. Yeah. Not that much. 10,000 bucks.

For 10,000 bucks, I would translate this all into Spanish every year. So there, I mean, that's a business opportunity for somebody. That's not chump change if you can automate it. Vinny, any plugs? Any plugs? You earned your, you're on your, you're in your, You're in your lunch here. Yeah, thank you. I mean, obviously, excited about what we're doing at Waitrum. And really we'd love to tell people about what that is. Yeah, Waiterum is basically a video conferencing platform that's going to be fully AI driven.

We're launching our features in May, the AI features. Our first feature will be probably catch up, which means that if you jump into a call late with your colleagues, it gives you a summary of what just happened before you got there. And I think that that's going to be rolled up. I mean, the features are rolling out the next month and two is going to be pretty awesome. So check out the website, Waitroom.com. I will say that in building Waitrum now, we're using OpenAI, it's really interesting because as we start working with companies to understand what their businesses are about

and integrating into their sales force and Notion, et cetera, we may have to start building our own custom LLM to just basically understand how to take conversations and meld them into something more useful to the company because you need context. around what the company does and training the language to understand the company better. We're using open-aird right now. Maybe it evolves so fast we don't need to, but it's something that when you're thinking about building features, you have to ask yourself, is it something you're building, which is LM sort of agnostic, or is that core to your business?

So I'm very interesting what happens over time where the companies build their own ones or take an open source one, fork it, and build some customized ones, or you use the standard. I mean, if there's a cloud available, like, why? I mean, unless you are Dropbox or YouTube, like you're going to rack your own storage. But if you're below Dropbox or box, you know, you're going to just use cloud storage. There's data privacy issues as well. And I know that Open AI is trying to deal with that. But some companies probably wouldn't feel comfortable with, you know.

Yeah. So you do on-prem. Yeah. Yeah. And then if you do that, then you have to have your own L.m because you can't really use Open AI for on-prem. Maybe you can. Do they have on-prem? They do.

They do. They have versions now that allow you to do. Sunny, any plugs? Any plans? Yeah, you know, like definitive AI, a lot of stuff that we're looking at here today, we're just enabling that within the enterprise. So reach out if you want to do that with your own private data. Everybody will see you next time on this week and service.

Bye-bye.