17:05Rick Mulready · Tutorial
7 Mind-Blowing Use Cases for Hermes Agent
How a free AI agent builds persistent memory about your business and runs parts of it for you.
June 11thThe argument in one line.
Decomposing any human job into discrete workflow steps is the only mental model you need to build an AI agent that does that job, and the leverage compounds the higher up the org chart you go.
Read if. Skip if.
READ IF YOU ARE…
- You run a business and want to reduce the cognitive load of tracking 15 simultaneous workstreams.
- You are already making content but the ideation-to-publish pipeline eats hours you do not have.
- You build SaaS products and want an autonomous loop from usage data to shipped feature without a full engineering sprint.
- You want to understand what real enterprise AI agents look like beyond generic ChatGPT prompt demos.
SKIP IF…
- You want step-by-step no-code tutorials — this video operates at systems-design level, not a build guide.
- You need tools available today. Stanley is proprietary and not yet publicly released.
The full version, fast.
A $40M company CEO demonstrates three proprietary AI agents: Stanley, a content agent that sources viral ideas from competitors and Reddit, remixes them in the creator's voice, generates carousels from personal photos, and auto-schedules posts; an AI chief of staff connected to Slack, Notion, and meeting transcripts that delivers a daily briefing, maintains a red/yellow/green scorecard, and proactively runs SQL analysis without being asked; and an autonomous product manager that reads usage data, billing, and support email to surface feature ideas, write PRDs, and prompt coding agents to build and A/B test without a human touching code. The unifying mental model: decompose any human job into its constituent workflow steps, then give those steps to an agent with the right context.
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00:00 – 01:30
01 · Introduction — the anti-hype premise
Establishes credibility ($40M company, $400M enterprise value) and frames the video as a counterpoint to generic AI content. Previews all three agents.
01:30 – 05:10
02 · Agent #1 context — why content is hard at scale
Explains the problem: consistent content creation is harder than it looks. Introduces the three-ingredient framework: ideas, production, distribution plus weekly review.
05:10 – 09:37
03 · Stanley demo — ideate, remix, carousel, schedule
Live walkthrough of the Stanley dashboard: sourcing outperforming content, pressing 'make it mine,' watching a carousel generate with personal photos, scheduling across platforms.
09:37 – 09:37
04 · Stanley video editing demo
Records a talking-head clip, uploads to Stanley, watches the auto-editor select best takes and stitch a final cut.
09:37 – 15:50
05 · Agent #2 — AI Chief of Staff
Daily morning briefing, red/yellow/green scorecard, proactive Slack messages with SQL retention analysis the CEO did not request. Built on top of OpenClot (OpenAI) with access to Slack, Notion, and Granola meeting transcripts.
15:50 – 20:43
06 · Agent #3 — Autonomous Product Manager
Usage data + billing + support inbox feed feature suggestions in Slack. Agent writes PRDs, triggers coding agents to build, A/B tests to 5-50% of users, monitors outcomes and reports back. Closes with the 'audacity of vision' motivational close.
Lines worth screenshotting.
- The secret to a great AI agent is having it own the entire workflow end-to-end, not just one step in the middle.
- Reddit is one of the most underused sources for real-time pain points your customers are already articulating publicly.
- An AI chief of staff that surfaces blockers proactively is worth more than one that just answers questions on demand.
- Giving an agent access to Slack, Notion, and meeting transcripts creates enough business context for it to run SQL analysis like a senior data scientist without being asked.
- The biggest unlock is not automating tasks you already do — it is having an agent do analysis you never had time to commission.
- A/B testing a feature built entirely by coding agents and monitored by an AI PM closes the product loop without a human engineering sprint.
- Decompose the job before you build the agent. AI chief of staff only works if you first write down exactly what a chief of staff does step by step.
- The resources barrier to building product teams is gone. The only constraint left is the size of your vision.
- An agent trained on your voice and personal photo library produces content that is harder to distinguish from your own than most human social media managers could.
- Proactive agents that send you findings you did not ask for generate more leverage than reactive chatbots by an order of magnitude.
- The weekly content iteration cycle — review performance, feed it back into the next slate — is the compounding mechanism most creators skip.
- Feature ideas sourced from correlated retention signals are higher signal than ideas sourced from vocal user complaints.
Decompose the job before you build the agent.
WHAT TO LEARN
Every AI agent that generates real business value was designed by someone who first wrote down the exact steps a human does in that role, then handed each step to a model with the right context.
- Map the job first: list every recurring task a person in that role does, from sourcing ideas to generating deliverables to sending updates. The agent can only automate steps you have explicitly named.
- Context is the unfair advantage: agents fed with Slack history, meeting transcripts, and live product data outperform generic LLM calls because they carry business memory the model otherwise lacks.
- Proactive beats reactive: an agent that surfaces a SQL analysis or a retention insight you did not ask for produces more leverage than one that waits for prompts.
- A weekly review loop is the compounding mechanism most creators skip: reviewing what performed, feeding that back into the next slate, and letting the agent re-calibrate is what separates growing audiences from stagnant ones.
- A/B testing closes the product loop: shipping a new feature to 5-50% of users and letting an agent monitor the outcome replaces a multi-week manual analysis cycle with an always-on feedback engine.
- The job decomposition mental model transfers to any role: content head, chief of staff, product manager. If you can write down what that person does step by step, you can give those steps to an agent today.
Terms worth knowing.
- Stanley
- Stan's proprietary AI content agent. Sources viral ideas from competitors and Reddit, remixes them into the creator's voice, generates carousels from personal photo libraries, and auto-schedules posts across platforms.
- Job decomposition
- The practice of listing every subtask a human performs in a given role, then converting each subtask into an automated workflow step for an AI agent to execute.
- Granola
- A meeting transcription tool that records and summarizes team calls; used here as the source of meeting-notes context fed into the AI chief of staff.
- OpenClot
- The speaker's informal name for the AI runtime powering the chief of staff agent (based on OpenAI); used to describe the orchestration layer with access to Slack, Notion, and meeting transcripts.
- PRD (Product Requirements Document)
- A document that specifies what a feature is, how to build it, and how to measure success. Historically written by a product manager; here generated by the autonomous PM agent in one click.
- A/B test (agent-driven)
- Shipping a new feature to a subset of users (5–50%) and having the AI PM monitor whether outcomes improve, then reporting results back for a final human decision.
Things they pointed at.
Lines you could clip.
00:06
“I am so tired of seeing all these AI hype FOMO driving videos where they say if you are not using AI in all these ways you are missing out millions of dollars, and then they show you really generic high level ChatGPT prompts.”
14:12
“What your job is to do in today's world is to compose all the things that you spend your time on, turn that into a workflow, and then give that to an agent that can then just do that for you in your sleep way faster and likely way better than you can.”
19:40
“The main thing now stopping you between the life that you are living now and the life that you want to build for yourself is no longer about access to resources. It is the audacity of your own vision.”
Word for word.
Read-along
Don't just watch it. Burn it in.
See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.
metaphoranalogy
00:00I'm gonna show you three AI agents that I use that have made me three times more productive as the CEO of a $40,000,000 company. And I'm gonna do so because I'm so tired of seeing all these AI hype FOMO driving videos where they say, if you're not using AI in all these ways, you're missing out millions of dollars, and then they show you really generic high level chatty biting prompts.
00:19Instead, my team and I have had to put so much hard work devoting AI agents that had generated real actual business value at a scaled enterprise worth $400,000,000 and not some overhyped Claude Maxey.
00:32And so number one, I'm gonna walk you through our automated content agent that is ideating content ideas on our behalf, scripting those, editing those on our behalf, and then distributing all that content to all of the different platforms to three x our content output and also increase our engagement per post by 50%. Number two is the AI chief of staff that I built for myself that has made me at least two to three times more productive, and more importantly, way less stressed as a person because I no longer have to keep track of the 15 different things that are going on in my business.
01:00Instead, I have an AI agent that's actually pushing the ball forward on all these different work streams without me. And then lastly, something experimental that we're working on that I genuinely think will change the game of product development where we now have an automated product manager who is watching how all of our users use our product and then is autonomously coming up and suggesting feature ideas before building those feature ideas and then shipping and testing those and sending us the results.
01:23So we have an autonomous product manager who is improving our product for us without us needing to be in the room at all. So let's get started with agent number one, our AI head of content. So the problem right now is that everyone's saying you should be making content to go viral to get distribution for your business.
01:38But the reality of actually making content and going viral and consistently building following is way freaking harder than that. Like, how do you make good content? How do you make sure it goes viral?
01:46How do you consistently create content when you're so busy with the rest of your life? It's much easier said than done. But the benefit of getting to sit in the seat that I get to sit in and the job that I have is that not only have I gotten to build my own following, but I've also now seen the data behind hundreds of thousands of creator entrepreneurs and seen the patterns that work on top of working with the very best creator entrepreneurs out there.
02:06So the Stephen Bartlett's, the Hormozies, the Gary V's of the world, and seen and understood their workflow and how they think about repeatedly creating great content down to a science, where we've basically taken my million dollar per year content that I currently invest in, and we've packaged and productized all of our insights and workflows into an AI agent, where when you think about the key ingredients required to build a following and distribution for yourself online, those key ingredients are number one, coming up with really good content ideas.
02:33Number two, editing and producing those super well. And number three, actually going out there and publishing those, and then using a weekly iteration cycle to review the performance of your content ideas to inform the next week's slate of content to make sure that you're creating better and better content over time. And so we've reverse engineered all of that workflow into an AI agent and a command center that we wake up to every single day.
02:56And so on our command center, our AI agent that we call Stanley has populated all the different variables we need to make the right decisions every day with our content. AKA number one, our agent is constantly sourcing the outperforming content in our market, where Stanley understands our particular content and our particular niche, and then it's going out to the universe of all other content out there and the top performers in our space, analyzing which pieces of those content fit our format to the kinds of formats of content that I make, and then it's suggesting those so that I can just press make it mine, and boom.
03:27It will literally take that video or take that carousel post and remix it into my specific voice language and style. AKA, it takes a proven outperformer in the market with validated data and then remixes it and writes a script specifically and uniquely for me, which if you're in the game of making content, you know that this is one of the top tactics that all the top creators and entrepreneurs use.
03:48But then on top of that, it's also stoking our own internal creativity, where it's constantly every day sourcing all of the trending news and topics within my specific niche, which is this creator entrepreneur investor niche, and flagging different interesting topics that we could talk about at any time. And sourcing this not only from Google searches and news, but then also Reddit, which is a really fantastic place to get great ideas where your customer is already talking about their number one pain points or the number one topics that matter to them right now.
04:14And then lastly, because our agent is so trained on my unique voice and story, it's coming up with a list of daily ideas every day that we can execute that I get to be the CMO of, just review as if it's just like on my iPad and I can swipe and say, I wanna do this one or I don't wanna do this one. And from here, the workflow gets so much freaking cooler, which is I'll take this first video here, which is that 90% of VCs goes to me before I raise $5,000,000.
04:38The deck wasn't the problem. We're I'm gonna take the idea that it's suggesting for me here, which is actually pretty good. I'm gonna say, make it mine.
04:45Let's make it. And what's gonna happen from here is it's going to land on my content calendar within this operating system we've built. And on this content calendar, I'm able to either generate a full script if I'd like to do it real, or on top of that, it will actually make carousels for me.
05:00And I'm not talking those cheesy camera graphic carousels that that look like AI slot. It's gonna make me a carousel with all of my favorite photos. So it's going to write copy in my particular voice and style.
05:11I get to edit it, and now it's going to pick my favorite photos from my photo albums that match the exact context of what I'm talking about. So here, we're talking about VCs and investing. Let's actually see what photos it picks from me.
05:22Damn. This is actually pretty good. So in this, you'll see that it's generating me a full carousel with all the lines here that is actually my specific story.
05:31So I was some kid from North Carolina, immigrant mom, no connections tech. It's actually picking the right frames that match the specific context. And so here, as the CMO of my brand, I'm able to go in and change the text, change the style, move it around a little bit.
05:43But what's really, really cool is our agent has started to understand my particular taste. So it's using the font that I always use in my carousels, and then on top of that, it's matching the photos that best match with the text on screen.
05:55And boom, just like that, truly from just, like, having an idea forty five seconds ago with you to now clicking it, I now have a piece of content that I genuinely would create on my own account. But we're not done here because the secret to making a really great AI agent is you have the agent own your entire workflow. I'm just gonna take this carousel, schedule it on the calendar, have it generate the captions across all the different platforms in my voice, which then I can then tweak and add my specific unique human taste to it.
06:20And then it's just gonna schedule this and post this without me having to lift a finger, which is really, really cool because you're like, holy shit. I don't have to put anywhere near as much work into doing all the muck of creating content where I can just start with really great ideas I either have myself, then I can co create that content with someone where I basically just have a really crack social media intern doing all the hard work for me, and I can just get to press posts and go from there.
06:43And that's just text or photo posts. Right? That helps us do well on LinkedIn or Twitter or some forms of Instagram, but we haven't even talked about video yet, which is the really cool part that my team has been working hardest on with the model that we're building specifically to edit our content for us.
06:58Where let's say I come up with an idea, a shower, on a walk, and specifically that idea is I wanna teach people on how to get your first 100 customers and tell the story of how I landed the first 100 customers for my business stand. And so I have a couple specific angles that I know I wanna hit. Like, I know I wanna talk about the hard work and cold deals that I had to send to get things off the ground, all the first videos I had to do, all the cringe I had to push through, because I know that I can package that story in some sort of way that's gonna helpful to someone.
07:21But the idea isn't fully formed. Right? Like, oftentimes, we have these content ideas, but they're not fully refined on the edges in a way that will make sure that that content performs.
07:28And so I'm gonna come here to Stanley and chat with Stanley here as my thought partners, my second rate, as my as my cracked content intern to help me refine this video. I'm gonna say something like, I wanna make a video on how to get your first 100 customers, and I wanna specifically share the parts of my story that are most helpful.
07:41And I think that these angles will work best, but let me know what you think as well. Like, how can we truly create outperformance here? And because Shanley is trained on both my content and what outperforms in my content, as well as the entire universe of all the content out there, which these standard LMs don't have access to, it's able to really help me punch out my content where it comes up actually with a pretty decent idea here.
08:01I'm able to add it to queue, And so I can now take my phone, put it right here next to the script that I've co written with Stanley, and I'm actually now just gonna film this particular video, and we're gonna see our auto editor actually cut this video together, grab the best takes, say, know I'm about to stumble probably a couple times, and then create a fully packaged done for you video right here.
08:18So I run a business called Stan that does $40,000,000 in revenue. But I wanna remind you that I started from literally nothing. Right?
08:23I started just in my grad school dorm room way back in the day with zero customers. I'm gonna teach you how I land my first 100 customers.
08:35With all that pushing, it will roll down the mountain. Cool.
08:39Alright. So that being said, I'm gonna upload this, and we're gonna see this baby auto edit. What it's doing is it's reading all the different takes that I set the same lines in or I screwed up very admittedly, and it's taking the best one, and now it's parsing and putting those together to now create me an automated finalized video that I can now review and make minor edits myself, add captions if I want to, essentially vibe edit my own video, which is insane.
09:01And then on top of that, choose any of the alternative takes I'd like to because maybe I did like the one before versus the one that it chose. So it's doing all of the work for me and then giving me the maximum flexibility to make those tiny little tweaks myself to make it perfectly in my taste. And so you can start to see how powerful this agent is that we've built here, where it has three x r output across all the different platforms here.
09:22And so stay tuned on this particular agent. If this feels like very complex to build for you, it's been a lot of effort to figure out the models on our end because this is something that we're going to ship to the entire world soon. So the next AI agent that I've built has generally made me feel like I truly uplevel as an executive.
09:37So if you're anything like me in running a business, you're constantly getting hit by 15 different things going wrong in your business, 15 different things that you need to keep track of, and then on top of that, you're like, okay. I was in meetings all day. What did I actually get done?
09:48And so you have to constantly hold all these 20 different things going on in your business in your head to make sure that those things are tracking forward, while also making sure that you know the one or two things that are actually the most important thing for you to focus on and maniacally actually deliver and move the ball forward on those work streams, which gets harder and harder to do the more and more successful you are because your business grows, your team grows, the number of customers grow, the number of people who have opinions that wanna share with you grows.
10:10And so this AI chief of staff I built has been able to take all that noise and just completely compress it and help me understand what are the most important things to do. And then most importantly, is proactive and helps me move the ball forward, autonomously takes actions on my behalf to move the ball forward on all those different worksheets.
10:26So I get to wake up every single day and have a really clean, simple beginning of day text delivered to me that anchors me to, first of all, the north stars of what is most important for us because I've given it our top priorities right now for the quarter. Then it lets me know what are our top three to dos for the day. And then lastly, it's already pre scraped my calendar and seen all the different meetings I have and sending me a briefing to prep me for every single one of these meetings.
10:48And then lastly and the most fun is it also sends me a visual dashboard. So I get to be smooth brained caveman and just look what is green, what is yellow, what is red of all of our different goals, and then get to see what is the blocker or the next step that I need to take to move that ball forward, which oftentimes when it spots a blocker like this, I actually just get to text it, go ahead and do this and this on my behalf, and then it moves it forward.
11:07And so how I bought this is I've given my OpenClot access to all of our Slacks. So there's Slacks specifically that I send or that someone sends me or anything that's going on in any of our different Slack channels between the team. And then I've given this agent access to all of our Notion.
11:20So every strategy document, every Wiki document out there to understand the full context of my business. And then lastly, I've also given access to my granola, which is automatically transcribing all of my meeting notes with my team. So it's able to ingest all the transcripts.
11:33It's able to understand all of the to dos and action items and decisions I've made with my team, and then use all of that context to constantly update our scorecard and visual tracker of our progress. So it takes all of that context, updates something from red to green or green to yellow or green to red, and then because it's smart enough and proactive enough, it's thinking about what is the next step.
11:49And so one of my favorite examples recently was that I was meeting with my data center team and my product team, and we were specifically trying to figure out why our retention for one particular customer setting up was so low. But to do that kind of analysis will take a human being multiple hours to dig into that level of of deep data.
12:04And on its own, my open call sent all the participants of that meeting, including myself, a Slack, really deeply diving into the deepest level of detail of our retention numbers for this particular segment because it figured out that because it had access to our underlying customer data, so who's retaining, who's subscribing, as well as some of their traits as personas, it can actually go out there and just do the SQL queries itself, do the analysis itself, and then actually find clear findings of what different user journeys or what different jobs to be done were actually most correlated to the customer retaining.
12:33And it found all these different insights that truly you would expect of, like, a pretty badass senior data scientist to find. Right? It was already cutting all of our customer cohorts in different ways, by different segments of usage, by different segments of the personas.
12:45And in that, it then ultimately provides a couple suggestions on what we should do next as well as some questions of the team to make sure that it did the job right. And I think this example starts to show you just how powerful these agents could be.
12:57Like, imagine your most eager, proactive, super talented intern constantly thinking about all the different ways it could truly push the ball forward for you. Like, think about how much leverage, how how much more chill and easy your life might feel when you can take a take a breath. You have .
13:14And so whether it's a deep product analysis you didn't even think to ask for, or it's helping you create an entire strategy document from just a simple voice memo, all the way to running a team of dozens or hundreds of people, specifically helping you keep track of how everything is going. Building yourself a proactive AI chief of staff is going to help you get so much more leverage on your time and feel so much less stressed.
13:34And then on top of that, I've made sure that every heartbeat, so every cycle that it runs, it's constantly running through a list of actions and to dos on my behalf. So it's understanding what are my top priorities, understanding what are my to dos that are checked and not checked off, and how can I move the ball forward there?
13:47It's then checking my Slacks, my emails, my calendar, and understanding what are things that I can practically prep John for. And then at the end, it's sending me a simple clean text of all the different things that can move the ball forward for and any prep materials that I'd like to review ahead of time. But beyond even this particular really exciting example, I think what's most valuable to give you is the mental model of how you can think about building a really effective AI agent.
14:09And that mental model is specifically that you should be decomposing the workflow of the jobs that you do. If you think about it, this AI chief of staff I built is really just me decomposing parts of the job that a CEO or a COO or a chief of staff would do, aka a CEO or a COO is in charge of driving a business forward.
14:27And so when you work backwards from what driving a business forward means, you start to realize, well, one of the things that you need to do to drive a business of people forward is you need to make sure that everyone's on track with the same goals and then you're moving towards those goals consistently over time. So what that means on a literal basis is that you often find the best CEOs will put together a scorecard for the quarter for their business, where they're tracking how are all the different metrics moving from green, yellow, to red, and then what are the top blockers in that.
14:50And then their job as a CEO is to meet with their team, so the owners of those particular metrics, and work with them through meetings to unblock what it is they're going to do to move that metric from red to green. And so someone has to facilitate those conversations and also track and measure all of those things so that the entire organization, including yourself as the CEO, knows what's going on.
15:07We've now given this task that before a human had to spend their time doing to an agent who, if you give them the right context, can also do the same thing. And so whether it's an AI chief of staff or some other job that you need done, what your job is to do in today's world is to compose all the things that you spend your time on, turn that into a workflow, and then give that to an agent that can then just do that for you in your sleep way faster and likely way better than you can.
15:30Where I think the first round of agents are just here to do stuff for us, But the next level agents that we're starting to unlock as a market is understanding how do I have an agent actually go out there and think for me? How do I have it autonomously move the ball forward for me, which was previously just the realm of us human beings?
15:45Which brings us to the last agent that we're starting to build here internally that I truly think is going to change the game, where we now have an AI agent who is constantly sending to us in a Slack channel potential feature ideas for our product based off of how our users are currently using our product, where we have hooked up this agent to all of our usage data.
16:02So specifically, it's able to see what our users are clicking on, where they're getting stuck, where they're getting frustrated, where they're spending the most time. And then on top of that, our billing data, our retention data, our email support inbox. And so it's able to suggest, for example, that we should build a native Notion integration because it's seeing that the customers who are retaining the most right now oftentimes have Notion integrated, aka their context integrated, and therefore, they're able to create much better outputs, and they are more integrated into the workflow or our product.
16:27And so it's understanding what our happy customers are doing. It's also correlating that with who's staying subscribed and getting the most value from the platform, and then it's therefore suggesting what action we should take next to improve our product. In the old age of product development of building a SaaS tool or an AI agent, you yourself as a human being had to go into the data and really start to think about what is it that I could ship that will improve this product to increase the retention or increase the success of my product.
16:49So you have look at all of your subscription data and see who is canceling, who is staying, and you have to look at the actual usage of your products. You have to watch tons of live user sessions and understand, okay. People are using it in this way, and people are aggregating on this tab, and people are getting stuck here.
17:03And then you have to talk to dozens of customers on top of that to understand their qualitative feelings about those conversations. It's extremely manual effort to understand the lay of the land to then be able to say, I think we should ship this feature because I have a hypothesis that this will actually improve our metrics.
17:18That's oftentimes a multi week process. But in today's world, we've now built an AI agent that has full access to all of that context. So it is ingesting all of our subscription data and seeing who's canceling versus who's staying and what are the demographics of that person that are leading to them being more likely to retain and succeed on top of also looking at all of our usage data.
17:37So it's saying where people are getting stuck. It's saying where people are engaging the most in our product. It's even plugged into our email support inbox.
17:44So it understands which customers are reaching out from here and what are they asking about and how can we solve that into one loop. And therefore, it is ultimately in a Slack channel now, pinging us out with feature suggestion ideas every time it comes across an opportunity. But the thing is, this is just the insights layer.
17:58It doesn't stop there. Because from here, it's immediately sending us a ton of different feature requests. And so we as the human being, because we have the full concept, have the full name, we're actually live with customers seeing them, feeling them, and understanding how they feel.
18:09All I have to do now is play the role of case curation, where I can just say, this specific feature idea, that's actually a pretty good idea. Why don't you run with it and make me the product requirements document? Which is before a long document that every product manager out there knows and every engineer knows is the bane of their existence to actually create, where it lays out what is the specific feature, how do you build it, and how do you make sure it's successful.
18:29And so our agent can now generate that with one click. I can then give edits and thoughts to it of like, oh, I think you should actually change this or make sure you think about this nuance, or I think you should actually make this section better. And then from there, well, what do you know about coding agents?
18:40It's now able to just build that without us ever having touched a single line of code ourselves and then integrate that into our product where, for example, we can send this net new feature to 50% or 5% of our users, and the agent will monitor whether or not that test is improving the outcomes of those customers, then reporting that back to us for us to make a final decision as the humans and the decision makers who then integrate that and ship that to all of our entire code base.
19:04And so I want you to think about and understand the power that these AI agents can have in improving your business. Because now, instead of having to have the resources and money and attention and time required to hire net new product teams and engineering teams and get them spun up, instead, you can have an AI agent entirely dedicated to improving the retention of your business or improving the onboarding flow of your app or improving the success rates of whatever service you offer to your clients.
19:27Like, we now have the ability to access a full time product manager and engineering team underneath them to then staff them across our business, but in just an hour of our time. Like, the amount of leverage in today's world that you can build for yourself is insane, where I want you to recognize that in the past, the only people that had access to this amount of resources and time were the largest of companies with massive war chests.
19:48But now you as a single person at home can spin up teams of agents that I'd argue are much more scalable and much more powerful than anything that those companies had access to before, and you can just absolutely run circles around them. And the main thing now stopping you between the life that you're living now and the life that you want to build for yourself is no longer about access to resources.
20:07It is the audacity of your own vision and foresight and your ability to actually go out there, take action, and make it happen. Despite how shitty the world is right now and how much people will lead you to believe it's shitty, there's also never been a better time out there to break out and succeed. And so my hope is that you can take everything that I've shared with you today as a jumping off point to start to think about what is the AI agent that I could build that will truly 10x my own impact and my own velocity.
20:32And so go out there and dream it up, crush it, and I will see you in the next phase.
The Hook
The bait, then the rug-pull.
Most AI productivity videos are just ChatGPT prompts dressed in FOMO. This one opens with a CEO who runs a $40M company saying he is tired of that genre — then spends twenty minutes showing you what agents that generate real, measurable business value actually look like from the inside.
Named ideas worth stealing.
02:15list
The 3-Ingredient Content Stack
- Ideate (find viral ideas)
- Edit and produce well
- Publish with a weekly iteration cycle
The three things required to build a following online, reverse-engineered from top creator workflows and automated into a single agent.
Steal forContent strategy, editorial calendar automation
14:12model
Job Decomposition Mental Model
To build any AI agent: list every subtask a human performs in that role, convert each subtask into a workflow step, give the agent context and tool access to run each step.
Steal forAny role-replacement or role-augmentation agent design
How they asked for the click.
VERBAL ASK
20:43subscribe
“Subscribe to follow the Road to $1B”
Full-screen title card at the very end, clean and direct. No verbal pitch — just the visual. Effective because it names a concrete journey rather than a generic 'subscribe for more.'
MENTIONED ON CAMERA
FROM THE DESCRIPTION
PRIMARY CTAWhere the creator wants you to go next.
OTHER LINKSAlso linked in the description.
Visual structure at a glance.
