If your AI startup uses the same foundation models everyone else can access, the moat slide cannot simply say “proprietary AI.”
Investors need to understand what becomes harder to copy as the company grows.
That advantage might come from data, workflow ownership, distribution, customer context, network effects, domain infrastructure, multimodality or execution.
The pitch deck needs to show how that advantage compounds.
Start by separating the model from the company
A useful test is:
If OpenAI, Anthropic, Google or an open-source model becomes dramatically better next year, does your company become stronger or irrelevant?
If better models make your product better, that can be a good sign.
If your whole advantage disappears when someone gets access to the same model, you need a stronger answer.
Bessemer has made a similar point in its work on vertical AI: foundation models themselves are becoming a less reliable moat, while specialised workflows, proprietary data and multimodal capabilities can create stronger defensibility. See Bessemer’s vertical AI playbook.
Five moat questions your AI pitch deck should answer
1. What data do you create or access that others cannot easily copy?
“Uses customer data” is not enough.
Ask:
- Is the data proprietary?
- Does usage create new data?
- Does the product improve as the dataset grows?
- Can competitors legally or practically obtain the same information?
- Does the data capture outcomes, preferences or context that foundation models do not have?
Andreessen Horowitz has argued that unique data becomes more valuable as AI makes high-quality information easier to use. Read the a16z analysis.
Show the mechanism.
Customer usage → proprietary context → better output → more usage
That is much stronger than writing “data moat” in a box.
2. Do you own part of the workflow?
The strongest AI companies often become part of how work gets done.
They do not simply answer a question.
They receive inputs, make decisions, take actions, write information back into systems and learn from the result.
In your deck, show the workflow before and after your product.
Where do you sit?
What does the customer stop doing manually?
What becomes dependent on your product over time?
3. Do you understand the customer better with every use?
Context can become a moat.
An AI system that understands company policies, previous decisions, customer preferences, domain terminology, historical outcomes and internal workflows can become increasingly difficult to replace.
Your slide should show how that context accumulates and why it improves the product.
4. Do you have a distribution advantage?
A better model does not matter if nobody sees it.
Distribution can come from:
- an existing customer base
- embedded integrations
- channel partners
- marketplaces
- proprietary communities
- industry relationships
- workflow integrations
- product-led loops
- a trusted brand in a sensitive category
For many AI products, the difficult part will be reaching users and becoming embedded before dozens of alternatives do.
5. What proof shows the advantage is already forming?
Moat slides often describe the future.
Investors also need evidence from today.
Examples:
- retention improves with usage
- customers upload proprietary datasets
- integrations make the product part of the workflow
- switching would require rebuilding historical context
- gross margins improve as your routing becomes better
- human review falls over time
- proprietary outcomes data grows with every transaction
- customers expand from one workflow into several
The best moat slide connects mechanism + evidence.
A simple AI moat slide structure
What we use
Foundation models, internal models, external data.
What we add
Workflow logic, customer context, proprietary data, integrations, domain infrastructure.
What compounds
Outcome data, embedded workflows, distribution, switching cost, performance.
Evidence today
Usage, retention, accuracy, expansion, data volume, customer dependency.
That tells the investor what is commodity and what belongs to the company.
Do not pretend the model is proprietary if it is not
You do not lose points because your startup uses OpenAI, Anthropic or another provider.
You lose credibility if the deck tries to make commodity infrastructure sound proprietary.
A clearer statement might be:
We use multiple foundation models. Our advantage is the workflow and proprietary outcome data created across 1.8 million completed transactions.
That gives investors something real to evaluate.
The moat should connect to the business
Technical defensibility by itself is not enough.
Ask what it creates commercially.
Does it produce:
- higher accuracy?
- lower cost?
- faster implementation?
- better retention?
- exclusive supply?
- better margins?
- stronger distribution?
- more customer expansion?
The pitch deck should connect the technical advantage to the economic result.
Frequently asked questions
Is using GPT a weakness in a pitch deck?
No. Many AI companies build on third-party foundation models. The question is what differentiated value your company adds above the model.
What are strong AI startup moats?
Depending on the company, defensibility can come from proprietary data, workflow ownership, accumulated context, distribution, network effects, specialised infrastructure, multimodal capabilities or process knowledge.
Should an AI startup have a separate moat slide?
If defensibility is likely to be an obvious investor objection, yes. In other cases, it can be stronger to prove the moat across the product, traction, competition and GTM slides rather than isolating it on one page.
Need help making your AI startup’s moat clearer?
If your AI company is strong but the deck still sounds like every other AI startup, Deck Studio can help turn the real moat into a clearer investment case.
