AI Startups: Protect Everything EXCEPT the Patent

Short version: For most AI companies, patents on “using AI for X” don’t protect you. They backfire by revealing your method while remaining practically unenforceable. The competitive advantage is in your data, processes, distribution, and customers—so protect those and build your business around the reality that competitors can do something similar.

The uncomfortable truth about AI patents

Most AI patents in the wild are:

1. Undetectable. You can’t see what’s happening inside a competitor’s GPU cluster. If infringement can’t be detected from the outside, you can’t enforce it in practice.
2. Narrow by design. “Using AI for [use case]” leans on conventional tools. Examiners force the claims into tight corners that are easy to design around. You think you are getting “broad” coverage when, in truth, it is very narrow.
3. Disclosure that helps competitors. You publish a roadmap of how you did it. They learn from your specification without taking on your cost or risk.

Put bluntly: thin AI patents backfire. You pay to teach your competitor while getting almost no protection.

Where the moat actually lives: data and operations

We never like the “big idea patents” that mistakenly try to be broad. We want the roadblock patents that close off the best way to solve a problem that your competitor will have to solve.

The best way to find those patents are to focus on what was the hardest technical thing we did. Where did we spend the most time and money? What was the technical part that caused us so much frustration? What was the solution to that problem that unlocked our value proposition?

Not only are these the “slide to unlock” of your product, they are the key problems that a competitor will have to solve to compete with you.

Using “AI to solve X” is a bland and obvious thing that anyone can imagine. Doing the heavy lifting to make it happen is much more valuable.

What took the work? It is not the AI solution; it’s everything around it:

• Collection: How you source raw data and secure rights to use it.
• Curation: Cleaning, de-duplication, labeling, harmonizing, schema design.
• Process: Feature/embedding choices, evaluation harnesses, guardrails, feedback loops, post-processing heuristics.
• Integration: How the model plugs into workflows, contracts, and service levels customers rely on.

As with any IP protection, you always default to trade secrets first. Only get patents where the trade secrets cannot be protected.

In this case, your methods for data collection, curation, processing, and integration are all easily protected by trade secrets. Should you get a patent on any of these? NO – because it is easy to design-around any of these processes, a patent would be undetectable, and you give away your hardest-earned information.

Trade secrets, not patents

If you can’t detect infringement, don’t patent it. Keep it secret.

What to keep as trade secrets

• Data sets and labeling recipes
• Curation tooling and pipelines
• Evaluation/evasion tests, red-team suites, safety filters
• Retrieval schemas, prompt orchestration, and post-processing
• Performance heuristics and deployment runbooks

How to protect them

• Contracts: Strong invention assignment, NDAs, confidentiality, and data-rights language with employees, vendors, and customers.
• Access controls: Least-privilege repositories, role-based data access, audit logs, and secrets management; restrict exports.
• Process discipline: Code reviews, change control for pipelines, and documented handling of sensitive data.
• Vendor posture: DPAs, clear IP ownership, and exit/transition clauses.

Trade secrets are the only route that actually protects the advantage here because they don’t require detectability.

Plan for competition—because you can’t stop it

Assume others will build their own dataset, their own schema, and their own curation process. They can, and some will. Bake that reality into the business model.

You cannot rely on the “safety net” of your AI patents to keep competitors away. Your competitive advantage is NOT THE PATENTS. It is everything else.

You need to compete the old fashioned way: execution.

Win on things a rival can’t copy overnight:

• Distribution: Channels, partnerships, and placement.
• Contracts: Long-term agreements, integrations, switching costs.
• Customer base: Adoption, support, renewals, references.
• Performance: Speed, accuracy, coverage, reliability, compliance.
• Brand and sales motion: Clear positioning, repeatable demos, proof that it works in production.

Stop leaning on a flimsy “AI use-case” patent as a safety blanket. Build the moat where it holds.

Valuation: two very different perspectives

1. Valuation to investors: revenue is the only metric for value

For equity investors, valuation rests on revenue and profitability. Revenue is proof that customers choose your product over alternatives, and profitability is proof that you can deliver it efficiently. Nothing signals competitive advantage better than a customer willing to pay, and keep paying.

Investors look at the company They want realized value—cash flow tied directly to product-market fit. That’s why your best story for investors isn’t a patent filing—it’s recurring revenue, renewals, and growth. In theory (but rarely in practice), the venture investor is supposed to price a company as a reflection of its future discounted cashflows.

See: The Company as the Product.

2. Valuation for lending: we only look at cashflow but sometimes data

When we look at a company for IP-backed lending, the lens shifts. A patent, standing alone, has almost no resale value—especially in AI, where the data and processes are tightly coupled with the business and rarely transferrable.

In this context, our lending underwriting is completely dependent on cashflow.

We cannot give credit for any “AI on X” patents, and we typically cannot give credit based on specialized trade secrets. In the “AI for X” businesses, your trade secrets are hard-fought and hard-won for you, but they often have very little resale value. Any acquirer will want the ‘package’ of the entire system, and you cannot separate out the IP from the company.

There is an exception: there are some businesses where the data can be resold on its own. There are emerging markets for data, especially carefully curated, cleaned, and updated data. This may be a separate line of business for the AI startup company, but it also my undermine the company’s products. In the resale market, data can be sold, but it is very difficult and you should not expect high valuations.

See: Patents that Protect a Business Advantage.

When a patent might make sense (rare)

File only when infringement is externally observable AND the claim blocks a capital-intensive alternative:

• Hardware or sensor designs with testable outputs
• Cryptographic watermarking or verifiable artifacts
• Algorithms that produce a distinct, measurable, externally evident behavior
• Systems where you can buy and reverse-engineer a shipped product

If you can’t test for it in the wild, think twice. Patent only what you can detect. Otherwise, don’t disclose it.

Action checklist for founders and boards

• Stop the “AI use-case” filings. Stop burning cash on undetectable claims.
• Map the data. What you collect, where it comes from, rights you hold, gaps you must close.
• Tighten contracts. Employee IP assignment, NDAs, vendor DPAs, and explicit data/IP ownership.
• Lock down access. Least privilege, logging, secrets management, export controls.
• Codify pipeline discipline. Reviews, change control, eval suites, red-teaming, incident playbooks.
• Focus budget on GTM. Distribution, integrations, case studies, renewals, and expansion.
• File selectively. Only on artifacts you can verify in the market.
• Pitch the truth. Lead with data rights, process advantage, distribution, and revenue—not a thin patent.

Bottom line

For most AI companies, patents on use cases don’t protect you—they backfire. They’re undetectable, narrow, and they hand competitors your playbook. Put your protection and your effort where the real value lives: your data, your process, your distribution, and your customers. Build around the reality that others can do something similar, and win by executing better.

Focus on everything but the patent.