Application layer AI companies cannot build sustainable moats against model providers through simple technical adjustments like fine tuning or model routing.

Product Shape is the Moat

Wrapper Laundering

Since 2022 there’s been a “wrapper laundering” shell game.

Founders build AI wrappers and continually come up with simple stories about why their work is “hard” and “defensible” in order to raise money.

Once one story expires, a new one quietly slips in to fill its place.

First it was fine tuning. Then it was evals. Now it’s model routing.

All of these can be explained in a few sentences and paired with scientific looking charts in order to seem credible—to feel legible.

But most companies that fine tuned their own models faded away. Evals are useful, but not everything. And it’s going to be difficult to beat the model providers at routing in the long run (a topic for another article).

Shape is the Moat

Obviously, we think application layer companies have a reason to exist at @SpellbookLegal . But we think the reality is more nuanced and less sexy:

The best advantage app layer companies have against model companies is Product Shape.

Look around you: almost every mature product you have is fit for purpose.

You can’t have a great kettle that is also a great toaster. You can’t have a great mug that’s also a great lamp. You can’t have a great writing app that’s great at audio production.

Evolutionary pressure, over the long run, causes products to become increasingly fit for purpose.

No matter how smart you are, you can’t create the best toaster that is also the best kettle.

We don’t use many Swiss Army knife products at all.

The competitiveness between everything in AI is a symptom of unoriginality.

Most companies are still focused on the terminal interface of AI: chat.

Yes: if you build a chat box with tools, you’re going to have to come up with some clever numbers that show why your chat is incrementally better than Claude for your vertical.

But that’s a losing game in the long run. If something can be accomplished well with a chat experience, Claude or ChatGPT is eventually going to be great at it. Anthropic will tune its chat-shaped product for every vertical it has data on (all of them).

What model companies can’t easily copy is your product shape.

An Example of a Rich Product Shape

Yesterday at Spellbook we launched Autonomous Contract Management, our deeply opinionated end-to-end infrastructure for managing agreements with AI.

0:35The shape of managing contracts with AI

The shape of managing contracts with AI

It contains a wide breadth of connected and optimized shapes that are fit for purpose:

• Surfaces for auto-ingestion and triage across the business

• Timeline views for following the steps of a negotiation in realtime

• Surgical redlining workflows that are specifically designed for lawyers

• Collaboration surfaces to manage escalations and approvals

• Storage and recall mechanisms that only make sense for contracts

And yes, we also have a data moat, which is realtime tracking of contract market data that we can use to provide better and better recommendations. That’s also a part of our product shape.

And we’re still only scratching the surface. There’s so much left to build, in order for us to create the best AI infrastructure for contracts. How should disputes get resolved? How do we link into finance for reconciliation? How do we ensure contract performance?

What Model Providers Can’t Do

OpenAI and Anthropic can’t be everything to everyone. Maintaining 1000 product shapes is not feasible for orgs in their positions. It would dilute their efforts away from higher leverage activity. And even if they could, it’s impossible for a brand to stretch that far. Would you buy a computer from an automotive manufacturer?

You might say: “Soon AI will create any UI you want on the fly. What about that?”

This is great for ad-hoc use cases. But anyone who has built apps in the real world knows that non-deterministic, dynamic UIs across an org can’t work in daily use. If we move one button in Spellbook, we get 20 irritated support tickets. People want UI that is as stable and intuitive as a washing machine.

App Layer Competitors are Different

In this article I’m talking about building a moat against the model providers. They can’t stretch across 1000 shapes.

However, you will certainly have app layer competitors who can copy your shape. In this case, you need to concern yourself with more traditional software moats: network effects, data, brand and raw speed. But at least here, you are not competing against companies with near unlimited money and talent.

The Cambrian Explosion of Product Shapes

I think that the Cambrian explosion of AI products has barely started.

The chat box is our common ancestor. And as social creatures, we struggle to stop copying eachother on this.

But evolutionary pressure will cause us to fork into a dizzying spiral of new, fit-for-purpose AI products.