Reid Riffs with Parth Patil on Enterprise AI Integration (Part 2 of 3)

This episode is our second installment of a special, three-part Reid Riffs miniseries focused on what it actually means to become AI-native. Instead of a news or headline-driven conversation, Reid sits down one-on-one with AI engineer and strategist, Parth Patil, for a deeper exploration of how AI is changing the way people and organizations work. In this second episode, they discuss why most enterprises are still talking about AI without truly integrating it (“AI theater”), and how the real shift begins inside everyday workflows rather than strategy decks. Together, they explore how language models and agents can reduce friction in communication and coordination, reinvent meetings, and turn unstructured information into actionable insight (with examples). They also examine how AI-powered analysis, automation, and parallelized agents are accelerating decision-making, reshaping roles, and moving work from execution toward orchestration. Parth and Reid both highlight how an open mindset and experimentation are required to collaborate effectively with these systems as AI evolves from a productivity tool into a foundational layer for thinking and leadership. Subscribe below to catch the third episode for startup founders and their early teams building AI-native companies. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/

• 0:00Speaker 00

  Hey everyone, Arya here. We want to kick off the new year with inspiring conversations about AI as well as practical and tactical guidance around the technology. So for the month of January, AI specialist and my dear colleague Parth Patel is joining Reed for Reid 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 Reed in the hot seat come this February. Thanks so much.

• 0:41Speaker 01

  Thanks for the kind word and warm welcome to Possible, Arya. As I talk with Reed 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 Reed's YouTube channel for the full experience. Thanks, and let's get into it.

• 1:05Speaker 02

  So let's talk a little bit about how big companies, all of which are talking about doing AI and talking about like what their plans are, setting up proof of concepts and doing stuff. Are there any big companies that you've noticed doing AI well?

• 1:20Speaker 03

  You know, outside of, I think, the hyperscalers, I don't, not yet, not yet. I think everyone is expected to have an AI strategy, both like teams, CEOs, board of directors, everyone's kind of pushing AI. And I think, I think a lot of people are talking about AI, but I haven't really seen an AI-native company outside of maybe the frontier labs, which are AI-native because they built the products, and especially done at scale. Because I think these large companies, enterprise, you know, it's a very, it's very hard to move a large ship. And even, even when you do see a new technology come online, it has to go through different layers of approval before people can even play with it. And so experimentation is also slower. So I haven't seen it yet. And also, the playbook is still a little

• 2:03Speaker 03

  unclear, like where do you, where do you infuse that? I think that's what we're going to talk about a little bit here.

• 2:07Speaker 02

  I completely agree. I do have some, you know, experience of some of the leaders of some of these companies reach out to me and talk to me. So I know that they're working on it and trying it, but, but it's still, if you use a baseball analogy, the players haven't even come into the field for the first inning yet, let alone anything else. And so

• 2:25Speaker 03

  Here I am calling it the first inning and you're saying they're not even there yet.

• 2:27Speaker 02

  Yeah. Well, they, they haven't even come out of the dugout.

• 2:30Speaker 03

  Yeah, yeah.

• 2:31Speaker 02

  And so they're talking about coming out the dugout. They're setting up a committee to study coming out of the dugout, but it's not, you know, it's get on the field. Let's think a little bit about how executives should think about AI because obviously one way to start experimenting is personally, um, you know, start using AI as a chief of staff yourself. But what are some of the things that you think are things that executives should say, look, I need to get my organization. I need to get my people. I need to start learning this. I need to start figuring out what our, as a company and our teamwork adaptations are to this. What, what's the advice you would give to an executive at a, call it tech company, but also not tech, like ignorant company?

• 3:10Speaker 03

  If I think about the tools that we now have through language models especially, I think that's probably where we could narrow our focus. I think, okay, language models inherently can accelerate any language related task. And the biggest language related set of tasks in a company is the communication, the coordination layer across huge teams, like large, large projects. And so I think about like all the meetings that we have, all the documentation we have for those meetings, who's writing that documentation? Like who's creating the action items? Who owns the action items? This is the layer I think that is very easy to implement AI and get value out of immediately is reducing the friction of coordinating across large teams. I think that's where maybe the advantage of the enterprise comes in. It's like, if you have a very like AI amplified kind of

• 3:55Speaker 03

  communication layer and coordination layer where people come into a meeting and maybe you have a meeting kind of like AI that's just also plugged into the company business intelligence that can surface the most relevant things about the problem that you're solving in real time, or even just like taking notes during the meeting and then deciding, okay, we've We've agreed we're here, we're gonna, we're gonna do it, do this, but did one of us write it down? But maybe it shouldn't require us to write it down because that, that kind of effort is no longer something that a human should maybe do. And, and then who owns that, uh, long-term? I think the communication layer is where the most initial, the initial, uh, obvious value is because it's mostly text-based tasks. I also think that in engineering and software, the gains in productivity are very, like, it's very obvious that you can get huge gains in engineering productivity.

• 4:39Speaker 03

  I think it used to take maybe 12 engineers, two years to do like a pretty massive migration type project. And the same group of engineers, if you were to give them Claude code, four engineers could do it in six months. That's the kind of like per person, uh, productivity increase and speed increase. So I think what happens is like, if the capacity to like solve problems is that much more accelerated, we actually have to look at all the meetings that we have and rethink, like, maybe like slash a bunch of them and like rethink what the essential meetings look like. And what's the essential core group of people that's gonna work on a workflow and like really accelerate that workflow? And figuring out what were we previously doing manually that no longer we should be doing? And freeing that time up to attack the net new problem space. But that's kind of like how I think about this.

• 5:20Speaker 02

  Not surprisingly, completely agree with you on this. I think another way to look at it in terms of thinking about it and say, hey, what if part of what we're doing is a lot of coordination problems? Um, cause it's problem solving, decisioning and so forth. Those can be used, AI can be helpful and all that can be generally deployed individually for that. But on the coordination, what do you do? And so like, for example, if you record every meeting and you have a transcript, not only do you have a transcript, one of the things that I actually do is not only recording the meetings, but then run it through AI with the kind of prompts of, cause you know, it has a prompt of, you know, here's all the projects, companies I'm involved with, et cetera. Here's the people that I'm collaborating with in various ways. Obviously in the company, it doesn't have to include only that, it could also just be everyone in the company. It's like,

• 6:04Speaker 02

  Well, who should I consult with on this? There's anyone I've not thought about consulting with on this? Who should I notify? What action items have fallen up? And obviously, as you begin to get agentic, you can use your kind of hotkey approach to go, "Oh, you were talking about this, you know, animation project. Should you notify Parth on this?" And I could just go, "Yes." Right? And that's one of the ways where keeping the human loop, you go, "Well, I'm nervous about it doing something I'm not happy with." But if you're running it as a kind of an agent that's checking in with you, then all of a sudden, our brains don't act like computers. Allow the AI to be a computer to remember it's like, "Oh, this project, yeah, I should talk to Parth about this." As a way of doing it.

• 6:48Speaker 02

  And those are the kinds of things that are building upon your fundamental correct thing, which is the way to start, is every single company on the planet works with communication. They all have meetings. The meetings may need to be reinvented. But by the way, one of the ways you start learning that is start transcribing them and see what happens. And by the way, then you can begin to break this problem of, "Well, everyone wants to be in the big meeting." Because it's like, "Well, I need to know what's going on." It's like, "Well, actually, in fact, we could have that meeting with the seven people because other people can then be consulted and informed and all the rest of the things." And what's more, say, for example, you're an executive and you know that that working group is happening. You could say, "Hey, I want to make sure this question is asked in the meeting." And your agent can then go say,

• 7:31Speaker 02

  "Oh, by the way, Reid wanted to make sure this question was at least considered in this meeting." And, you know, Parth might go, "Well, actually, that's not the right question. This is the right question. Here's why, dah dah dah." And as you're talking about it, then A, the whole group does it, but then that also gets back to me. And then all of a sudden, we don't forget things. We're accelerated. We're using these as catalysts for completely changing our gameplay. One of the ways to kind of look at this is there's these, you know, decision frameworks and assignments. And whether it's Dacey, Racy, et cetera. But now you can think of A as not just, you know, who's accountable, but also where the agent is and where the agent's playing. So one of the challenges that a lot of big companies typically come to

• 8:17Speaker 02

  You know, trying to integrate a new technologies, they set a little group, they do a proof of concept in a little group and so on. They don't actually start experimenting with and just integrate it into their actual workflow process, which is actually, I think, one of the things you have to do with AI. You can't just go, oh, these three people off in the closet are going to do this. So what are some of the things that you would say for big companies to say, look, here are some things to really think about as trying to integrate AI into your company?

• 8:42Speaker 03

  What I've noticed about AI, at least in this current form, is it exists at the workflow level. So like, the workflow is being transformed. Parts of the workflow are text based, and now language models are taking it, are taking up some of that stuff that was previously done very manually by people imprecisely. And so if the workflows are being updated, I think, actually, that's kind of like a bottom up, like, that's a bottom up thing within an organism. Everyone has their own workflows. Every single person knows how their job is done. And when you think about where does the AI fit in, it's the person who does the job is going to realize, oh, wow, we should be doing it in this new way. And so the gains often come in this bottom up approach from like, I'm working with our translator to work on our podcast translation, and I see how, how much work

• 9:26Speaker 03

  goes into like the translation piece. And it's like, well, actually, we should focus on the quality of the voice, and we should have language models work on more of the translation piece, and then maybe like, guide the language model. So we end up becoming more orchestrators in that workflow. And we're focused on where our unique human, like, our ability to listen to the accent actually ends up being more valuable than the, the literal like writing of the transcript. So I think that like, reimagining the workflow, and that being done in a bottom up manner, and how you do that is to create an environment that is sharing those wins across. Like, you want to reward that experimentation, and like, celebrate, oh, here's what we learned this week. Here's how much time we saved. Here's how many steps we skipped in this process. And like, now we actually don't need these three meetings now, because we have an automated solution here in place.

• 10:10Speaker 03

  I've seen companies that are more resist, they're not resistant to AI, they're just not, they're, they're very close minded about it. And then what happens is that the productivity gains end up, like, someone gets really good at doing something, but they don't feel like they can share that. And they don't, they're like, well, if I share that, then like, maybe I'll get in trouble, like, this new approach to something from, I just want to do my job faster, and no one needs to know. That kind of, Ethan Moll calls it the secret cyborg. And that's fine on a short term kind of thing for the individual, but really a team that rewards that experimentation and sharing the ideas, like where you and I are bouncing ideas off of each other, that team is going to go way further and that those learnings end up becoming like organizational learning, right? So it ends up being a collective kind of pursuit.

• 10:54Speaker 02

  As a funny parallel, what percentage of Hollywood writers do you think are secretly using it at home? And then since they're not allowed to bring it into the writing room, what would your guess be and what percentage it would be?

• 11:05Speaker 03

  At least 40%. I think it's at least 40%.

• 11:08Speaker 02

  I was gonna go with 70.

• 11:09Speaker 03

  70?

• 11:09Speaker 02

  Yeah.

• 11:10Speaker 03

  I mean, I've met some, I've met some and they're very, they won't admit it.

• 11:13Speaker 02

  Yes.

• 11:13Speaker 03

  But then when they talk about, when they do admit it, they're very much like, I don't like that it doesn't have perfect recall on every scene that I committed. And I'm like, well, okay, so clearly there's a, there's

• 11:22Speaker 02

  You're using it.

• 11:22Speaker 03

  You're using it. Yeah. And we need to make it better and the rappers need to get better. There's going to be an agent for that. That's going to be much more deterministic. But you can see that there is a strong desire to like bring them into the creative process.

• 11:34Speaker 02

  One of the things we were talking about was in integration, a lot of it has to do with communication. A lot of it has to do with team dynamics. A lot of it has to do with obviously individual amplification, but also workflows. Can you describe a team workflow that wasn't possible pre-agent large language model, but is now tractable? Something that maybe you've built or seen that is new in terms of how to conceptualize what kinds of reinvention of these workflows is possible?

• 12:03Speaker 03

  I'll show you, actually. We take a look at my screen. We have a Claude code agent. And in this folder, we actually have a bunch of data. We have a bunch of CSV data on order items for a fake toy company. Just a bunch of CSVs. So we're going to say, Claude, look at every single CSV in this folder, analyze the data, however you see fit, and then build me a dashboard so we can drill into the insights and visually understand what's going on in the data in our company. So I think that data analysis is one of these coding adjacent business intelligence and coding and coding agents are actually very closer together than I ever imagined. And that came from business intelligence, came from finance, came from data analysis. But then we do the analysis using code. And then when you take a coding agent and you plug it in, you give it access to the data as we're doing over here. It's

• 12:48Speaker 03

  looking at order data, refunds, order items, the same thing that I was, it's going to analyze the data with Python and then it's going to build a dashboard. The kind of thing that I would have done that would have taken me two or three weeks to build this kind of dashboard, I suspect we'll get the first version in under Like under a minute. And this is something that doesn't matter how large or small your company is. Like you have data and you may not have enough analysts to slice that data. But now you have this like extra cognition, we can just aim at the problem. And what used to take three weeks can now be done in a few minutes. So here we have a completely generated dashboard from this six raw CSPs. And so Maven Mutt Fuzzy Factory, so this is an imaginary toy company that these CSPs represent. And you can see the revenue grows over time, profit grows over time.

• 13:33Speaker 03

  Conversion analytics, rate of conversion over time. And it's really like a comprehensive, it's a pretty good dashboard for a single prompt of data analysis. And you could, you know, you could ask a follow up question. I think I'm sure we could ask more, the ability to drill down into this data. But a lot of this kind of an analytical work, business intelligence type of work. I mean, I think of it as like business intelligence is a subset of general intelligence. So we should be able to use AI to do business intelligence. I really think that BI is a subset of AI. This is how I imagine the role has already evolved. It's like you have a folder of data, point the AI at that data and have it make sense of it. You know, I asked for a follow up question and I asked for it to create like a McKinsey style presentation on top of the data. So let's take a look at that.

• 14:17Speaker 02

  And this does look like some presentations I've seen from McKinsey.

• 14:21Speaker 03

  We've got the classic McKinsey color scheme type.

• 14:24Speaker 02

  Just about everybody in McKinsey is actually at least experimenting with individual use, right? So like, I think this kind of thing is the kind of thing they will see.

• 14:31Speaker 03

  They will, they will. I mean, the moment I, I have, I have been looking at, ever since interacting with G P T four, I was like, wow, we don't have to build presentations by hand. And it was surprising to me that the AI will build a web application that looks like a presentation. So this is an HTML file with some CSS and JavaScript. But when you think about code as a general solution, then it's clear that code will be used to reimagine the business intelligence. And it makes it so that like, if this can be done in one minute, then we can go much richer and much deeper in our, in our depths.

• 15:01Speaker 02

  Well, part of the things that I think caused this acceleration is people don't realize how this acceleration completely changes the game. It's a difference in degree, but it also makes a massive difference in kind. And part of that's because like, an executive can just do this themselves and not like, well, I write an email to the, the data scientist, the analyst team. And they, they process it when they get to it in a couple of hours and they do the work and they get back to you the next day or the day after, et cetera. But like the learning and kind of thinking about that as a loop. Another thing is you can now ask a whole bunch of different questions that you hadn't asked before. Or you could do the interview me, your earlier prompt, until you go to get to the right kind of thing.

• 15:43Speaker 03

  The right artifact that would help you

• 15:45Speaker 02

  Yes.

• 15:45Speaker 03

  Impact the trends.

• 15:47Speaker 02

  So all of this stuff becomes possible everywhere within the company. And obviously, part of the transformation that's going to be very important in companies is like, yes, there will be some rework of the meetings, some rework of the team process. And those are good things to do. But a lot of it's also going to be individuals bringing their stuff. And as you mentioned earlier, being able to talk to each other about it and do collective learning. Like, how do we collectively learn this and adapt to this better than other organizations?

• 16:13Speaker 03

  Yep.

• 16:13Speaker 02

  As part of it.

• 16:14Speaker 03

  So I mean, we might be doing this in a meeting.

• 16:16Speaker 02

  Yes.

• 16:16Speaker 03

  In real time, we have the raw data and then you and I are prompting the same AI to unpack the insights in the company.

• 16:22Speaker 02

  Exactly. And so, you know, what are some of the easiest things you think companies should consider automating within the corporate stack?

• 16:31Speaker 03

  Oh, I think one of the easiest and highest leverage things. So before language models, a big complaint I always got for people are trying to think about data and data analysis and insights is that we don't have clean data. Our data is not clean, it's very messy. The interesting thing is that language models are uniquely very good at cleaning up data. Right? You can give them a whole like, like a customer complaint and turn it into action items. Right? These are the action items. This is the main takeaway, like the three things that we should take away from this in whatever structured format. And then that fits into your CRM, it fits into your traditional business logic, which is more fixed. So I think that eliciting structure from your unstructured, messy world of your business is the obvious first thing with language models.

• 17:16Speaker 03

  And, and then, then the next thing is figuring out the follow-up actions. Like, what should we do next? So here we have a slide, it says immediate next steps. And it's like, oh, we should review the mobile, the, the mobile UX because it seems like the, the conversion is lower. Certain products are more successful. It looks like certain, the AI is already doing this analysis that would have otherwise taken me like three weeks of just like being in the weeds. It, it is pretty shocking to me and I cannot imagine working with an analyst that is no longer, that is not doing this.

• 17:46Speaker 02

  Yes.

• 17:46Speaker 03

  Right? Like every analyst I hire and work with in the future is going to be working at this kind of speed of like question answering and data visualization that I kind of expect from this.

• 17:55Speaker 02

  Yeah, and people, I think, worry there's like, oh, a bunch of work goes away. But actually, in fact, what happens a whole bunch, this is an example of where a whole bunch of new work gets created. Because if you think, well, we only have been asking the most minimal questions before because it was so expensive to do it. Now it's a question of, oh, well, for example, on these conversion rates, does the conversion rate change by time of day? Does a conversion rate change by holiday? Does the conversion rate change by, like, give me all the variables that it might change by. Okay, well, initially that, you know, are there any surprising things that we might learn on conversion rate? Like, well, time responsiveness or other kinds of, and you could just be keeping going. And that's what the task becomes.

• 18:35Speaker 03

  That's the new job. The new job is asking the AI to do the right thing and figuring out what the right questions to ask are. It's no longer like, did you write the right SQL query? It's not the syntax of like, did we write the right query? But now we're at this like, we're all kind of at this more macro orchestration of, I think it's amazing. I think it's a total expansion of the analyst kind of role.

• 18:55Speaker 02

  Yeah, we both follow Sam Shalachi. And like, it's a little bit like his description of, look, there isn't coding anymore. There's system architects that is you're using it. And the same thing is, by the way, true of lots and lots, it's a parallel. One of the things that's interesting about all, like, one of the things our, our entire audience should take away from the thing is, even when you're talking about coding, coding is a parallel. It feels, oh, that's coding, that's different. It's like, no, coding is a parallel to data analysis. It's a parallel to creating memos or PowerPoint.

• 19:23Speaker 03

  Operations.

• 19:24Speaker 02

  Okay, it's a parallel to doing legal stuff. It's a parallel to doing auditing and risk analysis. It's a parallel to all of the stuff about like, how is it that you orchestrate for now? Being able to do a ton more work in a short amount of time greatly expands the kinds of things that you can be doing that are value creating. So it isn't that your previous thing, which took three weeks, now done two minutes, like, well, then I'm going to spend the rest of the three weeks playing Halo. It's like, no, I can actually do a whole lot more and create a whole much more value here. And so the, the job is still valuable.

• 19:57Speaker 03

  Yeah, we can fan out, we can, and this is where the computers are very useful is like, humans, like, we are a single threaded. We can only really work on one thing at a time. But if you say, let's expand our analysis and look at 10 different angles at the same time. That's the kind of thing that's unlocked when you lean on the parallelization of the computer and the coding agents. Agents that can work in parallel, you can spin up many of them, and then you can attack a problem with many angles at the same time, which is a totally new capability.

• 20:24Speaker 02

  Part of the thing that people don't like understand is that learning these tools is not learning how not to do your job. It's how to learn to do the job in a way that you have superpowers. And that's the whole part of super agency. So one of the things naturally is that, you know, most people are non-technical. Part of their fear and concern about adopting new technology is that they don't know how to use it. They don't know if something goes wrong. They don't know if like something, something happens. What do you think is the right mindset for kind of the non-technical executive for thinking about this and, and kind of why do you engage? And then what would they need to understand about like how the models work?

• 21:02Speaker 02

  Or, or what is kind of some of their potential experiments for how do they lead well in the AI age?

• 21:10Speaker 03

  I think it's less important that you know how a language model works. And more important that you know what it's like to work with a language model. Working with the tool is, is a new skill. Less important than say like, why does it predict the next token a certain way? I think it's more about like, what can this model, given access to these tools, do for you? And increase, I think the technical, you know, right now, the coding agents are the most powerful. But eventually we will get their counterparts that are non-technical friendly. I think Claude and ChatGPT and these tools will become even more powerful and more agentic. And, and for example, like they might be organizing your digital life, helping you organize all your files. You're like, you know, your healthcare records, your personal life, your work life, creating that context.

• 21:56Speaker 03

  Enriching that context, retrieving it when you need it. Those paradigms I'm already seeing in the coding agents, and I'm sure they will, they will end up cascading to the non-technical experience. If you're ambitious, and I think, I think more people should be ambitious because you can teach yourself anything today. I think, and this is something that some of my like non-technical. Like some of my old bosses, they've come to me and they've been like, I have time, I can learn something. What should I learn? And in those cases, I do push them to play with a Claude code or a Codex to get a sense of what it's like to have an AI on your computer, working side by side with you, organizing your work, creating daily automations. There's things that you can't do in ChatGPT. You would go to ChatGPT and you ask, generate a hundred images. It's only going to do like three and then it'll just kind of just stop there.

• 22:41Speaker 03

  But if you go to a coding agent, it will write a program that can generate a hundred images. And so the, if, once you want automation, when you want personal automation, you, you have to leverage code. It's just that right now, those tools are a little bit more for the technical person. I still think it's, it's never been easier than before to get Claude running. And if you've seen anything of how I interact with this, I'm not coding. I'm talking to a chatbot that writes code for me. So I'll generate thousands of lines of code without having to personally code them. It's mostly like I'm delegating to coding agents on my behalf. I think we will all be doing that eventually in some capacity. So if you're ambitious, pick it up now when it's a little bit like early and you get, you get that head start on it.

• 23:23Speaker 03

  Otherwise, take the most technical person you know and equip them with and invest in their coding agents. It'd be like, you should, you should be using Claude code. I have, but like if you're, if you're a non-technical leader, empower your technical counterpart, your CTO, to be using these tools. And to be, and to cascade that throughout the firm. Cause that is, is like unblocking them, making them, putting them in a place where they're not afraid to learn and experiment and discover the value is, is the most important thing you could do.

• 23:48Speaker 02

  I actually have directed a number of people who are non-technical to start vibe coding. And it's still a little rough for the non-technical person.

• 23:55Speaker 03

  Right, that's right.

• 23:56Speaker 02

  So for an executive, one of the things you can do is actually go get a technical person. And say, look, these are the kinds of things I'd like, set this up for me.

• 24:05Speaker 03

  Yeah.

• 24:05Speaker 02

  Right? Like do the vibe coding hack. Sometimes some of the things I ask you is like, look, set up this vibe coding thing for me so I could start using it.

• 24:12Speaker 03

  Yeah.

• 24:12Speaker 02

  Because then I'm getting that experience and that foresight into what.

• 24:18Speaker 02

  Being AI native, making it happen is, and I don't have to learn the current hard edges. That's right. Of vibe coding. That's right.

• 24:25Speaker 03

  Exactly. I think, and also I can create an environment that will prevent you from even like, you know, tripping over yourself.

• 24:31Speaker 02

  Yeah.

• 24:32Speaker 03

  And discovering the value problem more quickly, or like creating a custom agent for you that matters more to your use cases than to my own. So, yeah.

• 24:40Speaker 02

  And that's also part of our earlier conversation about the Zen of AIs. Put your ego aside. Like, yes, someone else knows how to do the vibe coding thing much better than you. Just like, learn to partner with them the way you learn to partner with AI or dance with AI and kind of say, okay, help me solve this problem. And then as you do that, that gets you into the learning. Because by the way, of course, part of, once you're down the road, where you're beginning to see what, you know, the agent can do for you, then you're like, oh, well, now I want this too. Now I want this too. Cause that's the way you learn it. You don't learn it by like, oh, I sat down with the AI for Dummies book. Right. And, and like thumb through it. Right. I learn it by doing it.

• 25:19Speaker 03

  There's no better way to learn this technology than by using the technology. And there's no, there's no alternative to that. And, and we, when we meet, sometimes you'll have a crazy, interesting, creative idea. And I'm like, we should just get the first version of it. There's no, there's no reason why we can't get the first version in the next three minutes and then validate some of these hypotheses of design and, and whether like there is something there. And I think that, like for me, every time that happens, I'm like, it's more like, oh, I gotta show Reid that this might be possible in like five minutes.

• 25:47Speaker 02

  Yes.

• 25:48Speaker 03

  Because I want you to update your priors on how long a certain kind of like old world job might have taken now, given we have this massive accelerant. Yeah.

• 25:57Speaker 02

  Cause what most people don't realize is through your life and your tool use, you have constrained your imagination to what you think is possible on the old tool set. You now need to re-release your imagination. There'll still be some constraints you'll learn, and those will change over time. But you now have many more capabilities than you imagine. Like for example, like one of the things you say, well, like, okay, I'm, I'm trying to figure out how to use AI for leadership. And I'm sitting there and I've just listened to this podcast and reading parts. But well, go talk to Frontier Agent and ask and interact some with it saying, well, what are ways that if I was trying to solve this kind of leadership problem, and let me describe it in more depth and, or the interview me prompt, like interview me about this problem

• 26:38Speaker 02

  until you have enough to say something interesting to me about how I can use you in order to help me with this leadership problem.

• 26:45Speaker 03

  Right. Or even take the transcript of our podcast and show that to an AI and have have it turn into a framework for thinking about how to explore these ideas.

• 26:54Speaker 02

  Or a framework for you, because you upload the transcript of the podcast and you say, hey, here's the problems I'm going to solve. How would you apply this conversation that Reid and Parth just had into a framework that would be useful to me in my company and company X and my role, et cetera, et cetera. Like in it. And part of the thing about the acceleration of AI, so there's, you know, AI is amplification intelligence. There's also AI is acceleration intelligence is you can do all that in minutes.

• 27:20Speaker 03

  Yep.

• 27:20Speaker 02

  Right. And that's part of the thing to start thinking about what the timeframe changes in terms of how you're capable of operating as an individual, how you're capable of operating as a team. And again, that's one of the reasons why you've get into the experimentation and learning. So when you start kind of learning that this is as a learning journey and as adoration, you begin to realize that actually, in fact, constructing tools to amplify yourself, amplify your team is now something that is doable for every individual, for every team. One of the things I think we're going to see a huge explosion work and amplification is by self amplification, where it's the self is somewhat the individual, but also the team. And it's because you're going to start doing tool development to

• 28:04Speaker 02

  just accelerate you for your particular work, for your particular group, for your particular company. So what are the ways that people should approach this learning about tool amplification? And where might some of the various, you know, tools that you've mentioned in your AI stack are things that people should experiment with? Replit, others, that sort of thing?

• 28:25Speaker 03

  I feel like I've gotten this mindset. It's it's it's an extension of vibe coding, but probably a more practical application of vibe coding. The instinct some people have is, oh, I'm going to build Facebook. I want to publish it. Maybe that shouldn't be the first thing you make. But because there, there, there's a learning curve to building these generating tools and building tools on the fly. But the safe way to experiment is to build internal tools. Build tools that where you know the user because it's you. It's like, oh, the user is Reid and maybe like Reid's team. And so like, if I have a relationship with you and your team, then I am getting feedback directly from the users. And this is the other thing is like traditionally you would have like a UX researcher, a product manager and an engineer. And that might be three different people. But now one person can kind of play all roles and it's actually better to be that generalist and to play all roles.

• 29:11Speaker 03

  Because your feedback loop and your iteration speed on the tools is very high. It used to be that you might spend a week building one feature. Now you can build six, seven features in a day. And for an internal tool, that means that you can go from like not having a tool in the morning to having a very good first version by the end of the day. If you're in that tight feedback loop with the end users on your team. And the other thing I think about tools is designing tools that are both for people and for the next agents that come online in your team. And so a lot of times I discover a workflow and then I go to AI and I say, okay, how do we make it so that my coding agents can also use this workflow? Like I can pull the analytics this way. Can they also pull the analytics?

• 29:50Speaker 03

  Because I want to ask them questions that I might not be able to quickly grasp much more quickly than as quickly as they can kind of synthesize that stuff. So I think building internal tools is something that like is the easiest way to get into like the unlocking the value. Because tools make it easier to do what we do today. Free up time for us to do more things tomorrow. And for me, Replit was that realization. I use Replit. Most of the things I'm making on Replit that I'm vibe coding are just for me. They're not, I'm not publishing or selling that software. It's personal software. It's custom software. And when it's really cheap to make software, you should just make much more of it. And in, it doesn't have to justify its own existence in some kind of revenue driving way. Then that is the validation where you can kind of build these tools.

• 30:35Speaker 03

  And a lot of times you don't even have to like, I realized. It might be easier and faster to build the first version yourself than to even go out and try to buy something off the shelf. That's one of the one of the realizations of vibe coding. Um, I do think there are still plenty of tools that you shouldn't even shouldn't even try to make yourself like maybe not build. Unless you really need a very unique CRM, you can just buy a CRM. Um, compliance tools, anything having to do with like SOC two or like, uh, legal. Stuff that where the requirements don't change over time, I think it's good to just buy something that's commoditized. But if it's unique to your problem space, your customer base, your user. If it's something that's very unique to that, that problem, it, I think you should build some custom internal tools.

• 31:17Speaker 02

  So in terms of your own use of Replit to launch your own tools, what's one of the probably more eccentric or funny? Like something someone wouldn't have thought of that you've built as an, as a Replit, you know, tool for yourself.

• 31:31Speaker 03

  I actually have one right here. This one I've been working on in Replit and with outside coding agents. I basically had an agent scan all of my, every project that I've coded in the last three years. I was just go learn all the tech that I've been using. I forget some of the stuff we used two years ago from Sam's team at Microsoft, Sam's Colatia's team at Microsoft, Microsoft Graphrag. The ability to like create these knowledge graphs on top of large corpus of data. I actually kind of think that you might end up with something like this inside of every company. Where some kind of centralized agent managed, um, wiki, interactive, evolving kind of wiki. And, um, I, you know, I built it in Replit because I was like, oh, it would be nice to talk to something that could reflect all of my technical.

• 32:16Speaker 03

  Um, explorations over the last three years, like read all my code, connect all the dots between all the technologies and then. And then we ended up, I ended up with this, this kind of like 3D graphics experience. I was like, it's all vibe coded, right? Entirely vibe coded. I don't even think I wrote even 1% of the code myself. And then when I had this, the interesting thing is it's a, it's a knowledge graph of everything I've learned about working with agents and all the different technologies inside them. Then I asked the graph itself. I said, why don't you ask yourself how to build a voice assistant that can control the graph? And then it added this, this little voice button. And so it modified itself into a voice agent that can query and understand the rest of like my corpus. Hi there.

• 32:57Speaker 04

  Hi there. How can I help you today?

• 32:59Speaker 03

  Can you show me, um, let's zoom in on the Claude code node and tell me about Claude code.

• 33:04Speaker 04

  I found the node for Claude code SDK. It's actually the former name of what's now called the Claude agent SDK. It was originally focused on code-related agent flows. Would you like to explore its connections or dive deeper into its details?

• 33:19Speaker 03

  Um, yeah, tell me about hooks related to Claude code.

• 33:22Speaker 04

  The main connection for the Claude code SDK is the Claude agent SDK.

• 33:26Speaker 03

  And so I basically designed this thing to represent my technical explorations, but allow myself to have a conversation with my own technical mind, right? So I, and I think that like building knowledge graphs for me and then connecting them to voices. It's just, I love having it on the side and just asking it questions about connecting the dots between all the projects. So I think something like that might actually be very valuable as like companies start growing and scaling. Like what, how do you make sure that knowledge inside the company is accessible by all the different teams and all the different people? This is obviously one for me, but I can imagine many of these being constructed as like artifacts that are useful for,

• 34:05Speaker 03

  like a new Intranet, new interactive Intranet.

• 34:07Speaker 02

  Well, imagine that it's like every person has their own Wikipedia, every group has their own Wikipedia. Every project has its own Wikipedia, every company has its own Wikipedia, et cetera, et cetera. And it's all easily accessible and brought to your, you know, your, your ear tips by agents.

• 34:25Speaker 03

  Exactly.

• 34:26Speaker 02

  Let's be imaginative. What is the corporate office of the future look like?

• 34:30Speaker 03

  As someone that works from home, um, it's been a while since I've been in an office. But I think about, I think about this, like all of these capabilities, language, voice, uh, knowledge systems, automation through natural language agents that can take action, parallelize. I think what's going to happen is certain people in, in a company, many people will have, I mean, everyone's at a different layer depending on your role, will have these like pods of agents that they interact with. And some of, uh, some of those agents will be shared. They'll be like assigned to a project. Like, um, you know, I have an agent working on this knowledge graph. Maybe you also talk to that same agent. So we're both giving that same agent feedback. So it's sort of like an extra,

• 35:13Speaker 03

  uh, perspective that both of us are creating, a character we're creating to play a certain role. And then the question becomes like, how many of these would you be interfacing with? And like, how do they show up? I think they'll show up in meetings in both like ambiently after the meeting ends. It's like, why don't we start these prototypes? Here's a couple of follow up actions. I've sent, you know, memos to XYZ stakeholders. I think that that kind of ambient layer is going to get unlocked. It's going to make everything feel like in motion and alive and mutable in a way that maybe software and infrastructure is felt fixed, where I feel that software is now becoming liquid and malleable and kind of like composable in a very like, sort of like spell casting, where if you can, you know, say the right combinations of words, like the agents start

• 35:58Speaker 03

  getting to work and they start creating a new interface, a new product, they start solving a problem for you, or they run in the background. And I think that that the idea of these processes running ambiently in the background as the people are aiming them is going to be very big. I already see it in my own life where I have like, you know, maybe 15 projects, each one has two to three agents on them. And some of them are working on one two day long project and they might come back, you know, two days with like progress. And the kind of progress that an agent can make in two days will might will blow your mind, but it also feels like then the time scales are collapsing for how quickly we can attack more ambitious projects where you would have previously required 10 people to attack a problem over two years.

• 36:40Speaker 03

  Maybe we create an agent that works on it for two days and we get the first version back. But then it's not that once that thing is out there, we just kind of wait. We're going to have multiple of these kind of jobs running where we're firing off more questions, more, uh, more queries, more explorations, more prototypes, uh, more hypotheses, more variations. I think even products might feel like very, in the same way that we A B test in software, I think products might actually evolve. Like I look forward to a day where I wake up and the agent is like, oh, by the way, I rebuilt your knowledge graph in six different ways. Do you like any of them? And then you get all these options and then you get to choose. Um, and they're working at night, right? So I think even in, like, imagine in healthcare, like, and research kind of roles, you go

• 37:24Speaker 03

  to sleep with a couple of ideas and you wake up with possible answers. That's, that's what I look forward to.

• 37:29Speaker 02

  Possible is produced by Palette Media. It's hosted by Ari Finger and me, Reid Hoffman. Our showrunner is Sean Young. Possible is produced by Tenasi Delos, Katie Sanders, Spencer Strasmore, Imo Zoo, Trent Barboza, and Tafadzwa Nirmurundwe.

• 37:44Speaker 00

  Special thanks to Surya, Yala Monchili, Sayida Sabieva, Ian Alice, Greg Beato, Parth Patel, and Ben Ralas.