SPEAKER_00: Hey, everybody, welcome back to this week in startups, we're doing another AI roundtable. And this is the best one ever Vinnie and Sonny join me again to demo chat GPT'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 a couple of CSVs that we grabbed off government websites, we upload it to chat GPT. 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 10s of 1000s 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 weekend 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

SPEAKER_03: over the weekend, and some stuff I'm doing in Python on a replet. It's gonna be a great show. It might even blow your

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SPEAKER_08: Hey, everybody, welcome to another episode of this week and startups with me again, Vinny Lingham, and Sonny Sandeep 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 1000x what's happening in crypto. So we'll skate to where the

SPEAKER_07: pot is going, uh, and continue our discussions about AI here. So this is our weekly AI roundtable. You have ideas, uh, for the producers here producers

SPEAKER_09: at this week and startups.com. If you see something interesting, uh, say something email producers at this week and startups.com. All right. So, uh,

SPEAKER_12: let's get right into it. You shared a link with us, uh, sunny on the group chat that, uh, some chat GPT users now have access to a code execution or code

SPEAKER_08: interpreter plugin. Uh, what is this and why is it important?

SPEAKER_13: Yeah. So this is really, really, uh, big and what it, what chat GPT has enabled

SPEAKER_15: open AI has enabled is the ability for, um, um, the interface to run code and what it's really, uh, what's interesting and you can now input data, um, via like an upload feature. Um, so one of the really cool examples that people are doing this weekend, as was just released on Friday, just go 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. Um, and so it's really, you know, the process to do that before would have been to, you know, go get a data scientist or write a Python program. And so it does all of this in line and very similar way to how we saw the plugins work. We're seeing

SPEAKER_19: that now for, um, you know, running code.

SPEAKER_21: And that code interpreter, if you were to just do a Google search right now for, if

SPEAKER_08: you do a Google search for, uh, chat GPT and you go into chat GPT on the dropdown, you see, uh, especially if you're paying the default, which is 3.5, uh, version of chat GPT, GPT four. And then you'll see some other things, uh, like GPT 3.5 with browsing, which is in alpha GPT four 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, uh, 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 I'm, is code interpreter available to everybody. Do you know?

SPEAKER_15: 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, um, you know, publishing stuff to the community to help educate everyone. So it's not widely available yet.

SPEAKER_29: Got it. And so an example of this might be what, uh, 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.

SPEAKER_22: Exactly. That that's normally how someone would deal with it. Yeah.

SPEAKER_35: So inside your organization, uh, Vinny, 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 just happens to be good at hacking this stuff together.

SPEAKER_12: 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, Vinny.

SPEAKER_43: Yeah. I mean, uh, it's, it's definitely a lot easier to, I mean, it's, you know, the barriers to do 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 got a friend who's a data scientist and, um, 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, they, some of the data science companies out there right now, they, 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. They, I think, uh, one's data dog, I think, and there's a couple of others, um, you know, and, and, uh, you know, open AI and chat GPT is basically, you know, reduce the ability to do this to, you know, SMEs enterprise individuals can do it. What I think is interesting though, um, on a slight deviation here is Google has got access to so much company data to the Google suite.

SPEAKER_44: So if you, if you like run a startup and you're on Google, Google drive, uh, you know, Google docs, Google sheets, everything. That information is incredibly powerful. So now Google just needs to take BOD and say, would you like to activate BOD on your company documents? And then, you know, create like, uh, you obviously have to figure out the privacy stuff and, you know, rights. I mean, but, but basically you have access to.

SPEAKER_46: That's already been done in an organization, right? Like generally speaking, the organization should have set their permission.

SPEAKER_44: So, uh, well, well, so just keep this in mind, right? If BOD starts learning across the company, it needs to be able to partition the knowledge and not infer information. Sure. That only you have access to.

SPEAKER_49: So 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?

SPEAKER_51: Yeah. And what's their compensation? You don't want that coming up in the results. Exactly. So that is an important permissions issue.

SPEAKER_54: Yes. But, but, but, but if you're the CEO, you should have, you know, do you have access to someone?

SPEAKER_57: Access to everything. Do you have access to everything? Or do you have access to, and what about like, if JKL's got a private doc, uh, sheets in there that no one else actually, are you allowed to see that? Of course.

SPEAKER_47: I mean, the, the organization owns it. This is like a fallacy that some employees have that I'm on my corporate account. Yeah.

SPEAKER_59: I agree with you. Yeah. If it's personal, if it's personal information, you shouldn't have it on the company's service anyway. Totally. I am amazed by that.

SPEAKER_57: If it's company information, it should not be, if it's company information, it should be available to your manager, your manager's manager. Right.

SPEAKER_64: So that's an important issue to flag.

SPEAKER_11: Um, 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 for slack for all eternity. Do not expect anything. Phone calls, phone calls as well.

SPEAKER_68: A lot of companies record all calls incoming in.

SPEAKER_11: I mean, and some of it's compliance and some of it's just the default. Yeah. 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.

SPEAKER_09: It's 2023. Don't do that because it's going to be indexed and then somebody's gonna be able to pick it up.

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SPEAKER_07: Do you have an example to show here, Sonny? If people are watching at youtube.com slash this weekend or on Spotify or the video feed?

SPEAKER_77: Yeah. Let's go for it. Let's just open up GPT-4 here. And I have some stuff that I was playing with this weekend that got interesting too that I'll share.

SPEAKER_26: 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.

SPEAKER_19: Okay. So we're inside of a chat GPT here, and we're going to upload this electric vehicle data set.

SPEAKER_07: And that, when you said send a message, there's a link on the right there. And if you on the left of send a message and that's where you upload from. Yeah.

SPEAKER_19: Right here. There's like a little like a, there was a, yeah, see this little plus icon and normally the, uh, so you can see the first thing.

SPEAKER_07: I didn't know that. Is that only for.

SPEAKER_84: That is only for the code interpreter.

SPEAKER_07: Got it. And so show just so people can see the interface here because we have never done this, but could just hit a new chat there and let me just show people the interface and then just describe that for folks.

SPEAKER_11: Um, so when you create a new chat, um, you click new chat in the top left, you hit this down arrow key. Now you can see all the different, um, items, plugins, default, et cetera. So you gotta sports cast us a little bit so people see it. And then it gives you a little description of what it is, um, and how good it is.

SPEAKER_09: And they get a sort of internal rating of what it does, but you picked pulled out code interpreter.

SPEAKER_87: Interpreter. Correct. Got it.

SPEAKER_19: All right. And then you hit that. And now, yeah, this is about, uh, I think a 29 meg file. And so it's gonna take, uh, you know, a few seconds to upload here. I see that. Yeah. And so now what it's gonna do, and none of us have really seen this file yet. Which is fascinating. It is fascinating.

SPEAKER_94: I'm by the way doing this alongside of you. Yeah.

SPEAKER_15: So this is the code. So it's generated this code. This is Python code here.

SPEAKER_95: J Kyle, you were asking about this weekend to read that file and it's still generating and it's understanding. Now it's now you can see here, it's starting to tell us, Hey, the data has been rolled into a data frame. And from the first few rows, we can understand that this is the data. So we're gonna let this just let this complete. And I'll tell you the next, the next piece, which what Vinny was talking about a second ago was like, you know, where you'd normally have to go get a data scientists. And so, uh, to do something like this.

SPEAKER_96: And so, and it, you know, throws some things up here and it says, okay, so it's done. So then my next question is gonna be this. Well, let's describe what it showed there.

SPEAKER_15: 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.

SPEAKER_08: So what producer Nick gave us was the electric vehicle population data.

SPEAKER_11: And, uh, it figured out what's in there and it's reflecting that back to you in plain English.

SPEAKER_84: 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, uh, like a, a Python library

SPEAKER_95: called pandas, which is what a lot of data scientists would use to start analyzing data.

SPEAKER_08: So there was a little carrot there that said, show the work. So after it uploaded it, when it finished work, it asked you to do that.

SPEAKER_12: And fascinating when it did yours for me, it did a different response to the same data, which is really interesting. Yeah. Like chat GP for work told me the data set contains information about electric vehicles with each row representing a specific electric vehicle. The columns in the data set are as follows. And it did it one through 10. It actually gave me a list of them. Yeah.

SPEAKER_35: Which is really like a totally more helpful response. That's very fascinating that we had two different.

SPEAKER_15: 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 BEV 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, 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.

SPEAKER_111: What's very interesting about that is that you did a very generic question, which is you asked the CEO question. All right. Thanks for the data. Yes.

SPEAKER_11: Data scientists in a meeting. Uh, why, why do I care? Just get to the point. What, what, what did you learn by studying the data?

SPEAKER_07: And it, it, it's basically just starting with some general ideas here to get you started and you could pick one to double click on.

SPEAKER_15: Yes, correct. And so it's now doing the work and what you can see here. Oh, again, like, you know, yeah.

SPEAKER_116: And so what you're seeing, remember, imagine people are listening, sunny. So sportscast.

SPEAKER_15: Okay. So it, it, it gave us five, uh, examples, uh, of things that to look at the data. So the first is the distribution chart of the different. 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?

SPEAKER_95: Maybe four to one in terms of there's way more battery electric vehicles and plug in electric vehicles, according to the data set that we were given. Okay.

SPEAKER_15: 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 that the chart there. Um, 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 three, then the model Y, then the leaf and so forth. If you look at this chart. And then when we look at by year, uh, and obviously, you know, this we're only part way into 2023, we can see that the by year, the distribution of electric vehicles has generally been increasing with a little bit of a slowdown in 2019 and 2020.

SPEAKER_95: And a pickup back in 2021 and a huge jump back in 2022. And we're only, you know, quarter, a little bit more than a quarter away to 2023.

SPEAKER_127: That would be my interpretation.

SPEAKER_46: But what's interesting here is now that you start to see some of these things, you could actually ask ChatGPT, why is there a spike?

SPEAKER_08: Uh, but you could just do that in another window at ChatGPT4.

SPEAKER_77: What's your takeaway here, Vinny, just to bring you in on the conversation?

SPEAKER_43: I mean, I'm, I'm going to start using this to analyze my wine collection. Fantastic. You have a CSV?

SPEAKER_131: Upload it. Tell me about it.

SPEAKER_41: That's exactly what I'm going to do. I'm going to go and pull it right now and see if I can go, you know, come up with some, some strange stats, you know, recommend other wines for me. Let's see what it comes up with.

SPEAKER_35: 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.

SPEAKER_08: It actually, uh, wrote some of these and it said top 10 most popular electric vehicles is 0.3.

SPEAKER_138: Tesla model three is the most popular electric vehicle model followed by Nissan Leaf, et cetera.

SPEAKER_12: So you start getting into some really interesting concepts here.

SPEAKER_36: And for mine, I let me, um, share mine. This will be very interesting to do if I may, uh, or did you have another one you wanted to do, um, Sonny? No, no, no.

SPEAKER_15: I, that's what, you know, I wanted to just show that capability. Cause that's the new, uh, feature they unlock is uploading the data set, which I know you've been thinking about a little bit, Jacob. Cause you have a lot of spreadsheets. I know.

SPEAKER_142: I got a lot of spreadsheets.

SPEAKER_08: I got, can you see my, uh, screen now? Okay. So I did the same thing. I uploaded the same file, but what you'll see here is that, um, 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 field population. And we need to analyze various aspects of the data set. Pretty generic.

SPEAKER_35: Let's explore the following three trends. Electric vehicle, vehicle adoption over time. Most popular electric vehicles make some models distribution of luxury all types like yours.

SPEAKER_149: And then it gave me a couple of charts. It did a different design style.

SPEAKER_08: Uh, which is weird, but electrical vehicle adoption over time, instead of using a histogram, it did a, uh, a line chart. Line chart. Line chart. It did the same thing. Most popular electric vehicles. And then it did the same thing, the distribution. And it too gave me, uh, some highlights here.

SPEAKER_36: And, uh, what I could do here is, uh, 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 and you're like, okay, we know Tesla is running the table on everything. But I, I don't care that the, I mean, we all know model three outsells everything because it's, you know, the greatest model.

SPEAKER_154: Why I think it's the greatest car ever made, but those two, but let's just take out all Teslas and see if it does that.

SPEAKER_36: Right. So now you're starting to be able to do things with data.

SPEAKER_155: I mean, it's just, this is just stunning. Uh, what could be done here?

SPEAKER_12: Um, I was over the weekend, uh, 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 GPT four. I think if you leave the screen, it will, uh, it can stop.

SPEAKER_35: Yeah. Sometimes. 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.

SPEAKER_09: Uh, like doing multiple queries simultaneously, like just for the love of God, uh, Greg and, and, uh, give me a corporate account here. Let me put all my people into chat GP four.

SPEAKER_11: Let all of this data be shared in a common repository. I need multiplayer mode for chat GPT four. 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.

SPEAKER_35: And hopefully everybody in my companies is actually doing this. Now, if you hear my voice, I've been like tweeting about just, oh, wow, here we go. Uh, let's see electric vehicles over time without Tesla. That's interesting. Uh, and then the models, uh, yeah, wow.

SPEAKER_08: 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 a 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 electric vehicles becomes much more normalized. So here peak sales in 2022, it looks like is 14,000 23 is not complete yet.

SPEAKER_163: Right. So that's why it's last complete year.

SPEAKER_08: It was 25,000 over 25. I'm sorry. Number of EVs.

SPEAKER_09: 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.

SPEAKER_165: Yeah. Probably that makes more sense.

SPEAKER_09: So it's, it is like, yeah, it says 14,000, but it actually means add, uh, probably two zeros. So 1.4 million. So you're just taking out a lot of vehicles. Probably. Yeah. Tesla sold. What looks like 500,000.

SPEAKER_167: Is that right? No, 50,000. Is this somebody in America?

SPEAKER_170: Close to a million. Yeah. This might be us. Cause it was, uh, it had state ed state vehicle and other information. What's the timeframe.

SPEAKER_171: What's the timeframe for this? Like a year, a month. Since 2000. Like a few years.

SPEAKER_51: Yeah. Yeah. I mean, this is just incredible. I mean, you, you just see like, we're lifelong technologists. We know how much time this kind of takes, uh, to do this kind of stuff.

SPEAKER_176: And imagine you take your website information or your podcast data, and then you start slicing

SPEAKER_15: and dicing that now. Yep. And imagine the work and the number of people it took and the time it took.

SPEAKER_177: Mm-hmm.

SPEAKER_15: You, Jason would want that answer right away. Where are the listeners from? Right. Which ones, you know, all the different, uh, you know, you're going to go after this, Jason and download all your data and you're going to be uploading it immediately is my guess.

SPEAKER_180: Right. Yeah.

SPEAKER_41: And I mean, well, well, I mean, does it, it doesn't need to have a developer account for that or like, what do you need to have to be able to use this?

SPEAKER_15: Right now you have to be, um, have a developer account and you need to be, uh, let in by open AI.

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SPEAKER_187: There is a wait list for plugins. And, um, but this is.

SPEAKER_44: It's compute intensive. Jacob, this goes back to the whole GPU shortage problem, right? This is compute intensive.

SPEAKER_136: If they gave everyone a hundred million users plus access to this, it would just fry the system. They don't have the capacity for it.

SPEAKER_36: Honestly, like I, um, I think we're getting to the point where this is so valuable for organizations that Azure, uh, and AWS should just start offering your own.

SPEAKER_12: Uh, what is it? A 100 is the NVIDIA.

SPEAKER_41: Amazon's working on it, but it's not that simple just to like spin these things up. It's going to take a couple of years to get.

SPEAKER_47: Oh, I mean, just racking them is going to take time producing them. You have to basically.

SPEAKER_36: Jekyll, there's a shortage.

SPEAKER_194: There's a chip shortage out there.

SPEAKER_36: I absolutely understand. But what I'm saying eventually was the word I use. Sure. 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 A 100, you know, cost $10,000.

SPEAKER_12: 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.

SPEAKER_36: And I was like, uh, which dates had the most growth in 2021 and 2022? Uh, and it said, based on this, the electric vehicles dates, 2021, here are the two top states with the most growth growth.

SPEAKER_12: Do you want to take a guess? Which dates had the most growth? Percentage wise? Without California?

SPEAKER_15: Without California?

SPEAKER_199: No, it included California. California and New York. California and New York. No, I said percentage growth though.

SPEAKER_200: A percentage.

SPEAKER_12: No, no. Actually, I said which dates had the most growth in 2021 and 2022. Interpreted that as percentage, not raw numbers. Okay. So it is in fact-

SPEAKER_206: Texas. I'd say Texas. Okay. Which are, okay.

SPEAKER_205: Keep going.

SPEAKER_181: I'm not going to say- Texas and probably maybe Florida. Maybe Washington. Yeah.

SPEAKER_209: I'd say, well, I say, yeah, like Washington.

SPEAKER_210: Smart. Sorry. Uh, a number of minutes in F bomb.

SPEAKER_08: Um, Washington is number one.

SPEAKER_36: They grew from 18 to 27,000. A growth of 9,000 EVs, 50% growth. And Texas was number two. Uh, yeah. Actually I got that wrong.

SPEAKER_64: It says number of EVs in 2021, three number in 2022, four.

SPEAKER_212: It's, it's Washington state data specifically. It's in Washington state.

SPEAKER_214: Oh, this data set.

SPEAKER_215: Yes. This data sets, Washington state data from Washington state.gov.

SPEAKER_12: Oh, sorry. Okay. So what we're looking at has nothing to do, uh, with by state.

SPEAKER_64: Okay. That's why the numbers were low. Okay.

SPEAKER_19: Great. Yeah. What I'm looking, I'm looking at the CSV though. It does have, uh, all kinds of counties and cities. Like I see San Diego.

SPEAKER_64: We just took, by the way, for the folks listening, we just took a random data set.

SPEAKER_12: The producers found and just uploaded it. So we found this data set doesn't have perfect information. And so just understand like the, we're, we're, 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.

SPEAKER_32: Uh, 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, uh, explain to the audience what you found and what you did while we were alive on air. Yeah.

SPEAKER_222: So I found a website where they have a bunch of, uh, CSV files from government data. One of which was the one that you just saw previously, the Washington state EV data. Um, I also found one which has something to do with the topic that we're, uh, covering today about FDIC bank failures. Um, which was from the actual FDC, FDIC.gov website, which you can see right here. Pretty amazing. I uploaded it.

SPEAKER_04: It found a formatting error on the CSV file. And I was about to look up how to fix it and chat GPT just fixed it itself. Found a Unicode error.

SPEAKER_222: Okay. Yeah. That's common. Just fixed it. Pretty crazy. I asked it. What are the most interesting ways to visualize this data? Gave me some examples. I said, okay, do that. Um, and here you go.

SPEAKER_226: Okay. So let's take a look here.

SPEAKER_12: Um, it said bank closures by year bank closures by state top acquiring institutions.

SPEAKER_32: 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. Uh, 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. This is fascinating.

SPEAKER_36: And obviously we see by year scroll down 160 or so in, uh, the financial crisis. And then it slowly went down. But what's interesting about that, Vinny, if you look at it, do 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.

SPEAKER_08: You're going to have some per year, but it still took, uh, it was basically four years of bank closures.

SPEAKER_44: Well, well, so just remember, so a lot of this was back in the days we had, uh, we, we've had a lot of like the smaller banks being consolidated up and then they passed 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, the, the amount of money that's in the banking sector has blown up in the past two, three months out of like five banks.

SPEAKER_57: We've had, um, more AUM blow up. I think in 2023, then in 28 and nine and 10, 11, like the whole banking crisis. We, in the past three months, we've had like dollar amount. By dollar amount. Yeah, yeah, yeah. Like, like, like, like Washington mutual, uh, versus Silicon Valley bank versus first Republic, et cetera. Like the scale is so different right now because these banks are so big. Yes.

SPEAKER_47: It's interesting also about what Nick found here is like, you could see some of these didn't have acquires. They just shut down.

SPEAKER_08: Some of them, uh, you know, were acquired by state bank and trust company, first citizens bank, Maris bank, US bank, NA. 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.

SPEAKER_36: This is what I've been trying to explain to people. I mean, maybe I have to go back to based Cal and start using all caps on Twitter.

SPEAKER_237: But I am finding that 30% of what I do can be done inside of champion for today. I'm finding my producers and you sort of Nick pull up his thing there.

SPEAKER_36: And I start 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.

SPEAKER_12: They're using chat GPT for all day long. Um, and even during shows. So this to me is what I would implore people to try to understand right now.

SPEAKER_11: Smart people who are using this are taking, I would say between 10 and 50% of their job and automating it.

SPEAKER_36: 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, we're doing a great job.

SPEAKER_12: 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 Vinny 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 GPT for people are not gonna need them. So we just get rid of half the data scientists.

SPEAKER_36: Now, take a moment to think about what I just said. There's been a competition for data scientists.

SPEAKER_64: Some organizations say, how many of these data scientists do we need?

SPEAKER_242: Well, I mean, well, I'd say right now, J.K.L., we probably don't have enough on a global basis.

SPEAKER_57: So I don't think there's gonna be a shortage of data scientists anywhere anytime soon. 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 gonna 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? Yes. 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, you know, 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 all very disparate. You still have to build data warehouses that have all information, et cetera. So it's not as simple as that. I think that, um, 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, the, the gap right now between the haves and the have nots in data science is very, very big.

SPEAKER_36: I don't know, Sonny, I might disagree this weekend. I started, uh, learning Python.

SPEAKER_249: You already called me Sonny right now. Vinny.

SPEAKER_36: No, I was going to Sonny. I was going to throw to Sonny.

SPEAKER_47: I thought you did too.

SPEAKER_251: I was like, Oh my God. Oh, you also thought so.

David Friedberg: I was going to throw up Sonny. Listen, you guys are Sonny and Vinny. You're two of my best friends. The names are different by one letter. Sonny and Vinny.

SPEAKER_261: Two letters. Sonny, Vinny. Two letters.

SPEAKER_263: Oh, right. Yeah. Yeah. Sorry. Sorry. I'm, I had a long weekend.

SPEAKER_36: 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, you know, kids, whatever got in the way. But I started.

SPEAKER_21: Warriors game.

SPEAKER_266: Warriors game. Knicks lost. Warriors won. Incredible.

SPEAKER_12: Shout out Steph Curry. Replit is like a coding environment. So I just signed up and I started taking their Python course. I was like, oh my God, this takes so much concentration.

SPEAKER_11: 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 bounty thing on Replit. And I put a bounty up and then I explained in details.

SPEAKER_29: I'd like an auto GPT agent that checks our database of already contacted companies by URL.

SPEAKER_09: 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.

SPEAKER_270: Then finds new startups on crunch based products on 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.

SPEAKER_29: 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.

SPEAKER_11: Um, and I put it up for $270. I got four applications. And as you can see here, um, 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. Would love to chat more about you and check my GitHub LinkedIn for resources. And he's done three bounties, uh, cribs, Jake out.

SPEAKER_29: I'm a fan of the pods. I've read your book, dumb luck. I'm poking around Replit and see what all the fuss about. I stumped you about regarding your bounty. I'd like to help, uh, ask you to flesh out your criteria. Yada, yada, yada.

SPEAKER_268: I do either of these free as long as we can dedicate pretty much of the time to you coaching me on my personal journey. I don't like taking free stuff, but anyway, my point here, Vinny. Yeah.

SPEAKER_280: And then I'll go to sunny.

SPEAKER_29: Um, 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.

SPEAKER_36: Just like I am using chat GPT four. And I feel like I'm on a collision course sunny between using chat GPT four with plugins and uploading stuff.

SPEAKER_237: Myself. And then working with the developer community to write tiny little scripts for $270 that add a $50 salary or $40 salary or $60 salary for, let's say an operations person in our organization.

SPEAKER_218: You know, that would take five hours.

SPEAKER_237: I can basically take what is 50 hours a week of work in our company to researchers doing 50 hours a week of work, $1,500 a week, maybe, I don't know, $2,000 a week, fully baked with benefits.

SPEAKER_47: A hundred thousand dollars a year of work. A hundred thousand dollars a year of work. And I can just automate it for 270 bucks.

SPEAKER_284: Am I crazy or is this going to change the world?

SPEAKER_15: No, I mean, you're, you're, we're 90 days away to 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, um, and it's like I said, we're entering a world where, um, the core framework is being absorbed by open AI. And so if you just saw what we did, um, that they're going to open, like they're taking their, they're taking their time right now from a safety perspective. That, um, 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. Yes. Which you know, 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.

SPEAKER_176: Uh, 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, inside there.

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SPEAKER_295: I would agree with Sunny on this.

SPEAKER_57: I mean, guys, this is the fifth generation language. Like 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 not even English, other languages as well. So actually, you can translate for you. So as long as you can... Translate it. 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, where digital, you know, digital, you know, software programs and machines could interpret what we're saying accurately. And because the human brain is so complex. So 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, or a describer or something. So like, you know, I posted a tweet, I think yesterday. You know, pull it up, Nick. I think this is a very important point that we should, we should probably touch on today and get your, your views on this. I think that, that, you know, in the next cycle. So we're in a, we're in a bear cycle right now, right? We're, are we heading to one or whatever you want to call it? Like obviously we, we, you know, 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. Um, 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, however you want to define the next cycle. Regardless, I think we're heading for deflation in a big way.

SPEAKER_302: And I think that this will become the number one driver of deflation. I think you're exactly correct.

SPEAKER_12: What's going to happen is massive efficiency will come to the companies that get on this early.

SPEAKER_11: 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 nineties. 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.

SPEAKER_36: And that was back then when, you know, you got to keep your job for a long time.

SPEAKER_12: There wasn't as much turnover for boomers, but there were boomers who were like, literally when I was installing computers in the early nineties, 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, 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, you're literally a dinosaur. You're literally a dinosaur. That's my belief.

SPEAKER_11: So you're exactly correct. This will be 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.

SPEAKER_29: Let's say if everybody got, maybe let's say everybody got 10%. And every year, every seven years, people doubled their efficiency.

SPEAKER_11: 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.

SPEAKER_176: What do you say, Sonny? Well, I think there's a great example, J Cal, and I've seen it, but, uh, Nick, if we can pull it up in terms of efficiency.

SPEAKER_15: So, um, this is someone who's working on a do not pay plugin. Oh, Josh Browder.

SPEAKER_310: He's been on the program. Yeah, yeah.

SPEAKER_178: Oh, there you go. So maybe.

SPEAKER_29: Just, you know, Josh Browder is Bill Browder, who wrote the book, red notice his son.

SPEAKER_11: He's an entrepreneur and he has do not pay as the name of company. He's been on the podcast. And his whole thing was to help you like, um, get out of like, um, reoccurring subscriptions, et cetera. But he's also. So let's do a reaction thing.

SPEAKER_26: J Cal, why don't you read this? Cause you've seen it. So go for it.

SPEAKER_314: I haven't seen this. So what did it, what did it say? Okay.

SPEAKER_15: So, so this is, you know, do not pay. It's a app on top of a chat GPT leveraging it. It goes to ask, how can I help you? He says, find me money. Is this connect? The app says connect your bank account. Uh, it connects account and then it, uh, finds the subscriptions. That this person is paying. It obviously. Whatever. And then it says, what do you want to do? 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.

SPEAKER_266: I had. It scan all about 10,000 bank transactions.

SPEAKER_317: Okay. So it found $80 and 86 cents leaving his account every single month and offer to cancel, uh,

SPEAKER_314: offer to cancel those. Great.

SPEAKER_15: Okay. Let's keep scrolling. Okay. Okay. Then the bots basically got working mailing letters in the case of gyms. Right. And it used a USPS, um, API, um, and chatted with the agents to basically start working on the cancellation. And so, um, like we, we can scan through this and we'll maybe drop the link in the notes, but the beauty here is going back to efficiency. Hmm. Think about the time and effort. There's one last example. If you can go back there, uh, Nick, where it actually found a bill for a wifi connection and he, it, uh, it turned around and asked, Hey, was that, um, did the wifi work properly? When he said, no, it drafted a letter to send to, you know, go, go, whoever the wifi company was, uh, asking for a refund for that, for that. And we, we all experienced that where we pay for it and it doesn't work or it's bad. Yep. And basically. Yeah.

SPEAKER_09: And so, uh, very similarly negotiation process. Yeah. To cancel that and get a refund.

SPEAKER_15: Yeah. And then similarly, it started a negotiation process for, with Comcast. It's just, that's what I'm saying, Jake. Now we're, these are apps that are being built on top of the technology.

SPEAKER_95: So we are almost where you're talking about as I said, less than 90 days away from incredible things happening for us, which then aligns the deflationary argument.

SPEAKER_09: 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. Um, yeah, you're not really following how fast this is. Yeah.

SPEAKER_36: I started playing with, um, I'm giving a speaking gig on Wednesday, uh, in Laguna down in the Orange County. Um, doing my paid speaking gig thing. It's a corporate gig and I'm talking about travel. And so I started, uh, testing some, uh, I was like, you know, in this luxury hotel kind of situation, I wouldn't say which one.

SPEAKER_195: Um, but let me share my screen here.

SPEAKER_12: Uh, so I started using the GPT forward browsing, uh, web browsing. I don't know if you've played with this, but it doesn't work very well. I had said on all in and Chamath and Saks laughed about this, that, hey, you're going to need to start citing your sources.

SPEAKER_36: And then getting permission from them, et cetera, or else this thing is going to become gnarly. And all these lawsuits have already been filed.

SPEAKER_12: But when you, uh, 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.

SPEAKER_11: It found this link 20 from a website, EHL. And then it read the content. Yep. 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.

SPEAKER_29: And then it basically just cribbed it. So now you can kind of see what's happening with chat GPT four. It is cribbing a lot of data and just rewriting it. Um, and then it does some thinking on top of it.

SPEAKER_314: I want to, I want to, I want to clarify something. Okay.

SPEAKER_15: Um, 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.

SPEAKER_82: It's two very different use cases that we have to be aware of here.

SPEAKER_11: So anyway, this EHL insights had written this. Um, and you know, you can see it basically took what they had on their website and it summarized it a little bit better.

SPEAKER_29: And then way down here, it gave a citation. You see that 12, it gave a little tiny citation. Um, and then I said, which hotel chains are known for having the best hotel workspaces. None of them offer dedicated work desk and high speed internet over ethernet connections. And it started browsing the web.

SPEAKER_11: It's actually doing it right now. Cause it's failed so many times. I want to show you another, uh, one I did here.

SPEAKER_12: And, uh, this one was a fascinating. Um, I said, what are the major trends and luxury hotels? And it gave me, um, up to September, 2021, 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.

SPEAKER_237: 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, uh, it said personalization, smart technology and authentic experiences. And I was like, huh, the first two definitely authentic experiences.

SPEAKER_35: I was, I don't know if that's actually like culturally immersive activities, January connecting to the destination. I was like, I don't know. This feels a little woke to me. I was about to say, I was about to say, I took my time.

SPEAKER_344: I was about to say that it's well cheapy tea.

SPEAKER_237: 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.

SPEAKER_11: 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. Um, like temperature preferred lighting, curated experiences, personalized dining options, customized spa treatments, dedicated concierge service, flexible room configurations, tailored in room entertainment. Uh, personalized turndown service. I said, you know what? Expand that list to 25 ideas. And it just went to town, you know, and. Yeah. Customized minibar. I'm like, well, that's a great idea. I, I've never experienced a customized minibar.

SPEAKER_350: I've had that, I've had that, I've had that idea before personally.

SPEAKER_07: Personalized business and wellness programs, uh, customized transportation options, customized bedding and linens.

SPEAKER_11: I've heard about that. Actually Chamath has that at, uh, the peninsula where they have CP pillowcases with his initials on them. Uh, so he had talked about that pet friendly personalization, uh, scent experiences. That's dumb, but interesting. Um, personalized communication. They do that in Vegas.

SPEAKER_354: They do that in Vegas. Yeah.

SPEAKER_11: Communicate with guests through their preferred channels, such as text, email, or phone. That's actually a really important one. Some people, everybody's got a different one. Um, so then I was like, okay, well, let's take this to the next level. And I said, let's, 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 a hundred 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 a hundred room luxury hotel expert expertly fuses modern technology, personalized experiences and authentic cultural immersion. Create a truly unparalleled parallel retreat from the moments guests arrive.

SPEAKER_270: They are welcome into a world of bespoke services and innovative amenities, all meticulously designed to cater to the individual preferences and needs.

SPEAKER_11: 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.

SPEAKER_09: Yep. Incredible.

David Friedberg: I said, rewrite that in half the number of words. And so it did it in half the number of words. So it was a little tighter.

SPEAKER_237: 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. So Cal Serenity Retreat, Pacific Sands Haven, Coastal Bliss Retreat, Azure Shoreline Sanctuary. I said, please do that again and come up with one word names. Microsoft sponsored number four.

SPEAKER_360: Exactly.

David Friedberg: So it came up with Wave Crest, Sun Haven, Tide Song, and Beach. Those are much better. Much better. Yeah, those are much better.

SPEAKER_237: Like, not terrible. Yeah. And then I said, give me four more, but none of the names should include beach, water, or wave concepts. Cause I was like, that's too obvious.

SPEAKER_365: Well, I like Elysian. Yeah. That's good.

SPEAKER_35: Just to go with that one.

SPEAKER_366: Zephoria, Elysian, Solsti, Eden Vista. And this is where I left off in this insanity.

SPEAKER_176: Yeah. So Jacob, can I challenge something that you said? You said 30% more efficient. Yeah.

SPEAKER_15: 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.

SPEAKER_36: I would say an average college educated person getting paid the average national salary for an operations position or an administrative assistant position. Yeah. You know, like a non programming non sales position is 60,000 a year, 70,000 a year, which if you divide by 2000, you know, is, you know, something in the range of 30 to $50.

SPEAKER_11: Right. Uh, 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. Yeah. You would basically surf the web for 20 hours.

SPEAKER_280: You would write down all your ideas. You would go eat a bunch of bagels and donuts.

SPEAKER_15: You'd have come 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, each time that's 30 minutes in interaction with you. Yeah.

SPEAKER_270: 50 hours of work. I put it out times 40 bucks, $2,000, maybe a hundred hours of work. Yeah. To get this. And then forget about asking, come up with names. You know, that's like a very specific thing. That's an agency.

SPEAKER_09: We charge you $20,000 for those four names at the end, I think. Yeah.

SPEAKER_19: Um, and so it's not 30% more efficient. I think it's 300%.

SPEAKER_382: Yeah, I could be wrong.

SPEAKER_12: Then I wonder if the gains are sustained because these feel like early gains.

SPEAKER_269: So now my question back to you, Sonny is, are these like massive gains, 300% gains for the first year of AI?

SPEAKER_384: And then we get to 30% a year, or is it compounding and 300 turns into 3000?

SPEAKER_18: That's a good question. I hadn't thought about it, but my, my guess is, you know, this is hard.

SPEAKER_15: Well, when the iPhone first came out. Right. And even to this day, and we don't get as many Ubers and Airbnbs, but it's still, it's still, it's compounding on itself. Yeah. And we're 10 plus years. I mean, we're 15 years. And when I was saying 10, right? Yeah. We're 15 years in and an iPhone still compounds. Crazy.

SPEAKER_386: Yeah.

SPEAKER_388: So I think it compounds.

SPEAKER_57: This is back to, this is back to the whole thing. Like human beings are really bad at, um, being able to see like the, you know, compounded growth charts. Like we, you know, exponential growth.

SPEAKER_388: And 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.

SPEAKER_231: We, we, we have our brains are not wired to understand the curve. Yeah.

SPEAKER_09: That's really. Yeah. We have an evolutionary, not an exponent exponential mindset.

SPEAKER_280: Exactly.

SPEAKER_392: Exactly.

SPEAKER_11: We, we only understand evolution and even evolution.

SPEAKER_280: Took thousands of years for humans to accept the idea that we evolved from primates and primates evolved from, you know, reptiles or whatever.

SPEAKER_09: I don't know what the exact forking was. That took thousands of years for us to understand this.

SPEAKER_57: We, we, we have, we have 3 billion people, 3 to 4 billion people who are, I would say, you know, activated in the global economy. So they have an internet connection. They have, you know, they have access. Like it's a highly networked place. Like we think about this, right? Like a hundred years ago. I mean, the most connected network of people would be maybe people living in New York or, or London or like, and that's maybe, maybe a hundred thousand people. Yeah. And that was like a network because it was, it was separated by, by obviously distance. Uh, and maybe, you know, once the telephone came out. Had knowledge. Yeah. And, and, and, and access. And when the telephone came up, now you had like a wider connection.

SPEAKER_400: Uh, so you could access people, you know, over space and time quicker, but that, you know, it took airplanes. It took for airplanes, transport. Now we've got, now we've got this. I mean, this is thinking this, this is like taking the number of people.

SPEAKER_57: Like if you like work out some sort of, let's just say for example, you said the number was, um, you know, um, let's say it's a hundred million people.

SPEAKER_400: 20 years ago squared was the number, right now it's, and then you bring the internet in. That's what it was right now.

SPEAKER_231: Now you've got 3 billion people squared. Like that number is orders of magnitude more than a hundred million squared. It's insane.

SPEAKER_402: What, what's really gonna happen here. I think it's such a great point.

SPEAKER_36: Um, is the, think about the, the impact of giving somebody internet access, then high-speed internet access. Now you give them this.

SPEAKER_237: So for somebody who's a knowledge worker, I S I said, oh, 30% more efficient.

SPEAKER_36: Uh, and Sonny said 3000. And now imagine you are a person 300, 300%, sorry, 300%. Now you're a person in San Paolo and you just, you had low speed access sometimes flaky internet access. Now imagine you get a starling connection and you've got a hundred megabits, uh, down and you get chat GPT four.

SPEAKER_11: And instead of you having to figure stuff out, you start asking it 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 to code. This is crazy. Like those people are going to experience. They're gonna 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 cause they found me on LinkedIn.

SPEAKER_328: But yeah, this is, it's hard to comprehend what happens when a billion people have access to this.

SPEAKER_231: So if you take it down to like the biological compute stack of the human being, right? We've got this like 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, 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 offloaded.

SPEAKER_57: And with, with mobile as well, we've all floated the, the storage layer to the internet. So whenever you wanted to know something, you didn't have to remember, remember all these facts and figures, you go 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 get, 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.

SPEAKER_400: What, what, what, what OpenAI and ChatGPT and AI in general is doing is basically, you know, the compute function for the human brain is being now is the same process as happening to the storage. So, so now we've got storage on the internet and now we've got compute on OpenAI. 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 try and figure out the financials of a company. Heck now. I'm going to take the company financials, stick it into OpenAI and say, okay, this public company, I, you know, 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, you know, Buffett style of investing, this is a great investment. I know it's a really shitty investment. And that happens in minutes.

SPEAKER_410: I can analyze the entire company's financial statements in minutes. Oh, no, it's funny.

SPEAKER_400: So, so let me finish the point. So what's really, what's really happening with the human brain right now. So we've, we've offloaded storage, we've offloaded, uh, or we offloading, uh, you know, compute. We're starting to. The third thing, which we're not offloading and we shouldn't, and this is where the, the debate gets in is, is, um, decision-making, right? Because the, these systems are not making decisions for us. Morality, ethics, decision-making.

SPEAKER_413: Exactly. Exactly. So morality, ethics, decision-making.

SPEAKER_400: And then, and then when you have this, like, now it says, this is what it looks like. This, this kind of company looks like a good investment. Now you make the decision, do I want to deploy my capital in there?

SPEAKER_57: Now you can automate that eventually, but that's, you know, and the financial decisions are the easy one, but the morality stuff is where we, we're going to have these conversations.

SPEAKER_226: Let me, uh, go to you, Sonny, in a second, but I just want to give a shout out to Cora's Poe.

SPEAKER_36: And if you, you can log into it at the web, at the web now it's poe.com and they have something called Sage, but they also have GTP for Claude plus Claude instant Nevis AI.

SPEAKER_11: They got everything here and that you can create bots. It's they're really cooking with oil over there. Um, and it said, I asked it, what are the major trends in luxury hotels to try to, you know, do the, the core data set. 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. Um, and then I said, okay, um, give me 10 specific trends around points two and four. And I said, sure here are 10 specific trends around personalization and technology. Again, the same as I was doing in the other chat GPT for instance. Um, it gave me all these things. And so then I just clicked on smart room systems. Cause I, I didn't know that smart room systems was a category. So I, I clicked smart room systems and it appended, tell me more about to that.

SPEAKER_36: And, uh, it started explaining, you know, one of the key features, adjust the room's lighting, temperature, all that stuff. Um, and, um, it gives, gives you prompts now. So it's actually telling you what to ask next. This is really getting interesting.

SPEAKER_35: So it's, it, this is pre cog. If you watch, uh, minority report, sunny, where it like knows you're going to commit a crime. It knows what you want to do next. Uh, and it kind of gives you the next one. Uh, what are some examples, smart room systems, how they, and now you can say, what are examples?

SPEAKER_11: And boom, you can just keep Philip Hugh. You, so now I'm like, you start thinking about the research again, back to your point of like how many hours this would take. Um, 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. So the way I can 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 time talking to founders as opposed to researching stuff. All right, let's wrap up here. Any final thoughts, sunny, we have Vinnie's.

SPEAKER_270: 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?

SPEAKER_209: Yeah.

SPEAKER_15: 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 enterprises can harness this. It's not as straightforward for most enterprises to just go to chat GPT for just yet. But, you know, we're working on that problem alongside it. I think, too, what we have to kind of focus in on is how does 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 what it's told us where we started today's conversation. We can see if you give it a data set, it can be very, you know, 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 Vinny's framework of memory and compute. It wasn't doing either. It was kind of doing the cheating thing of humans. And so I think 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.

SPEAKER_18: Right. I think the speed forces you to basically use the technology to its maximum capability.

SPEAKER_420: You have the folks. You can run literally 30% faster every week, compounding week after week.

SPEAKER_11: If you embrace these tools and you use them, stop what you're doing.

SPEAKER_280: 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. We're excited about it.

SPEAKER_29: All of that stuff, you know, had, you know, different levels of impact. This is different. This is just very different.

SPEAKER_280: Um, and it's compounding at a pace that I think is a self-fulfilling prophecy on the way to AGI.

SPEAKER_36: I mean, we're getting to artificial general intelligence. It's so clear. I mean, we're, 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, if I took this and I put it into a presentation and I gave you that pitch on your luxury hotel.

SPEAKER_328: You would think like a bunch of McKinsey people spend three months on it.

SPEAKER_15: 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, we won't listen to it, but maybe we can drop it in the notes. But, um, 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, uh, uh, AI voice treatments. And so what it does is it takes his voice and what he's asking, um, 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. 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.

SPEAKER_401: Yes. Startups as in Spanish, but it would be in my Spanish.

SPEAKER_371: It would be in your voice. That's bonkers. Yes.

SPEAKER_84: And he built that and all the code is there and it's just incredible. Yeah.

SPEAKER_19: 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, metas of the world. Oh, and it wouldn't be done.

SPEAKER_36: 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.

SPEAKER_64: Yeah. And they're like, we hire act 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. Yeah. And they're like, you can 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.

SPEAKER_11: Yep. And put it into 10 languages and then have 10 different websites with it. I would do it. Yeah, for sure.

SPEAKER_427: I would do it.

SPEAKER_29: Yeah. And I would pay 50 bucks to do that. I wouldn't pay 500 though.

SPEAKER_11: Um, 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.

SPEAKER_09: 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 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 could automate it.

SPEAKER_434: Vinny, any plugs?

SPEAKER_27: Any plugs you earned your, you earned your, you earned your lunch here.

SPEAKER_435: Yeah. Thank you. Uh, I mean, obviously excited about what we're doing at Waitroom.

SPEAKER_437: Um, and, uh, really would love to see.

SPEAKER_57: Tell people about what that is. Yeah. Waitroom is basically a video conferencing platform. That's going to be fully AI driven. We're launching all features in, in May, the AI features that 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, the features we're rolling out the next month or two is going to be pretty awesome. So check out the website, waitroom.com. Um, I will say that in building Waitroom now on, we're using open AI. Um, 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, uh, LLM. Just, just basically understand how to take conversations and, and meld them into, um, you know, something more useful to the company because you need context around what the company does and train, training the language to understand like the company better. We're using open AI right now. Maybe it evolves so fast. We don't need to, but it's something like when you're thinking about building features, you have to ask yourself, is it something you're building, which is LLM sort of agnostic or is it, is that core to your business?

SPEAKER_59: So I'm very interested to see what happens over time where the companies build their own ones or, you know, take an open source one, fork it and, and build some customized ones, or you use the standard.

SPEAKER_439: I mean, it would be, if there's a cloud available, like why I'm, 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.

SPEAKER_442: Well, 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.

SPEAKER_57: Yeah. So you do on-prem. Yeah. You just do on-prem cloud.

SPEAKER_442: And then, and then if you do that, then you have to have your own LLM because you, you can't really use open AI for on-prem. Maybe you can. Do they have on-prem, Sonny? They do.

SPEAKER_443: They, they, they have versions now that allow you to do that as well. Sonny, any plugs? Yeah.

SPEAKER_15: Yeah. You know, like a definitive AI or a lot of stuff that we're looking at here today, which is enabling that within the enterprise.

SPEAKER_78: So reach out if you want to do that with your own private data. All right, everybody.

SPEAKER_449: We'll see you next time on this week's service. Bye-bye.