Unbundle Expensive Services into AI Apps
Find a service only the rich could afford, do it with a general chatbot, then spin the working ones into apps
- Difficulty
- Moderate
- Time to result
- ~months to results
- Steps
- 4
- Confidence
- 90%
Every's product-incubation playbook targets services that were historically so expensive only rich people or big companies could buy them — a chief of staff, a lawyer, a ghostwriter, someone to organize your files. Cheap intelligence makes these affordable, so Every first satisfies its own latent demand using general-purpose tools (ChatGPT/Claude), validates that it actually works, then unbundles the proven use case into a standalone app — testing internally first, since Every's own AI-first team mirrors its audience.
Origin
Dan Shipper's articulation of how Every incubates products (Kora, Sparkle, Spiral, Monologue); he notes it 'only sort of snapped into focus recently' after doing it intuitively.
Core principles
- 01Cheap intelligence stimulates demand for services only the wealthy could previously afford
- 02Validate with general-purpose tools before building anything bespoke
- 03Software is becoming content — a totally new, greenfield design space with no established playbook
- 04Your internal team, if it mirrors your audience, is your first and truest test market
- 05Playing at the edge means you're doing today what everyone will need in ~3 years
How to run it
- 1
Find the latent expensive-service demand
Notice the moments you wish you had a ghostwriter, a lawyer, a chief of staff — high-value services you can't afford enough of because they're so expensive.
Pro tip Good candidates are services with far more demand than can be fulfilled at their current price.
- 2
Try it with a general-purpose tool first
Use ChatGPT or Claude to do the job. See if it's actually useful and actually works before committing to build.
Watch out Don't skip straight to building — the general-tool test is your cheap validation gate.
- 3
Unbundle the working use case into an app
If the general tool proves the value, extract that specific job into its own dedicated product, the way spreadsheet workflows were unbundled into B2B SaaS.
- 4
Test internally, then ride the audience pipeline
Ship it inside your own team first; judge success by whether it's 'a banger' internally. Because your audience shares your team's vibe, internal winners become your first external users.
Pro tip Measure early success by real internal adoption ('everyone just started using it'), not projections.
In the wild
A chief of staff was historically a service only executives could afford. Every built Kora to manage email with AI. All-in, including salaries, they spent maybe ~$300k — a product Shipper says was technically impossible three years ago at any budget because email summarization and auto-responses needed modern models.
→ Kora launched publicly with 2,500 active users and millions of emails flowing through it, built by two engineers.
Products like Monologue were adopted internally first — 'everyone just started using it and we're like okay we've got something here.' Because Every's readers share the team's AI-forward vibe, internal winners convert into the first external user base.
→ A repeatable pipeline: internal banger to external launch.
Common mistakes
Dismissing GPT wrappers as low value
Shipper argues 'GPT wrappers' are maligned for no reason and are enormously valuable — the general-tool-then-unbundle path is exactly how you find real products.
Is it for you?
Best for
AI-first founders and small teams whose own daily work surfaces demand for premium services, and whose audience mirrors their team
Not ideal for
Building a conventional zero-to-one SaaS app with no technical skill — Shipper says that's 'not even within sight'; this works for new, content-like software forms
From the transcript
“there are these things that were historically really expensive um that only rich people or big companies could buy”
“we start to then use like CHBT and Claude first, these general purpose tools to try it and see is this useful”
“if it does, we will like unbundle it into its own separate thing that um becomes an app”
“we measure the success of the product by like is it a banger inside of every”
“organizations like ours people who are playing at the edge we're doing things that in like 3 years everybody else is going to be doing”
From the episode
The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code
Dan Shipper (co-founder/CEO of Every)