Five industries walked through — what AI-native looks like in the wild — step 1 of 9
Reading other people's businesses is the cheapest education in the AI era
Important framing before you start. The five industries we walk through below are illustrative scenarios, not actual deployments. The Klarna anchor case at the end of this lesson is real, with documented receipts (Anthropic press release, OpenAI case study, Sebastian Siemiatkowski's May 2025 walk-back). The HVAC, insurance, recruiting, support-ops, and software-dev walkthroughs are "what if you applied this framework" explorations. They're useful for pattern recognition. They are not proof the framework has shipped at any of those specific companies. Read them as thought experiments. Read the Klarna section as a case study.
This lesson is a theory lesson. There is barely any code. That is on
purpose. The previous lesson gave you a framework
(is_ai_native_ready(spec)); this lesson gives you five examples of
that framework applied to real industries. The point is pattern
recognition. By the end you should be able to look at any service
business and answer the question "what does this look like when 80% of
the operational work is handled by agents?"
If you've read the curriculum this far, you've spent most of your time on code. That's the right move when you're learning to read what AI ships you. It's the wrong move when you're trying to figure out which company to build, or which workflow at your current job to rebuild first. For that question, the bottleneck is not Python. It is pattern intuition — the ability to look at a messy real-world process and see the underlying machine.
That intuition comes from reading case studies, not from drilling syntax. So this lesson is mostly prose. Skim it once, then come back when you're sketching a real project.
The five industries
Greg Isenberg's writing on AI-native services has named categories where AI-native companies will out-execute incumbents: agencies, brokerages, law-adjacent services, accounting firms, compliance shops, healthcare admin, real estate operations, education services, logistics coordinators, BPOs. We're going to walk through three of them in detail, then add two from elsewhere in the discourse (a process-debt-in-software-dev example, and a support-ops example).
The five we'll cover:
- Home services — the inbound-job-to-paid-job lifecycle
- Insurance brokerage — the document-heavy renewal cycle
- Recruiting — the source-to-place pipeline
- Support operations — the ticket-to-resolution flow
- Software development — process debt and the QA bottleneck
For each, we'll show:
- What the workflow looks like under the OLD model (humans coordinating, tools sprinkled)
- What the workflow looks like under the AI-NATIVE model (agents executing, humans supervising)
- Where the wedge sits — i.e., the one or two specific decisions that, once automated, cascade through the rest of the business
- The metric that proves the rebuild worked
What you're looking for as you read
The same three signals across every example:
- High volume + existing rules + over-coordinated humans. This is the step 1 criterion. Every case study below hits all three.
- Implicit knowledge made explicit. Watch for the moment in each case study where some piece of tribal knowledge ("Sarah handles that one") becomes an explicit rule the agent can read.
- The human's role becomes more leveraged, not less important. The recruiter stops being a data janitor and becomes a relationship closer. The support lead stops routing tickets and starts designing escalation logic. Watch for this transition in every case.
If you can see those three signals in each industry, you can probably see them in your own industry. That's the whole point.
A note on what this is not
This is not a "here's how to use ChatGPT for your business" lesson. This is a lesson on what it takes to restructure a business so agents can run inside it. The central thesis (echoing Isenberg's writing on AI-native services):
An AI-native company is not a company that uses AI. It is a company that has been rebuilt so AI can actually operate inside it.
The case studies that follow describe rebuilds. Bolt-on chatbot implementations don't count. Custom GPTs labeled "Brand Voice Assistant" don't count. The bar is: could a small team operate a business at 5-10× the productivity of a comparable incumbent because the workflow itself is machine-readable?
If yes, it's AI-native. If no, it's AI-assisted.
Five industries walked through — what AI-native looks like in the wild — step 1 of 9
Reading other people's businesses is the cheapest education in the AI era
Important framing before you start. The five industries we walk through below are illustrative scenarios, not actual deployments. The Klarna anchor case at the end of this lesson is real, with documented receipts (Anthropic press release, OpenAI case study, Sebastian Siemiatkowski's May 2025 walk-back). The HVAC, insurance, recruiting, support-ops, and software-dev walkthroughs are "what if you applied this framework" explorations. They're useful for pattern recognition. They are not proof the framework has shipped at any of those specific companies. Read them as thought experiments. Read the Klarna section as a case study.
This lesson is a theory lesson. There is barely any code. That is on
purpose. The previous lesson gave you a framework
(is_ai_native_ready(spec)); this lesson gives you five examples of
that framework applied to real industries. The point is pattern
recognition. By the end you should be able to look at any service
business and answer the question "what does this look like when 80% of
the operational work is handled by agents?"
If you've read the curriculum this far, you've spent most of your time on code. That's the right move when you're learning to read what AI ships you. It's the wrong move when you're trying to figure out which company to build, or which workflow at your current job to rebuild first. For that question, the bottleneck is not Python. It is pattern intuition — the ability to look at a messy real-world process and see the underlying machine.
That intuition comes from reading case studies, not from drilling syntax. So this lesson is mostly prose. Skim it once, then come back when you're sketching a real project.
The five industries
Greg Isenberg's writing on AI-native services has named categories where AI-native companies will out-execute incumbents: agencies, brokerages, law-adjacent services, accounting firms, compliance shops, healthcare admin, real estate operations, education services, logistics coordinators, BPOs. We're going to walk through three of them in detail, then add two from elsewhere in the discourse (a process-debt-in-software-dev example, and a support-ops example).
The five we'll cover:
- Home services — the inbound-job-to-paid-job lifecycle
- Insurance brokerage — the document-heavy renewal cycle
- Recruiting — the source-to-place pipeline
- Support operations — the ticket-to-resolution flow
- Software development — process debt and the QA bottleneck
For each, we'll show:
- What the workflow looks like under the OLD model (humans coordinating, tools sprinkled)
- What the workflow looks like under the AI-NATIVE model (agents executing, humans supervising)
- Where the wedge sits — i.e., the one or two specific decisions that, once automated, cascade through the rest of the business
- The metric that proves the rebuild worked
What you're looking for as you read
The same three signals across every example:
- High volume + existing rules + over-coordinated humans. This is the step 1 criterion. Every case study below hits all three.
- Implicit knowledge made explicit. Watch for the moment in each case study where some piece of tribal knowledge ("Sarah handles that one") becomes an explicit rule the agent can read.
- The human's role becomes more leveraged, not less important. The recruiter stops being a data janitor and becomes a relationship closer. The support lead stops routing tickets and starts designing escalation logic. Watch for this transition in every case.
If you can see those three signals in each industry, you can probably see them in your own industry. That's the whole point.
A note on what this is not
This is not a "here's how to use ChatGPT for your business" lesson. This is a lesson on what it takes to restructure a business so agents can run inside it. The central thesis (echoing Isenberg's writing on AI-native services):
An AI-native company is not a company that uses AI. It is a company that has been rebuilt so AI can actually operate inside it.
The case studies that follow describe rebuilds. Bolt-on chatbot implementations don't count. Custom GPTs labeled "Brand Voice Assistant" don't count. The bar is: could a small team operate a business at 5-10× the productivity of a comparable incumbent because the workflow itself is machine-readable?
If yes, it's AI-native. If no, it's AI-assisted.