Every consultancy is “AI-enabled” now. LinkedIn is drowning in the phrase. What does the label actually mean when you strip the marketing off?
We think there’s a real distinction — and it’s not the model you use.
AI-enabled
You bolt an LLM onto an existing workflow. The chatbot is a widget. The copy assistant is a plugin. When AI breaks or the API goes down, everyone falls back to how they used to work.
There’s nothing wrong with this. Most enterprises need to be here first. But it doesn’t change how the org runs.
AI-native
The workflow itself is designed around the assumption that AI is one of the collaborators. That looks boring from the outside — a Notion tab, a Cursor session, a couple of internal tools — but it changes:
- Who does what. Junior engineers ship senior-level PRs because the review layer is a model, not a Slack DM to a busy staff engineer.
- What “done” means. The definition of done moves upstream, because AI is fast enough to catch the boring parts. The interesting work happens where the AI can’t reach.
- How you hire. You stop hiring for “can this person write a for-loop” and start hiring for “can this person tell when an LLM is wrong”.
Why it matters for a studio
If we say “we’re AI-native”, it means the team you get paid for is smaller than the team you effectively get. A three-person milbo squad ships like a six-person Y2020 team. That maths is only true because the tooling and the process were built together — not one bolted onto the other.
The test
Ask a “AI-enabled” agency: what would break if the OpenAI API went down for a week? If the answer is “not much, we’d still hit the deadline” — that’s actually the honest one. They’re using AI as a productivity tool. If they claim “everything would break”, they’re either lying or in over their head.
We’re closer to the first answer than most people expect. The tools are commodity. What we sell is the judgement about when to reach for them.