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Anthropic’s CEO: How to Build a 1 Person Business with Claude — Transcript

by Nate Herk | AI Automation · 2,842 words · 428 segments · language en · Watch on YouTube

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0:00So, the CEO of Anthropic just said that
0:01the first one person billion-dollar
0:03business will be created this year using
0:05Claude. He explained the three things
0:07that this business will have, and these
0:08can be implemented by anyone. Even
0:09Instagram's founder said that he could
0:11probably build and run Instagram from
0:13scratch with just Claude and his
0:14co-founder. So, today I'm building a $1
0:16million business using Claude and three
0:18elements that Dario said are required to
0:21be able to pull this off. I'll show you
0:22how I built it, what it does, and how I
0:24made sure that it can run with zero
0:26employees. So, let's get into it. So,
0:27the reason that we're building a
0:28million-dollar business instead of a
0:30billion-dollar one is because a billion
0:31dollars is a great headline, but a
0:33million-dollar business is way more
0:35approachable and realistic for the
0:36average person looking to get started.
0:38Let's start with the three things that
0:39Dario actually talked about. Now, real
0:40quick, Dario didn't publish like an
0:42official three-step checklist. He was
0:44answering a question in an interview
0:45about what a one-person billion-dollar
0:47company could look like. I'm turning the
0:49examples from his answer into three
0:51filters that we can actually use
0:53>> [music]
0:53>> today. So, the first filter is a
0:54business that trades or deploys its own
0:56capital. Dario's example was a
0:58proprietary trading firm. The same
1:00general model could be a real estate
1:01flipping company or even a used car
1:03dealership. The business uses its own
1:04money to buy something, improve it, or
1:06trade it, and hopefully sell it for
1:07more. The benefit here is that you don't
1:09need thousands of customers or a massive
1:11sales team, but you do need money,
1:13expertise, and a willingness to take on
1:15real financial risk. So, for this video,
1:17that filter helped me rule out the
1:19capital heavy route. I wanted something
1:20that a normal person could start without
1:22putting a bunch of their own money at
1:23risk. Now, the second filter is software
1:25because normal people can build useful
1:27software with just Claude code now. And
1:29the options here are basically endless.
1:30You could build software that writes
1:32content or even runs a cybersecurity
1:33audit. But being able to build software
1:35doesn't automatically make it a good
1:37one-person business because you could
1:39still end up with a product that needs
1:40custom onboarding, constant support, and
1:42a salesperson on every single deal. So,
1:44that final filter is that sales and
1:46customer support need to be highly
1:47automated without the experience
1:49becoming terrible for the actual users.
1:50And that filter narrows the list quite a
1:52bit. The offer should be repeatable and
1:54need very little customization, and it
1:56should be easy to start using for the
1:58users without, you know, heavy
1:59regulation or tons of different support
2:01questions. Those types of support
2:02questions need to be able to be answered
2:04by an AI agent. That's why simple
2:06products like a file converter or an ad
2:08reviewer, things like those make sense
2:10cuz the customer understands what
2:11they're buying, they can get the result
2:12quickly, and they don't need like a
2:14custom consultation in order to get
2:16value out of the product. So, the first
2:18filter ruled out a capital-heavy
2:19business. The second led me to software,
2:21and the third narrowed it to a product
2:23that could run without hiring a massive
2:24team. Or, I guess a team at all. Now, I
2:26gave Claude three ideas to compare. One
2:28was a scheduling tool, so something like
2:30Calendly. Another one researched
2:31companies and drafted cold outreach
2:33messages. And the last one stress tested
2:35customer-facing AI agents before a
2:37business actually launched them. So, I
2:38asked Claude to run through all these
2:39different examples, you know, play
2:41devil's advocate, spin up, you know,
2:42like a war room debate panel, and I
2:44asked who would pay for each idea,
2:45whether the result could be delivered by
2:47software, and whether one person could
2:49realistically sell and support [music]
2:51it. So, like the scheduling tool was
2:52very easy to use, but it would be
2:53entering a market full of mature
2:55products. The outreach tool was super
2:57easy to explain. It doesn't prove that
2:58those emails will convert. Now, the
3:00third idea had a much clearer result. A
3:01company connects its AI agent, the
3:03software puts it through difficult
3:04customer situations, and the company
3:06gets a report showing where the agent
3:08failed. So, that's the business that I
3:09decided to build today, and Claude and I
3:11named it Agent Report Card. In simple
3:13language, it's quality assurance
3:14software for AI agents, AI eval
3:16software, essentially. So, an AI agency
3:18might build customer support bots for 10
3:19different clients, and before they hand
3:21one over, they need to know that that AI
3:23agent won't invent a new policy or
3:25refund the wrong person or expose
3:27private data, things like that. So,
3:28basically, what they need to do is have
3:30proof that the AI agent will actually
3:31perform as expected rather than just
3:33going on vibes. And without software,
3:35somebody has to test all of those
3:37conversations manually. And whenever the
3:38agency maybe updates the agent with a
3:40new prompt or a new AI model, its
3:42behavior is going to change. So, Agent
3:44Report Card will run the tests, save the
3:46evidence, help diagnose the failures,
3:47and create a report that the agency can
3:49give to its client. Now, the tool stack
3:51is pretty simple. Claude does the AI
3:52work, Claude Code helped me build the
3:53product, the app stores the test
3:55history, and then Clay helps find
3:57potential customers. And just to be
3:58clear, this business doesn't literally
3:59trade its own capital. That was the
4:01route I used the first filter to
4:02eliminate. It does fit the software
4:04route, and the product is repeatable
4:05enough that sales and routine supports
4:07can be automated around it. And by the
4:09way, you can get everything that I'll
4:10build to start this business for free.
4:11I'll attach the skills, the prompts, and
4:13the frameworks from this video inside of
4:15my free school community. So, if you'd
4:16like to follow along, you can get them
4:18for free by joining with the link in the
4:19description. If you have any doubts or
4:20problems, someone from my team or a
4:22member of the community will help you
4:23out. So, let's get back to the $1
4:25million business. So, I divided the
4:27one-person business into three parts.
4:28First is the actual work the customer is
4:30paying for. Second is the agent that
4:32handles sales and customer support, and
4:33third is the workflow that finds
4:35potential customers and prepares the
4:36outreach messages. So, let's start with
4:38the product. I've connected a customer
4:39support agent to Agent Report Card. And
4:41you guys can see the connection right
4:42here. The app runs that agent through 16
4:44tests. Think of them like mystery
4:46shoppers. Some ask normal questions,
4:48while others try to get the agent to
4:49take a risky action or answer without
4:51enough information. And this is
4:52essentially our golden data set that
4:53we're testing the agent against because
4:55we know what the correct answers should
4:56be or what the correct agent actions
4:58should be. So, the first completed run
5:00right here scored 88. 14 tests passed
5:02and two failed. So, now we can open up
5:04these failures, and we can see the
5:06customer's question, the answer the
5:07agent gave, and why that answer actually
5:09failed. So, this customer here
5:10threatened a billing dispute. So, the
5:12agent should have stopped and send the
5:13conversation to a human, but it didn't
5:15do that clearly enough. I sent that
5:16failed conversation to Claude. Claude's
5:17able to diagnose the problem and suggest
5:20a tighter instruction for billing
5:21disputes. [music] I approved that new
5:22policy version and ran the same 16 tests
5:25again, and the score was still 88. So,
5:27what happened here was the billing
5:28problem was fixed, but a different test
5:30failed because these agents can respond
5:32a little differently from one run to the
5:33next because they're AI agents. They are
5:35non-deterministic. So, fixing just one
5:37example doesn't prove the whole agent is
5:38reliable, which is why in this example
5:40we're doing 16, but realistically, the
5:42bigger the golden data set, the more
5:43confidence you can have in the quality
5:45and performance of these AI agents. So,
5:47anyways, I ran the suite again and this
5:49time the score moved to 94. Both
5:51original failures were fixed, but the
5:52agent still mishandled a request to
5:54export private customer data. So, you
5:56can see exactly what improved and what
5:57still needs work. The app isn't forcing
5:59a perfect score just to make the result
6:00look good. It's helping you diagnose and
6:02fix. Then after all this, I click create
6:04report and this is the actual
6:05deliverable. The client can see the
6:06score, the test that were run, what
6:08changed, and the issue that's still
6:09open. The private conversations and full
6:11prompts stay inside the agency's
6:12workspace and that is the core business
6:14workflow. The customer isn't paying for
6:15the dashboard, they're paying for proof
6:17that their agent was tested before it
6:19reached real users and put their
6:20reputation or their business at risk.
6:22All right, so now part two. Now the
6:23business needs a way to handle new leads
6:25without me taking the same introductory
6:27call all day. So, a potential customer
6:29can submit this trial form. In this
6:30example here, the agency manages 14
6:32agents, still test them all manually,
6:34and has already seen one agent try to
6:36refund the wrong order. So, what Claude
6:37will do here is read what they
6:38submitted, explain whether the company
6:40is a good fit, and recommend a small
6:41trial using its human risk agent. You
6:44can see right here the reason it
6:45qualified the lead it created. And I
6:47still make the final decision before
6:48anything moves forward. So, the
6:49repetitive part of the first sales
6:51conversation is pretty much handled.
6:52Claude doesn't send an email, charge a
6:54card, or promise the customer anything
6:55on its own. Now, customer support works
6:57very similarly. I submitted a normal
6:59question asking how to rerun only the
7:00tests that failed. Claude found the
7:02answer in the product guide and polished
7:03it to the customer support page. So,
7:05then I submitted a request for a refund
7:07and permanent account deletion and what
7:08Claude did is drafted a response and
7:10sent the ticket to me, but it left the
7:11actual refund and deletion completely
7:13untouched. So, routine questions can
7:15keep on moving through while decisions
7:17involving money or customer data,
7:18essentially decisions that are high
7:19risk, still come to the founder. And so,
7:21obviously when I say zero employees,
7:23right now I don't mean that nobody
7:24works. You know, it's it's a one-person
7:26company, one person running the company,
7:28meaning me. But the software handles the
7:29repetitive work and I can handle the
7:31decisions that require judgment and
7:33think about how do I actually grow this
7:34whole operation. Now, the last part,
7:36which is part three, is finding
7:37companies that might actually need this.
7:39So, what I do here is I use clay to find
7:41businesses that are publicly deploying
7:42AI agents. And then Claude checks the
7:44public sources, it explains why that
7:46company might be relevant, and drafts a
7:47message to them based on the evidence.
7:49Now, the reason we're using Clay here is
7:50because it just has the best B2B data
7:52out there. And in order to successfully
7:54do cold outreach, you need to be able to
7:56build a high-quality list of
7:57decision-makers that actually fit your
7:59ICP. You need to be able to enrich those
8:01leads so that you can actually
8:02personalize the messages at scale. And
8:04then you can also schedule all of the
8:06sending inside of Clay as well. This
8:07software will pull data that isn't
8:08accessible with other tools or agents.
8:10So, we're getting the highest-quality
8:12stuff right here. And also, in this
8:13specific example, we did use Claude to
8:15generate the personalized messages based
8:17on the enriched leads, but Clay could
8:19actually do that as well. So, it's
8:20really a one-stop shop. And if you guys
8:22want to check out a deeper dive video
8:23that I did with Clay and Claude Code,
8:25I'll tag that right up here. But
8:26anyways, now if I open up one of these
8:28companies, you guys can see the source
8:30and the message that Claude wrote. And I
8:31can review and approve the draft, but it
8:33stays marked [music] not sent. And that
8:34matters because finding a relevant
8:36company and writing a good message is
8:37not the same as getting a customer. So,
8:39this workflow automates that slow
8:41research and preparation. And the next
8:43real test is obviously sending the
8:44outreach and getting replies, seeing
8:45whether companies will pay, and being
8:47able to customize that actual process
8:49because there's multiple steps in that
8:51cold outreach funnel where clients may
8:53drop off. Now, at 499 bucks per month,
8:55Agent Report Card would need 168 active
8:58customers monthly to pass $1 million in
9:00annual recurring revenue. So, I now have
9:02the product workflow, the sales and
9:03support system, and the client
9:04acquisition workflow that one founder
9:06would need to operate this type of
9:07business. What I don't obviously have
9:09yet here is 168 paying customers. So,
9:11the first milestone is getting five
9:12agencies to connect their own agents,
9:13use the report, and pay for it, and
9:15figure out what type of feedback we get,
9:17and how we need to improve the process.
9:18[music] So, now I would just need to get
9:20very, very clear on what I call the AI
9:22monetization readiness assessment, which
9:23is the three P's: pain, promise, person.
9:27Actually, no, I like to go pain, person,
9:28promise. So, what is the very specific
9:30pain point that you're trying to solve?
9:31What is the exact person that you're
9:32trying to solve that pain for? And how
9:34can you promise that your software is
9:36going to solve that exact pain point for
9:38that exact person. So, for Agent Report
9:40Card, for example, I'd say that the pain
9:41is that agencies are manually testing
9:43customer support agents and can't prove
9:45the quality of them before pushing them
9:46into production. The person is an AI
9:48automation agency who is deploying
9:50customer support agents for their
9:52clients. And the promise is that Agent
9:53Report Card runs your agents through 16
9:56or more high-risk scenarios and shows
9:58you exactly where those agents fail and
10:00creates a client-ready reports on that
10:01evaluation. So, after I read off my
10:03three P's, you might be wondering why
10:05focus specifically on customer support
10:06agents instead of just general AI
10:08agents? Because saying all agents is
10:10very broad. You know, sales agent,
10:12finance agent, sport agent, they all
10:13have different types [music] of tests,
10:15different processes. And if we tried to
10:16cover everything, the product would
10:17become generic and the promise would get
10:19a little bit more vague. It has to be
10:21very specific and strong. And the truth
10:23is here, there are already other
10:24products out there that do evals or QAs
10:26for AI agents. And those other companies
10:28probably already have customers, more
10:30capital, and a reputation. So, customer
10:32support agents gives us a repeatable,
10:34high-risk situation that we can test and
10:36we can get really good at. Things like
10:37refunds, billing, disputes, account
10:39deletion, private data requests, knowing
10:41when to involve a human, you know, those
10:43escalations, things like that. It allows
10:44me and my software to become experts at
10:47the specific process. We can then, if we
10:48need to, later expand into other agents.
10:51But we need to get a good foundation
10:52laid. And starting narrow gives us a
10:53specific customer, a super painful
10:55problem, and a promise that our software
10:56can actually deliver on. And because of
10:58the way that we're looking to start the
10:59pricing, we would need 168 customers to
11:01pay us each monthly to pass $1 million
11:04annually. And that's obviously not going
11:05to happen quick and it's not going to be
11:06super easy, but 168 customers is
11:08realistic in that niche. Okay. So, in
11:10this video, I kind of talked a lot about
11:12a one-person software business. But what
11:13if you wanted to go down the
11:14service-based route, which is actually
11:16what I did? I started out as an AI
11:17freelancer, and then once I passed
11:19around 10K per month just by myself, I
11:20decided to start bringing on developers
11:22and sales people and eventually scaled
11:24the whole operation with some
11:25co-founders as well. So, if you guys do
11:26want to learn more about that road map,
11:27there is a link in the description for
11:29that exact road map. But anyways, that
11:30is going to do it for this one. So, if
11:32you guys enjoyed the video you learned
11:33something new, please give it a like it
11:34helps me out a ton. And as always, I
11:35appreciate you guys making it to the end
11:36of the video and I'll see you on the
11:37next one.
11:38Thanks, everyone.

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