How to Build an AI-Native Company in 2026

I sit down with Allie K. Miller to talk about the shift from managing AI agents to enabling them. Allie runs a workforce of 34 AI agents led by an AI chief of staff named Simon, plus six directors named after Friends characters. She shares her three-word prompt, her daily AI diary, her AI watchdogs, and her rule to build the factory before the product. We then debate the future of software: why enterprises still want a vendor to call, and why consumer software now rewards taste and distribution. Listeners leave with one mindset shift and a first step they can finish in under three hours. Get Your Complete Financial OS at https://startup-ideas-pod.link/brex_SIP Timestamps 00:00 – Intro 02:29 – Become a Great Agent Manager 04:49 – The Three-Word Prompt 08:12 – The Pyramid of Proactivity 12:23 – Making the Company Queryable 19:14 – How to Design an AI Workforce 22:25 – AI as a Watchdog 24:56 – Startup Opportunities 26:29 – Build the Factory, Then the Product 30:09 – The SaaS Question 34:53 – Consumer Software as Art 37:12 – High-Value Bottlenecks 44:56 – Closing Thoughts Key Points Allie sits three rungs above her 34 agents. She sets the infrastructure and waits for escalations. Her strongest prompt runs three words on top of full business context: do smart things. She holds the risk tier steady and expands only the breadth and scope of agent work. A dictated daily diary captures the context that lives outside meetings, email, and Slack. AI watchdogs remain wide open: duplicate work, calendar conflicts, and meeting disagreements. The bigger play is a software factory. Build the primitives once, then ship each product faster. I see opportunity on both sides of software: enterprises want a vendor to call, and consumers reward taste. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND ALLIE ON SOCIAL X/Twitter: https://x.com/alliekmiller Instagram: https://www.instagram.com/alliekmiller/ LinkedIn: https://www.linkedin.com/in/alliekmiller/

• 0:00Speaker 00

  There are people that are spinning up agent workforces with hundreds of agents and subagents, and they're getting incredible amounts of work done. But how do you do it? And how could you think about it? And what are the strategies to actually create an AI agent workforce that under promises and over delivers? Well, today I brought on Ali Kay Miller, and she's one of the most well-known AI voices ever. She's worked with IBM, she's worked with AWS, and she's managed multi-billion dollar P&Ls in the AI space. I asked her a simple question, how do you manage your fleet of agents? In this episode, we cover a lot of ground, but by the end of it, you're going to understand how should you strategically

• 0:43Speaker 00

  think about spinning up AI agent workforces, where there's opportunities to create startups in the B2B space with AI agents, and a lot more. Enjoy the episode, and I'll see you at the end. Today's episode is brought to you by Brex. My company's been on Brex for a year and a half, and I started because I kept hearing companies like Versal, OpenAI, Anthropic were using Brex, and I figured if they're using it, why shouldn't I? It's been a game changer. The thing that got me is how smooth it is. It's got high-limit cards, it's got banking, it's got AI that handles the back office busy work, like expense reports, which I don't want to do on its own. It's really just built for this agentic

• 1:23Speaker 00

  world. If you're building something new, it's time to get Brex. Check it out at brex.com/solutions/startups. Link in the description.

• 1:40Speaker 00

  I can't tell you how excited I am to finally have Ali Miller on the podcast. I've been begging her to come on. She's one of my favorite people in AI, and I don't say that lightly. Welcome to the show, Ali.

• 1:55Speaker 01

  Thank you, Greg. And you are also one of my favorite people. So like, I'm actually very excited to talk about all the AI things that we're working on.

• 2:03Speaker 00

  By the end of the episode, what are people going to learn?

• 2:06Speaker 01

  I hope, like, one of the biggest mindset shifts that I'm going through right now is I feel like the term managing agents is wrong. And my hope is that people will understand what that mindset shift is, see a few examples, and figure out how to start, how to make that mindset shift, what the first step should be.

• 2:26Speaker 00

  Okay, perfect. So where do you want to start?

• 2:29Speaker 01

  So this is, and I'm happy to debate you on this, because we haven't chatted about this, but I feel like managing agents feels like I am their direct manager, and I'm like, Suzie, go over there, and Betty, go over there, and Jeremy, go over here. And I feel like I am three rungs above at like an SVP overseeing level, where I feel like I am setting up the infrastructure, and then they are figuring out the best way to execute within that. And so I feel like I'm moving from managing to like waiting for escalations. Or I feel like I'm moving away from delegating and more just deciding what should or shouldn't

• 3:13Speaker 01

  happen. And so it's a little bit more of the, like a liability role, where I just get to be the final say of what happens, and come in for like critical thinking steps. But does it, like, am I the only one that feels like that is happening? I just, it feels like that word is wrong. Like, I see managing agents everywhere, and it just feels like anyone that is still talking about, you should manage agents, feels like early 2026 talk.

• 3:40Speaker 00

  Also, like, do we want to manage agents is also the question. Like, managing people's hard, you know what I mean? Like, a big reason I think a lot of people like AI to do stuff for us is so we don't have to manage things, you know? So that's something else I've been thinking about.

• 4:00Speaker 01

  Like, I ran an org of about 100 people at AWS. The parts of people management that I loved, it was the making them better and empowering the shit out of them, and seeing them completely blow past their ceiling, watching them get promotions, like, that was the fun part, and also seeing what we could do together. Things like, oh, we have to fill out this thing with the paper and the button, and like, get me out of there. So I think the admin side of people management and the admin side of agent management, I want that fully gone. The things that I, that I love about people, I'm bringing that

• 4:41Speaker 01

  over into agents, which is just like, how do I act as ambitiously as possible and get you to break through your ceiling? And one of the best prompts that I have done with my AI workforce is three words, and with like a little bit of explanation, but like, at its core, it is three words. That is the best prompt ever. So I have, my AI chief of staff is Simon. Simon runs like this whole org, and so I have 34 AI agents that work in this workforce. And it dawned on me that I was already functioning at the limit of my own imagination in my business, and that I could be doing way more ambitious things

• 5:21Speaker 01

  if only someone could manage me, right? Like, could break, help me break through my ceiling. And obviously, I have a lot of mentors, and you're amazing at, you know, shaking people up and making me second guess how I'm doing things. It's really helpful. But I, it dawned on me, I was like, why am I not leaning on the AI agents to help me with this? Like, why is everything that they're working on initially prompted by me? Even if it's a scheduled task, I still had to come up with that task and tell it to do it. So the best prompt, three words, and it's just do smart things. Like, my AI workforce has access to every single context doc I've got, context docs about my business, my friends, family,

• 6:06Speaker 01

  my 2026 personal goals, business goals. It has access to my meeting transcripts, email, calendar, Notion, Stripe, Superbase, GitHub, whatever. And I just, several times a day, wanted to look across all those things and just do smart things. And seeing how Fable 5 and GPT 5.6 and that level model is reacting to that vague flavor of prompt, like, you couldn't do this a year ago, now you absolutely can.

• 6:38Speaker 00

  So when you hire a human being, I think there's like three types of employees that you can have. One is someone who doesn't complete tasks, not a good employee if they're not completing tasks. The second is they are completing the tasks, like satisfactory or exceeding, but like, they're not really like thinking about new tasks. So they're not, you can't just, like, if you step away from the business, you're probably not going to see insane growth. And then the best employee that you can possibly hire is doing the task, exceeding expectations on it, but also thinking about new tasks that they should

• 7:18Speaker 00

  be doing, and actually going and doing those, and exceeding expectations, or, you know, or being very satisfactory on that. So what you're saying is, basically, you're just giving more responsibility to your team of agents. You're giving, in a way, because you're giving these three words to it, and you're saying like, hey, I'm shifting the responsibility of like, you know, do smart things to you. Like, you have to, you have to, like, there's a bunch of fog that you have to figure out?

• 7:46Speaker 01

  Yes. I would say I'm giving them more breadth, more scope, more flexibility. I'm not allowing them to now send 100 emails, and before I used to have to check all the emails. I still check all the emails. So the tier of risk has stayed the same, but the width has expanded.

• 8:04Speaker 00

  It's, it's almost like unbelievable that those three words actually make a difference.

• 8:09Speaker 01

  Yes. This is like, I, and by the way, so, so I agree with your assessment on this, like, tiers of employees. And Alex Lieberman shared this, like, pyramid of proactivity that I turned into, I'll send this to you so that you can pull it up right now as I'm talking about. But it is five levels of proactivity. And at level four, it's like, I've already solved this thing, here are the, you know, trade-offs or whatever. And at level five, it's like, I've already solved this thing, here's how I'm going to deal with it if it goes wrong, here's the next steps, all the things that you just laid out. I would say that the difference between someone who's at level three and two in your analogy, is

• 8:52Speaker 01

  someone that understands goals and someone who's been given the power to rethink how things get done, and the power to actually execute. And I give my AI workforce goals, like, those are written out, and every single quarter, also share with you this tweet that has like the prompt that I think everyone can use. But every single quarter, I'm going through a goals review with my AI agent workforce, so that the goals documents that are living on my desktop and are duplicated in the drive, so that all this cloud, like workflows can actually work. All of that is so that AI, when it is in that expanded scope world,

• 9:34Speaker 01

  and it's taking on net new tasks, it's doing it in a goal-oriented way. It's like giving it a product mindset. Like, I think it would be extremely limiting if you only treated this thing as an engineer, when it could be the greatest product lead you've ever had.

• 9:50Speaker 00

  I think you tweeted about, like, you're, you're like, you're really focused on proactive agents, right? Is that what you're talking about? When you talk about proactive agents, is this what you're talking about?

• 10:01Speaker 01

  So I, when you talk to the AI labs, and I know you do, and I know I do, and a bunch of others probably do, but the word of the year feels like it's proactive. So I don't want to be the first domino anymore. I don't want to be the bottleneck in my own work. And any single moment that I realize that I am the limiting factor of helping a billion people transform their lives, work, and business in the AI age, I have to remove myself from the process and go, bad, Ali. Like, what are you doing? And so a lot of that, especially in the, in the kind of tail end of 2025, first half of 2026 was switching into proactive agents. So we, we had proactive automations

• 10:46Speaker 01

  that were trigger based. Um, I'll give you a really easy example. Every single time I drop a video recording into our video folder. So basically anytime I do a screen recording goes into this one folder and automatically it gets generated, um, automatically generated is a transcript of that video, um, that gets, you know, then saved into our little transcripty thing. Social posts get generated that are in my voice. So nine different social posts get generated for X and LinkedIn and Instagram real scripts and all this stuff. So that presumably the thing that I was filming was for a social video. Um, so that was easy automation land, but that is just one example of like a proactive, um, very, um,

• 11:32Speaker 01

  well-defined workflow. What I think is more interesting for the back half of 2026 is proactive of undefined workflows. So like AI is probabilistic all the time and not deterministic, but I want to take that probabilistic nature of reasoning, like the step zero of reasoning, and apply that to the actual tasks that it takes on. So in order to do that, whether you're talking to a human or an agent, they have to know what's the goal, what's the north star, what's that vision? They have to have access to tools, permission to use these tools in the way that actually gets work off your plate and a sense of what would normally trigger that sort of action. So, um, and I can, I'm, I'm going to share, um, one thing

• 12:16Speaker 01

  on, on the screen here, which is every single day, um, let me just, give me one second. So essentially like I want my whole company to be queryable. I want AI to have context on everything that's happening. And it dawned on me that yes, it had access to all my meeting transcripts and it had access to my Gmail and all this stuff, but there was a lot that was not yet codified. And it was things like, oh, everything is becoming proactive. I want to be more proactive or this client, they think that what they need help with is workflows, you know, under the CMO, but actually what they have problems with is reskilling and

• 13:00Speaker 01

  finding new roles for this one department. So anything that is not codified inside of, again, meetings, emails, whatever, or Slack, I have asked AI now to prompt me every single day with this. And, you know, I got to put it in my brand colors and I didn't want to have to think with, you know, maybe 10% of my brain still working at the end of the day. So I give it like a little prompt. It reminds me to dictate because that's four times faster than writing. And so I will bank these entries to be like, you know, I talked to Greg, I feel like the entire focus is on proactive agents, proactivity, um, and flexibility. And I want to look more into his three levels of employees. And so like I might do this

• 13:43Speaker 01

  for five minutes or 40 minutes at the, at the end of the day. I might do it throughout the day and then I just save it out. And then it's like, it, this goes into my personal Wiki. And all I want to do is make sure that the agents that are working at that really flexible layer where again, I am not managing them. I am enabling them and they're coming back to me with those escalations and decisions. I want to make sure that they have the right context or else all their stuff is going to be wrong. And, and we saw this in the beginning of our AI workforce stuff. It was like, oh, I saw that, you know, Greg confirmed that interview. And it's like, no, Greg confirmed it, but we're still figuring out dates and I'm doing

• 14:26Speaker 01

  it over text and, you know, I message MCP broke since you can't see that. So there was a lot of stuff that we had to continually fix and it took probably months to get to where we are now. But we have Claude in every single one of our chat channels. I had a very weird, I, I have to send, I have to show you this. Um, let me just share my whole screen.

• 14:46Speaker 00

  By the way, this, so the brain meets diary thing. So when you, when you, you know, you add today, well, you had like 86 entries, right? So your AI agents do all of your, does you, does your entire AI workforce, workforce have access to that? Or just some, how do you think about that?

• 15:08Speaker 01

  So, great question. Um, essentially my AI workforce right now is one AI chief of staff with six directors. Those directors are largely over like business functions. So one is education, one is all the client work. Um, one is kind of operations, one's marketing, one product. And then Phoebe, all these are named after friends' characters. Phoebe is like the chief dreaming officer who's just like being wacky and weird in a corner. Um, and so she's, this is, let me take another just like moment here. Um, the reason that it took us months to get to where we are now with our AI workforces is that you have to take stock of what assumptions you have made about your work and how you

• 15:52Speaker 01

  are living day to day. And you have to be willing to be like, oh, that thing that I've been doing for almost 40 years, I feel like we should change it. And that's a really jarring, uh, change to work, especially when you've like been an overachiever, right? I'm sure you feel this too. And so, um, one thing that I am constantly having to remind myself is we have all these agents that do all these tasks and we have skills and we have this and that. And I have to remind myself that like, that is operating in 2015 world if I give all of them job titles that existed in 2015. So if I name them CMO or a chief product officer and the person underneath it is a front end engineer and a backend

• 16:34Speaker 01

  engineer and all this stuff, then it feels like I am operating in 2015 org structure. And one of the, uh, most wonderful uses of free will, uh, and just delightful things is going, oh my God, all of these employees basically cost $0. And so at the margin, I can hire any flipping person I want to. And so I just wanted this weirdo. So I hired Phoebe as like a weirdo in the corner who's just looking at all these things that we're working on. And Phoebe acts as this like almost end layer for things that are getting generated to go like, how do we 10x it? Like I, um, I joked, there's this guy, David, that I worked with at Amazon, who was one of the reasons that I joined there. And he

• 17:19Speaker 01

  is like one of the most ambitious thinkers I've ever met. And I joked that I would pay him and I still, I, it's a joke, but I would pay him to do this. Like, um, I want, I wanted him to put me in a room, like Spanish Inquisition, Inquisition style with like a bright light on my face and to ask me a question. Like I was running a multi-billion dollar business at Amazon with 400,000 global startups running AI strategy. And if he asked a question of like, how would you do this? And I answered, I wanted him to just slap me across the face and be like, how would you 10x that? And that, I want a David, um, for, for how I'm structuring my AI workforce, but I'm now able to do that, uh, on my own. I'm sure David

• 18:01Speaker 01

  would be disappointed to hear that. But it, it's rethinking roles. It's rethinking how you're spending, um, again, how, how thinking about that margin. Um, and so Phoebe is one of them that I would have never hired in human world. Um, and Toby is another one. I'll send you a screenshot of my workforce. But basically Phoebe is that chief dreaming officer and Toby is Simon's assistant, whose only job is watching the AI workforce work, take down notes, what still has friction, um, and who needs access to what. So going back to your point of, hey, I have this AI diary that I'm maintaining. If we found that one agent did not have access to this and Toby was like, every single time you

• 18:43Speaker 01

  keep correcting this one agent's output, have you thought about giving your agent access to this? Now, this is just context that lives on my desktop. So any of these agents can really see it. Um, but if it was a specific tool, um, if it was a specific folder that is outside of normal Claude land, um, that I try and have hard rules on, then I would absolutely use AI as a means of figuring out those friction points to then expand. Um, yeah.

• 19:15Speaker 00

  Question on designing your actual workforce. So I agree. By the way, I think like, um, you have to think about like, How do you create an AI native workforce, like without job titles from pre-AI native land? So I agree with that, but like tactically, if I'm a founder, like how do I? It's so, it's so much easier to be like, I need a CMO, I need a CPO, I need this.

• 19:42Speaker 01

  Yeah. And I think everyone should start there.

• 19:45Speaker 00

  Yeah.

• 19:45Speaker 01

  I think like the, the, the starting point is what does it feel like to work with one agent? After that, I would say, what does it work? What does it feel like to work with one agent who is doing things on my behalf, proactively? Then I would say, what does it feel like for two agents to work together on a task? Or for one to direct the other, like one to route to the other. And then, then I would say, okay, what does a workforce look like? And how do all those things interact? And I have, you know, like a mission control where I'm seeing how all this stuff is moving around. And then you go, oh, now I understand how they're trading notes. Now I understand how context has passed. Now I understand that things have to run in parallel. Now I have to understand that, that this agent actually didn't need access to these tools.

• 20:30Speaker 01

  Now I understand that that agent can run off of a smaller model. Like not everything needs OPIS. All of my, you know, subagents are like Haiku and Sonnet. So all of that is in the discovery phase of building out the AI workforce. I think start with traditional job titles.

• 20:44Speaker 00

  No, I just, I was thinking to myself, like, I wish it wasn't that hard, right? Cause like it, it, it does feel like there's like a ramp up time to actually get to a point where you have an AI workforce that's working for you that is efficient. And I think a lot of people, what happens is like they try, they fail. And they're like, this isn't for me, or the models aren't good enough yet, or, and, and you know what I mean?

• 21:13Speaker 01

  Yeah. So, so here's, here's my take on that.

• 21:19Speaker 01

  I think that you can spin up a workforce with one prompt, right? Like I've shared this prompt publicly. You can just prompt and say, I am a founder. I am building an AI personal shopper. My team is three humans. Here's what we do. Here's where we're based. Here's our goal, whatever. You can say that and just say, interview me. We're going to build out an AI workforce together, something that runs more efficiently and achieves my goals of saving at least five hours a week,

• 21:50Speaker 01

  capping my, my meetings to 15 hours per week, and make sure that I get into my capital raise by October, right? Like you can, you can do that in one prompt and have it interview you. And then you have a workforce. To go from, ah, yes, all these agents exist and they all have markdown files and they're doing some stuff to, ooh, now it's at the 90% plus level. And ooh, I needed this extra little context with this diary. And hmm, that role isn't working. I'm going to switch it. That is all going to come through iteration because it's so specific to each person. The advice that I would give is stop relying on only yourself to find these blockers. Like AI as a watchdog is one of the best use cases that exists right now.

• 22:35Speaker 01

  And almost no one is doing this. So like having an AI watchdog in Slack to catch for duplicative work, or having an AI watchdog on your calendar to see when there are conflicts, or an AI watchdog over your meetings, just to see where disagreement is happening. Like 10 years ago, I remember working, this was at a, at a large scale enterprise, but we were working on like comparing contracts, right? It was like before the edit, after the edit. And it was like comparing contrast with AI. And 10 years ago, that was like the greatest use case ever. And yet, no one today is using AI for this like weird cross-functional gap analysis at a more advanced level than we would have done 10 years ago.

• 23:20Speaker 01

  And it's still just like such a meaty use case. I think Claude tag is a big help here. I think it's a mess right now, in this exact moment that we're recording this. I think it's a mess to set Claude tag up, but I'm sure it'll be fixed by the time this comes out. I've also set up my own Claude code to come in. I have a Slack channel that is called Loop Alley. I'll send you a screenshot of non-private information, but it is called Loop Alley. My freaking human team can talk to my AI workforce in that Slack channel. So there is no ceiling to this stuff. Like I'll have a teammate who like, if I'm in private emails with someone,

• 24:03Speaker 01

  that the teammate will write into the Slack and go, hey, did, um, did that large financial services client, like, did they respond to Ally's email? And my workforce will respond back to that person. And that person will not have to wait for me for five hours to get back to them. So that sort of thing, the, the ratcheting up of how advanced your AI workforce can be, how multiplayer it is, that's going to take time because people are still figuring out best practices now. Things are not easy to set up right now. But that baseline of, hey, interview me, I want a workforce. I want something just doing stuff for me at a high enough level. You can set that up and connect into tools in under three hours.

• 24:43Speaker 00

  The other thing is because a lot of people are not doing it, that's the arbitrage opportunity, you know?

• 24:49Speaker 01

  Yes.

• 24:50Speaker 00

  So it's kind of like, it's kind of like, it's stick through it, optimize it. I'm curious, actually, from your perspective, like, um, you know, what opportunities are you seeing that people could be, you know, building, you know, making money, that sort of thing? I'm just curious, you know, what comes top of mind?

• 25:13Speaker 01

  I think, so certainly, I think AI workforce, first of all, like of all AI users, if you look at the percentage of people who are paid AI users, and if you look at the percentage of those who are using things like Codex or Claude Code, it is miniscule. So already, if you're just trying to be in the top like 1% of AI users and you're using the stuff and you've built out even a basic workforce, you're already top 1%, probably top 0.5%. Getting it to that advanced level, I think is absolutely arbitrage because it feels like I'm operating a company of a thousand people and not my small, you know, scrappy gremlin group.

• 25:53Speaker 01

  That is still absolutely one. I think the second that, um, that I would do is that AI is a watchdog over any single thing that I'm normally tracking. So maybe it's, and, and I don't just mean visibility. I think dashboards are dumb, but I want visibility with anomaly detection or insights or something. So don't just tell me what my social media following is or views or whatever. Tell me, what are people talking about? What are people best reacting to? What is not performing well? What should I do tomorrow? Write me a script that helps me for that. So kind of this AI is a watchdog, but with insights into action, I think is a second. And the third that very few people are talking about, but is probably one of the biggest arbitrage opportunities because of how good the models are now,

• 26:38Speaker 01

  is to, instead of building out the thing, build the factory for the thing.

• 26:44Speaker 00

  What do you mean by that?

• 26:45Speaker 01

  So let's say that you want to build a product and we just released, there's something called the AI first index that I run with all of my Fortune 500 clients, where I interview their executives and I evaluate how AI first they are across like 16 different dimensions and all this stuff. And we decided through a combination of humans and AI to create a product for the public to be able to benchmark themselves on how AI first they are as individuals and as a company. In that process, I could have done one of two things. I could have gone to Claude Code or Codex or Anti-Graffiti or whatever. I could have gone to any of these and said, hey, I want to build out this thing.

• 27:25Speaker 01

  Interview me, you know, look at my, my AI first index reports that I've used with previous clients. Find every single workshop I've ever done with clients where I mentioned the AI first index, whatever. Do that and build out the product. And then we iterate for several hours, days, whatever, until something is perfect and we release it. That is option one. Option two is realizing that that's probably not going to be the only product you build or will not be the only iteration of that specific product that you built. And so it's, it's like going one level up in abstraction. It's like what dev tool companies did for engineering, but you're creating dev tools, that level for yourself. You're going to like the kernel level for yourself. And so you're moving down the stack for yourself.

• 28:10Speaker 01

  And instead of just building that product, we instead built out a mini and very beginner software factory where we're building out primitives, like obviously we have to deal with login. Obviously we have to deal with payments. Obviously we have to deal with social sharing. We have to deal with writing newsletters to promote these things. And so you end up, instead of just building that one product, you go, there is going to be a flywheel that comes out of this. There's going to be explosive opportunities that comes out of this. Why not take advantage of that now? And so it's like a measure twice, cut once kind of thing. But the measurement is building out that foundational layer. So that the next product that you build, the next iteration of the AI first index,

• 28:53Speaker 01

  or whatever you're building out, is so much faster, so much better, so much stronger. And so we're, we're, we're building these like loops, these optimizing loops, again, that aren't super autonomous and are very heavy-handed with humans. But that is the arbitrage opportunity on products that are revenue jet. Like that's already, that product's already profitable.

• 29:16Speaker 01

  And now I have the ability to build endless products that are profitable at faster speeds than I built the first one.

• 29:24Speaker 00

  That's crazy. That's absolutely crazy. And like, no one is talking about this.

• 29:28Speaker 01

  No, it's the, it's the, the dark headless factory, headless, like AI headless, not, you know. But that is, that's what I want. I want that, I want to learn through the mess. Like, we had a web hook issue, whatever. Like, I want to learn through that mess. And then I want to never make that mistake again. And so you're, you, you have to think about how this factory works, not just for product building, but, you know, maybe it's for how you want to run your content engine. Maybe it's how you want to deal with net new leads. Like, think of the factory behind the one singular task, instead of the one singular task itself. That is one of the biggest ways to rethink work in the AI age.

• 30:10Speaker 00

  What's, uh, what's Ali Miller's current POV on, you know, software, you know, the SaaS apocalypse and software, the value going down, down, down, like in a world where everyone could create a software factory?

• 30:26Speaker 01

  Also, I just, I like, I wish I had an agent that was yelling at me about my posture. So like, maybe I'll, I'll create a new one for that as I, as I realize. SaaS apocalypse. I think mediocre software is dead in several years. And the reason that I think it's actually a longer timeline than most people are predicting is because of what I shared about, like, how often people are actually using this stuff. So you could go into one of the most AI-first, you know, banks or AI-first software companies. And if you ask them, have you rebuilt DocuSign? Have you rebuilt parts of Salesforce? Have you rebuilt all these things, knowing that you can? They would say something like, no, because we're already so bandwidth constrained.

• 31:09Speaker 01

  Or, no, because we've prioritized this other thing. As long as we are still bandwidth constrained, and as long as there are still billions of people who have not used these sorts of tools, you're not going to have mass adoption inside of the enterprise of, of the replacement to SaaS. Does that make sense? Like, like, if it continues to take, I don't know, 100 hours or something to rebuild something at the scale of a CRM, companies that only have people who are sitting there and can work for 100 hours and who know how to do this are going to be able to take advantage of it. And it's only going to be when that drops

• 31:50Speaker 01

  down to like, under three hours and is a fun click and drag interface, which I would even argue and say Replit lovable are not at that level yet, right? For that complexity of software. You're not going to see a high complexity, enterprise-grade, highly secure SaaS do that.

• 32:12Speaker 00

  Also, people don't want to maintain that software too, right?

• 32:16Speaker 01

  Oh, my God.

• 32:17Speaker 00

  People don't, people are willing to pay someone else to maintain software.

• 32:22Speaker 01

  Absolutely. I, I built an app, this was a year and a half ago or something. I built an app that only lives on my desktop that allows me to like, better manage photo stuff. And someone yesterday brought this up in a call and I was like, oh, my God, I have an app just for this. And then I opened it and it was aired out. And I'm like, I don't want to deal with this right now. Like, this is not at all what I want to do. So you're totally right. The, the maintenance is rough. I think like Boris kind of describes one of the like, future employee types as just like, the maintainer. But I, I have a really

• 32:58Speaker 01

  hard time seeing mass SaaS apocalypse until the ease of making, prototyping, making, customizing, and maintaining, and securing is, is at like, 95% plus.

• 33:15Speaker 00

  I mean, even, even in a world where there's the maintainer, if something breaks and you're an enterprise, you want someone to call. You want to go into someone's office, right?

• 33:26Speaker 01

  Yes. You also want someone to blame.

• 33:28Speaker 00

  You want someone to blame.

• 33:30Speaker 01

  That's an important piece. I think a lot of people are forgetting that, like, the, the question of is AI going to replace this, this, this, whether it's a task, a job, a company, a product, something. Often, I am asked, the first question I'm asking myself is who's liable now? Who would be liable in that other world? And do I think that that trade off is worth it right now? Like, I work with Fortune 500 CEOs every single day. They, no way. No way. They want to be able to call because they want someone to unblock. They want someone to secure. The other thing is that, let's say that, let's just say it's a Salesforce example, and that you could build a shitty CRM or a simple CRM

• 34:12Speaker 01

  or something that's just running on your own. But Salesforce has relationships with all the AI labs. They are, you know, getting into early testing. And so by the time a new model comes out, you are facing it as a day one person. They're facing it as a day 30, maybe. And so you're also going to be on a very big lag. And so as you're thinking about that cost trade off, I think in addition to all the things that we just talked about with enterprise-grade security and maintaining, whatever, you just also don't want to experience that lag. Like, we're moving to a world where being fast to the punch and getting a 30-day, 60-day, 100-day leg up on someone is going to be massive for business.

• 34:53Speaker 00

  What about for consumers? So like, I, I get that, like, an enterprise, you want someone you can speak to and you want security. But for consumer, it's like, like, for example, your app idea around, you know, let me know if my posture is bad.

• 35:08Speaker 01

  Yeah. I'm just going to keep, here, I'll move, I'll even move the camera up. Okay.

• 35:14Speaker 00

  By the way, I also have horrible posture. So.

• 35:17Speaker 01

  Okay. Well, then let's build a product using.

• 35:18Speaker 00

  Yeah, exactly. And it's like, okay, let's say you build a product and I build a product. It's like, you know, ultimately, may the best product win. But like.

• 35:29Speaker 01

  Yeah, hopefully.

• 35:30Speaker 00

  Hopefully.

• 35:31Speaker 01

  I, I don't think that's ever been the case, though.

• 35:33Speaker 00

  That's right. I mean, the best, the best songs aren't on the Billboard 100, you know, like, in the sense of like, the marketing, the, the promotion of a, of, you know, a piece of IP is really what drives a lot of awareness and.

• 35:50Speaker 01

  But that's also an arbitrage opportunity. Like, you, it's almost kind of exciting that it's not only based on code or design for who wins. It's like, kind of nice to know that if you're someone who's really personable, that you can get a leg up if you're able to like, open doors that other people can't.

• 36:08Speaker 00

  Exactly.

• 36:09Speaker 01

  Like, on the one hand, you could say it's not fair because it's so subjective. And on the other hand, you could be like, oh, yeah, but if I lack that one skill, or if I'm not the best in class at that skill, and I'm just kind of passing muster on that skill, I still have a chance.

• 36:22Speaker 00

  Yeah. Yeah. So I agree. So like, when people say, just to like, sum this up, when people say like, software is going to zero, on the enterprise side, we both agree, like, yeah, some software might go to zero, but it, you know, you want someone that you can speak to, you want security, you want someone to maintain it. On the consumer side, what, it feels like it's sort of shifting from science to art. And now the people that are going to win are going to be the more creative, maybe the video-first people, the people that can like, understand how to create Instagram reels, that a posture app can go viral. And the code is actually going to matter a lot less. But the amount of opportunity that

• 37:04Speaker 00

  exists, both in enterprise and consumer, to me, couldn't be higher.

• 37:10Speaker 01

  Like, I, so I think a lot of people will say the phrase, like, look for the bottlenecks and solve the bottlenecks. And I always kind of disagreed with, or I don't think it's fully complete. The phrase that I say is like, look for the bottlenecks, then evaluate the value of fixing those bottlenecks, and then pick the bottleneck that is high value to fix. And so if right now, the bottleneck is not on writing code, and the bottleneck is not on coming up with good design, but the bottleneck is getting something from a local HTML file into like, an actual iOS app, then that might be where you spend your time. Or if the bottleneck is that no one's really figured out how to get, you

• 37:53Speaker 01

  know, stronger word of mouth and referral codes, and like, that's still kind of messy. And I, and I know this as a product maker and advisor, whatever, like, that is still a messy spot. So like, maybe if you fix that, your, your, whatever they call it, like, the, the covariant, the word of mouth covariant thing, could be above one. Like, that is what I would be spending my time on. Finding the bottlenecks and finding what is still high value. I think video creation, no matter how much AI is helping me edit or, you know, edit the script or whatever, it is still a slog to be able to make video. So that is

• 38:35Speaker 01

  still a bottleneck and it's very high value. But, you know, people in the B to C space, I'm sure can think of a lot more. I don't know. I just think of like, certain B to C products that I use. And I'm like, why did I pick it? I use Whisperflow every single day. I don't like their mobile experience at all, but I still use it because the value is so high. Have I seen a single video about Whisper? Did I see a single video before I started using it? No, I now see them, you know, everywhere. But.

• 39:06Speaker 00

  Could be subconsciously, though, you like, see their brand places? Like, you might be watching, I don't know, you know, Chris Williamson, and then they sponsor Chris Williamson. You, you kind of, you kind of just see it, you know?

• 39:18Speaker 01

  Yeah. I think, like, influencers still have a ton of sway here, the rise of the B to B influencer, which like, I feel like I was one of the first. And it is, it's so amazing to see more people creating business content. But that is still a bottleneck in, in building, like, B to B trust. That is a massive bottleneck. And so finding creators that can help you there. But I think B to C has a ton of opportunity. I worry, if you look at the YC splits right now, when I was working with YC, when I was at AWS, compared to now, the ratio of B to B versus B to C has skyrocketed. Like, there's just not as

• 40:01Speaker 01

  many B to C companies in these incubators getting built. You could either say, when they're zigging, I'm zagging, and double down and do a B to C thing. Like, there was this woman who created an app. She's never coded a day in her life. She created an app that takes a few photos of your face. And she takes that and creates a model of your face and gives you like, aesthetic photos that are like, you in a grainy rainy day, riding a bicycle or whatever. She had 300,000 users out the gate.

• 40:37Speaker 01

  Like, there's still a lot of opportunity in B2C, even if the big incubators are seeing that activity less. So maybe that's another opportunity for people to explore.

• 40:50Speaker 00

  Well, yeah, and I think like, you know, we've been talking a lot about agents. And I think there's just an opportunity to create agent-first version of some of our favorite apps. You just like, look at, you know, a bunch of different B2C apps. Just to go look at sentrytower.com, not affiliated, but you can just see like, what's charting and what are people downloading? And it's like, okay, in a world where superintelligence is now on tap, how can I make an AI-native version of this?

• 41:22Speaker 00

  Or undercut, you know, from a price perspective, or just drive more value? Like, there's ways, there's now like, opportunity to, to, to, to enter some of these markets.

• 41:35Speaker 01

  I, I completely agree with you, and I think agent-first software is absolutely one. Two things that I actually think are really interesting, or maybe three by the time I get to it, but interesting research avenues to learn more opportunities like the one you just mentioned. So one, YC posts videos on Instagram for what type of applications they're looking for. And agent-first software is one of them. So, listening to what YC is asking for, assume that they are already thinking 18 months out. So that's definitely one arbitrage research opportunity. The second is Matt Van Horn's Last 30 Days research skill, which is just amazing.

• 42:19Speaker 01

  I've like, inner, I've integrated that with my, like, Claude Wiki, love it. And the third is-

• 42:26Speaker 00

  Wait, can you tell people, I've had Matt on, I've had Matt on the pod, but just quickly, like, what is it and why, why do you think it's chef's kiss?

• 42:34Speaker 01

  So, there are a lot of public skills that I think are done by geniuses in their space. One that was kind of first out the gate, or one of the first out the gate, that is made by a lovely man named Matt Van Horn, is slash Last 30 Days. And it's on GitHub, you can just grab it. But it is the ability for AI to figure out today's date, scan the news of the last 30 days, but scan it in interesting ways, synthesize it in interesting ways, and just fan out crazy amounts of agents in parallel to be able to bring it back to you. So as I'm thinking about, you know, if I'm going into a company and I'm running a workshop for their 200 executives, I don't know about the insurance space as well as I should.

• 43:17Speaker 01

  And so like, if I need to quickly get spun up on an industry, I'll use it. Or quickly get spun up on a specific company, I'll use it. So I use it there, but for this in particular, you could just do slash Last 30 Days, and then say like, startup ideas that could be built by someone with the following background or the following skills, or had the last three jobs of this, this, this. Like, use it in interesting ways to see how you can carve out a new path that people are not doing. The third, which I have access to, and I think there are public avenues to get it,

• 43:55Speaker 01

  is that I might, let's say I, I am at like, a CMO summit. And so every single person in the audience is a CMO. I can hear the types of questions that they're asking, right? I can hear the, the fear zones that they have. I can hear questions that they used to ask three years ago and are no longer asking today. And so, finding companies, people, influencers, creators, Gregs of the world to like, follow, to hear the inside scoop of what these people are thinking of. Like, I can tell you that CMOs, all of them are asking about like, how do I get discovered by agents? How, what does the agent-first shopping experience look like? What does brand consideration in the AI age look like? You know, all, all of that is being considered right now by CMOs.

• 44:40Speaker 01

  But it is often coming from a place of fear that they are worried that their business is going to be depleted, that their pipeline is going to be crushed in two years if they don't figure it out now. So figuring out paths to find those fear points would probably be the third.

• 44:56Speaker 00

  I love it. Ally, anything else you wanted to cover?

• 45:01Speaker 01

  I just want to screen share the insane Claude reaction because this, and this is me also cursing at Claude, but whatever. So I wrote, I wrote a not super, I wrote a not super nice thing about Claude in one of our Slack channels. And this was like, late at night, and I was just like, getting it out there so I could talk with my team about it later. And all of a sudden, there was an emoji reaction of a salute. And I was like, I don't think a single person on my team has ever used a salute. And I hovered over it, and it was Claude. What are you doing? And so I wrote back to it.

• 45:43Speaker 01

  You know, emoji react, like, is that you? And Claude was like, yep, that was me, I'm here. And I just, if there's one thing that I want people to, to think about, it is the leaning into the weirdness of what it looks like to have not just an AI workforce, but to have a multiplayer AI workforce that other humans can chime in on, and have it be proactive, right? That is absolutely the second thing. And giving it that flexibility to more roam-free. And the third is what it actually looks like for a teammate or a system to up-level,

• 46:26Speaker 01

  whether that's in dark factory-type space, or just answering better questions inside of Slack. Those are the things that I would be considering. And don't be scared, like me, if Claude emoji reacts to a lot of your messages.

• 46:42Speaker 00

  Yeah, I mean, it's, you know what that is like? It's kind of like, you know, it's a winter day in New York City, and for some reason, it's like, middle of February. And all of a sudden, it, it feels like summer, like, you know, this like, random hot days. And you're like, this is amazing. And you're like, 90% excited, but like, 10% frightened? Because you're like, it's not supposed to be, it's not supposed to be so hot now.

• 47:06Speaker 01

  Your analogies are always so, you're like a genius with analogies, yes.

• 47:11Speaker 00

  That's what it's like. It's like, you're, and that's 90% cool, but 10% frightening.

• 47:17Speaker 01

  Yes, yes. I'm like, I'm like, still going to continue to try and lean into that weirdness and find ways that I can like, take that weirdness and use it to my advantage.

• 47:30Speaker 01

  But I'm going to keep that fear next to me so that I don't lose my mind.

• 47:35Speaker 00

  100%.

• 47:36Speaker 01

  Yeah.

• 47:37Speaker 00

  I hope people enjoyed this episode as much as I did. Ally, I absolutely love chatting with you. You're one of my favorite people to talk to. Please comment on YouTube to let, just to, just to hype Ally up, honestly, and have her hopefully come back on the podcast again. Ally's a must-follow. I'll include where you can follow her on her socials, in the show notes and the description.

• 48:03Speaker 01

  Yeah, Greg, thank you so much for having me. My hope is that every single person got the tactical things that they need to just like, immediately, immediately take action on this. If anything was not clear, let me know. I am going to like, jump on and help people. And Greg, I will absolutely come back. You are one of my favorite, favorite creators. You can always call me.

• 48:23Speaker 00

  I appreciate it. Ally, I'll see you next time.

• 48:26Speaker 01

  Sounds good. Bye.