Alex is the co-founder and CEO of Patentext. He’s spent over a decade drafting patents for startups, unicorns like Uber and Dropbox, and everything in between. When he’s not obsessing over Patentext or running his climate tech-focused IP firm, he’s likely training for a triathlon or chasing a very fast border collie.
Whether the question comes up the week before a fundraise or right after the team spots a competitor launching something suspiciously similar to their product, every AI startup founder asks this question eventually: can you actually patent AI?
The short answer is yes, with caveats. Here's what startup founders need to know about AI patents in 2026.
Can you patent an AI system or model?
Yes, AI-related inventions can be patented, but not everything AI-related qualifies. The USPTO evaluates AI patent applications using the same legal framework as any other technology, which means the invention needs to be novel, non-obvious, and patent-eligible under 35 U.S.C. § 101.
The last requirement, patent eligibility, is where most AI-related rejections happen. Under current USPTO guidance (and the related body of case law stemming from Alice Corp. v. CLS Bank), abstract ideas are not patentable on their own. AI systems built around mathematical concepts, statistical models, or generic data processing steps are often considered abstract and therefore ineligible unless the application describes a specific, practical application that produces a concrete technical improvement.
In other words: an AI patent needs to describe what the system does differently and better, not just that it uses machine learning.
What kinds of AI inventions can be patented?
The most defensible AI patents tend to focus on specific technical improvements rather than the general idea of using AI. Here are the kinds of inventions that tend to do well:
- Novel architectures or training methods: A new neural network structure, a modified attention mechanism, or a more efficient training pipeline can be patentable if it produces measurable improvements over prior approaches.
- Domain-specific AI applications: AI systems that solve a specific, real-world problem in a non-obvious way (medical diagnosis, materials discovery, predictive maintenance) tend to fare better than general-purpose AI tools.
- System-level integrations: How the AI interfaces with hardware, sensors, data pipelines, or other systems can be patentable even if the underlying model is conventional.
- Novel data preprocessing or output generation: Inventions that transform data in a new way, or that produce outputs (images, drug candidates, structural predictions) through a non-obvious process, can clear the eligibility bar.
What can't be patented?
There are categories of AI-related inventions that are unlikely to survive examination:
- Training a model on existing data using conventional techniques: Fine-tuning GPT or applying gradient descent isn't patentable on its own.
- Abstract mathematical relationships: If the "invention" is essentially a formula or statistical technique applied generically, it likely won't qualify.
- Pure software that doesn't produce a technical effect: Software that just manipulates information without producing a real-world result often fails eligibility.
- AI as a named inventor: As of 2026, AI systems cannot be listed as inventors on U.S. patents. The USPTO has been clear that inventorship requires a human contribution.
How do you patent an AI invention?
The process is the same as any other patent, but the framing requires care:
- Document the invention thoroughly: Capture what makes your system different. Architecture diagrams, training procedures, benchmark comparisons, and concrete use cases all help.
- Work with a practitioner experienced in AI patents: Not all patent agents or attorneys are equally equipped to handle software and AI claims. Look for someone with a CS or ML background who understands the specific pitfalls of Section 101 rejections.
- File a provisional application early: A well-drafted provisional locks in a priority date before you publish, present at a conference, or release a product. It gives you 12 months to continue developing the invention and prepare a more detailed non-provisional.
- Draft claims that emphasize technical effects: Claims that tie the AI system to specific, measurable improvements are more likely to be allowed.
How much does an AI patent cost?
AI patent costs generally follow the same structure as other software or tech patents:
- $2,000 to $7,000 for a well-drafted provisional application with professional drafting, plus $65 to $325 in USPTO fees.
- $8,000 to $18,000+ for a non-provisional application, depending on complexity and who's drafting it.
- Additional costs for responding to office actions, which are common in AI-related patents given the eligibility issues above.
For early-stage startups, a provisional is usually the right first move. It preserves your filing date, signals IP defensibility to investors, and buys time to decide whether a full non-provisional makes sense.
Should AI startups bother with patents?
It depends on the business, but for most AI startups building on a core technical differentiation, the answer is yes.
Patents don't just provide legal protection. They signal to investors that the technology is defensible, provide leverage in partnerships and acquisitions, and can become valuable assets in their own right. Investors consistently cite patent portfolios as a factor in valuation, especially at the Series A stage and beyond.
That said, patents aren't the only form of IP protection available to AI companies. Trade secrets (for training data, fine-tuning procedures, or model weights that aren't publicly released) can be a complementary strategy, particularly for companies that don't want to disclose technical details in a public patent filing.
What does the current patent landscape look like for AI?
AI patent filings have grown sharply over the past decade. Large tech companies, academic institutions, and defense contractors have been building portfolios aggressively. But early-stage AI startups are underrepresented in this landscape, often because founders assume patents aren't relevant until they've scaled.
That assumption is expensive. Priority date is everything in patent law. The longer a startup waits to file, the more prior art accumulates, and the narrower the available claim space becomes.
If you've built something technically differentiated, the best time to start thinking about your IP strategy was six months ago. The second-best time is now.
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Disclaimer: This article is for informational purposes only and does not constitute legal advice. Patent laws are complex and vary by jurisdiction. For personalized guidance, consult a qualified patent attorney or agent.