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How I'd Build A 1M$ Business With Zero Employees In 2027 (GPT Astra Only)

Laura of AI Founders maps the architecture she would build if the target were a one million dollar business run by one person on GPT-6 Astra, the OpenAI frontier model released a month before filming. Her frame is a seven component cockpit: radar, flight plan, communications, engines, instruments, autopilot, and the pilot, the one control she refuses to delegate. She declines to project revenue at all, asking the model for the assumptions under four routes instead of a number, so the title's million works as a design constraint rather than a forecast. This page rebuilds every step, checks her Census and plan limit figures against the sources, and marks which numbers are hers and which are published.

Published Oct 5, 2026 18:49 video 44 min read Added Oct 8, 2026 Open on YouTube →

At a glance

Laura, who runs the AI Founders channel, lays out the architecture she says she would build if the target were a one million dollar business run by one person, with GPT-6 Astra as the single intelligence across the whole operation. Her frame is an aircraft cockpit with seven components: radar that finds a market signal, a flight plan that is the offer and the arithmetic underneath it, communications that create demand, engines that produce and deliver the work, instruments that tell you whether any of it is working, autopilot for the jobs that have to happen whether or not you remember them, and the pilot, which is the one control she says she would never hand to a model. She is unusually direct that this is not a revenue forecast: thirty four seconds in she says flatly that it is "not a promise that you will make a million dollars," and when she reaches the business model section she refuses to project revenue at all, asking Astra for the assumptions underneath four routes rather than a number at the end of one. The video's single sharpest claim is operational rather than financial, that Astra closes the gap between describing a thing and having the finished thing, and the mistake she spends the last three minutes attacking is building the machine before anyone has said yes.

The information age is over

She opens on the old arithmetic. A million dollar business used to require people: someone to research the market, someone to build the website, someone to find Customers, someone to prepare proposals, someone to deliver the work, and someone to keep the whole operation moving. Her argument is that GPT-6 Astra changes that equation, and she is specific about why. It is not that the model helps you think or create, which earlier models already did. It is that it can research, build finished assets, operate software, and complete work on a schedule even when you are not sitting at your computer.

Then the disclaimer, delivered early and without hedging, at 0:34: "that is not a promise that you will make a million dollars. I'm sharing with you the architecture that I would build if that were the target." She also sets up the ending in the same breath, promising to show the one control she would never hand over, "because that is the control that decides whether you build a real business or an impressive machine that nobody needs."

The manifesto section follows. The information age is over. The permission economy no longer exists, and she says the word twice: it is dead. What has replaced it, in her telling, is an age of leverage, "one where value is the only brand, AI is the workhorse and the only gatekeeper left is your own indecision." Corporations used to win with resources; she argues the next generation of founders will win with leverage and a point of view.

But she immediately qualifies the leverage claim, and this is the line that sets up the entire structure of the video: "leverage without direction is a rocket burning in place. All of that power, all of that thrust is going nowhere." That is the thesis. Everything that follows is an attempt to give the thrust a direction.

The cockpit: seven components, one intelligence

Her definition of a one person business is worth quoting against the caricature, because she names the caricature first. It does not mean one person doing every single job, "20 tabs open, exhausted by Thursday." It means one person can actually see the whole thing, make the calls that make a difference to the business, and point a system at everything else.

Then the cockpit, enumerated at 1:40. You need radar to see the market, a flight path that turns an opportunity into something that adds up, communications that put the business in front of people, engines that produce and deliver the result, instruments that tell you whether it is actually working, and autopilot for the jobs that happen without you having to remember them. Astra, she says, can operate across all of them. And then the hook she leaves hanging for fifteen minutes: "There's a seventh one, the pilot. We will come back to that one because it is where most people make the expensive mistake."

Figure 1. The architecture as Laura enumerates it at 1:40, with the seventh component placed where she places it, outside the delegated set. The whole video is organized around this diagram, and its load bearing feature is the dashed line: six components are jobs she hands to one model, and the seventh is the judgment that decides whether the other six are pointed at anything worth doing. Her phrasing for the failure mode is a plane with nowhere to go.

The five things a serious business used to require

Before the pre flight check she makes a narrower and more defensible version of the opening claim. Up until now, building a serious business meant getting hold of five things: five people, money, technical ability, infrastructure, and some way to reach anyone. Her claim is deliberately not that these disappear. "Astra does not make those five disappear, but it makes them a lot cheaper to get to."

What it can do instead: build without you coding, research without a team, produce without a department, use software that was never designed to connect, and keep working without waiting for another prompt to be written. That last clause is the one that distinguishes this video from the same pitch made a year earlier, and it is the capability she returns to twice more.

Her summary of the shift is the best compressed line in the first three minutes: "One person did not suddenly become capable of doing everything. One person just does not have to anymore."

What a business used to need	The staffed version	What she says changes	What it still costs
Five people	Market researcher, web developer, salesperson, proposal writer, delivery, operations	One model operating across all six cockpit components	Her claim, stated as architecture, not a result she shows
Money	Payroll, tooling, infrastructure, agency retainers	Subscription tiers rather than headcount	Verifiable: Astra API pricing is $10 per million input tokens and $50 per million output
Technical ability	Someone who can code, and someone who can integrate systems	"It can build without you coding," and it can operate software through the interface a person uses	Verifiable: OpenAI reports 72.6 percent on the OSWorld 2.0 computer use benchmark, so roughly one task in four still fails
Infrastructure	Hosting, CRM, reporting stack, integrations built per tool	No connector required, because the model uses the portal, the menus and the fields directly	Her claim. The video describes this capability rather than demonstrating it on screen
A way to reach anyone	Ad budget, an audience, a sales team, or a list	Research driven outreach, one prospect at a time, plus being findable by AI systems	Her claim, and the one place the video hands the demo to a sponsor's product

Figure 2. The five prerequisites Laura names at 2:13, with her own qualifier attached: she says Astra "does not make those five disappear, but it makes them a lot cheaper to get to." Green entries are figures published by OpenAI and checkable today. Amber entries are her claims, stated as the architecture she would build rather than results she demonstrates, which is how she frames them.

Pre flight check: the five reasons people wait

At 2:54 she runs what she calls the pre flight check, and she is careful that it is not about the machine. It is about five things people believe they need before they are allowed to start: a team, money, a brand new idea, an audience, and the ability to code. She takes them in order and answers each with one line.

The team. "There are 29.8 million businesses in the United States with no employees at all. About four out of every five of them. So the team is not the thing." That figure is real and it is the one hard statistic in the video. The US Census Bureau's 2022 Nonemployer Statistics counts 29.8 million nonemployer businesses, firms with no paid employees and at least $1,000 in annual receipts, and they account for about 82 percent of all US small businesses. Her "four out of five" is accurate. Worth adding what she leaves out, because it cuts both ways: those same 29.8 million firms produced roughly $1.7 trillion in receipts, which averages out to well under $60,000 each. The existence of solo businesses is proven. The existence of solo businesses at a million dollars is a much smaller set, and the statistic she cites does not speak to it.

The brand new idea. "Steve Jobs did not invent the smartphone. He just made it better." A compressed version of a real point about Apple entering a market that already had BlackBerry and Palm in it, and a reasonable argument that novelty is not the entry requirement people treat it as.

The audience. "A business that serves 10 people properly is a real business." Which is both the smallest claim in the section and, read against a million dollar target, the one that quietly implies a high price per Customer.

The coding. Here she makes a specific promise about the video itself: "in about 10 minutes, you are going to watch a model open a piece of software and use it the way a person would. Clearly, you don't need to know how to code."

She closes the section with a dateline rather than an argument: "Every one of those reasons was a real one to wait from starting your business in 2021." The implication is that they expired, not that they were never true.

The first control: radar, not an idea generator

At 3:51 she names what she thinks is the most common and most expensive first move. "Most people start a business by asking AI the worst possible question. Give me 10 business ideas." The failure mode is precise: it gives back ten plausible answers, you pick the most exciting one, and you spend three months building on a guess. Her verdict: "That's not strategy. That is guessing with extra steps."

Her replacement is a distinction between inventing and investigating. "If I were starting from zero, I would not ask Astra to invent an idea. I would ask it to investigate an opportunity."

What she feeds it first is everything she already has, and she lists five inputs: her skills, her experience, the things she is unusually good at, who she knows, how much time she really has, and the markets she already understands.

Then the research brief, and this is the most actionable passage in the video. She asks it to go and look properly:

Find people actively complaining about expensive problems
Read the reviews, the forums, the competitor pages
Pull the language Customers use when they are frustrated, "not my interpretation of it"
Find what they have already tried
Find what they pay for now
Find where the existing options let them down

And then the output constraint, which she emphasizes by repetition: "bring me three opportunities, not 10, three." For each one she wants five things: the Customer, the painful problem, the evidence that it is real, what solving it might be worth, and how crowded the market is.

Two more questions sit on top of that, and she flags the second as the important one. Why might she be the one to win it, and "what would have to be true for it to work at all." Her reason for singling that out is the sharpest epistemics in the video: "a confident answer and an answer that makes money are not the same thing."

She closes the section with the distinction that gives the component its name. "The breakthrough is not that Astra can search. Search gives you information. Radar tells the signal from the noise." And then the principle, stated as the first of the business: "Do not begin with what you can make. Begin with what the market already wants changed."

Flight plan: the offer, and the maths underneath it

At 5:37 the flight plan arrives, defined as the offer and the arithmetic beneath it, and she opens by clearing out what an offer is not. "A website is not an offer. A course is not an offer. An AI agent is not an offer. Those are vehicles. They're containers."

Her definition: "An offer is a promise to solve one meaningful problem for one recognizable person in a way they choose over the alternatives." Three constraints in one sentence, and the third is the competitive one people skip.

The method is adversarial. She gives Astra the radar evidence, asks for three versions of that promise, and then, in her phrase, makes it "attack its own work" with seven questions:

Which one solves the most valuable problem?
Which is easiest to trust?
Which gets somebody a result fastest?
Which is simplest to deliver?
Where is the Customer still carrying the risk?
What would stop the sale?
"If this offer works, does delivering it get easier or does it eat more of my time with every new Customer?"

She calls the last one the question a lot of people forget, and her reason is scalability. "A good offer is easy to say yes to. A good offer that scales is also easier to deliver."

The four routes, and the refusal to project

Then the money section, and this is where the video departs hardest from the genre it belongs to. She says there is no one correct business model if the target is a million dollars, only a few broad routes, and she names four: sell something expensive to a few people; turn one repeatable result into a package and sell it to a lot more; build something that travels without you, like a course, software, or a set of templates; or do a mix.

She asks Astra to run the numbers on all four. And then she states, explicitly, what she does not want back: "Not to predict a million dollars, to show me the assumptions underneath each route because what I want back is not a number. I want to understand the assumptions."

The four assumptions she names are the ones that actually drive a revenue model: how many Customers, at what price, how long each one actually takes her, and how many will stay. Then her own standing question, which is a stress test rather than a forecast: "which one of these am I most likely to be wrong about? Because that is the one that decides everything and nobody ever checks it."

This is the reason there is no revenue chart on this page. Laura gives no prices, no conversion rates, no traffic assumptions, no Customer counts and no timeline arithmetic anywhere in the eighteen minutes. The million dollar figure in the title is used exactly once as a quantity, and she reframes it immediately: "The million is not the strategy. It is a design constraint. It forces you to confront whether you are building a business that can grow or just a better paid job."

Her closing line on the model is the most concrete sequencing advice in the section: "Your first money probably comes from selling your time. Your first bit of scale, however, comes from teaching your business to produce the result without eating more time of yours."

Route to the target	Her shorthand	What the arithmetic rests on	Where it breaks, on her own axes
High price, few Customers	"Sell something expensive to a few people"	Price per Customer, and trust at that price	"Does delivering it get easier, or does it eat more of my time with every new Customer?" Bespoke work usually fails that test
One repeatable result, packaged	"Turn one repeatable result into a package and sell it to a lot more"	Customer count, retention, and time per delivery	Volume has to arrive before the delivery cost does. Her "which am I most likely to be wrong about" lands on demand here
Something that travels without you	"A course or software, a set of templates"	Reach, conversion, and the audience she already said you do not need	Zero marginal delivery cost, but the pre flight answer "a business that serves 10 people properly" does not reach a million on this route
A mix	"Or you can do a mix"	All of the above, sequenced	Matches her own sequencing advice: time first, then scale. Also the only route she does not caveat

Figure 3. The four routes to the target, as Laura names them at 6:32, against the four assumptions she says she wants back instead of a revenue number: how many Customers, at what price, how long each one takes her, and how many stay. Every cell here is qualitative because she assigns no numbers to any route. The last column is this page mapping her own seven offer questions onto the four routes; no price, conversion rate, Customer count or timeline appears anywhere in the video, and none is invented here.

Communications: the object, not the words

At 7:51 communications is defined broadly: everything the business says and everything the Customer sees. Her list is the website, the proposal, the emails, the posts, the follow up, and the proof.

Then she concedes the obvious, which is a mark in the video's favor. "AI has been able to write these things for years. That's not impressive anymore." The claim she makes instead is about distance: what changes with Astra is "the distance between describing the thing and getting something that you can actually use."

Her two examples are specific. Instead of giving her the words for a website, it can build a page and publish it. Instead of outlining a proposal, it can produce the document in her template, with her brand and the pricing logic already inside it.

The compression of that argument is the best line in the video and the one the whole middle section rests on: "The older model gave you the words, but Astra gives you the object."

Her reason for why that matters is not about quality, it is about attrition. "The gap between a draft and the finished thing is where most one person businesses stall. Generating the draft is usually easy. Finishing it is the part that steals the evening."

Then she turns from production to purpose. "Producing more content is not the goal. The purpose of communication is to create the right conversations." So the brief she gives Astra is to find the people most likely to have this problem right now, research each one, and write outreach that proves the research happened. Her standard: "Good outreach is not volume. It's relevance." And the three part test for whether a message qualifies: it says I understand your specific situation, I can see what it costs you to leave it alone, and I have a credible way to help you.

She closes with a second forward promise at 8:38: "in a few minutes, I'm going to show you the business doing its own work overnight on a schedule. Nobody at the keyboard."

Audience: can AI systems find you, and the first hire you do not make

The chapter named Audience opens on a question the cockpit cannot answer from inside itself: "Can AI systems actually find you and recommend you?"

That sets up the sponsor read, and her framing of it is genuinely the best definition in the video, independent of the product. "Zero employees sounds great until you work out what an employee actually is. It is not the person. It is the job that has to happen again next week and again next month."

The partner is Ahrefs. The captions mangle both names in this passage, rendering the company as "Hrefs" and the product as "Let"; the product is Letaido, Ahrefs' AI marketing platform. Her split is "Ahrefs is the tool. Letaido is the worker," and her description of it is a workspace where you describe the job in plain English, "or whichever your language is," and it goes and does it on a schedule without you.

The demo she runs is a single sentence instruction: check every week whether people can find us in Google and in ChatGPT, and what our competitors rank for that we do not. Her emphasis is on what she is not doing: "I'm not building anything. I'm describing it and it comes back with the finished thing next week too whether I remember to ask it or not."

The price she states is "99 a month sitting on an Ahrefs plan for the data." Letaido's own pricing page lists $99 per month for one workspace with unlimited users and AI credits included, plus built in hosting with one free domain. One correction worth making, since she presents the Ahrefs plan as a prerequisite: Letaido's FAQ says it works on its own, and that if you are already an Ahrefs Customer it reads directly from your Site Explorer, Keywords Explorer, Site Audit, Rank Tracker and Brand Radar data at no extra API cost. So the Ahrefs subscription improves the data rather than being required for the product to run.

Her line to close the read is the one that ties it back to the thesis: "That is the first hire that we do not make."

Engines: where a one person business turns into a burnout machine

At 10:17 the engines, which she defines as everything a Customer gets after they say yes, plus all the invisible admin that has to happen to make that work.

Her description of the failure mode is the most vivid writing in the video. "This is where a one person business usually turns into burnout machines. One person factories trying to do the job of 10, duct taping tools together and moving information across by hand and filling out forms and doing the same small steps every day because somebody has to." Then the verdict: "if your business needs you every day to survive, you do not have a business. You just have a very demanding job."

Her diagnosis of why automation did not previously fix this is the most technically substantive claim in the video, and it is correct about the old constraint. "Up until recently, AI could only get into that software that agreed to let it in. If the thing you use had a proper connection built for it, you could automate quite a bit of it. If it did not, however, the work came straight back to you." That is the API and connector problem, stated plainly: automation reach was bounded by whichever vendors had shipped an integration.

What she says Astra changes is that it uses the software the way you use it: "opening the portal, clicking through the menus, moving the information across, filling in the fields, and coming out the other side with the finished thing."

Her reason this matters is the best argued point in the eighteen minutes, because it identifies a specific economic gap rather than a capability. Small businesses are full of work "that was never hard enough to justify custom software but was repetitive enough to eat somebody's weekend sometimes, or evenings at least." Her four examples are concrete enough to recognize:

The supplier system from 2009
The client portal with nothing useful to connect to
The form that has to be filled in one field at a time
The report that starts with information spread across four different places

And the conclusion: "For the first time, one person could automate the work that stayed manual purely because the software was too old, too closed, or too obscure to connect to."

The three way split, which she tells you not to skip

Then the guardrail, and she flags it: "I would not hand Astra the whole engine and walk away. So here's how I would split it. And this is the part that I suggest you don't skip."

Her three tiers, each defined by its own test rather than by task type:

Some of it it just does, "because if it gets that wrong, nothing breaks"
Some of it it gets ready, and then "I need to look and approve before anything gets sent out"
Some of it stays with her, "because there is a person on the other end and I need to make sure that we keep the standards high"

Her summary: "This is how a one person business gets capacity without getting reckless." And then the line that is the thesis of the back half: "AI does not make you unnecessary. It just makes your judgment worth a great deal more."

Figure 4. The split Laura describes at 11:52, with the test she states for each tier. The three tests are hers, verbatim. The items placed in each column are this page mapping her own examples from across the video onto the tiers she defines, since at this moment she states the tests without enumerating tasks. The right hand column is drawn from her explicit list of what the pilot never hands over at 15:41.

Instruments: five questions, five decisions

At 12:24 the instruments, and she makes the point with the metaphor intact. "Every cockpit has instruments for a reason. You cannot fly by how busy the cabin feels. And you cannot run a business by how productive the AI looks."

The five questions she says the instruments must answer:

Are you creating demand?
Are conversations turning into Customers?
Are Customers getting the result that we promised to them?
Is the work making money?
Where am I still the bottleneck?

That last one is the unusual inclusion and the one that fits the thesis. The others are standard funnel metrics; "where am I still the bottleneck" is a metric about the operator, which is the only resource a one person business cannot buy more of.

What she asks Astra to build from the business data is deliberately small: "a simple weekly review, not 50 charts, dashboards, five decisions." The five:

What changed?
Why did it change?
What needs me?
What should stop?
What is the single best move for the next seven days?

"That's it."

Then the second appearance of the finished object argument, now applied to analysis rather than assets. "It can build a spreadsheet with the formula and the scenario inside it, not just paste numbers into a table." And the use she puts that to is the thing that makes the flight plan section load bearing rather than decorative: "It can compare what actually happened with the flight plan and point out where reality has broken the original assumption, because it will."

Her principle: "the point of a dashboard is not to admire the numbers or show them in a beautiful way. It's to change a decision."

Autopilot: positions you hire, not automations you collect

At 13:31 the component she says changes the whole relationship, and she sets it up by conceding what came before. "Everything we have done up to now can still start with me. I open the system, I ask, and it works. That is not much better version of doing everything myself. But it is still me starting the engine."

Her definition of autopilot is jobs that run on their own, and her examples are phrased as instructions rather than features:

Check this every Monday
Watch that and tell me when it changes
Review the pipeline every morning
Prepare the client report on the first of the month
Compare this week against the plan before I start work on Friday

"You define the job once in your language. Let's say English. And it keeps going without waiting for another prompt."

Then the one piece of hard product detail in the video, which she volunteers because, as she says, people always ask in the comments: "three scheduled jobs can work on free, and then 10 on business and edu, 15 on pro and enterprise." She adds the caveat that it may have changed depending on when you are watching.

Those numbers check out against OpenAI's own documentation on scheduled tasks in ChatGPT, with one gap. The active task limits are 3 for Free and Go, 5 for Plus, 10 for Business and Edu, and 15 for Pro and Enterprise. Every tier she named is right. The tier she skipped is Plus, and it matters, because five is exactly the number of jobs she goes on to recommend.

Figure 5. Laura's stated plan limits against OpenAI's published figures. The three tiers she names are accurate. The gap is Plus, highlighted in amber, which allows exactly five active tasks, the same number as the five autopilot jobs she recommends hiring first. The practical reading: her five job starter set does not fit on the free tier, fits exactly on Plus with no spare slot, and gets its first real headroom on Business. She adds her own caveat that the numbers may have moved since filming.

The five positions she would hire first

Her reframe of what an automation is, is the single most useful sentence for anyone actually building this. "These are not automations that you collect. They're positions that you hire."

And if she only had five, these are the five, in her order:

A weekly market and competitor brief
A daily monitor of new opportunities and prospects
A pipeline and follow up review
A delivery and quality check
A monthly money and performance review

Note how precisely those five map onto the cockpit: one for radar, one and one for communications, one for engines, one for instruments. The autopilot component is not a sixth function, it is a schedule laid over the other five.

Her closing instruction is a constraint rather than an ambition: "You don't have to automate everything. Automate the five things that the business cannot afford to forget. A business with five jobs that always happen is, trust me, a lot better than one with 40 that mostly don't."

And her summary of what the stack adds up to: "this is the moment that Astra becomes much more important. It starts behaving like the thing the business runs on." Radar, flight plan, communications, engine, instruments and autopilot. "One intelligence across the whole operation."

Then the turn: "But none of that means the business can reach a million dollars. That depends on the last component."

The pilot: the one control she would never hand over

At 15:41 the seventh component arrives, fifteen minutes after she promised it. The pilot, and what she defines it as is not a skill but an authority: "the decision about what deserves to even exist."

She is scrupulous about granting the model everything else first. Astra can research the market, find patterns, compare opportunities, build assets, use software, and tell her what to do next. What stays with her is a list of six:

Which Customer matters
Which problem is worth solving
What promise she is prepared to make
What good looks like
What the business will refuse to do
"Also, whether the destination is even worth reaching at all"

That fifth one, what the business will refuse to do, is the one most absent from this genre, and it is the one that makes the list a strategy rather than a preference.

The expensive mistake

Then the mistake the whole video has been built to deliver, and she labels it as the number one thing she sees: "They build the system before they have sold anything. They spend three months building a beautiful cockpit for a plane with nowhere to go. They have no validated offer, no Customer, no first sale. Just a very impressive system waiting for a business to arrive."

Her verdict, in the metaphor: "I think that's a no go."

And the sequence she prescribes instead, which is the actionable core of the video and the direct inverse of its own structure: "You need to build the offer, get the first yes, and then build the machine around the thing that actually works."

That is worth sitting with, because it recommends doing the seven components out of the order she presented them. Radar and flight plan first, then a human selling to a human, and only then communications, engines, instruments and autopilot. The cockpit is what you build after the first sale, not before it.

What leverage cannot buy

Her three limits on the model are stated as flatly as anything in the video. Astra "can give one person the capacity that used to take a team, but it cannot give a weak business strong economics. It cannot make an irrelevant offer valuable, and it cannot care about the Customer on your behalf. That is why judgment becomes worth more and not less."

Then the economic argument underneath the whole thesis, and it is a real one: "When execution was expensive, being able to build something was the advantage. Now that execution is cheap, choosing what should be built is the advantage, in my opinion. The advantage goes to the people who know what is worth doing and can turn that into a system that works without eating their lives."

Full circle, and the ask

At 17:20 she brings it back to the opening. The information age is over, the permission economy is dead, and "the new game, the one where one person with one clear offer and one intelligent interface can outexecute a larger team still stuck in disconnected systems, is being played right now."

Her one sentence restatement of the architecture, which doubles as the chapter list: "It is radar that finds the signal, a flight plan that adds up, communications that create demand, engines that produce the result, instruments that protect the model, and work that keeps happening after you leave your keyboard."

And the most carefully worded claim in the video, which is also the most honest: "GPT-6 Astra does not give you the million dollar idea. It gives you enough leverage to stop a good idea being trapped inside your calendar."

The close is the hand off to her own funnel, and she names the gap it fills: "if you're sitting there thinking, fine, but what would I actually sell? That is the problem we solve next." The invitation is to the AI Founders HQ community by QR code or the link in the description, where there is a course on answering exactly that question. She runs the free community, AI Business Trailblazers, and a paid one, the AI Founders Hive, alongside her co founder Dan.

Her last line is the thesis compressed to a single question: "the question was never whether AI could help run a one person business. The question was always whether you would be the one in the pilot seat."

What GPT-6 Astra actually is

Because the title says "GPT Astra" and she says "GPT-6 Astra" throughout, and because this genre routinely builds on models that do not exist yet, the status of the model is worth stating plainly.

It is real and it had shipped before this video. OpenAI announced GPT-6 Astra on 3 September 2026, about a month before this video published on 5 October 2026, describing it as its frontier reasoning model for complex professional and enterprise work. Rollout went in phases: Pro, Enterprise and Business Premium users in ChatGPT Work and Codex first, with Plus and Business following. It is also available through the OpenAI API and on Amazon Web Services and Azure AI Foundry.

The published figures, for grounding the capability claims she makes:

Context window of 1,050,000 tokens, with a maximum output of 128,000 tokens
Knowledge cutoff of 30 April 2026
API pricing of $10 per million input tokens, $1 per million cached input tokens, $12.50 per million cache writes, and $50 per million output tokens
OpenAI positions it as state of the art on computer use, browsing, software engineering, cybersecurity, science and professional work, and names Agents' Last Exam, AutomationBench, ScreenSpot Pro, FrontierMath Tier 4, ARC-AGI 3, Terminal-Bench 4.0, Terminal-Bench Science 0.1 and HealthBench Pro among its benchmarks
On OSWorld 2.0, the standard computer use benchmark, reported at 72.6 percent against 65.7 percent for its predecessor GPT-5.6 Sol
It is OpenAI's first model to reach its own "Critical" internal cybersecurity capability threshold, which is why its launch drew the regulatory attention it did

So the capability she builds on, a model that operates software through the interface rather than through an API, is a shipped product with a published benchmark number attached to it, not a rumour. The forward looking part of the title is the business, not the model: "In 2027" is her timeframe for building, and the plan is written for a tool that already exists.

One naming note. The title's "GPT Astra" is shorthand. The model is GPT-6 Astra, and the captions on this video render it as "GPT6 Astra" throughout.

Key takeaways

The architecture is seven components, not six. Radar, flight plan, communications, engines, instruments and autopilot are all delegated to one model; the pilot is not, and the pilot is the component the video exists to argue for.
"Leverage without direction is a rocket burning in place" is the thesis, and the number one mistake she names is its consequence: building the cockpit before there is a destination, which means before there is a validated offer, a Customer, or a first sale.
Her prescribed order is the inverse of her presentation order. Build the offer, get the first yes, then build the machine around the thing that worked.
Do not ask a model to invent an idea. Ask it to investigate an opportunity, feed it your actual position, and cap the output at three opportunities with evidence attached, not ten plausible ones.
"What would have to be true for it to work at all" is the question she singles out, because "a confident answer and an answer that makes money are not the same thing."
An offer is a promise to solve one meaningful problem for one recognizable person in a way they choose over the alternatives. A website, a course and an AI agent are vehicles, not offers.
The million dollar figure is used as a design constraint, not a forecast. She assigns no prices, conversion rates, Customer counts or timelines to any of the four routes, and explicitly asks for assumptions rather than a projected number.
The capability claim that actually distinguishes this from last year's version of the same video is computer use: automating the work that stayed manual because the software was too old, too closed or too obscure to connect to.
Delegate in three tiers, by the cost of being wrong: it just does it when nothing breaks, it prepares and you approve when something is leaving the business, and it stays with you when there is a person on the other end.
Automations are positions you hire, not tools you collect. Five jobs that always happen beat forty that mostly do not, and the five she would hire first map one to one onto the other cockpit components.
The limits she states on the model are the useful part: it cannot give a weak business strong economics, make an irrelevant offer valuable, or care about the Customer on your behalf.

Chapters

The video ships seven chapters, six of them titled. Those six appear below in bold, verbatim. Chapter one is untitled in the source, so its label here is this page's, as are the finer sub beats, which are drawn from the timed transcript because seven chapters across nineteen minutes leaves an eight minute stretch in the middle undivided. One quirk worth knowing before you click: the video's own "The Pilot" marker sits at 16:46, but the pilot section actually starts at 15:41, so the marker lands after the argument has already begun.

0:00 The information age is over
0:34 Not a promise that you will make a million dollars
1:08 Leverage without direction is a rocket burning in place
1:40 The cockpit: seven components
2:13 The five things a serious business used to require
2:54 PreFlight Check
3:51 The First Control
4:19 The research brief: what to feed it, what to ask for
5:22 Search gives you information, radar tells the signal from the noise
5:37 Flight Plan
5:57 Seven questions to make the offer attack itself
6:32 The four broad routes to a million
7:35 The million is a design constraint, not a strategy
7:51 Communications
8:06 The older model gave you the words, Astra gives you the object
8:29 Audience
9:12 Sponsor: Ahrefs and Letaido, the first hire you do not make
10:17 Engines: where a one person business becomes a burnout machine
11:22 Too old, too closed, too obscure to connect to
11:52 The three way split you should not skip
12:24 Instruments: five questions, five decisions
13:31 Autopilot: jobs that run without you starting them
14:35 Scheduled job limits per plan
15:08 The five positions she would hire first
15:41 The pilot: the six decisions she never delegates
16:14 The expensive mistake: building the system before selling anything
16:46 The Pilot
17:20 Full circle
17:52 The ask: community and course
18:26 Outro

Notable quotes

Leverage without direction is a rocket burning in place. All of that power, all of that thrust is going nowhere. Laura, AI Founders, 1:08

One person did not suddenly become capable of doing everything. One person just does not have to anymore. Laura, on what the one person business actually means, 2:45

Most people start a business by asking AI the worst possible question. Give me 10 business ideas. Laura, opening the radar section, 3:51

That's not strategy. That is guessing with extra steps. Laura, on picking the most exciting of ten generated ideas, 3:51

I would not ask Astra to invent an idea. I would ask it to investigate an opportunity. Laura, 4:04

A confident answer and an answer that makes money are not the same thing. Laura, on why "what would have to be true" is the question that matters, 5:08

Search gives you information. Radar tells the signal from the noise. Laura, 5:22

Do not begin with what you can make. Begin with what the market already wants changed. Laura, stating the first principle of the business, 5:22

A website is not an offer. A course is not an offer. An AI agent is not an offer. Those are vehicles. They're containers. Laura, 5:37

What I want back is not a number. I want to understand the assumptions. Laura, on asking Astra to run the four business models, 7:03

The million is not the strategy. It is a design constraint. It forces you to confront whether you are building a business that can grow or just a better paid job. Laura, 7:35

The older model gave you the words, but Astra gives you the object. Laura, on the shift from drafts to finished assets, 8:06

The gap between a draft and the finished thing is where most one person businesses stall. Generating the draft is usually easy. Finishing it is the part that steals the evening. Laura, 8:06

Good outreach is not volume. It's relevance. Laura, 8:38

Zero employees sounds great until you work out what an employee actually is. It is not the person. It is the job that has to happen again next week and again next month. Laura, opening the sponsor read, 9:12

If your business needs you every day to survive, you do not have a business. You just have a very demanding job. Laura, 10:50

For the first time, one person could automate the work that stayed manual purely because the software was too old, too closed, or too obscure to connect to. Laura, on what computer use changes, 11:22

This is how a one person business gets capacity without getting reckless. Laura, on the three way delegation split, 12:24

AI does not make you unnecessary. It just makes your judgment worth a great deal more. Laura, 12:24

You cannot fly by how busy the cabin feels. And you cannot run a business by how productive the AI looks. Laura, opening the instruments section, 12:24

The point of a dashboard is not to admire the numbers or show them in a beautiful way. It's to change a decision. Laura, 13:31

These are not automations that you collect. They're positions that you hire. Laura, on autopilot jobs, 14:35

A business with five jobs that always happen is, trust me, a lot better than one with 40 that mostly don't. Laura, 15:08

They spend three months assembling a beautiful cockpit for a plane with nowhere to go. Laura, on the number one mistake people make building with AI, 16:14

You need to build the offer, get the first yes, and then build the machine around the thing that actually works. Laura, 16:14

It cannot give a weak business strong economics. It cannot make an irrelevant offer valuable, and it cannot care about the Customer on your behalf. Laura, on the limits of the model, 16:45

When execution was expensive, being able to build something was the advantage. Now that execution is cheap, choosing what should be built is the advantage. Laura, 16:45

GPT-6 Astra does not give you the million dollar idea. It gives you enough leverage to stop a good idea being trapped inside your calendar. Laura, 17:52

The question was never whether AI could help run a one person business. The question was always whether you would be the one in the pilot seat. Laura, closing line, 18:26

Resources mentioned

AI Founders, the channel, hosted by Laura with her co founder Dan, and its site AI Founders HQ
The AI Founders Hive, the paid community and course she points to at the close, alongside a free community, AI Business Trailblazers
GPT-6 Astra from OpenAI, the model the entire plan is built on, with the launch announcement and API model listing
ChatGPT, where she runs the Astra workspace, and OpenAI's documentation on scheduled tasks, the source of the per plan job limits she quotes
Codex, one of the two surfaces Astra shipped into first
Ahrefs, the sponsor, and Letaido, its AI marketing platform at $99 per month, which is the on screen demo. The captions render these as "Hrefs" and "Let"
Google, named in the demo instruction alongside ChatGPT as the two places she wants to be findable
The US Census Bureau 2022 Nonemployer Statistics, the source of the 29.8 million figure, plus the Census explainer on the smallest businesses
Steve Jobs and Apple, her example that the brand new idea is not the requirement, against predecessors like BlackBerry and Palm
Benchmarks named in Astra's launch materials, for anyone checking the capability claims: OSWorld, ScreenSpot Pro, FrontierMath, ARC-AGI and Terminal-Bench
Amazon Web Services and Azure AI Foundry, the other two places Astra is served
Her earlier video on the same architecture with a different model, How I'd Build a One-Person AI Business Using Only Claude In 2026, which is useful for seeing which parts of the thesis are model specific and which are not

An honest footnote

Three things are worth saying after the rebuild, and none of them belong at the front.

The architecture is better than the title. The title promises a million dollar business and the thumbnail sells a number, but the video's actual content is a delegation framework, and a good one. The seven component split, the three tier delegation test, the "positions you hire" reframe and the refusal to project revenue are all more disciplined than the genre's norm. The strongest moment is the one that undercuts the premise: her number one mistake is building exactly the system the video spends sixteen minutes describing, before anyone has paid for anything. A video that tells you to do its own content last is doing something unusual.

The promised demo is not the promised demo. At 3:15 she says "in about 10 minutes, you are going to watch a model open a piece of software and use it the way a person would," and at 8:38 she says "in a few minutes, I'm going to show you the business doing its own work overnight on a schedule." What arrives at 9:12 is the sponsor read, and the product running the job on a schedule is Letaido, not Astra. Astra's computer use, the single capability the whole plan rests on, is described but never shown. That matters because it is the claim a viewer most needs to see tested. The published number, 72.6 percent on OSWorld 2.0, is genuinely state of the art and also means roughly one task in four still fails, which is exactly the kind of thing a live demo would make visible and a description does not.

There is no path to a million in the video. This is a feature and a limitation at once. She is honest that she is not forecasting, and her reasons for refusing are sound: the assumptions matter more than the output, and the one you are most likely to be wrong about decides everything. But it does mean the title's number is never connected to the architecture by any arithmetic. Four routes are named and none are priced. The one figure that bears on the question, her own 29.8 million nonemployer businesses, averages under $60,000 per firm. A reader who wants the unit economics has to build them, and the video's honest answer is that this is the pilot's job, not the model's. On its own terms that is consistent. It is also the reason this page has no revenue chart: there were no numbers to chart, and inventing them would have been the dishonest option.

Full transcript

======================================== A million-dollar business used to require people. Someone to research the market, someone to build the website, someone to find customers, prepare proposals, deliver the work, and keep the entire operation moving. But GPT6 Astra might change that equation because it does not just help you think or create. It can research, it can build finished assets, it can operate software, it can complete work on a schedule even when you're not sitting at your computer. So, in this video, I'm going to show you how I would use one Astra Workspace to design and operate a oneperson business capable of reaching $1 million in revenue. But that is not a promise that you will make a million dollars. I'm sharing with you the architecture that I would build if that were the target. And by the end, I will show you the one control that I would never hand over to it because that is the control that decides whether you build a real business or an impressive machine that nobody needs. The information age is over. The permission economy no longer exists. It's [music] dead. We have entered a new age of leverage. One where value is the only brand. AI is the workhorse and the only gatekeeper left is your own indecision. Corporations used to win with resources. The next generation of founders, however, will win with leverage [music] and with a point of view. But leverage without direction is a rocket burning in place. All of that power, all of that thrust is going nowhere. So, let me show you the cockpit. A one person business does not mean one person doing every single job, 20 tabs open, exhausted by Thursday. It means one person can actually see the whole thing, make the calls that make a difference or an impact for the business, and point a system at everything else. Think of it like a cockpit. All right? You need radar to see the market, a flight path that turns an opportunity into something that adds up, communications that put the business in front of people, engines that produce and deliver the result, and instruments that tell you whether it's actually working, and autopilot for the jobs that happen without you having to remember them. Now, Astra can operate across all of them. There's a seventh one, the pilot. We will come back to that one because it is where most people make the expensive mistake. And look, up until now, building a serious business meant getting hold of five people. Getting hold of money, getting hold of technical ability, of infrastructure, and some way to reach anyone. Now, Astra does not make those five disappear, but it makes them a lot cheaper to get to. It can build without you coding. It can research without a team. can produce without a department, use software that was never designed to connect and keep working without waiting for any other prompt to be written. That is why the one person business [music] is changing. One person did not suddenly become capable of doing everything. One person just does not have to anymore. Now before we take off, there is a pre-flight check and it is not about the machine. Okay? It is about five things that people believe they need before they are allowed to start. a team, money, a brand new idea, an audience, and the ability to code. Now, there are 29.8 million businesses in the United States with no employees at all. About four out of every five of them. So, the team is not the thing. Not only that, but when it comes to having a new idea, you need to remember that Steve Jobs did not invent the smartphone. He just made it better. So, the brand new idea is [music] not a thing either. Also, a business that serves 10 people properly is a real business. So, the audience is not a blocker either. And then the coding. Well, in about 10 minutes, you are going to watch a model open a piece of software and use it the way a person would. Clearly, you don't need to know how to code. Every one of those reasons was a real one to wait from starting your business in 2021, right? The pre-flight is done. Let's start with the first control. Now, most people start a business by asking AI the worst possible question. Give me 10 business ideas. Guess what happens? It gives back 10 plausible answers and then they pick the most exciting one and spend 3 months building on a guess. That's not strategy. That is guessing with extra steps. If I were starting from zero, I would not ask Astra to invent an idea. I would ask it to investigate an opportunity. So what I would do is give it everything that I have actually got. My skills, my experience, the things that I'm unusually good at, who I know, how much time I really have, and the markets I already understand. Then I would ask it to [music] go and look properly. Find people actively complaining about expensive problems. Read the reviews, the forums, the competitor pages. Pull [music] the language that customers use when they're frustrated, not my interpretation of it. Find what they have already tried, what they pay for now, and where the existing options let them [music] down. And then bring me three opportunities, not 10, three. And for each one, show me the customer, the painful problem, the evidence that it is real, what solving it might be worth, and how crowded the market is. I would also ask it why I might be the one to win it, [music] and what would have to be true for it to work at all. That last question is the one that will make a difference. You'll see because a confident answer and an answer that makes money are not the [music] same thing. The breakthrough is not that aruck in search. Search gives you information. Radar tells the signal from the noise. And this is the first principle in a million-doll business. Do not begin with what you can make. Begin with what the market already wants changed. Once the radar finds that signal, you have to decide whether it is worth flying towards. Now, let's talk about your flight plan. Your flight plan is your offer and the maths underneath it. And this is where most people confuse a product with an offer. A website is not an offer. A course is not an offer. An AI agent is not an offer. Those are vehicles. They're containers. An offer is a promise to solve one meaningful problem for one recognizable person in a way they choose over the alternatives. [music] So I would give Astra the raider evidence and ask for three versions of that promise. Then I would make it attack its own work. [music] Which one solves the most valuable problem? Which is easiest to trust? Which gets somebody a result fastest? Which is simplest to deliver? Where is the customer still carrying the risk? What would stop the sale? And then one question that a lot of people forget, if this offer works, does delivering it get easier or does it eat more of my time with every new customer? [music] because you want scalability. All right? A good offer is easy to say yes to. A good offer that scales is also easier to deliver. Now, if the target is a million dollars, there is no one correct business model. There are only a few broad routes. You can sell something expensive to a few people. You can turn one repeatable result into a package and sell it to a lot more. [music] You can build something that travels without you, like a course or software, a set of templates, or you can do a mix. And I would ask Astra to run the numbers on all four of them. Not to predict a million dollars, to show me the assumptions underneath each route because what I want back is not a number. I want to understand the assumptions. How many customers at what price? How long each one actually takes me? How many will stay? And then the question that I always ask, which is [music] which one of these am I most likely to be wrong about? Because that is the one that decides everything and nobody ever checks it. The million is not the strategy, right? It is a design constraint. It forces you to confront whether you are building a business that can grow or just a better paid job. Your first money probably comes from selling your time. Your first bit of scale, however, comes from teaching your [music] business to produce the result without eating more time of yours. So now we know where we're flying and [music] whether the trip is worth taking. The next problem is getting anybody else to notice. [music] Communications is everything the business says and everything the customer sees. [music] That's your website, the proposal, the emails, the posts, the follow-up, the proof. AI has been able to write these things for years. [music] That's not impressive anymore. What changes, however, with Astra is the distance between describing the thing and getting something that you can actually use. So instead of giving me the words for a website, it can build a page and publish it. Instead of outlining a proposal, it can produce the document in my template [music] with my brand and the pricing logic already inside of it. The older model gave you the words, but Astra gives you the object. And that is not a small improvement. The gap between a draft and the finished thing is where most oneperson businesses stall. Generating the draft is usually easy. Finishing it is the part that steals the evening. But producing more content is not the goal. The purpose of communication is to create the right conversations. So I would ask Astra to find the people most likely to have this problem right now. Research each one and write outreach [music] that proves the research happened. Good outreach is not volume. It's relevance. It says, "I understand your specific situation. I can see what it costs you to leave it alone, and I have a credible way to help you. And in a few minutes, I'm going to show you the business doing its own work overnight on a schedule. Nobody at the keyboard. That one's [music] coming. But before you start sending anybody to the business, there is one more thing that the cockpit cannot see from inside itself. Can AI systems actually find you and recommend [music] you? So, zero employees sounds great until you work out what an employee actually is. It is not [music] the person. It is the job that has to happen again next week and again next month. Which is why for this one we are partnering with Hrefs. Hrefs is the tool. Let is the worker. It is a workspace where you describe the job in plain English or whichever your language is and it goes and does it on a schedule without you. [music] Watch. I'm telling it in one sentence to check every week whether people can find us in Google and in chat GPT and what our competitors rank for that we don't. I'm not building anything. I'm describing it and it comes back with the finished thing next week [music] too whether I remember to ask it or not. It is 99 a month sitting on an HRF's plan for the data. That is the first hire that we do not make. Link is below. Thank you Hrefs for partnering with us on today's video. Now the market can see the business but attention without delivery is [music] just noise. So it's time to start the engines. The engines are where the business creates value. everything a customer gets after they say yes and all the invisible admin that has to happen to make that work. This is where a oneperson business usually turns into burnout machines. One person factories trying to do the job of 10 duct taping tools together and moving information across by hand and fitting out forms and doing the same small steps every day because somebody has to. And if your business needs you every day to survive, you do not have a business. you just have a very demanding [music] job. Up until recently, AI could only get into that software that agreed to let it in. [music] If the thing you use had a proper connection built for it, you could automate quite a bit of it. [music] If it did not, however, the work came straight back to you. But with Astra, it's different. It can use the software the way that you use it, opening the portal, clicking through the menus, moving the information across, filling in the fields, and coming out the other side with the finished thing. And that is important because small businesses are full of work that was never hard enough to justify custom software but was repetitive enough to eat somebody's weekend sometimes or evenings [music] at least. The supplier system from 2009. The client portal with nothing useful to connect to. The form that has to be filled in one field at a time. The report that starts with information spread across four different places. For the first time, one person could automate the work that stayed manual purely because the software was too old, too closed, or too obscure to connect to. But I would not had Astra the whole engine and [music] walk away. So here's how I would split it. And this is the part that I suggest you don't skip. Some of it it just does because if it gets that wrong, nothing breaks. Some of it it gets ready and then I need to look and approve before anything gets sent [music] out. And some of it needs to stay with me because there is a person on the other end and I need to make sure that we keep the standards high. This is how a one person business gets capacity without getting reckless. [music] AI does not make you unnecessary. It just makes your judgment worth a great deal more. And to use that judgment, you need to know what the machine is actually doing. Now, every cockpit has instruments for a reason. You cannot fly by how busy the cabin feels. [music] And you cannot run a business by how productive the AI looks. The instruments need to answer five questions. Are you creating demand? Are conversations turning into customers? Are customers getting the result that we promised to them? Is the work making money? And where am I still the bottleneck? I would give Astra access to the business data and ask it to build a simple weekly review, not 50 [music] charts, dashboards, five decisions. What changed? Why did it change? What needs me? What should stop? And what is the single best move for the next seven days? That's it. And this is another place where the finished object is important because it can build a spreadsheet with the formula and the scenario inside it, not just paste numbers into a table. [music] It can compare what actually happened with the flight plan and point out where reality has broken the original assumption because it will. Because the point of a dashboard is not to admire the numbers or show them in a beautiful way. It's to change a decision. And once Astra knows what makes a difference, the cockpit does not have to wait for you to open it. Now, let's talk about autopilots because this is the part that changes the whole relationship. Everything we have done up to now can still start with me. I open the system, I ask, and it works. That is not much better version of doing everything myself. But it is still me starting the engine. Autopilot is different. These are jobs that run on their own. Check this every Monday. Watch that and tell me when it changes. [music] Review the pipeline every morning. Prepare the client report on this first of the month. Maybe compare this week against the plan before I start work on Friday. You define the job once in your language. Let's [music] say English. And it keeps going without waiting for another prompt. Now, there is a limit to how many of these can run on each plan. And I want to say because I know many times people comment three scheduled jobs can work on free [music] and then 10 on business and edu 15 on pro and enterprise. So keep that in mind as you build your workflows. [music] And this might have changed by the way depending on when you're watching the video. What I'm trying to say is that these are not automations that you collect. Okay? They're positions that you hire. If I only had five, I would [music] start with these first. a weekly market and competitor brief. Second, a daily monitor of your new opportunities and prospects. Third, a pipeline and follow-up review. Fourth, a delivery and quality check. And five, a monthly money and performance review. You don't have to automate everything. Automate the five things that the business cannot afford to forget. A business with five jobs that always happen is, trust me, a lot better than one with 40 that mostly [music] don't. And this is the moment that Astra becomes much more important. It starts behaving like the thing the business runs on. We have radar, flight plan, communications, engine, instruments, and autopilot. One intelligence across the whole operation. But none of that means the business can reach a million dollars. That depends on the last component. [music] And that last component is the pilot. And this is the part that I would never delegate. All right? the decision about what deserves to even exist. Astra can research the market. It can find patterns and compare opportunities and build assets, use software, and tell me what to do next. But I still have to decide which customer matters, which problem is worth solving, what promise I'm prepared to make, what good looks like, and what the business will refuse to do. Also, whether the destination is even worth reaching at all. This is the number one mistake that I see people make when they build with AI. They build the system before they have sold anything. They spend three months assembling a beautiful cockpit for a plane with nowhere to go. They have no validated offer, no customer, no first sale. Just a very impressive system waiting [music] for a business to arrive. I think that's a no-go. You need to build the offer, get the first yes, and then build the machine around the thing that actually works. All right. Astra can give one person the capacity that used to take a team but it cannot give a weak business strong economics. It cannot make an irrelevant offer valuable and it cannot care about the customer on your behalf. That is why judgment becomes worth more and not less. When execution was expensive, being able to build something was the advantage. Now that execution is cheap. Choosing what should be built is the advantage in my opinion. of the advantage goes to the people who know what is worth doing and can turn that into a system that works without eating their lives. [music] So, let me bring this full circle. Like I said, the information age is over. The permission economy is dead and the new game, the one where one person with one clear offer and one intelligent interface can outexecute a larger team still stuck in disconnected systems is being played right now. The one person business is no longer a theory. [music] It is radar that finds the signal, a flight plan that adds up, communications that create demand, engines that produce the result, instruments that protect the model, and work that keeps happening after you leave your keyboard. GPT6 Astra does not give you the million-dollar idea. It gives you enough leverage to stop a good idea being trapped inside your calendar. And if you're sitting there thinking, fine, but what would I actually sell? That is the problem we solve next. So, if you [music] need an answer to that question, I would love to invite you to join us in our community, you can use the QR code here or the link down below. We have an entire course that helps you answer it as well as put that into practice with the help of AI because the question was never whether AI could help run one person business. [music] The question was always whether you would be the one in the pilot seat. So, thank you so so much for watching. Like this video if you did. Be sure to subscribe if you haven't done so. Share this with anyone in your circle of friends or family or co-workers who would like to start a oneperson business and is looking for ways to use AI to do that. And until next time, I suggest you go ahead and watch this video here and I'll see you there. Bye.