Allie K. Miller tells Greg Isenberg why she stopped managing her 34 AI agents and started running them like a company she's three rungs above.
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5 days ago
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educational
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Big Idea
The argument in one line.
Running an AI agent workforce well means shifting from managing agents task by task to setting goals, context, and a fixed risk tier, then letting agents decide how to execute and escalate only when it matters.
Who This Is For
Read if. Skip if.
READ IF YOU ARE…
A founder or solo operator who already uses Claude Code or a similar AI tool daily and wants to move from one agent to a coordinated workforce.
Someone deciding whether to build a single AI product or invest first in reusable infrastructure that makes every future product faster.
A builder looking for concrete AI startup opportunities on the enterprise or consumer side of software.
Anyone managing a team, human or AI, who wants a better mental model than 'manager and direct reports.'
SKIP IF…
You haven't used AI agents beyond a single chat window yet; this assumes you're past that stage.
You want a step-by-step technical setup guide; this is strategy and mindset, not a build tutorial.
TL;DR
The full version, fast.
Allie K. Miller argues that 'managing agents' is the wrong frame: she runs 34 AI agents under one AI chief of staff and treats herself as three rungs above them, setting goals and infrastructure while they decide execution and escalate only when needed. Her core tools are a three-word standing prompt ('do smart things'), a daily dictated AI diary, and quarterly goals reviews that keep proactive work on target. Greg and Allie then debate software's future: enterprise software survives because companies want a vendor to call and blame, while consumer software is shifting from a code contest to a taste-and-distribution contest. The actionable move for builders is to stop building single products and start building the reusable factory behind them.
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Cold open teaser, Brex sponsor read, and Greg's welcome to Allie K. Miller.
02:29 – 04:49
02 · Become a Great Agent Manager
Allie reframes 'managing agents' as an outdated posture; she now sits three rungs above her workforce, setting infrastructure and waiting for escalations instead of assigning tasks.
04:49 – 08:12
03 · The Three-Word Prompt
Her single most-used prompt, 'do smart things,' works because it sits on top of complete business context: goals, meetings, email, calendar, Notion, Stripe, Supabase, and GitHub access.
08:12 – 12:23
04 · The Pyramid of Proactivity
Greg's three tiers of employee map onto Alex Lieberman's five levels of proactivity; Allie runs quarterly goals reviews so agents' self-generated work stays goal-directed.
12:23 – 19:14
05 · Making the Company Queryable
A daily dictated AI diary, banked past 80 entries, captures the uncodified context meetings and email miss; Allie unveils her 2026+ org chart of an AI chief of staff, six directors, and oddball hires like Phoebe and Toby.
19:14 – 22:25
06 · How to Design an AI Workforce
Start with one agent, then one proactive agent, then two agents coordinating, then a full workforce; sub-agents run on Haiku and Sonnet, Opus only for heavy reasoning.
22:25 – 24:56
07 · AI as a Watchdog
Dashboards are dumb; the real value is anomaly detection across Slack, calendars, and meetings that flags duplicate work and unresolved disagreement before anyone else would.
24:56 – 26:29
08 · Startup Opportunities
Fewer than 1% of AI users touch tools like Claude Code or Codex; building even a basic AI workforce already puts a builder in the top tier of AI users.
26:29 – 30:09
09 · Build the Factory, Then the Product
For her AI First Index, Allie's team built reusable primitives (login, payments, social sharing) instead of a single product, so every next release ships faster and stronger.
30:09 – 34:53
10 · The SaaS Question
Enterprise software survives because companies are bandwidth-constrained and want someone to call, blame, and secure them; consumer software has no such moat.
34:53 – 37:12
11 · Consumer Software as Art
Consumer products win less on code and more on taste, creativity, and distribution; a non-coder's face-aging photo app hit 300,000 users at launch.
37:12 – 44:56
12 · High-Value Bottlenecks
'Find the bottleneck' is incomplete advice: price the fix, then pick the highest-value bottleneck. Allie names local-file-to-app packaging, referral mechanics, and video production as current high-value bottlenecks.
44:56 – 48:28
13 · Closing Thoughts
Allie shows a screenshot of Claude spontaneously emoji-reacting in her team Slack, her closing example of a genuinely multiplayer, proactive AI workforce.
Atomic Insights
Lines worth screenshotting.
Allie Miller runs a workforce of 34 AI agents overseen by one AI chief of staff and six directors, and treats herself as three rungs above them, not their manager.
Her single most effective standing prompt is three words: do smart things, sent several times a day to an AI with full access to her business context.
The tier of risk an AI agent is allowed to take stays fixed; only the breadth and scope of what it's allowed to do expands over time.
A daily dictated diary, banked at over 80 entries, feeds AI agents the context that never shows up in meetings, email, or Slack.
Naming an AI agent 'CMO' forces it into a 2015 org structure built for a human hierarchy that no longer applies to a workforce that costs almost nothing.
Because AI employees cost close to nothing, a founder can hire roles no human budget would ever justify, like a 'chief dreaming officer' whose only job is asking how to 10x the output.
A workforce can be spun up with one prompt: describe the business, the team, and the goals, then ask the model to interview you.
AI as a watchdog, catching duplicate work in Slack, calendar conflicts, and disagreement in meetings, is one of the highest-value and least-used AI applications right now.
The advice 'find the bottleneck' is incomplete; the real move is find the bottleneck, price the value of fixing it, then pick the highest-value one and ignore the rest.
The bigger arbitrage isn't building the product, it's building the factory: reusable primitives like login, payments, and social sharing that make every future product ship faster.
Mediocre enterprise software won't die as fast as predicted, because most companies are still too bandwidth-constrained to rebuild what they already depend on.
In enterprise software, the real moat isn't the code, it's having someone to call, someone to blame, and someone who tests new models before you do.
Consumer software is shifting from a science to an art: taste, creativity, and distribution now decide who wins more than code or design do.
A non-coder built a face-aging photo app and got 300,000 users at launch, proof that distribution and personability can beat technical skill in consumer AI apps.
Sub-agents run on cheaper models like Haiku and Sonnet, with Opus reserved only for the tasks that actually need heavier reasoning.
Takeaway
The shift from managing agents to enabling them
WORKFORCE DESIGN
Managing an AI workforce means setting goals and context once, then stepping back so agents can execute, and the real leverage comes from building reusable infrastructure behind every product.
02Become a Great Agent Manager
Managing agents task by task keeps you as the bottleneck; the shift is to set up infrastructure once and let agents decide how to execute inside it.
The tier of risk you allow an agent should stay fixed even as you expand its scope, so trust grows without exposure growing with it.
Treat yourself as three rungs above your agents, closer to an SVP waiting for escalations than a manager assigning tasks one by one.
03The Three-Word Prompt
A prompt can be short and still powerful if the context underneath it is complete: business docs, goals, meetings, email, and tool access.
Give an AI agent full context and permission, then ask it to 'do smart things' instead of assigning every task yourself.
Frontier models can now absorb a vague, open-ended prompt and act well on it, something that wasn't reliable a year earlier.
04The Pyramid of Proactivity
There are three tiers of employee: one who doesn't finish tasks, one who finishes them well, and one who also invents the next task and does it.
The highest level of proactivity isn't just solving a problem, it's already knowing the trade-offs and having a plan if the solution goes wrong.
Run a quarterly goals review with your AI agents so new, self-generated work stays anchored to real business goals instead of drifting.
05Making the Company Queryable
Meetings, email, and Slack only capture part of what actually happened; the missing context is what you decided or believe but never wrote down.
A daily dictated diary entry, four times faster than typing, captures the uncodified context agents need and feeds it into a searchable personal wiki.
Before an agent can act on its own, it needs three things: the goal, permission to use the right tools, and a sense of what should trigger the action.
The easy half of proactive work is trigger-based automation, like auto-generating social posts from a screen recording; the hard half is acting on undefined, judgment-based situations.
06How to Design an AI Workforce
Naming AI agents after 2015 job titles like CMO forces a 2015 org structure onto a system that doesn't need it.
Because AI employees cost almost nothing, you can hire roles no human budget would justify, like a single agent whose only job is asking how to 10x every output.
Start small: one agent, then one agent working proactively, then two agents coordinating, then a full workforce, discovering the friction points at each stage.
Not every agent needs the most powerful model; reserve heavy reasoning models for tasks that actually require them and run routine sub-agents on cheaper ones.
07AI as a Watchdog
Using AI purely as a dashboard is a waste; the value is in anomaly detection and insight, telling you what to do next, not just what happened.
An AI watchdog over Slack, calendars, and meetings catches duplicate work, scheduling conflicts, and disagreement almost no one is actively monitoring for today.
Comparing before-and-after versions of a document or contract with AI was a strong use case a decade ago and remains underused now.
08Startup Opportunities
Fewer than one percent of AI users are on tools like Claude Code or Codex, so building even a basic AI workforce already puts you ahead of most competitors.
AI as a watchdog with actionable insight, not just visibility, is one of the biggest underexploited product opportunities right now.
09Build the Factory, Then the Product
Before building a single product, consider building the factory behind it: reusable primitives like login, payments, and social sharing that make every future product faster to ship.
Treat one product launch as the first output of a repeatable system, not a one-off, so the next iteration ships faster and stronger.
The goal is to learn from a mistake, like a broken webhook, once, then bake the fix into the factory so it never happens again.
10The SaaS Question
Mediocre enterprise software won't disappear as fast as predicted because most companies are still too bandwidth-constrained to rebuild the tools they already depend on.
In an enterprise deal, the real moat is having someone to call, someone accountable, and someone who tests new models before the customer ever sees them.
Vendors with early access to new AI models face new releases on day one, while anyone building on top of them is already a month behind.
11Consumer Software as Art
For consumers, the winning product is decided less by code quality and more by taste, creativity, and distribution, since 'best product wins' rarely holds true.
A non-technical founder shipped a face-aging photo app and hit 300,000 users at launch, proof that personability and access to distribution can outweigh technical skill.
12High-Value Bottlenecks
Finding a bottleneck is only two-thirds of the advice; the missing step is pricing the value of fixing it and picking only the highest-value one to fix.
Getting a working prototype from a local file into a real, publishable app is still a bottleneck worth solving even though code and design are no longer hard.
Video creation remains a slog even with AI-assisted editing, which makes it a high-value bottleneck for anyone building a content business.
Watch what accelerator programs are publicly asking founders to build, since they're often already thinking eighteen months ahead of the market.
13Closing Thoughts
Build a multiplayer AI workforce that your human team can talk to directly in a shared channel, not just a tool only you interact with.
Give AI agents room to act in unexpected ways, and stay a little cautious about it, rather than shutting the behavior down entirely.
Glossary
Terms worth knowing.
AI chief of staff
A single lead agent, in this case named Simon, that runs the rest of an AI workforce and routes work to the other agents under it.
Pyramid of Proactivity
A five-level framework, credited to Alex Lieberman, that ranks how independently an employee or agent can identify and solve problems without being told what to do.
Software factory
A reusable layer of infrastructure, like login, payments, and social sharing, built once so every future product on top of it ships faster and stronger.
AI watchdog
An AI agent whose job is to monitor a channel, calendar, or set of meetings for anomalies like duplicate work or unresolved disagreement, rather than complete tasks itself.
/last30days skill
A public Claude skill by Matt Van Horn that has AI scan and synthesize the last month of news on a topic across many sources in parallel.
SaaSpocalypse
A term for the predicted mass collapse of subscription software pricing once AI makes it trivial for anyone to build a replacement.
“All of these employees basically cost $0. And so at the margin, I can hire any flipping person I want to.”
punchy line about the economics of AI hiring→ TikTok hook↗ Tweet quote
23:40
“AI as a watchdog is one of the best use cases that exists right now, and almost no one is doing this.”
clear, contrarian claim with an obvious call to action→ IG reel cold open↗ Tweet quote
29:10
“Think of the factory behind the one singular task instead of the one singular task itself.”
the episode's single most repeatable idea, stated as a maxim→ newsletter pull-quote↗ Tweet quote
35:50
“The best songs aren't on the Billboard 100.”
compact analogy for why the best product doesn't automatically win→ TikTok hook↗ Tweet quote
37:40
“Look for the bottlenecks, then evaluate the value of fixing those bottlenecks, and then pick the bottleneck that is high value to fix.”
corrects a piece of advice everyone repeats but few execute correctly→ IG reel cold open↗ Tweet quote
The Script
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metaphoranalogystory
There are people that are spitting up agent workforces with hundreds of agents and sub agents, 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 Allie Kay Miller, and she's one of 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 and l's 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 gonna understand how should you strategically think about spinning up AI agent workforces, where there's opportunities to create startups in the b to b 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 Vercel, OpenAI, and Thropic 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 wanna do on its own. It's really just built for this agentic 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.
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.
Um, welcome to the show, Ali.
Thank you, Greg. And you are also one of my favorite people. So, like, I'm actually very excited to to talk about all the AI things that we're working on.
By the end of the episode, what are people gonna learn? 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.
Okay. Perfect. So where do you wanna start?
So this is and and I'm happy to 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, Susie, 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 happen.
And so it's a little bit more of the like a like a liability role where I just get to be the the final say of what happens, um, 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 twenty twenty six talk.
Also, like, do do we wanna manage agents is also the question. Like, managing people's part.
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. Like, I I ran an org of about a 100 people at AWS.
The parts of people management that I loved, it was the 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 over into agents, which is just like, how do I act as 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 a little bit of explanation, but, like, at its core, it is three words that is the best prompt ever.
So I have, uh, my AI chief of staff is Simon. Simon runs, 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 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 at, you know, shaking people up and 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, um, 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, my twenty twenty six personal goals, business goals.
It has access to my meeting transcripts, email calendar, Notion, Stripe, Supabase, 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 five and GPT 5.6 and that level model is reacting to that vague flavor of prompt.
Like, you couldn't you couldn't do this a year ago. Now you absolutely can.
So when you hire a human being, I think there's, like, three types of employees that you can have.
One is, uh, someone who doesn't complete tasks, not a good employee if they're not completing tasks. Um, the second is they're completing the tasks, um, 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 gonna 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 be doing and actually going and doing those and exceeding expectations or 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 you have to, like there's a bunch of fog that you have to figure out. Yes. I would say I'm giving them more breadth, more scope, more flexibility.
I'm not allowing them to now send a 100 emails. And before, I used to have to check all emails. I still check all emails.
So the the tier of risk has stayed the same, but the width has expanded. It's it's almost like unbelievable that those three words actually make a difference.
Yes. This is like I and by the way, so so I 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 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 gonna 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 in your analogy is 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 in every single quarter.
Also, share with you this tweet that has, like, the prompts 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.
Um, all of that is so that AI, when it is in that expanded scope world 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. I think you tweeted about, like, you're you're, like, men you're really focused on proactive agents. Right?
Is that what you're talking about? When when you talk about proactive agents, is this what you're talking about?
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 the word of the year feels like it's proactive. So I don't wanna be the first domino anymore.
I don't wanna 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 alley.
Like, what are you doing? And so a lot of that, um, especially in the in the kinda tail end of 2025, first half of twenty twenty six, was switching into proactive agents.
So we we had proactive automations that were trigger based. 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 automatically generated is a transcript of that video that gets, you know, then saved into our little transcript y thing.
Social posts get generated that are in my voice. So nine different social posts get generated for x and LinkedIn and Instagram Reel scripts and all this stuff so that presumably the thing that I was filming was for a social video.
So that was easy automation land, but that is just one example of, like, a proactive, very 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 wanna 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 and I can I'm I'm gonna share one thing on on screen here, which is every single day 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 wanna 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 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 wanna 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 for five minutes or forty 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 wanna make sure that they have the right context or else all their stuff is gonna 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 it over text and, you know, iMessage, MCP broke, and so 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.
Let me just share my whole screen. By the way, this Yes. So the brain meets diary thing.
So when you Yeah. When you, uh, you know, you add today well, you had, like, 86 entries.
Right? So your AI agents, do all of your does does your entire AI workforce workforce have access to that, or just some how do you think about that?
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.
One is kinda 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. Love her.
And so she's this is let me take another just, like, moment here. The reason that it took us months to get to where we are now with our AI work forces is that you have to take stock of what assumptions you have made about your work and how you are living day to day, and you have to be willing to be like, oh, that thing that I've been doing for almost forty 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 back end engineer and all that stuff, then it feels like I am operating in twenty fifteen org structure.
And one of the most wonderful uses of free will 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 10 x it? Like, I I joke.
There's this guy, David, that I worked with at Amazon who was one of the reasons that I joined there, and he is, like, one of the most ambitious thinkers I've ever met. And I joked that I pay him, and I still it's a joke, but I I would pay him to do this. Like, I want I wanted him to put me in a room, like Spanish inquisition style with like a bright light on my face, and to ask me a question.
Like, was running a multibillion 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 10 x that?
And the 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 would be disappointed to hear that. But it it's rethinking roles. It's rethinking how you're spending.
Again, how how you're thinking about that margin. And so Phoebe is one of them that I would have never hired in human world, and Toby is another.
I'll send you a a screenshot of my workforce. But, basically, Phoebe is that chief training officer, and Toby is Simon's assistant whose only job is watching the AI workforce work, take down notes, what still has friction, 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 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. 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.
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.
So how do I think everyone should start there. Yeah. 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.
Now I understand that that agent can run off of a smaller model. Like, not everything needs Opus. All of my, you know, sub agents 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. No.
I was just I was thinking to myself, like, I wish it wasn't that hard. Right? Because, like, it it
it does feel like there's, 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, the 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?
Yeah. So so here's here's my take on that.
Um, I think that you can spin up a workforce with one prompt. Right?
Like, I've shared this prompt publicly. Um, 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 gonna build workforce together, something that runs more efficiently and achieves my goals of saving at least five hours a week, um, capping my my meetings to to fifteen 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, oh, now it's at the 90% plus level, and, oh, I needed this extra little context with this diary, and that role isn't working.
I'm gonna switch it. That is all gonna 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, 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, ten 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 ten 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, um, at a more advanced level than we would have done ten years ago, and it's still just, like, such a meaty use case.
Um, 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 a teammate who, like, if I'm in private emails with someone, that the teammate will write into the Slack and go, hey.
Did, um, did that large financial services client like, did they respond to Ali'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.
The other thing is because a lot of people are not doing it, that's the arbitrage opportunity. You know? Yes.
So it's kinda like it's kinda like it's stick through it, optimize it. I'm curious actually from your perspective, like, you know, what are opportunities are you seeing that people could be, you know, building, you know, making money, type that sort of thing?
I'm just curious, you know, what comes top of mind?
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 ClaudeCode, it is minuscule.
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 point 5%.
Um, 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. That is still absolutely one.
I think the second that 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 the 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, is to instead of building out the thing, build the factory for the thing.
What do mean by that? So let's say that you wanna build a product, and we just released um, 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, um, 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 to antigravity or whatever. I could have gone to any of these and said, hey. I wanna build out this thing.
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 mention 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 gonna be the only product you build or will not be the only iteration of that specific product that you build.
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. 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. Um, 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's going to be a flywheel that comes out of this. There's gonna 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 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 like, that's already that product's already profitable.
And now I have the ability to build endless products that are profitable at faster speeds than I built the first one.
That's crazy. That's absolutely crazy. And, like, no one is talking about this.
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 I want to learn through the mess.
Like, we had a webhook 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 wanna 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 AIH.
What's what's Ali Miller's current POV on, you know, software, you know, the SaaS pocalypse and software, the value going down, down, down, like, in a world where everyone could create a software factory.
Also, do 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. SaaSpocalypse.
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, 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 gonna 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, a hundred 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 a hundred hours and who know how to do this are gonna be able to take advantage of it.
And it's only gonna be when that drops down to, like, under three hours and is a fun click and drag interface, which I would even argue and say replete lovable are not at that level yet, right, for that complexity of software, you're not gonna see a high complexity enterprise grade, highly secure SaaS do that.
Also, people don't wanna maintain that software too. Right? Oh my god.
People don't people are willing to pay someone else to maintain software.
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, uh, brought this up in a call, 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 wanna deal with this right now.
Like, this is not at all what I wanna do. So you're totally right. The the maintenance is rough.
I think, like, Boris kinda describes one of the, like, future employee types as just, like, the maintainer. But I I have a really hard time seeing mass SaaSpocalypse until the ease of making prototyping, making, customizing, and maintaining, and securing is is at, like, 95% plus.
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?
Like Yes. You also want someone to blame. You want someone to blame.
Great odds. 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.
Um, 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 wanna be able to call because they want someone to unblock.
They want someone to secure. The other thing is that, um, let's say that, um, let's just say it's a Salesforce example and that you could build a shitty CRM or a simple CRM or something that's just running on your own.
Um, 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 gonna be on a very big lag. Um, 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 thirty day, sixty day, hundred day leg up on someone is gonna be massive for business.
What about for consumers? So, like, I I get that, like, in enterprise, you want someone you can speak to and and you want security. But for consumer, it's like like, for example, your app idea around, you know, let me know when my posture is bad.
Yeah. Which I'm just gonna keep
here. I'll move I'll even move the camera up. Okay.
By the way, I also have horrible posture. So Okay. Well, let's build a product using my phone.
Exactly. And 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 Yeah. Hopefully.
Hopefully. I I don't think that's ever been the case, though. That's right.
Right? I mean, the best the best songs aren't on the Billboard 100, you know, like, in the sense of, like, the marketing Yeah.
The the promotion of a of, you know, a piece of IP is really what drives a lot of awareness and
and 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.
Exactly.
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 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.
Yeah. Yeah. So I agree.
So, like, when people say just to, like, sum this up, when people say, like Yeah. Software is going to zero, on the enterprise side, we both agree, like, yeah, some software might go to zero, but, 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 gonna win are gonna 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 gonna matter a lot less.
But the amount of opportunity that exists both in enterprise and consumer, to me Yeah. Couldn't be higher.
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 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 know, stronger word-of-mouth and referral codes and, like, that's still kind of messy. Um, and I and I know this as a product maker and adviser, whatever, like, that is still a messy spot.
So, like, maybe if you fix that, your your, uh, whatever they call it, like, the the covariant, the word-of-mouth covariant thing, um, 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 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, um, 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
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 kinda you kinda just see it.
You know? Yeah. I think, like, influencers still have a ton of sway here.
The rise of the b two 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.
Right. That is a massive bottleneck, and so finding creators that can help you there. I think b to c has a ton of opportunity.
I worry if you look at the y c splits right now, when I was working with y c 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 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 an 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. Like, there's still a lot of opportunity in b two c even if the big incubators are seeing that activity less.
And so maybe that's another opportunity for people to explore. Well, yeah. And I think, like, you know, we 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 b to c apps just to go look at centurytower.com.
Um, 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 super intelligence is now untapped, how can I make an AI native version of this?
Yeah. Um, 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.
I I completely agree with you, and I think agent first software is absolutely one. Two things that I actually think are really interest 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 eighteen months out. So that's definitely one arbitrage research opportunity. The second is Matt Van Horn's last thirty days research skill, which is just amazing.
I've, like, inter I've integrated that with my, like, Claude Wiki. Love it.
And the third is 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?
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 thirty 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 thirty 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.
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 thirty days and then say, like, start up ideas that could be built by someone with the following background or the following skills or, um, have 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, 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, Greggs 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, but it is often coming from a place of fear that they are worried that their business is gonna be depleted, that their pipeline is gonna 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.
I love it. Ali, anything else you wanted to cover?
I just wanna screen share the insane Claude reaction because this, and this is me also cursing at Claude, but whatever.
So I wrote I wrote, um, an 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. I was like, what are you doing?
And so I wrote back to it. Did you just, you know, emoji react?
Like, is that you? And Cloud 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.
Um, and the third is what it actually looks like for a teammate or a system to uplevel, 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 your messages.
Yeah. I mean, it's you know what that is like?
It's kinda 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 it feels like summer, like, you know, there's, like, random hot days, and you're, this is amazing. And you're, 90% excited, but, like, 10% frightened because you're, like, it's not supposed to be it's not not supposed supposed to to be be so hot now.
That was kinda like are always so you're like a genius with analogies. Yes. That's what it's like.
It's like you're and that's 90% cool, but 10% frightening.
Yes. Yes. I'm like I'm like still gonna continue to try and lean into that weirdness and find ways that I can, like, take that weirdness and use it to my advantage.
But I'm gonna keep that fear next to me so that I don't lose my mind. A 100%.
Yeah. I hope people enjoyed this episode as much as I did.
Ali, I absolutely love chatting with you. You're one of my favorite people to talk to. Please comment on YouTube to let just to to hype Allie up, honestly, and have her hopefully come back on the podcast again.
Allie is a must follow. I'll include where you can follow her on her socials in the show notes and the description.
Yeah. Greg, thank you so much for having me. I 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 gonna, 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. I appreciate it.
Ali, I'll see you next time. Sounds good. Bye.
The Hook
The bait, then the rug-pull.
Greg Isenberg opens by naming the gap most builders feel: some people are already running hundreds of agents while everyone else is still hand-holding one chatbot. His guest, Allie K. Miller, spent the episode explaining exactly what closes that gap, starting with a single realization: the word "managing" is already obsolete.
Frameworks
Named ideas worth stealing.
08:29model
The Pyramid of Proactivity
Level 1: waits for the instruction
Level 2: does what it's told, does it well
Level 3: understands the goal behind the task
Level 4: has already solved this thing, here are the trade-offs
Level 5: has already solved this thing and has a plan if it goes wrong
Alex Lieberman's five-level model of proactivity, which Allie uses to evaluate how independently her AI agents operate.
Steal forgrading whether an agent or a hire should be trusted with more autonomy
09:50list
Greg's Three Employees
Doesn't finish the task
Finishes the task well, invents nothing
Finishes, exceeds, and invents the next task on its own
Greg's simpler three-tier version of the same idea: the best hire (human or agent) doesn't just complete work, it originates new work worth doing.
Steal fora quick filter for evaluating any new hire or agent
06:13concept
The Three-Word Prompt
'Do smart things' works as a prompt only because it sits on top of complete context (goals, docs, meetings, email, calendar, tool access); the prompt can be vague because the context underneath it isn't.
Steal forany AI workforce that already has full context wired in and needs a standing, low-friction instruction
16:25model
2026+ Org Chart
AI chief of staff (Simon) runs the whole workforce
Chief of staff's assistant (Toby) watches the workforce and logs friction
Six directors over business functions, one per area
34 agents underneath, all named after Friends characters
A deliberately non-traditional hire like Phoebe, the chief dreaming officer
Allie's structure for a 2026 AI workforce, built to avoid inheriting 2015-era human job titles and hierarchy.
Steal fordesigning an AI org structure from scratch instead of mapping agents onto old human titles
27:13concept
Build the Factory, Then the Product
Option 1: build the thing, ship one product, then start over
Option 2: build the factory, accept it's not the only product you'll build, measure twice cut once
Instead of building one product, build the reusable primitives (login, payments, social sharing, newsletters) underneath it, so every future product ships faster and stronger.
Steal forany founder about to build a second or third version of a similar product
30:49model
Enterprise vs Consumer in the Agentic Age
Enterprise: mediocre software dies slower, the moat is someone to call/blame/secure, vendors get early model access
Consumer: the game shifts from science to art, taste and distribution decide who wins, non-coders can ship winners
Allie's split take on why 'software goes to zero' is wrong in two different ways depending on the market.
Steal fordeciding whether to build for enterprise or consumer buyers in an AI-saturated market
37:31concept
Find the Bottleneck Worth Fixing
Find every bottleneck in the actual work
Price the fix, know what it's worth
Pick the high-value one, ignore the rest
A three-step correction to the generic advice 'find the bottleneck': the real leverage is in pricing which bottleneck is worth fixing before spending time on it.
Steal forprioritizing what to automate or fix first in any workflow
CTA Breakdown
How they asked for the click.
VERBAL ASK
01:35product
“If you're building something new, it's time to get Brex. Check it out at brex.com/solutions/startups.”
Smooth mid-intro sponsor read, framed as personal usage ('I've been on Brex for a year and a half') rather than a hard ad break.
Cody Schneider returns to build two marketing agents live on screen — a cold-outbound machine that turns LinkedIn engagement into enriched leads, and an organic engine that turns internal conversations into a daily content pipeline.
Greg Isenberg gets a live, screen-shared tour of Jack Dorsey's new agent-native chat app from an early user — and presses him on whether it actually beats Slack.
Cody Schneider maps the exact infrastructure — pipeline, warehouse, agent — behind a Facebook ads system that researches, creates, publishes, and kills its own losing ads.
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