Reid Riffs with Parth Patil on AI-Native Startups (Part 3 of 3)

0:00Hey, everyone. Aria here. We want to kick off the new year with inspiring conversations about AI as well as practical and tactical guidance around technology. So for the month of January, AI specialist and my dear colleague Parth Patel is joining Reed for Reed Riffs to talk about how everyone from individuals to enterprises to startup founders can harness AI to level up their work, retrofit legacy orgs for the AI era, and build AI native businesses right from the jump. So tune in. You're in very good hands with Parth. And I will be back putting Reid in the hot seat come this February. Thanks so much.

0:41Thanks for the kind word and warm welcome to Possible, Aria. As I talk with Reid this month, I'll be walking through some of my AI projects, demos, and tools on screen. While I'll do my best to describe what I'm looking at for our audio-only listeners, consider switching to the video version of the episode on Spotify or watching on Reid's YouTube channel for the full experience. Thanks. And let's get into it. An area close to both of our hearts, which is entrepreneurship, starting companies, starting products. So everyone is going in AI today. What does it mean to actually be AI native? I think most people misunderstand this, but matter of fact, of all the people I know, you are the most AI native person.

1:28So say a little bit about what being AI native is. and how does it change the kinds of problems entrepreneurs might choose or the way they might go after starting a company, going after a market, thinking about how they operate from the very earliest days? This is something, and actually I think I'm one of the early AI natives, but I have a feeling the next generation will be even more AI native. There are people I've met that have never even used a keyboard, and for them that's like an alien experience, a mouse and a keyboard. They're used to the trackpad, laptop kind of experience. But I think even when I think about AI native, I think pretty much every, I have this like realization multiple times a week where I see something that a model can do.

2:14I see that, oh, Codex can now work for two days straight if you allow it to plan very deeply. Same thing with Opus, God Code Opus. You can give it a planning framework. If you allow it to take notes on its own progress, it's able to work for two days straight. And then I read that I read the paper and then I fire it up on a project and I see it working and it's like three hours deep doing like productive work for me and I'm just like, oh my god, then I go for a walk. And I'm just thinking I'm like, like, I'm walking right now, but I'm also being productive, right? I have these processes running.

2:44And then my mind starts going and I'm thinking like, where are we like, where do we apply this new capability, but this happens like every other week, right? Some new capability comes online. And then I have to go for a walk and think, well, where does this apply? and a lot of times I think I take like okay let's just describe the workflow as it is how we normally do it the normal problem that we're trying to solve and then I go to a language model I say given we now have xyz capabilities how might we reimagine this workflow so that we can do it in a more parallelized way in a more extensible modular way how can we reduce the drudgery of the human experience in this work.

3:25And, and then like, maybe write the first version of that. And I usually allow it to think for like a whole day and work on that for a whole day. And it is something that I try to get a pattern of where I describe the problem, I take it to the smartest model I know, I tell it to think about a couple different plans. And then I tell agents to start working on those plans, executing those directions. A lot of times you don't get like something that works, you know, on the first try, but you get a lot of promising directions, or you get like 80 % of the way there. And all of a sudden you've eliminated like 100 hours of work a week.

3:58And, or like now the few people that are working on this can go way further. They can think in a more, like there's the parallelization of cognition now, right? Where it used to be that every human was the bottleneck in any job. But now you take a process that someone does, you have them equipped with agents and then they're parallelized across many different parallel streams in that process. And that's something that's amazing. I think not rethinking of yourself as like being able to atomize a task, decompose it, and then parallelize subcomponents of it and then lean on the computer for those pieces.

4:33It just feels like a superpower. So give me a real life example. So one thing that I'm known for in my own friend group is as this guy, like the guy that's just like deep in the coding agents, deep on the frontier of like, what came out last week? What's the new superpower that we have? and a lot of my friends they still you know they work at normal companies they have normal jobs they work building their own companies and a lot of them are programmers and so for me it's like i get to experience the first the first version of the superpower but my friends are better engineers than me and so i'm kind of like is it because i'm a noob or is because i'm like kind of just teaching myself everything that it all feels magical or does someone who's also much more experienced than me experience a different kind of amplification so i'll take so for example earlier this year we We got, Cloud Code came out and started, it started taking off like wildfire within my workflows.

5:21And then I was like, well, is it just me? So then I took it to, I called my, all my best programmer friends in SF. We got dinner and we were sitting there at dinner and I was like, guys, Cloud Code, this feels like the step forward in coding automation that I've been like waiting for, like looking at different angles of, and here's what it, here's what it does for me. And these are the same guys that saw me when I first interacted with Cursor. So they were like, if he's right, we should get early on this. And so I had my buddy, Amila. Amila is a startup founder, and he's working on a company called Palette.

5:58And it's just two of them. It's just two engineers working on this company. And the company, what they do is JavaScript optimization. So they're trying to make the web interface, and the web interfaces faster. So tools like Notion, how do you make them responsive, low latency experiences? and his entire thing is how do we make the web faster like there's a lot of javascript how do we make it all faster and so i showed we were sitting there at dinner and i was talking to him about the cutting edge of coding agents and i was like here's why i feel like it's amplifying me here are the problems i can solve and i think you should be using it i mean you're a startup founder right like you should be using cloud code before you even think about hiring anyone and and at first he was a little dismissive and this i think is usually because this is because he's a really good JavaScript programmer.

6:42I think he's probably one of the best that I know. That comes with this pride and also a very high expectation. Yes, in some cases AI-generated code can be slop, but that doesn't make it useless. We work with people that are not perfect. We are not perfect. To get zero value would be shocking. I give him Cloud Code and then a couple months later I'm like, oh, Codex is also very good. And now OpenAI's Codex is competing on a similar plane. And so we get dinner four months later. And this is just like three months ago now. We get dinner again. And he pulls me aside and he says, Parth, I'm so glad you showed me these coding agents because now we are launching an enterprise partnership.

7:30It's still two of us. And we're just like, it's nailing extremely hard migrations. Codex is able to like understand and solve an arcane problem in 20 minutes that would have otherwise taken me a week with all of my time. And he's like, if you think about yourself as a solo founder or like a two-person team, you have many other responsibilities other than programming. And that he can delegate to these intelligent co-pilot systems that can actually like solve some of these multi-day problems for him. It means that like he's seeing this, like he really feels like it is something that is, he can't imagine hiring people that don't interact with these tools as well.

8:07And so it's a totally new play style. and my thing is like okay cool I need to figure out the next part of that game right the like orchestration of multiple of these and you know everyone's at a different level I think a lot of people are interfacing with chat GPT or they install cloud code they have one agent and I'm kind of at that point of like okay what if we have many of these a fleet that's kind of on deck some of them are active idle and some of them are working continuously and then can I put these tools in front of the best builders that I know and what impact does it have on them and it is it is staggering how much more effect it has on them they become much more ambitious they're like reaching milestones earlier than they would have imagined and then they reimagine the team that they're building around this kind of new play styles this is why I love working with startups because if you think about startups as opposed to enterprises like you have no baggage you're actually just already dead, like your default debt, like you don't have you don't exist.

9:06And and and you don't have this this like calcification of a bureaucracy and 1000 employees, you don't even have enough employees to do what you're trying to do to stay alive. And so then they lean into the new technology. And so my job, I view it as like scout the new technology and put it in front of the right person. And then it reveals to me, like, I get more validation. I was like, Oh, it is truly that kind of that important of a technology, it ends up in their daily workflow. It ends up being something their whole team is like collectively contributing to. And I get a lot of feedback then because I can use it myself and learn.

9:37But if I infuse my network with it, then I get a lot of feedback. And people are coming back to me six months later and they're like, I learned this new trick. Here's this crazy new ability that we have and I'd love to show it to you. Yeah, the collective learning, the network, the allies, friends in learning, the iteration in learning is super key. So walk us through what a modern example would be, something concrete of like a product or a feature that a frontier model in the loop from day zero could do. Like, you know, what does that process look like in the kind of the, you know, in a few steps?

10:18Well, actually, you know about this one. So we've been working on the Possible podcast. You're obviously the host of Possible. and I've been kind of supporting the team behind the scenes. The big kind of push for the last couple of months has been, can we internationalize this podcast? And internationalize, not just like release the podcast and then translate the transcripts, but what if we were to recreate the same conversation, but natively in many different languages? So your voice, your co-host Aria, you and Aria both are the hosts of Possible, but can we re-release the podcast using your voices in Chinese in French, in Hindi, and how many languages can we do that in, and how quickly can we expand into many different languages.

11:02And this was an interesting project because when we mentioned we were interested in translating, you've been working with translation of your content for a long time, but I was like, oh, this is perfect for agents because it is a coding problem. It is a problem that can be sliced up into a bunch of different small chunks, and then it can be largely paralyzed and so you're kind of cheating time and you're cheating the the like you're reaching into the general capabilities of language models and the increasingly general capabilities of voice voice models from 11 labs and so i basically and also you know at my last company at clubhouse when i was working there we had an internationalization team it was like 20 people 25 people and it took a long time to just launch in a new one new market and that was in the pre language model world, but now the language model speaks every language.

11:54The voice agents can generate almost 68 to 70 languages of the most popular languages on the planet. So you can almost think of a new kind of creator that emerges that's natively localized all over the planet where, yeah, you might be an English first creator, but imagine if everyone could experience you in their first language. And so that's always been on my mind, but the second piece of like, can we do it with a very small team? And so I took this problem, as I described it to you, and I went to Codex, I pulled up Codex, and I just talked about this problem for 10 minutes. And then I said, let's build a Gentic workflow that breaks down, atomizes this problem, and then reanimates the podcast in, say, five different languages.

12:41And a combination of Codex and Cloud Code built the first version of that in one day. And at the end of that day, we had this I mean, let's see. I run the app locally so I can show it to read. Here we go. So we'll pull up the pipeline. But I went to this coding agent, described the problem. And I was like, we need to atomize this. Strip away how it was done. Look at every new technology I show you that is now on our table that we have access to. And then re-solve this problem using AI agents. And so we end up with this pipeline. Basically, you think we have a transcript. We have a transcript with two speakers.

13:15And the first step is to parse the transcript for into terms. So we take each person's terms. Then we need to translate into a different language and transcreate. So we need to preserve the meaning of the original conversation. You don't want to do a literal translation because then you have like cultural idioms that come into play. And this is a lot of this is what we learned when we partnered with the human experts on each language. And it's been a very interesting journey because one technical person paired with a few language experts can actually localize an app or an experience or a podcast very quickly.

13:51And when I had the first version of the pipeline, it was like, great, we have the French translation pipeline working. And then Codex was like, would you like me to enable the other 68 languages? And that's when I was like, yes. I mean, we're not ready yet, but let's do it. I mean, I want to see that we don't have all the human reviewers that we want in every language. but I was like, this is the awesome thing about thinking about it as like a natively, like an AI native approach. It's like your agents are today, you know, right now they're in English mode, but then they could be, you could just change one word, switch the language.

14:22And now they're transcreating the content into French, into Chinese, into Portuguese, into every single language, even like my, my mother tongue Marathi. Right. And I showed, I showed like a sample of the podcast to my parents and they were like, well this is this is it feels like npr but like from marashtra in india it sounds it feels like the quality was so in their jaws dropped so and so i think about this as like we we did the first version in a day and the agents were just ready to enable the next level of scale and like we just need to get enough experts around it so we can raise the quality bar up to our expectations but But it's something that I could not imagine.

15:03I mean, we tried to do it, and it took like 25 people and several months to do it before. And now it's like, it's pretty much like... Next couple of days. Next couple of days. Yeah. Well, and one of the things actually was particularly funny, and this is part of your general point that's important here is, look, there's a huge amplification that comes from the agents. But so we did French, and we did French early because, you know, I've been spending some time in the French ecosystem trying to help various things. And so we released French as the very first Green's Riffs. And then we went to some of my French friends and they said, well, that sounds like Canadian French.

15:37That's right. Right. And I was like, oh, and we didn't know enough to know. But that was the reason why it's still worth cross-checking. And so then we redid it, again, using agents to be, you know, Parisian French. To delineate between all the, yeah. And the naive approach is that everyone who speaks French speaks the same. But no, actually French is spoken differently in the different parts of the world. Yes. And then I went back to the agents and I was like, guys, guys, I was like, guys, we have to actually localize this. This isn't about every language is one version, but actually every locality gets its own unique version.

16:11And then and then it was like, well, actually, we need to retrain the voices. So we need to create a French read. We need to create like a Parisian French read. We need to create a Parisian French area. And then realizing that we could do that. Eleven Labs has some very cool voice remixing tools. and realizing we could do that i was like well it seems like this same approach might work for every locality in other languages as well so the idea that you're like solving this problem and the next problem and the next problem at the same time is very interesting and also realizing that the models like the models are getting way better and when we started we were using an 11 labs model that didn't have intonation and then now we're using the v3 model which can you can actually prompt inject emotional context and we can create more animated it's not just a robotic kind of recitation of the podcast it's more like talking to someone that's very animated and so the models i think that's the huge thing there is the models are getting better and it was it was a leap in codex's capabilities that showed me that i could do it in one day but this is something that every week every two weeks there's some leap in capabilities and i sit down on a fresh project and i'm just like i aim a very hard problem and i just say hey let's see where we can go and i'm shocked at where you can go in just one, two hours of iteration, eight hours of it thinking, and then that first version.

17:30And you're just like, this used to be 25 people in like six months. And now it's a day to the first version. And now we're like, okay, let's become more ambitious. Let's see how quickly we can like get this out there. And by the way, one of the things, again, it's the re-broaden your imagination for stuff. And like, for example, this conversation hadn't occurred to me. But one of the fun things we might want to try with read riffs and probably using your agents in order to do this is to essentially say, well, let's try Scottish English, Northern English, Welsh English, right? Classic English English, and then release four versions of it with that kind of locality tuned, because that would be fascinating.

18:14Yes, I agree. It's like, what's the extent of this like I think of it as hyper local yes like I even cloned my own voice and then like went very local into like India and I was very like recreated my own voice is like a local in like six different languages in India and I was very like this is incredible like now we can reach everyone in a very like in a way that they feel like like yeah like natively like heard yeah exactly do you want to show something with a tool um yeah I guess I could show you. Yeah. It's up to you. You got to launch now. Let's go with the possible FM. Find a transcript. Podcast transcripts.

18:56And then let's go with RIP the computer keyboard. Oh, that one has to name. We don't have his voice. Let's go with read an ARIA. So here we're going to take two paragraphs of the possible podcast. Paste it into our translation tool. so we have paris custom version yeah so we have the custom voices that are parisian french we also have beijing shanghai which we're working on and we have a couple other markets we're looking at um let's do paris and french i had to delineate between canada and france because it was a it was a point of like it was a point of feedback that we got so we have a podcast transcript we have you and aria the hosts of possible and i'm going to click run and i'll explain what's happening So the first thing the system does is break it down into turns.

19:45And so this is your turn. This is Aria's turn. And then it's going to tag each turn with the emotional context appropriate for that moment in the conversation. It's like these agents are basically role-playing you guys in the conversation. Now it's tagging the conversation. And so we'll see these same turns of conversation where it's going to infuse emotional context. And that's the cool thing about the new 11 Labs V3 model, which is extremely realistic voice. So here we have frustrated, serious, and then emphasizing. And then you're making a very strong point in this turn of conversation. And so the AI is starting to assign that emotional context.

20:24Smiling, so hopefully Arya's response is gonna be very positive, curious, when she asks the question, serious, for her final point. And now it's actually translating the conversation into French. And so the next column will appear soon. And this is gonna be the French translation column.

20:45One other version, we should try this for fun at some point, it's Klingon. Oh, Klingon. Yeah, we could release the podcast in Klingon. Just to kind of show the fact that the future is here. Yeah. So now here, this cell right here has just come up, and this is the first draft French translation of this conversation so far. And so now it's in French. And what's happening is 11 Labs is generating the audio. It's basically reassembling the conversation using your voice clones, but now in French. Kind of as we're doing this pop-up level, this is like an example of our workflow. Yeah. Where something that was previously a massive stretch, maybe too expensive to do, then becomes something easy to start prototyping.

21:36And actually, even for read riffs, we've deployed it in French. Yep. Right, it's the, and a huge amount of acceleration through agents, but then selective intelligent use of humans in the loop. Exactly. For getting the product right, et cetera. And this is the parallel to, for example, a founder who might be thinking about, like, okay, what's the way, like, we'll just start doing it. But where are the things where you use the AI to accelerate you and what you're doing, and then what are the places you bring in, you know, experts, feedback, presidential customers, etc. So it looks like we have a French translation.

22:18We'll play a couple seconds of it. You would like to be managed on the territory. To make the American intelligence, it's also to have the whole whole chain. complete. We are completely over because our administration has to believe that it's a race on social networks. Tracy, with you today. We have seen a lot of articles recently talking about the boom of data centers, which already wreaks a big wave of fraud in the construction and technical professions.

22:58like the other Indian dialects, but you're like, I don't speak that language. And yet that is my voice speaking that language. It's kind of like accessing the multiverse. Yes. It's like, imagine if you were French. Yes. Here's a glimpse into that. And this is all like kind of a very concrete dive for kind of saying, look, this is how much, how to operate, how to do quick internal tooling, how to explore various versions of product market fit, all of the things that, you know, I think basically, frankly, any credible founder today that like has to be showing AI data. If you're not, then you basically shouldn't be doing a company.

23:35Yeah. And we should be thinking like, like what parts of this workflow do we absolutely want to start aiming AI at? Even just to get a baseline of its performance, even before we are like, oh, it's good enough. And I just asked our coding agent to tell us how many agents are we using in this system? Because, okay, here we go. We're using six agents. So the first agent tags each turn of conversation with emotions to guide the delivery of the voice. Then there's an agent for single turn tagging. Then there's an agent that translates it into the target language. Then there's an agent that validates it, making sure that the transcript is still holding the conversational tags.

24:12Then there's an agent that listens to the generated audio. So we generate the audio, then it transcribes it, and then it listens. It's like, yep yep yep that looks like what we're aiming for and then and there's a second agent that's verifying language on the other end and and part of this is like thinking like how much of this can we double check and triple check and generate and regenerate before the person has to come back in and do the final approval and i'm pretty excited about how much of this we could do before our experts come in because our experts are then like let's focus on this part of the idioms making the tech references especially how do you preserve the meaning of like something that is hard to explain in different language, you have to have an expert in that culture, in the idioms of the culture.

24:55Then you get into transcreation. All of our agents are using GPT 4.1, and it runs on the Agents SDK. But the reason this system is possible is because I said, use the Agents SDK to solve this workflow. So I went to the smartest model, and I said, we're going to use Agents and then create the next version of this pipeline. So part of this is amazing amounts are now doable by individuals. But so how does that reconceptualize possibilities in founding teams? So what might now be possible in terms of founding teams, what they should look like, what might be different from a founding team five years ago?

25:39I think the biggest difference is, of course, you want to embrace the technical velocity that we have accessible. So if you're, you know, your CTO, the first technical person should be learning how to use cloud code or codex, maybe both. And then very quickly moving to a level where they can orchestrate a small fleet of, say, 20 of those at the same time. that there is a slight learning curve there but it is so much more worth it for the most senior first technical person attacking anything to adopt that mindset and you should be willing to spend the money on these tools because actually like it cascades through the rest of the hires that you make the rest of the people that you bring on you're going to want each person to be individually amplified and so that's different is that maybe each person has some kind of like compute spend which is maybe even equivalent to like you know a contractor hire equivalent of like cloud code spend and a coding agent spend in aggregate and an agent spend in aggregate like it is almost like you think about that as like a first party kind of approach to the problem and i think that the person who embraces these workflows is going to see at least 50 to 70 percent increase in productivity and then looking for generalists people that quickly adapt into multiple roles.

26:59I think the PM that can vibe code that can quickly convince you of a new design choice, right? It's like, Oh, let's maybe it's not production grade, but it, it, it gets you the team thinking about a new way that the product could be designed. Potentially weeks faster. Yeah. Weeks faster. Yeah, exactly. And I think that like starting with that expectation of speed and then because, you know, as companies grow, like, or, you know, we tend to get slower as we have the coordination tax builds up, but starting very quickly, quickly unpacking the hypotheses before, and you'll reach these realizations before you even have to raise money.

27:33Or then when you do raise money, you raise for different reasons. And I see that in the startups that I'm advising is that they can go much further with a very small team and a strong core of like agentic tools. The thing I would add is, you know, kind of classic, you know, call it two decades ago, three decades ago was you have a business person or technical person, you know, as the kind of co-founders doing something. And if you had only a business person, they would hire a technical person. If you had only a technical person, they'd hire a business person. And I agree with you about that. But I also think that one of the first jobs for the technical person is to similarly make sure that the business person is amplified by way too.