For AI start-ups, filing a patent may seem like crossing the finishing line. An invention has been found, the application is ready and the company can then get on with building the product.
In reality, filing is much more like the starting line. The pace of AI development is making that distinction increasingly important. The World Intellectual Property Organization’s 2026 data shows just how quickly the landscape is changing: published GenAI patent families rose from approximately 14,000 in 2023 to more than 37,800 in 2025. More than 56,000 were published across 2024 and 2025 combined, exceeding the total for the preceding decade.
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The challenge for start-ups is therefore no longer just determining whether an invention is patentable. It’s about deciding what really deserves protection, how that protection fits into the broader company picture and whether the end result will be a useful IP position as the technology continues to evolve.
An IP portfolio can become quite large without ever being strategically valuable.
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A patent is not automatically a competitive moat
Start-ups are frequently encouraged to build a patent portfolio because patents can signal technological capabilities to investors, potential acquirers and competitors. However, the quantity of applications filed is an extremely poor substitute for the quality of the underlying IP strategy.
An AI company might hold patents on individual model architectures, data processing techniques or application workflows whilst its competitors can quite easily design around those claims. On the other hand, another company could have fewer patents but protect a genuinely difficult technical advantage that lies at the heart of its product.
The really important question is therefore not just “what can we patent?”
It is rather “which elements of our technology will have a real impact on our competitive position if a competitor reproduces them or designs around them?”
That requires founders and their technical teams to think about patents alongside trade secrets, know-how and contractual protections as well as the practical realities of developing a product itself.
Freedom to operate matters as much as ownership
There is another risk in focusing too heavily on filing: owning a patent does not necessarily mean having freedom to commercialise the underlying technology.
AI systems frequently combine multiple technical components, including models, infrastructure, data-processing methods, optimisation techniques and application-specific workflows. A start-up can develop something genuinely innovative while still operating in an environment crowded with third-party patents.
That makes freedom-to-operate analysis increasingly important.
Before committing significant resources to a product direction, start-ups should understand where their technology sits within the existing patent landscape. The objective is not to prove that every conceivable legal risk has disappeared. It is to identify meaningful areas of overlap early enough to influence engineering and product decisions.
The same principle applies to prior-art research. A search should not necessarily be treated as a report produced immediately before filing and then placed aside. Its findings can influence claim strategy, reveal crowded areas and expose technical territory where a start-up may have more room to differentiate. This is why the relationship between search and drafting deserves more attention. Early prior-art research can give inventors and patent professionals an opportunity to consider claim scope and fallback positions before an application is locked into a particular approach.
Discovering a potential conflict just before launching a start-up is very different from discovering one after clients, investors and revenue depend on the product.
AI also changes the evidence behind an invention
AI-assisted development is on the rise, adding even more complexity.
Engineering teams are employing AI tools ever more frequently to explore technical approaches, create code, evaluate alternatives and investigate potential solutions. This makes the journey from problem to invention a lot less direct than it used to be. The U.S. Patent and Trademark Office’s revised November 2025 guidance makes clear that the same legal standard for determining inventorship applies whether or not AI was used in the inventive process. AI systems may assist human inventors, but only natural persons can be named as inventors.
For start-ups, this makes disciplined documentation a lot more than just a routine administrative task.
Teams should be able to explain how an invention came about, what human technical choices they made, how alternatives were assessed and where the inventive contribution actually came from. Good records will help create a clearer sequence of development if inventorship, ownership or even the scope of an invention are ever questioned later on.
It’s especially important as start-ups rely more and more on AI itself during both research and development.
Not everything valuable should become a patent
There’s also a temptation to patent just about anything – patents are visible and relatively easy to measure.
This line of thinking can sometimes backfire.
Some innovations may be better protected as trade secrets, especially if the underlying process is difficult to reverse-engineer and disclosing it through a patent would provide competitors with valuable information. Other advantages may come from accumulated engineering know-how, proprietary processes, datasets, implementation details or operational knowledge that cannot easily be captured in a patent claim.
So the right question isn’t so much whether something can be patented at all, but which type of safeguard will create the strongest long-term position.
That decision might also change over time. An early-stage start-up might hold onto certain implementation details confidentially while the technology continues to evolve, then later decide to patent a wider range of technical developments when the commercial direction becomes clearer.
Build an IP strategy around the business, not the filing calendar
The strongest AI start-ups are likely to treat intellectual property much more like part of their product and corporate strategy, instead of seeing it as just another legal matter running alongside them.
This will mean involving technical leaders, product teams and legal advisers early. It will mean regularly mapping out key technological developments against the existing patent landscape and competitor activity. It will mean spotting which inventions are crucial for future products, which should remain secret and where the company may face third-party IP constraints.
It will also involve resisting the notion that having more patents always equates to having better protection.
A smaller portfolio based on a few strategically important inventions might actually be worth more than a much larger collection of narrowly drafted applications with little connection to the company’s actual competitive advantage.
The AI patent race will undoubtedly lead to even more filings. However, the companies that are best set up for the next phase of the market won’t necessarily be the ones filing the most.
They’ll be the ones that understand what their patents are supposed to do. Filing establishes a legal position. It doesn’t on its own create the kind of sustainable competitive advantage you need.
For AI start-ups, the biggest challenge will be building an IP strategy that links invention, evidence, freedom to operate and commercial strategy. It also means knowing when a patent is just one part of the overall picture.
Author Bio:
C Renooj Jacob is the co-founder & CEO of Esgenix with 20+ years of experience in IP and technology. He previously served as senior strategist at LexOrbis and founded IP Astra. A three-time founder, keynote speaker, and certified patent valuation analyst, he specialises in IP strategy, patent valuation, and technology innovation.