I suspect many think GPT-4 wrappers can just count on LLMs to become basic infrastructure like databases or anything else, and count on differentiating based on how they store/manage prompts to solve specific problems. Maybe they need to build some small models or machinery on top of the LLM outputs to make them truly useful, and maybe building those things isn't that easy to copy because they require domain-specific expertise (in this case law knowledge).

I'm not a part of this startup, but here's a few potential moats.

* Prompt Engineering. Give someone a blank textbox and have them type in a simple query (Brown vs Board of Education || Motion to Dismiss), and detect the correct prompt to feed GPT to generate the best results of the end user. You can also prompt on behalf of the user. For example, have a "summary of relevant cases" page and the user doesn't even need to type in a query. Providing GPT with the correct context. Sharing context across team members.

* Training Data. I think a lot of legal data would be public access, but an ETL pipeline to load documents quickly and completely would be valuable. There's lots of courthouses, so it is non-trival to read it and put it back into the system. You could also acquire non-public training data that could be a moat. Potentially you could establish a data-sharing system with your clients, in which you get access to their data.

* Brand recognition. What's Coca-Cola moat when there are dozens of cheaper knock-offs that are competitive in the Pepsi Challenge.

* Trust. If a tool helps you do your job, you quickly establish trust. It can be hard for competitors to take that from you with similar products.

Historically applied analytics companies have lower moats, more professional services and lower gross margins than pure software. Palantir might be the quintessential company in this category but also Datarobot and it’s ilk.

It will be interesting to see if LLMs change this pattern. If having a corpus of expertly curated training data is sufficient then they’ll get better operating leverage than those firms. But my expectation is they’ll spend a lot of time building use-case-specific wrappers that don’t really scale.

Branding and legal precedent. Look at Docusign for an idea of their strategy.

Sales? Just having the vernacular and value adds for law firms could be all they need.

They merely have the opportunity to build a moat. Go really fast -> build capability, brand, scale, IP and integrations and hope like hell that's enough insurance when the fast followers arrive.

Distribution can be the moat.

Some of these GPT4 wrappers exist because they’re able to build specific integrations into existing systems that make it very easy to use. That’s a moat too.

I believe their moat is that they have early access to OpenAIs newer models as they come out so they are always a bit ahead. Combined with some big partnerships they have already landed perhaps, which could yield highly valuable proprietary datasets to train v2 of whatever they are doing.