SPEAKER_02: Number one, you consumed a bunch of information to you did the work and learned how to code and build products. And number three, you got on a plane, and you went to where the action was happening. And you met people you networked. Yeah, this is so easy. And the industry is so wide open. So for people in America, who are saying, Oh, my God, I don't know how to break into tech, consume every bit of content you can about what you want to do and being an entrepreneur. Number two, learn how to build products. So all the information is on the internet. And number three, go to wherever the act most action is. It happens to be the Bay Area. But there's also stuff happening in Dubai. There's stuff happening in Tokyo, there's stuff happening in Sydney and Melbourne. Sometimes you just got to get on a plane and meet people in network. It's very simple 123 you figured it out kid. I love it. This week in
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SPEAKER_02: startups. covering this AI trend well for over 10 years. But the last year has been absolutely extraordinary since chatgpt launched specifically 3.5 and then later 4.0. Everybody else has gotten in on the action and auto GPTs and baby GPTs were all the rage a couple of months ago. What were those? They were like little user agents, they would perform tasks anonymously, maybe string a couple of them together, like go do a search for a flight and then buy it for me. This is what the internet and intelligent agents were always supposed to do, but they never worked. And it's a brilliant idea, obviously a little bit scary to some people, but it's the obvious future. And as is the case, founders always try to make the future get here a little bit quicker. And today we're having a founder on the show, Aryan Sharma. He is the CEO and co founder of induced AI. And what they're building is agents that live in a browser and it's called, as I said, induced AI. Welcome to the show. Aryan.
SPEAKER_00: Thanks for having me. I'm scared to be here. Yeah. And now you heard my thanks for coming. You heard my
SPEAKER_02: intro there. Explain to the audience what you're building and why it's important.
SPEAKER_00: Yeah, I can give you a quick background on why this is even a consideration and why how we came to the concept of agents. I think one of the first traces of this, this the whole agent idea stems from the RL world, which is reinforcement learning. It's one of the earliest AI research areas. And what it's basically means is you can have these models that are learning how to do different things. And they can they can try a bunch of different patterns, and they get rewards when they're doing something right. And then they get penalties, think of it as penalties when they're doing something wrong. If you're reading one of these models, how to play tennis, every time they hit a good shot, they get plus one every time do something wrong, they get minus one. And they're the way intelligence is baked into these systems is you just allow them to do a bunch of patterns, and you keep rewarding and sort of giving them penalties when they're going on. And over time, they try out a bunch of different patterns and they become smarter. That was kind of the earliest traces of what agents meant where they have kind of agency to figure out and then self develop a little bit of intelligence. We used to do all of this, the whole agent premise was restricted to reinforcement learning for a while because you want to try out a bunch of things. And that was kind of the only architecture that allowed people to work and build these agents. There's this paper called World of Bits that OpenAI wrote in 2016. And it was Andrikar Pathi and a bunch of others. That was the first version of these agents where they were teaching agents how to control the web, how to play some games, Minecraft and a bunch of environments. And they kind of tried building it in a reinforcement learning first way. What has happened since then is obviously we've had transformers and we've had these amazing language models come in. And they've opened up a new set of capabilities. And after chat GPT came out last year was evident that it's it's, it's got a bunch of interesting capabilities, you can ask questions, it will give you responses, it can sort of reason on its own, you can, there was interesting patterns that people the developer community was creating auto GPT, baby AGI versions of this where you have a prompt prompting itself, and then chat GPT can talk to itself and have these loops and a little bit of that loop that is important and reinforcement being created. Give an example of that. Yeah.
SPEAKER_00: screen example that is you, let's say you give chat GPT a task come up with a copy for Jason's new fund. And it comes up, you give some description, this is highlighted the funds, this is what we're focused on, etc, etc. And it comes up with a three line copy. And then you take the three line copy, and then it's you just give it back to the system, analyze this copy that you came up with, and make it better or give me three criticisms, to get gives you like three criticisms, these are the things that you can improve. And you just kind of keep continuing the loop where it takes those three criticisms goes back and sort of trying to self heal or self correct in some sense. But it's all happening at the prompt level. So this is super easy for anybody to do. Nobody, there's no reinforcement learning, there's no model architecture, nothing in word is just smart use of language models, you're stringing them together in interesting ways to create this sort of agentic behavior. And what what was interesting for a lot of people in the developer community was that now that we have these language models, and we have these interesting new capabilities, maybe we can go back to the premise of agents, but design them in a language model first manner instead of reinforcement learning or some of the older versions of agents that were created. And auto GPT, baby HIV, or first versions of that where you have a language model sitting at the center, to go and give them a command, maybe go to Google search up for something and then give me a summary and extract top three highlights from the summary. And then that model that is sitting and taking in your input has access to a bunch of tools, it can choose which tools it wants to use, you can decide and go to this website, perform this action, capture this data processes it and other my so it has a little bit of this reasoning engine built in to itself. And you can execute the task using those tools. So it's not the language model is no longer restricted to its training corpus, it's no longer restricted to just giving you text responses, it has a little bit of agency to use just text input output to interact with the outside world to interact with the internet leverage API that utilizes different tools, and do more stuff. Do that
SPEAKER_02: currently, with chat GPT, if you try to get it to do things on the internet, I did the other day, I said, Hey, tell me the best pairs of boxer shorts. It gave me six different types of boxer shorts. And I said, Okay, buy me one pair of each and medium 32 waste. And it was like, I can't do that. So in your world, it could do that it could go search the web for those six brands, put it in the cart, check it out, use my card, know my address, and then ship it. That's what you're building, correct?
SPEAKER_00: Essentially, we are building, we kind of think of them as many digital workers, they are these language models that are sitting and you can give them instructions and they have full access to a browser. We are only doing browser stuff. Now, there's different ways of doing this, where you can have it at desktop level, you can have it at mobile level, you can have it at the API level. But we sort of think that the browser is the most so exciting, and the broadest way to do it.
SPEAKER_02: So I could create a personal shopper, using your software, I could tell the personal shopper, your job is to find the best item, make a list of those report back on this, and then I'll tell you which ones I want to order. And then you go order them and make sure they get shipped based on this criteria. Or I could even take that out, just say, get me the three best coffees, the highest rated ones, and ship them to my home address. And it would actually do it. Yeah, so the one caveat there is there are two ways to think of
SPEAKER_00: these agents. One very exciting premise of these agents is running them fully autonomously, which is what auto GPT and a bunch of other agents originally started with. You're just given a text description like this, go find me shoes or go find me flight tickets, and then it automatically figures out where it needs to go. It'll ask you questions to figure out what you want, and then automatically kind of figure out whatever needs to be done to get you your output. That is great. That's kind of the future that we are building towards. But if you have a lot of time, you can go and get your shoes. And that's what I'm trying to do. And that's what I'm trying to do. So I'm going to do it in three words, but in in the near term, the next four or five or six months, based on the current capabilities of the models that we have, it is a great vision. It's a great demo. But when you actually implement it, there's problems with the reliability because fundamentally, these models are non deterministic, and they come up with new outputs every time. So you cannot be sure that every time more likely than not, you'll get lost because these there's no rules that are surrounding them. There's no guardrails around these models.
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SPEAKER_00: is it's all stuff that was done previously with RPS software, the UI path. And, you know, some of these like large RPA companies that have existed for several years and decades. Their idea was, let's bring together tools that don't have APS, and we can connect them together and build these bots. What did you refer to those as? What kind of companies?
SPEAKER_00: It's called robotic process automation. It's the RPA, industry, a robotic process automation, as opposed to
SPEAKER_02: business process automation. RPA is these repetitive tasks, robotic process automation. Interesting. Yeah, and there, it just you have these robots that are
SPEAKER_00: created mini digital scripts and workers that do a bunch of repetitive tasks. And that has existed for a long time. So it's one of it's a last band category for a lot of enterprises. That's how enterprises automate work, especially when they're dealing with tools that don't have APS and that you cannot just, you know, string together those app here or existing API tools. LinkedIn come to mind, right? Lincoln slows you down. It
SPEAKER_02: doesn't have an API, everybody wants an API. They don't want to give an API because they know they go fast. But with robotic process automation, you set up a browser, you search, it goes and does these things automatically looking for I don't know, CTOs, chief technology officers, puts them into a database, you know, and sends them a link and email, whatever tries to guess that tries to validate, guess their email and then validate it with an email validation service. And that's how people have these databases if they ever try to sell you databases. Based on LinkedIn data. It's these RPAs that have done that searching and of course, they get turned off if they load too many pages. So it's a bit of a cat and mouse thing, correct. LinkedIn is a great example, you can look at a bunch of legacy
SPEAKER_00: industries like healthcare that have insurance platforms and claims processing platforms, all of these don't have API. So a lot of real legacy industries rely on RPA. The problem is that RPA has existed for a long time is just used to be done in a very manual way. Even though it's the eventual goal is you want to automate a workflow, it takes a lot of effort to kind of set up these processes because these RPA companies go top down. It's almost like for every dollar you spend on implementing RPA, you have to spend five or $6 on consultants who will actually come and understand your process. They will, you know, buy one of the software from one of the vendors. And then the reason it's so expensive and time consuming is that traditionally, if you want to string together and automate a workflow on the browser, you have to script every step. So simple Google search for launch or this week in startups is go to Google, you will have to click on the field, get the selector, the HTML selector, which is behind the scenes, the DOM, or whatever of the web page, then click on that field, type in your text, get the identifier of the button, then go to the page. It's just every field button element that you'll interact with on the web in completing your workflow, you have to manually go and script it. And the problem with that is scripting takes time, but also these scripts can break because if these, you know, websites keep changing layouts all the time, they keep changing selectors and class names all the time. And because you're hard coding it to selectors and class names from the HTML, if any of that changes, your scripts will break. So you have to constantly keep maintaining these scripts. So that's kind of one part of why... And this is where a language model might come in, because the
SPEAKER_02: language model would look at the page if your profile page gets updated, or I should say LinkedIn changes profile pages, and they just move the HTML around and people are looking for what city you're in. And they called it location instead of city. Well, that breaks the RPA, right? And so now he just said, what's the location, the language model should be able to figure out what's the location in the first I don't know, that you know, 500 words of text on the page.
SPEAKER_00: Yeah, the language model is doing real time inference on every run. So it can, it can basically handle these changes, it can handle, you don't have to spend as much time scripting, just broad directional input of, you know, the city, you don't need to pinpoint where the city is, you look at the page, get the thing reliably, you can obviously set guardrails around that as well. That's kind of one problem of RPA that solves in this new world. The other problem is that with traditional RPA, you can only, because it didn't have any reasoning skills, you can only automate things that are, you know, ruleset based. Just go to Google, put in this exact text, click on the first link, then go to this exact page. You cannot do things like, you know, go to this LinkedIn, analyze if it fits my ideal customer persona, or analyze if this falls into the five cities that I want to target. And then basis that, you know, do this action or draft a custom message. So any level zero cognitive reasoning tasks that you language models can be pretty good at, you cannot do them with traditional RPA. So we've, we've kind of taken this, this whole industry of how RPA was done, and designed in an AI native way of how can we make setup and you can show us actually how this
SPEAKER_02: works? Yeah. Yeah, I can pull out a quick demo, which gives
SPEAKER_00: you a good idea of how the base version works. So for people who are listening, we'll describe for you what's
SPEAKER_02: happening on the screen. Yeah, so you just go go to the
SPEAKER_00: induced platform you have, it's just empty screen with no workflows right now, I'm going to click on new workflow on the top right. And just ask you, you can either design the whole work from from scratch, where you, you know, give step by step input, or you can use AI assistance. And I'll go through what that means. But just ask for a workflow name, I'm going to put in employee timesheet, and I'll run through what workflow I'm making. So I just put in an employee timesheet. And then I'm going to put in a step by step, this is just English description of workflow that I want to design. And for context, the workflow is basically think of a construction company or a company that has warehouses or physical centers across the country, and they have employees coming in and filling in paper timesheets. There is basically a log of when they're coming into work, how many hours they're working, what the breaks they're taking, etc, etc. And this company takes in all of these paper timesheets, puts them on an air table, and they have to manually calculate the role for every employee because it's all on. So you have to take whatever is on paper, understand it, then run it against a company policy doc to calculate, you know, this guy worked five hours, this is a deduction, this is overtime, and then go and enter whatever payroll you've calculated back into an air table. So this was we did this one of our early pilot customers, they have a physical, they have a real back office of 15 people in their finance team that finance an ops team that does this. But with kind of in the new world with some of these agents, you can just describe this on our platform. That's crazy. So it has, hey, identify, access the air table
SPEAKER_02: payroll, navigate to the employee payroll base from stored variables, identify a relevant employee, search for employee with the payment status, review timesheet data, open the employees timesheet, etc. And then it says calculate the payment access the employment payout info sheet on Google Docs. Using the timesheet data, note the total hours work and time minus start time deduct any breaks, multiply the hours worked by the hourly rate mentioned in the Google Doc to get the gross amount deduct any break time expenses, or other deductions. And so this is the step by step process that some human did, you're just describing it in essentially plain English, not code. Yeah. And then you just click create workflow, what we'll do with the
SPEAKER_00: AI systems is unlike a lot of traditional traditional is a bad word to use, because it's all very new. But unlike a lot of other autonomous agents, we don't directly start running it based on English. And this is actually the first time we are ever showing this product on media, like a podcast or a video stream. But this is just this weekend startups exclusive. Thank you. It takes in whatever input you've given it compiles it, we have this middle layer in between, which is just the input that you've given, but structured into smaller steps. So all of the all of the steps are again, it just broken them down chunk them in. So it's just basically access a table, then loop through the entries, pick one entry, just smaller, smaller chunks of whatever I described. And the reason this is useful is one for visibility for who's designing this workflow, they can see whatever input they can watch the final translation. And then what you can also have visibility into is we split it into a bunch of different action types. So it's, you know, going to the web page is one action, clicking, filling, all of those are standard web actions. Then a bunch of data actions like looping, you know, identifying filtering that you can do on the page. And then we have the agent blocks, which all of the smart actions. So once you go to an employee's timesheet, any calculation that you want to do, you can use the agent block to delegate and get input from model. So it's basically a bunch of different block types, depending on what your workflow step is, that we automatically identify and put in, you can obviously edit, you can obviously make changes to this. And at the end, you can have standard, you know, ELT or outputs in whatever format. So after the workflow is done, you know, you can have triggers that if you API call, put it in a Google Sheet or anything that you need. So just put some in into these formats. And if you notice, for those who can see the screen, they automatically puts in these variables across the steps. So if your workflow involves capturing data from one place, and then using it in another step later on, it's basically I'll talk more about the browser environment. But this is basically a runtime that is designed for agents. So it has access to its own file system, its own memory, it can store data, retrieve data, basically, basically firing up a computer in the cloud,
SPEAKER_02: essentially, or a browser session, I don't know if you're using Chrome or Chrome OS, or Windows, you can light up a virtual browser or a virtual machine anywhere, AWS, etc. So you're basically popping up a desktop and then running this stuff? Yeah.
SPEAKER_00: Yeah. So we spin up a chromium fork on the cloud, it's a virtual machine with a chromium fork, it's a custom browser that we've designed specifically for running bots and autonomous agents and systems like that, we shouldn't go into but that's kind of it's been set up, which is why it has access to all of these tools that you can use in the flow. And then once you're happy with the workflow, just click Run. And this is kind of interesting, because the way it runs is it's been the browser that is spun up on the cloud, you can see a real time live stream of the browser when the workflow is running. So it's as if you're watching in a team member screen, you can see the stuff that's happening on the remote browser stream live on the left. And on the right, you can see step by step what's being done. So you can see the stream goes to a table, it loops through the entries, it picks one of the employees, opens the employees data, opens up this timesheet. And then now it's going to run OCR. And it can use tools on its own, as long as you're giving a step description, or use OCR document processing to capture text input from this document, store it in memory, then go to this Google Doc, which has information about how you calculate payroll based on the timesheet data, you'll capture this in memory as well. And then once it has both of these things in memory, it's going to compare both the data points on a reasoning step, which is why reasoning steps usually take longer, but it's going to take both of those things in calculate a final payroll amount. And you can see the variables being referenced here, we'll go back to our table. And it's just going to fill in, we've got a bunch of fields that were to be calculated, just going to come back, fill in total hours worked is nine, break duration is one, overtime zero, total payable is seven. We'll put in the hours, we'll put in total amount. And the interesting thing is, because this is all running remotely, we can we kind of call this the mission control view of the platform where you can have a bunch of different browser instances doing different things, or the same thing running at the same time. So it's like a real back office, you can scale up scale down, you can have 50 instances running at once you can have five, you've got one that's you've got the air table instance, you've got one running LinkedIn, we've got one on AWS, the tech crunch thing here, and you can kind of see live streams of all of them. So for the task of a manager who's managing 15 member back office team goes from actually delegating tasks to real people to just sitting and watching 15 of these screens. And crazy if something goes wrong, just go in and flag it. But otherwise, you can kind of have a top level view of everything. So if I'm
SPEAKER_02: running some insurance company, sales team, customer support, whatever, my startup has the six or 15 agents running, doing the tasks that humans were previously done, and you just watch them and make sure it's doing it correct. And it feels like you've been working on this for less than a year. How many months into this are you?
SPEAKER_00: We started working on this around April of this year, March April is when we started. So you're six months into this process? Yes. And we've kind of identified. So just just as a disclaimer for this, this run was very well described. So I gave you know, explicit input, I described everything, and that's how it run. It doesn't require me to manually skip the whole thing. And it runs reliably once I've given input, but it requires explicit input. And I think we took that the approach that we've taken architecturally, which I was talking about, was was actually is like the most important thing in this six month process that I think has helped us a lot with instead of designing an automation product that sits at a Chrome extension, and then you use it synchronously on our computer where I have an extension, I record something and then I can replicate, but I have an extension that is an assistant, I give it commands on my Chrome and it's doing things we designed everything to be remote and have its own environment. So we took we took chromium, forked it, made made some changes to it of how the DOM comes out how the HTML comes out. Has anybody started using this yet in like a real
SPEAKER_02: world situation? Or are you still in the laboratory? We we
SPEAKER_00: launched early October, and then we went live with a couple of folks. We're live with about 15. Now that is across different sizes, small companies, mid sized companies, we've kind of found our sweet spot in these mid mid market to mid to up market companies that are operating sort of older industries like healthcare, you know, financial services, etc. So it's live with with a few of them, we are constantly we're changing the form factor a little bit because the text input that you saw where you have to describe your workflow and then it translates to just the actual workflow that's running. That is not it. It's the easiest way to start. That's how most AI language model products start, but still not very intuitive. You think of it from the user's perspective, because when you're typing the text description for your workflow, you basically have to have a browser screen that's open and then you're typing out every step. I'm going to this and I was just like, type out, okay, go to this, then what am I clicking on? I just type out, you know, click on this. And what we are designing now is it's sort of like, and this is the video that I will share. So you can probably edit and because it's it's not even an alpha right now. We're gonna put it out in a few weeks, where it's sort of like think of a Colab notebook, a Google Colab notebook, where you have a bunch of cells. And if you want, I can show you Google Colab as a reference. But it just we have, this is this is how Colab works. It has these bunch of cells. And then you can basically give in like you can run each cell. So I can, I can be like, you know, print, hello world, this is how regular Colab works. And then you can add like each cell can be done. And then you can have a bunch of more cells that come together and you can string together cells to create something. We are creating a new interface for how we set up workflows. But instead of you having to type everything at once, and then edit them, and then run it, and then come back and debug, we have this environment that is set up for you to create workflows. So you kind of just come in and instead of typing out code snippets, you have, you just type out your English steps. And we have in the base of this section, on the right, we have the stream that we had opened up. So you just come in and go to Google. And then you can actually see it go to Google. And you're like, click on search for this weekend start actually see what happened. So it's basically real time, you know, setting it up once you're happy with every step, just click confirm, and then it automatically migrates to workflow is much more intuitive, it's easier. If you had a press monitoring service,
SPEAKER_02: you could say search the web for this person's last name, or this person's name. Go to Google News, click on the link, summarize it, email it to this person, let them know they were in the news. You know, are mentioned in a news story. That's a job that PR people do for a living. Now, are you going to get the, you know, you'll probably do some mistakes and probably will put old stories and it's gonna, you know, not be perfect in your world. But it would certainly make the person who does PR clipping, and what they call media monitoring or web monitoring, it would make them bionic, they would be able to do 100 times what they do every day. So essentially, that's what you're doing is then anybody who has a business process can basically script this. Starting a business used to be such a painful process, you needed to get a lawyer, there were tons of fees, it was a mess, but not anymore. Just check out Northwest registered agent, they're going to help you form your company fast, remember, speed matters. And then they're going to get you the docs you need to open a business bank account instantly. Then they're going to provide you with mail scanning and a business address. And they're going to do all that keeping your personal privacy intact. Northwest can form LLC is corporations and nonprofits. And here's why founders love Northwest, there's no hidden fees, there's no upselling, you can call them or cancel at any time. And Northwest has the best of both worlds solution. It's simple and self served. But they can be hands on if you need help with their amazing registered agent service. Northwest provides everything you need to start and maintain your business. And they're giving twist listeners a 60% discount for just $39 plus state fees, they'll form your LLC corporation or nonprofit. So visit Northwest registered agent comm slash twist today, Northwest registered agent comm slash twist. So what happens to the business processing outsourcing industry in India where you're from, and I know you have investment from Sequoia into India or what is now there's a new name for that firm. But in peak 15 Yeah, so you have an investment from them. When you show this to the people who are in the business process outsourcing to their heads just blow up and go Oh my god, what's gonna happen to these 100 million people employed in the business processing world? Or is this just been a continuation and there's always going to be more business to process?
SPEAKER_00: Yeah, I think there's one it's it's an extremely large market where this where outsourcing happens. And it's just increasingly we are all the tech is growing, but it's like the legacy industries and there's all sorts of unique things that keep coming up, which keeps growing the business process outsourcing at the back office or the like, the Philippines, India, all these kind of work, the things that get delegated outside, and you have these economies that are created for people that are remotely doing these tasks. So I think the market is huge, they always need more automation. So every time we go and we've had a lot of people who run these globe, they're called GCC. So the global, you know, back back office centers, and like a bunch of different words for them, we got a lot of them reach out. And they're always looking for automation, even though they have, you know, 50,000 people sitting and doing these things, because they want to make it more efficient, they want to be able to take on more business. So a lot of them want to be customers of this so they can improve internal processes. But I think in general, the trend that we're seeing is this will kind of take the low level things first. So the extremely repetitive things that have very little cognitive involvement are almost always easy to automate even with traditional RPA. With these models coming in, we'll be able to do a little bit of cognitive tasks as well, a little bit of thinking, filtering, you know, profile validation, lead enrichment, things like that will go in. And but then there's always, you know, complex tasks or sensitive tasks that you want these back offices to do like payments, etc. So I think it'd be a slow migration. But this kind of just trajectory of tech where credible, GCC stands for
SPEAKER_02: global capability centers. This is India, when you want to outsource a back office, you were Uber or Airbnb, and you have a huge amount of I don't know, refunds to process, or claims to process or complaints, whatever, you would hire a GCC to work with you on the best practice hire some people in a lower per hour location, and just get it off your plate in America where, you know, people candidly don't even want to take these jobs, no matter what you pay them, they just wouldn't take them. And, you know, that's what's been happening for, I don't know, for all time, but certainly in the last 30 or 40 years, this process of outsourcing. So I guess since you're so close to all of this, and you're building it, you're talking about very low level things, people who are doing data entry, I guess, data cleanup, SDRs, these are, you know, very, the lowest paying white collar jobs, I would say, right? These are Yeah. How, you know, in three years, what job you think you could do? Can you do a bookkeeper and accountant in five years? Could you do a paralegal in 10 years? Can you do every job? Could you do, you know, a salesperson's job in full, you're sending stuff, negotiating, etc? What, where do you see this winding up? Um, I think, I think the way I think
SPEAKER_00: about this, and I can share a few tweets, if you want, there should be like, interesting to see, as reference, there was this, there was this paper that that Jim from Nvidia released, and this was blew up on Twitter, you've probably seen it before, where it was basically GPT for playing Minecraft. And it's the way this operates is called Voyager, what it's doing is, it does a bunch of things. And then once it does a bunch of things, it analyzes things, and it buckets them into skills. So you know, chopping the tree, it's done a bunch of times, it sees what the reactions come in, and then it's going to bucket that into a skill of shopping. And then every time it does chopping, it kind of feeds into that loop of refining the chopping skill. And basically, what's written here is, it unlocks a new training paradigm, where training is execution. And it's the runs that this agent is doing in Minecraft, helps iteratively compose a bunch of skills that it can slowly like learn, but it's all confined to Minecraft. It's a constrained environment where this agent is operating, it's developing skills and continuously improving its skills. I think the way to think about how this slowly these agents become powerful is we will not have, in my view, generally capable, super autonomous global agents that can do everything. The way we get closer to these areas being more powerful is the way you said bookkeeping, you take three tools that are involved in book maintenance, maybe QuickBooks, maybe, you know, Excel or Google Sheets, and maybe like a bunch of code interpreter calculation stuff. You get these agents to use these tools, you build this repository of skills across these tools, or you kind of let them explore and build these repository of skills. And then the more they run, the better those skills get. As much training data as we can put into them, they slowly get better as using those three, four tools, doing those three, four kinds of interaction on those tools. And then when you kind of go into that agent, like do this calculation for me on my QuickBooks, and then run this math function, or like do a prediction or regression for me, it's able to use those three tools for well and start doing things. So I think it's going to happen sooner. I don't think it's two, three, four or five years out, it will start we'll start seeing specialized versions of these agents and specialized agents that can use a bunch of tools coming in in the next few months. It's just the way to approach that is get as many real world use cases, get as many real world tools, get these agents to learn these real world use cases, tools, run them a bunch of times, and then kind of keep improving how this gets exciting, that it will slowly go in skill by skill. And so what happens when some bad
SPEAKER_02: actor pops up 15 windows to go cause chaos on the open web across services? What do you think the potential there is? Because unlike chat, GPT, if you ask it, you know, chat, GPT is not going to go out and start trolling somebody on Twitter or Reddit or harassing them, let's say. But you could start very easily with your software. And I'm not saying you would do this, obviously, but it's obviously on the path. And people do this already. But you could fire up 15 accounts, or 15 browser windows, 15 different accounts, and then try to maybe swing an election, right? We saw the Russians had boiler rooms doing this. And they were, you know, all we found out about it. That was part of the Mueller report, trigger warning, Russia gate, but they were actually doing this, but they were using humans, right? And that you do have those boiler rooms, I think, in Manila, India, and other places where people do fake reviews of products. So somebody here could fire up 15 of these windows, and start posting pro Palestinian pro Hamas, pro Israel, whatever, comments, or just generally causing chaos. So how do you think about that? Because your tool would allow a neophyte, a non intelligent person, you know, a bad actor to go absolutely buck wild and destroy everybody else's experience on the web. I think so. There's a first part is there is a lot of this is
SPEAKER_00: what open has also dealt with, with their browsing being limited to only a few websites. And they had to take down browsing in between because it was bypassing authenticated pages and giving you paywall content through its scapers without actually, so there's like a bunch of things that are happening there. So I think it's important to add guardrails. And in the way we think about this is, because we've designed this as a browser environment that is meant for bots, we can build it up in so we don't need to build it up, like human browser environment, we can add a bunch of guardrails in a browser specific to these bots that like allow them only a limited set of capabilities. And a bunch of websites are just out of bounds, a bunch of capabilities are just out of bounds. And the users that are controlling these things, they can define a bunch of rules. But there are a bunch of global rulesets that just prevent that thing. So your terms of service
SPEAKER_02: and then what it's allowed to do, you could say, Hey, listen, we don't want you using these bots to go post to social media or to the bots and ruin them. So you could just your I assume you're just banning the ability to do that these bots can't go out and post on Reddit or whatever. Yeah, and we are
SPEAKER_00: banning a bunch of these, we are going safer than we should be going right now, just because it's easier to build up the safety spectrum then come down. And the other way we think about this is slowly it's going to evolve with the platforms where you know, maybe if a bunch of bots are being run on Airbnb, in the decision making process of what kind of bots are allowed or not allowed, and there'll be some sort of transaction that will eventually happen given the rate of progress that these autonomous agents and browser bots are seeing that websites will have to define. And this has happened for a long time. So robots.txt files exist on the internet for a lot of websites and OpenAI recently, a couple months ago, open source their scrapers, and they opened their signature. So a website can choose if they want to let OpenAI scrape them or not. And I think we'll see similar versions of that with these bots and agents where you'll be able to allow bots to run on certain parts and some sort of web standard. Somebody wanted to go to Airbnb. And they thought, Oh, let me just go to the checkout
SPEAKER_02: and check out with a fake account, then cancel the reservation just to ask the person a question, right? So once you have the book, you can I guess have a dialogue with the person and it wanted to fish for information or whatever, you could just say, you know what, that's not an allowed use case. And then you have a conversation with Brian and the team over at Airbnb. And they say, Yeah, we don't mind somebody building an agent that looks at up to 50 pages per week or something. But after that, we want you to go through the API, or we want you to get a license or something. And you'll just be a good actor watching that, of course, bad actors will do what you're doing, and not require that. So there's going to be a bit of chaos, I think we'll all predict on the open web in the coming year or two. And I this is a little bit of sci fi, but I think it will sort
SPEAKER_00: of be a new version of capture, where you have the way we have captures differentiating humans and bots, you probably have a version of captures that differentiate good bots and bad bots. And you have some way of proving what you're going to do. Maybe there's some digital signature exchange that happens there. But I think it's just, we are all in this new world. And a lot of I think the reason a lot of the larger companies are also moving slower now is because they're afraid of, you know, infringing policy or content policy, terms of service, etc, of different web products, and we slowly evolve both sides where they get more clarity on what they want to allow. And then, yeah, builders of bots like us get more clarity on what should be allowed for users.
SPEAKER_02: If you run a business, you know that having reliable vendors is non negotiable. And whether you need office snacks, holiday gifts or wholesale ingredients, you need to check out nuts.com. That's it nuts.com. That's a crazy amazing domain name and uts.com. And nuts.com is your one stop shop for the highest quality foods for your business. Again, they offer delicious office snacks, corporate gifts and wholesale ingredients. I got a gift pack. I have been eating these beautiful roasted nuts, and other amazing premium products like chocolate covered sweets. I love the trail mix popcorn. You know, I stopped eating the candy. So I went for the dried fruit, but they also have wrap candy as well. And my favorite jerky. And of course, they have all the gluten free stuff or whatever dietary option you're into over 50,000 companies choose nuts.com for their business needs from offices to hotels to restaurants to retail stores. nuts.com has something for every business. And so here is your call to action nuts.com makes ordering for your business quick and easy. And right now, nuts.com is offering new business customers a free gift with purchase and free shipping on any orders of $125 or more at nuts.com slash twist. Go check out all the delicious options at nuts.com slash twist, and you'll receive your free gift and free shipping when you spend 125 or more. That's nuts.com slash twist. Here's a crazy idea. People who are building these bots have credit cards, and real names and are validated as real humans and have a tax ID. And if you want to have a bot doing things in the real world, you have to put a credit card in you have to have a social security number and or whatever the business number is. And you have to have a valid phone number and you have to have a valid email and you have to be authenticated on the phone or have a driver's license on file. So to use these things you could just have like if you want to buy a gun or have a car, you have insurance, you have a driver's license. So if it's deemed to be too dangerous, you can look at the amount of danger this causes in the world or chaos it could cause. And then just like cars and guns are treated differently than pens and paper and you know, maybe there's fertilizer that you know, if you buy it, we know that people can make bombs out of fertilizer, you know, you have to have your passport driver's license, and you can only buy a certain amount of fertilizer and there's a waiting day, right? So all of these things we have in the world. Yeah, we have some version of KYC or you know, throttling when people can get it. We have cool off periods with guns and you know, hopefully the good folks in the world like yourselves who are building this stuff are thoughtful about it. What I love about this is I think this is work that people don't want to do. It's soul crushing work in most cases, repetitive tasks, it's you know, going out and chopping wood would be more pleasurable, I think for most people and healthier than doing some of the jobs that you're going to eliminate. And I think there are jobs that should be eliminated. Nobody wanted to be a phone operator and sit there and plugging cables all day for 4050 hours a week. It was arduous and painful. Same thing here. Nobody wants to be in the fields down on their knees picking strawberries. Robots should do that better. It's the same kind of analogy. I wish you great success with this. What have you I know you're very young. I didn't want to bring that up because when I was young and people referred to me as the 23 year old founder of this magazine, I always found it kind of annoying that that was in the first sentence, but you are 19 years old. You have raised a couple million dollars from Sam Altman and the former Sequoia India and I get that AI grant right from Daniel gross to I understand. Yeah. So for folks who are young founders out there, how the heck did you do it? I think I had a very interesting story because I
SPEAKER_00: didn't go up into the area of the US. I was out sort of an outsider in that sense, but I grew up in India and I always I used to see your podcast. I used to see who I see videos, I used to see a bunch of things from outside and I just be like, you know, this is something something happening. What was
SPEAKER_02: the youngest age you watched one of my podcasts? I think 12 or
SPEAKER_00: 13 when I was 13 in India watching this week and
SPEAKER_02: starters, you have no idea how much that fills my heart with joy because I always said, you know, I think there are people around the world who might hear this podcast and be inspired to start a company or just get stoked to be an entrepreneur. And to hear you actually say you listen to this at 12 or 13, halfway around the world, and now you're on the program six years later, is just mind blowingly joyful for me. So thank you for that. No, thank you for doing the show. I
SPEAKER_00: remember the clip that you did with Patrick, where he spoke about how they started stripe and how they raise money from Sam and how they came to the valley and started doing stuff. It's like a bunch of these things that I used to see him outside and I this was stuff that I wanted to do. I started writing code very early. So I was already working in tech while I was here. I was taking up remote jobs while I was still in school. I've been like a bunch of projects. And then as soon as I made a little bit of money, I started making trips to the US just to come to the Bay Area, stay in these hacker houses, try to meet people go to these events. That's how I met a lot of these investors and people who eventually kind of invested or became a part of the company. But I just started and that was I sort of had this brute force way of breaking in Twitter was in fact also super useful by users called him a bunch of people reach out to them, pitch them. Yeah, it's like a recap. Number one, you consumed a bunch
SPEAKER_02: of information to you did the work and learned how to code and build products. And number three, you got on a plane. And you went to where the action was happening. And you met people you networked. Yeah, this is so easy. And the industry is so wide open. So for people in America, who are saying, Oh, my God, I don't know how to break into tech, consume every bit of content you can about what you want to do and being an entrepreneur. Number two, learn how to build products. So all the information is on the internet. And number three, go to wherever the act most action is, it happens to be the Bay Area. But there's also stuff happening in Dubai. There stuff happening in Tokyo, there's stuff happening in Sydney and Melbourne. Sometimes you just got to get on a plane and meet people in network. It's very simple 123 you figured it out here. I love it.
SPEAKER_00: I think the other thing that's great about the valley in journalists culturally, everybody's open to taking meetings. And that that sort of people like, I think the classic, it's a play. It's an advantage for young founders, even though it sort of like we're building for a very old industry. It's like legacy industries. You can have all these questions around how do you get to these customers? How do you talk to them? They will like, how do you break in stuff like that. But I think on the other side, everybody gets excited when they see no large market opportunity, you know, the young team that wants to move fast. It's kind of the classic story that people get excited about. So I think everybody had been very open, grateful for everybody who took meetings with us since like helps helped us in the process. But I think it's, yeah, it's very doable. If you put in the work and to show up, people like backing people who are doing the work and have interesting stories. You know that people
SPEAKER_02: who are of action, people who are doing stuff in the world are one in 100 of the people who generally interact with us. So I get hundreds of emails where people tell me their ideas, tons of DMS, people tell me their ideas. And then once in a while, I get a link to a product or a screenshot of a product or a loom or a, you know, a quick demo or a figma. And I click the link. And I go, Wow, what you built is frickin cool. Let's get on a zoom or meet somebody on my team, you know, or come to our accelerator, or maybe we can invest. And that really does differentiate you. I think you figured it out, or in and, man, your parents must be so proud of you. What may I ask, what do your parents do? And what do they think of all this? Because they're in India. And you are 19 years old, and you raised over $2 million. Are your parents like entrepreneurs themselves? Or? No, they're both doctors. So they come from the opposite
SPEAKER_00: end of spectrum. They were pretty disappointed when I was not going to college. And they it's still not off the books for me, like some point, maybe you want to reconsider and, you know, maybe apply and get into some school into it. But I think they are generally they've kind of become more supportive. Over time, where it's like, you're doing, you're not doing something wrong. So as long as you're not like a criminal, and you're doing what's a pretty good benchmark, you're not a
SPEAKER_02: doctor, which are also not a criminal. So there's something in between those two things that is acceptable. They don't have to bail you out. So shout out to your parents, but message to your parents. Not everybody is going to just go through and do the standard thing. Some people have a lot of creativity, and they have more energy than those career paths allow for. And so I think I was I would have just been full mode out of college,
SPEAKER_00: even if I would have gone just because I think the way in which stuff is accelerating, it's almost like that the opportunity cost of going is is just it's huge. Like I was same thing
SPEAKER_02: happened on the way in the dot com era. And I told everybody, like, if you're going to college during the dot com era, when all this was changing, it's a big mistake, because I've never seen a gold rush like this. And then I saw a second one, which was in mobile in 2008 910 1112. And now this is the third one I've seen in my lifetime was really three very unique ones, the internet, the dot com era, the mobile shift, and then now AI and they come along every 10 years. And it's like this incredible season where there's a ton of snow on the mountain and you really ski really well. Oh, that's just great waves to serve. There's not always great waves to serve. I mean, you can build a great company anytime. But listen, I am so proud of you. And I'm not your parents, but I'm super proud of you that you're doing it. And I wish you great success. And my only regret is I didn't get a chance to be in the seed round. But hey, maybe you'll raise money again. And I can slide a quick maybe your your your uncle Jake out could slide a quick hundred K or 250k into this, I think you're gonna knock it out of the park, by the way. Congratulations and take your time. Focus on product, you know what to do. You've listened to all these talks and podcasts and you got great investor Sam's amazing. It's all about just focusing on the product and the customers and you seem incredibly product and customer focused. You must have picked that up, I guess from Sam and just watching our videos and Y Combinator videos and blog posts. Yeah.
SPEAKER_00: Yeah, I think that's the only way to do it. I've had versions of doing things. Otherwise, it just doesn't work. You have to learn only two things matter is just be heads down studying the space. I'd like to be very analytical. So all of these tweets and like data points that I consume, they are actually how I think and I can translate the little bit of this macro reasoning of what's happening with just product, what the customer is saying and how they're going to sort of strings together in an additive, which I think is equally important as we're doing the work. So it's, yeah, that's the only way that I think things can happen. And we've had we had a good launch. So it's just now iteration, we have a lot of backlog of demand. So kind of slowly serving out and figuring out how to get the capacity.
SPEAKER_02: All you gotta do is delight those customers, everything will be fine. Everybody check out induced.ai i n d u c e d.ai. And you can follow our in he's on Twitter as you mentioned x r a r y x n s h a r m a. Go ahead and follow him and all the links are in the show notes and we'll see you all next time on this week in service. Bye bye.
