AIaaS, DaaS, Subscription — What Business Are You Building?

“How should we charge for AI?”

That question is now showing up in founder meetings, board reviews, and product strategy sessions everywhere.

It resurfaced recently in commentary around Tesla’s Full Self-Driving subscription. The claim was simple: Tesla isn’t fundamentally a car company. It’s an AI and data company. The vehicle is distribution. The subscription is the durable revenue stream. A software-enabled-device?

Whether that thesis proves entirely correct is less important than what it signals.

AI is no longer being discussed as a feature enhancement. It’s being treated as a structural shift in the business model.

This Is Not a Pricing Debate

Inside most companies, the conversation narrows to packaging and pricing mechanics.

Should AI be bundled into existing tiers? Should it be a premium add-on? Metered by usage? Sold as AIaaS or DaaS?

These are legitimate options. But they are downstream decisions.

They assume you’ve already decided what role AI plays in your exchange of value with customers and how customers will depend on you because of it.

Often, that clarity doesn’t yet exist.

The Founder-Level Tension

In a recent strategy session with a start-up, we mapped how value was being monetized across their offerings.

We found seven different value exchange models operating at once.

Subscription in one segment. Usage-based in another. AI bundled in one offer, separated in another. Enterprise licensing layered on top.

When we asked whether that complexity was intentional, the room paused.

Multiple models are not automatically wrong. But when they emerge without design, they signal drift.

Each model reflects a belief about what customers rely on and what the company intends to be known for.

That is not a pricing choice. It is a strategic declaration.

Where This Connects to Value Exchange

In our previous piece, we argued that “we lost on price” is usually a failure in value exchange design.

AI raises the same issue at a larger scale.

Does AI fundamentally change how customers rely on you? Or does it simply enhance existing workflows?

If it strengthens what you already provide, it likely belongs within your current profit stream.

If it changes frequency of use, integration into operations, or makes you harder to replace, then the structure of your revenue should evolve with it.

This isn’t about charging more.

It’s about aligning revenue structure with real dependence.

The Applied Frameworks Reframing

This isn’t an AI feature decision.

It’s a profit stream design decision.

Before debating packaging or pricing mechanics, leaders must examine how AI reshapes customer clusters and the combinations of capabilities they rely on.

Does it create a new center of gravity in your offering?
Does it deepen the capabilities customers cannot easily replace?
Does it shift where your durable advantage sits?

If reliance shifts, your profit stream should shift.

If it does not, forcing a new model adds complexity without strengthening the exchange.

AI monetization without profit stream clarity becomes experimentation without direction.

Why Tesla Is a Useful Case

Tesla’s move to subscription for Full Self-Driving reflects a belief about where long-term reliance will sit — in continuously improving AI capability supported by accumulated data.

The revenue structure signals that belief.

It is not simply a pricing tactic.

Every founder confronting AI decisions is making the same kind of bet, whether explicitly or implicitly.

What will customers depend on us for five years from now?
What combination of capabilities will feel indispensable?
What revenue structure best reflects that reliance?

Those questions precede monetization mechanics.  It follows a solution lifecycle with intent. 

Where Profit Streams Makes This Visible

Profit Streams exists to surface these choices.

It maps customer segments, the combinations of capabilities they rely on, and how that reliance supports recurring revenue.

Only once that logic is clear does packaging and pricing become coherent.

AI may deserve its own stream. It may reinforce an existing one. Or it may simply increase expectations without changing dependence.

The difference is not in the algorithm.

It is in how the value exchange evolves.

AI is not a pricing tactic.

It is a decision about how your profit streams evolve — and what business you are building.

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Applied Frameworks
Designing profit streams that stand at their price