How Founders Are 10xing With AI

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Episode: Reid Riffs with Parth Patil on AI-Native Startups
Official source: https://www.possible.fm/podcasts/riffs043/

This transcript was generated from the local video and formatted by detected speaker. Minor transcription or speaker-label errors may remain; use the official source for the edited transcript.

[00:00] Reid Hoffman: ... 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.

[00:22] Parth Patil: (laughs)

[00:22] Reid Hoffman: So 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, you know, starting a company, going after a market, thinking about how they operate from the very earliest days?

[00:39] Parth Patil: This is, this is something... And I 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 key- a mouse and a keyboard. They, they're used to the track pad, laptop kind of experience. But I think even when I, when I think about AI native, I think pretty much every... I, I have this, like, mul- m- realization multiple times a week, where I see something that a model can do. I see that, "Oh, Codex can now work for two days straight if you allow it to plan very deeply." Same thing with Opus, Cloud 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, 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? The, the... I have these processes running, and 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? Um, how can we redu- reduce the drudgery of the human experience in this work?" And, 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're, you've, you've eliminated like 100 hours of work a week. And, or like, you, now the, the few people that are working on this can go way further. They can think in a more, like... Th- there's the parallelization of cognition now, right? Where it used to be that every human was the bottlenecked in, in, in any, in any job, but now you take a process that someone does, you have them equip with agents, and then they're parallelized across many different, uh, parallel streams in that process. And that's something that's amazing. I think not... Rethinking of yourself as, like, being able to itemize a task, decompose it, and then parallelize sub-components of it, and then lean on the computer for those pieces, it just feels like a superpower.

[03:29] Reid Hoffman: So give me a real life example.

[03:31] Parth Patil: So one thing that I'm known for in my own friend group is as this guy, like the, the guy that's just like deep into coding agents, deep on the frontier of, like, what came out last week, what, 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're 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 it 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, uh, amplification?" So I'll take... So for, for example, earlier this year we got... C- Cloud Code came out and started, it started taking off like wildfire within my workflows. And then I was like, "Well, is it just me?" So then I took it to... I, we, I, I called my, all my best programmer friends in SF and 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, I've been, like, waiting for, like, looking at different angles of, and here's what it, uh, here's what it does for me." These are the same guys that saw me when I f- when I first interacted with Cursor. So they were like, "M- yeah, if he's right, mm-n-d like, we should get early on this." And so I had my buddy, Emile, Emila. Emila is a startup founder, and he's working on a company called Palette. And it's just two of them. It's just two engineers working on this, this company. And it's a, the company, what they do is JavaScript optimizations. So they're, 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. In fact, I think he's, like, probably one of the best that I know. And that comes with this, like, pride, and also a very high expectation. So yes, in some cases, like, AI generated code can be slop, but that doesn't make it useless, right? We, we, you know, we work with people that are not perfect, and, and-

[05:52] Reid Hoffman: Mm-hmm. We ourselves are not perfect.

[05:53] Parth Patil: ... we are not perfect, right?

[05:55] Reid Hoffman: Yeah.

[05:55] Parth Patil: So, so but to, to, to get zero value would be, would be shocking. And so I give him, I give him Cloud Code, and-... and then a couple months later, I'm like, "Oh, Codex is also very good now." OpenAI's Codex is, is, is competing on a, on a similar plane. And so we get dinner four months later, and this is just, like, uh, say three months ago now. We get dinner again and he pulls me aside and he says, "Parth, like, I'm so glad you showed me these coding agents because now we are launching an enterprise partnership." It's still two, two of us, and we're just, like, it's nailing incr- extremely hard migrations. Codex is able to, like, like, uh, 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, these intelligent copilot 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, it is, it is something that is... He can't imagine hiring people that don't interact with these tools as well, right?

[07:01] Reid Hoffman: Mm-hmm.

[07:01] Parth Patil: And so it's a totally new play style. And my, my thing is like, okay, cool. I need to figure out the next part of that game, right? The, the, like, orchestration of multiple of these, and, you know, everyone's at a different level. I think a lot of people are interfa- interfacing with ChatGPT, or they, 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-

[07:49] Reid Hoffman: New play style.

[07:50] Parth Patil: This is why I love working with startups, because if you think about startups, uh, as opposed to enterprises, like, you have no baggage. You're actually just already dead. Like, you're default dead.

[07:58] Reid Hoffman: Yeah. (laughs)

[07:58] Parth Patil: Like you don't have, you don't exist, and, and, and you don't have this, this like calcification of a bureaucracy and, you know, a h- a thousand 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-

[08:27] Reid Hoffman: Yeah.

[08:27] Parth Patil: ... contributing to, and I get a lot of feedback then, 'cause I can use it myself and learn, but 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 love to show it to you."

[08:43] Reid Hoffman: Yeah, the collective learning, the network, the, the allies, friends in learning-

[08:47] Parth Patil: Yeah.

[08:48] Reid Hoffman: ... you know, the, the i- the iteration and learning is, is super key. So walk us through what a modern example would be, something concrete of, like, a product or feature that are frontier model-in-loop from day zero could, could do. Like, you know, what does that process look like in the kind of the, in a, in a few steps?

[09:12] Parth Patil: Well, actually, y- y- you know about this one. So we've been working on the Possible podcast, and you, you're, you're obviously the host of Possible-

[09:18] Reid Hoffman: Mm-hmm.

[09:19] Parth Patil: ... 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?

[09:26] Reid Hoffman: Mm-hmm.

[09:26] Parth Patil: And internationalize, not just, like, release the podcast and then translate the transcripts, but what if we were to re- 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? And this was an interesting project because when, when we mentioned we were interested in translating, you, you've been working with translation of your content for a long time. But I, I was like, "Oh, this is perfect for agents because it is a coding problem."

[10:10] Reid Hoffman: Mm-hmm.

[10:10] Parth Patil: It is a, it is, 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, you're reaching into the general capabilities of language models and the increasingly general capabilities of voice, voice models from ElevenLabs. And so I basi- ... 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-

[10:42] Reid Hoffman: Mm-hmm.

[10:43] Parth Patil: ... and that was in the pre-language model world. But now the language model speaks every language. The voice engines can generate almost 78, uh, 70, 68 to 70 languages, of, of the most popular languages on the planet. So you can almost think of a new kind of creator that come, 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?

[11:14] Reid Hoffman: Mm-hmm.

[11:15] Parth Patil: And, and so I took this problem, as I described it to you-

[11:18] Reid Hoffman: Mm-hmm.

[11:18] Parth Patil: ... 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 agentic workflow that breaks down, atomizes this problem, and then reanimates the podcast in, say, five different languages." And a combination of Codex and Cloud Code built the first version of that in one day.

[11:38] Reid Hoffman: Mm-hmm.

[11:39] Parth Patil: And at the end of that day, we had this... I mean, let's see. Uh, run the app locally so I can show it to Reid. Okay, here we go. So we'll, we'll, we'll pull up the pipeline, but I, I went to this coding agent, described the problem, and I was like, "We need to atomize this. Strip away how it, 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, and the first step is to parse the transcript for, into turns. So we take each person's turns. 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 wanna 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, 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. And 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, let's do it." I mean, I wanna see that... We don't have all the human reviewers that we want, uh, 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, and now they're transcreating the content into French, into Chinese, into Portuguese, into... In 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, "Whoa, this is, this is... It feels like NPR, but like from Maharashtra in India." It sound- it feels ex- it... The quality was so in-... It wa- their, their jaws dropped. N- and so I think about this as like we c- 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 ba- qu- the quality bar up to our expectations, but it's something that I could not imagine... I mean, I... 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-

[14:03] Reid Hoffman: Next couple of days.

[14:04] Parth Patil: ... next couple of days. Yeah.

[14:06] Reid Hoffman: 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 Read Riffs. And then we went to some of my French friends and they said, "Well, that sounds like Canadian French."

[14:30] Parth Patil: That's right. Yeah.

[14:31] Reid Hoffman: 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-

[14:39] Parth Patil: Yep.

[14:40] Reid Hoffman: ... to be, you know, Parisian French.

[14:43] Parth Patil: To delineate between all the... Yeah. There's... It's in... And the naive approach is that everyone who speaks French speaks the same.

[14:48] Reid Hoffman: Yes.

[14:48] Parth Patil: But no, actually French is spoken differently in the different parts of the world.

[14:51] Reid Hoffman: Yes.

[14:52] Parth Patil: And then I went back to the agents and I was like, "Guys, guys." (laughs) I was like, "Guys, we have to actually localize this."

[14:58] Reid Hoffman: Yeah.

[14:58] Parth Patil: "This isn't about every language is one version, but actually every locality-

[15:02] Reid Hoffman: Yes.

[15:02] Parth Patil: ... gets its own unique version." And then, and then it was like, well actually we need to retrain the voices, so we need to create a, a French Read, we need to create like a Parisian French Read, we need to create a Parisian French

[15:13] Reid Hoffman: Mm-hmm.

[15:13] Parth Patil: ... Read. Yeah. And then realizing that we could do that, ElevenLabs has some very cool voice remixing tools, and realizing we could do that I was like, "Whoa, 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 ElevenLabs 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, 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 and you're just like, "This used to be 25 people and like six months and now it's, it's a day to the first version" and now we're like, "Okay, well let's become more ambitious. Let's, let's see how quickly we can like get this out there."

[16:34] Reid Hoffman: And by the way one of the things again, it's the... Uh, rebroaden 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-

[16:57] Parth Patil: Yep.

[16:58] Reid Hoffman: ... right? Classic English English, and then release four versions of it with that kind of locality tuned because that would be fascinating for people.

[17:07] Parth Patil: I... Yes. I-

[17:08] Reid Hoffman: So we should try that.

[17:08] Parth Patil: I, I, I agree. It's like what's the extent of this? Like I think of it as hyper-local.

[17:12] Reid Hoffman: Yes.

[17:12] Parth Patil: Like I even cloned my own voice and then like went very local into like India and I was very li- like recreated my own voice as 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 re-... They, they feel like, like-

[17:30] Reid Hoffman: Yeah.

[17:30] Parth Patil: ... like natively like heard, you know?

[17:32] Reid Hoffman: Yeah. Exactly. Do you wanna show something with the tool?

[17:36] Parth Patil: Um, yeah. I, well I guess I could show you. Yeah.

[17:38] Reid Hoffman: It's up to you. You, you gotta launch now.

[17:40] Parth Patil: Let's go with the Possible FM. Find a transcript. Podcast transcripts. And then, uh, let's go with RIP the Computer Keyboard. Oh, that one has Tanay. We don't have his voice. Let's go with Read Aria. So here we're gonna take two paragraphs of the Possible podcast and paste it into our translation tool. So we have-

[18:07] Reid Hoffman: (laughs) Paris custom version.

[18:09] Parth Patil: Yeah. So we have the custom voices-

[18:10] Reid Hoffman: (laughs)

[18:10] Parth Patil: ... uh, that are Parisian French. We also have Beijing, Shanghai, which we're working on, and then 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 Arya, 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, and so this is your turn, this is Arya's turn. And then it's going to tag each turn with the emotional context appropriate for that moment in the conversation. It's as if they're, it's like these, these agents are basically roleplaying you guys in the conversation. Now, it's tagging the conversation, and so you, 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 ElevenLabs v3 model, which is extremely realistic voice. So, here we have frustrated serious, and then emphasizing, and then, like, you're making a very strong point in this, in this turn of conversation and so the AI is start, is starting to assign that, uh, that emotional context. Smiling, so hopefully, like, Arya's response is going to be very, like, positive. Curious, when she asks a 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 going to be the French translation column. We're almost done.

[19:38] Reid Hoffman: But one, one other version we should try just for fun at some point is Klingon.

[19:42] Parth Patil: Oh.

[19:43] Reid Hoffman: Right?

[19:43] Parth Patil: Klingon?

[19:44] Reid Hoffman: Yeah.

[19:44] Parth Patil: Yeah. We could release the podcast in Klingon. (laughs)

[19:48] Reid Hoffman: Yes, to kind of show the- the- the-

[19:49] Parth Patil: The range?

[19:50] Reid Hoffman: ... the fact that the future is here.

[19:52] Parth Patil: Yeah. So, now, here, this, this cell right here has just come up, and this is the, the, uh, first draft, uh, French, uh, translation of this, uh, conversation so far. And so now it's in French, and what's happening is ElevenLabs is generating the audio. It's basically reassembling the conversation using your voice clones, um, but now in French, and so-

[20:14] Reid Hoffman: Kind of as we're doing this, pop up a level, this is, like, an example of our workflow-

[20:20] Parth Patil: Yeah.

[20:20] Reid Hoffman: ... where something that was previously a massive stretch, maybe too expensive to do, then becomes something easy to start prototyping-

[20:30] Parth Patil: Yeah.

[20:30] Reid Hoffman: ... and actually even in, you know, for Read Riffs, we've deployed it in French.

[20:34] Parth Patil: Yep.

[20:34] Reid Hoffman: Right? It's the end, a huge amount of acceleration through agents, but then selective intelligent use of humans in the loop-

[20:45] Parth Patil: Exactly.

[20:45] Reid Hoffman: ... 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," and, but, like, where, where are the things where you use the AI to accelerate you in what you're doing, and then what are the places you bring in, you know, experts, feedback, potential customers, et cetera?

[21:09] Parth Patil: So, it looks like we have a French translation. I'll play a couple seconds of it.

[21:13] Demonstration audio: (French)

[21:13] Parth Patil: Arya likes it.

[21:13] Demonstration audio: (French)

[21:37] Parth Patil: Yeah, and so that's Arya's voice.

[21:42] Reid Hoffman: All the way back to when we did this with the Perugia speech-

[21:44] Parth Patil: Yeah.

[21:45] Reid Hoffman: ... it just, it's so mind-blowing to hear your own voice speaking a language. Like, it's like the-

[21:51] Parth Patil: Yeah.

[21:51] Reid Hoffman: ... you know, like, the other Indian dialects, but you're like, "I don't speak that language, and yet, that is my voice speaking that language."

[21:57] Parth Patil: It's kind of like accessing the multiverse.

[21:59] Reid Hoffman: Yes.

[21:59] Parth Patil: It's like, imagine if you were French.

[22:01] Reid Hoffman: Yes.

[22:01] Parth Patil: Here's a, here's a glimpse into that.

[22:03] Reid Hoffman: Yeah, 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, like, has to be showing AI native. If you're not, then you basically shouldn't be doing a company.

[22:28] Parth Patil: Yeah. And we should be thinking, like, n- 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.

[22:43] Reid Hoffman: Yeah.

[22:43] Parth Patil: Because, okay, here we go. We're using six agents. So, the first agent tags each conver- each turn of conversation with emotions to guide the delivery of the voice.

[22:53] Reid Hoffman: Yeah.

[22:53] Parth Patil: Then there's the, uh, 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, then 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 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, the, making the, the tech references especially." How do you preserve the meaning of, like, something that is hard to explain in a different language? You have to have, like, an, an expert in that, in that culture, in the, in the idioms of the culture, and, like, then- then you learn, you get into transcreation. All of our agents are using GPT-4.1 and, uh, and it runs on the agent's SDK. But the reason this system is possible is because I said, "Use the agent's 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."

[24:09] Reid Hoffman: So, you know, part of this is, is amazing amounts are now doable by individuals, but so how does that reconceptualize possibilities in founding teams? Right, so what might now be possible in terms of founding teams, what they should look like, what they, what might be different from a founding team five years ago?

[24:33] Parth Patil: I think the biggest difference is, uh, of course, you wanna embrace the technical velocity that we have at Possible. So if you're, you know, you're 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, it, it cascades through the rest of the hires that you make, the rest of the people that you, you, you bring on. You're gonna want each person to be individually amplified. And so what's different is that maybe each person has some kind of, like, compute spend, which is maybe even equivalent to, like, you know, a con- a contractor hire equivalent of, like, Cloud Code spend and, uh, coding agent spend in aggregate. And, and, and 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 gonna see at least 50 to 70% increase in productivity. And then looking for generalists, people that quickly adapt into multiple roles, so I think the PM that can vibe code, that can quickly convince you of a new design choice, right? Like, oh, well let's, maybe it's not production grade but it, it, it gets you, the team thinking about a new way that the, the product could be designed.

[26:03] Reid Hoffman: Potentially weeks faster.

[26:05] Parth Patil: Yeah, weeks faster. Yeah, exactly. And I think that, like, starting with that expectation of speed-

[26:10] Reid Hoffman: Yes.

[26:10] Parth Patil: ... 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.

[26:17] Reid Hoffman: Yeah.

[26:17] Parth Patil: But starting very quickly, quickly unpacking the, the hypotheses before, and, and you'll reach these realizations before you even have to raise money or then you, 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, you know, with a very small team and a strong core of, like, agentic tools.

[26:37] Reid Hoffman: 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, a business person, a technical person, you know, as the kinda 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 with 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 that way too.

[27:02] Parth Patil: That's right. That's right, yeah.

[27:03] Reid Hoffman: Like, it's not just, okay, this is the way I'm doing my workflow for DevOps and for experimenting with product design, for product market fit and all the rest. Yes, yes, yes, but also amplify.

[27:15] Parth Patil: Yeah, that's right, that's right. Yeah, and I think, I think they should be using state-of-the-art models as well. Like, e- ev- everyone should, in your, in your small team should be using state-of-the-art models that help them with all aspects of their work, right?

[27:27] Reid Hoffman: Yeah.

[27:27] Parth Patil: Your general copilot using the best one available, you know.

[27:32] Reid Hoffman: So you know, one of the things that, you know, there's a, a, partially because we, we, we live in a media environment and, and obviously, you know, Hollywood's t- tied itself into knots about AI and all the rest of this stuff and you live down there.

[27:44] Parth Patil: Yeah.

[27:45] Reid Hoffman: You know, so you see a lot of it, like, the, "Oh, we're using it but we're not telling anybody because, you know, it's kind of unpopular."

[27:50] Parth Patil: Yeah.

[27:50] Reid Hoffman: But even though it's such a clear amplifier, it's kind of like the, like there, there needs to, as opposed to a Masonic handshake, there needs to be an AI handshake now.

[27:58] Parth Patil: Or it's like, I finally get to tell a story. I was never even in Hollywood.

[28:01] Reid Hoffman: Yes.

[28:01] Parth Patil: You know, I could just, oh, like, I have an idea.

[28:02] Reid Hoffman: Yeah.

[28:02] Parth Patil: Now we can put the first vers- It does, it c- for $300 you can make the first version and, like, in the same way that we're vibe coding prototypes-

[28:08] Reid Hoffman: Yes.

[28:09] Parth Patil: ... we're vibe coding storytelling or, like, animating and creating these worlds as, like, concepts. And they may eventually become bigger things in a traditional format.

[28:17] Reid Hoffman: Yeah.

[28:18] Parth Patil: But they don't have to either.

[28:18] Reid Hoffman: Yeah, but the speed of using that exploration, interval development, it's the same thing where you, you, you learn by doing. You learn by seeing what you did on your first iteration.

[28:28] Parth Patil: Yeah.

[28:28] Reid Hoffman: Like the first, like you said, okay, let's use agents to build it. Oh wait, we need an emotional tagger.

[28:33] Parth Patil: Yeah.

[28:33] Reid Hoffman: Da-da-da-da is kind of as, as ways to doing this. So it's kind of our, our last question for the moment for kind of AI and kind of startups is, what sign, what, what ways should, do you look at when you look at startups and see, is that AI as marketing or is that AI as real? How should people think about that themselves? Like, am I being real enough? Am I being AI native enough? I'm not just using AI as a buzzword bingo to try to get money or attention or anything else.

[29:05] Parth Patil: Yeah.

[29:05] Reid Hoffman: Like, and, and what's that, what does that, that real AI traction look like?

[29:11] Parth Patil: Yeah, there, there is a lot of that I see going around these days, which is this buzzword kind of, like, this buzzword era of AI this, AI-enabled, AI-powered and then, and you're kind of, and maybe it's because I interact with all these models, I'm kinda like, "But which model? What AI? Like, what, what..." Actually, do you even need the AI or are you just shoving it in there so that it, you can say that it's AI? And for me it's like if you don't mention, if you don't go one level deeper, it makes me very skeptical. It makes me wonder if there's anything of value here at all or if it's just pure signaling in order to get attention or, like, to send a kind of message to people that wouldn't be able to discern. And I think that the other way to put it is, can you describe what it is you're doing without using the letters AI? If you can't, then maybe the AI isn't the important thing here. And then the other thing I see is, like, it's not even about the AI. Like, the AI power, it's like, do, does anyone care what database technology Uber is sitting on?

[30:09] Reid Hoffman: Yeah.

[30:09] Parth Patil: No, like, the actual person, it's like, if you were sit, to sit in the car and you were like, oh, like, the average person just wants to get from point A to point B-

[30:15] Reid Hoffman: Yeah.

[30:16] Parth Patil: ... uh, safely and, like, so a lot of that is, like, not even relevant to the end user and it's actually, I, like, I imagine a world that I wanna live in where AI is under the hood and taken for granted because it's just so good at what it does and it stays out of the way and it's not making itself the whole, like, it's, that is not the purpose, right? It's in service of the actual objective that we have, which is maybe create a new artifact, learn something, uh, or make, you know, build a product. And I think that AI is, like, when AI blends into the background, that's the best, the best version of this.

[30:47] Reid Hoffman: Possible is produced by Palette Media. It's hosted by Aria Finger and me, Reid Hoffman. Our showrunner is Sean Young. Possible is produced by Tenasi Dilos, Katie Sanders, Spencer Strasmore, Imo Zu, Trent Barboza, and Tafadzwa Nemarundwe.

[31:02] Credits: Special thanks to Surya Yalamanchili, Vida Sepieva, Ian Alice, Greg Beato, Parth Patil, and Ben Relles.