A patent draft that reads like a finished application can now take minutes to produce. That's the appeal for cash-strapped founders, and it's also the trap. The document can look complete while the protection underneath it is thin, and the gaps usually don't show up until an investor or acquirer starts digging.
General-purpose AI tools can search existing patents, organize an invention's details and spit out a plausible draft. The catch is that AI's performance is uneven. It can handle some steps very well and still miss the issues that decide what a patent is worth commercially. A polished application can offer little real protection, and that's the part a founder can't easily judge from the outside.
Where AI earns its keep
Used well, AI is a sensible tool for the groundwork. Founders can use it to organize technical documentation, summarize prior patents and flag possibly relevant prior art. Turning messy engineering notes into a clean invention disclosure can trim preparation costs and free up counsel to focus on strategy rather than transcription.
The bigger shift comes when a firm builds AI into the whole workflow instead of bolting it onto the end. In that setup, counsel coordinates invention discovery, drafting, drawings and quality checks. AI can help explore alternative implementations with the inventors, point out gaps in the disclosure and test whether the description supports the claims. Attorneys still direct the work, judge the output and tie each step to what the business needs protected.
That's a different thing from generating an application with a chatbot and handing it to a lawyer for a quick review afterward. The first approach shapes the filing from the start. The second often just polishes a draft that was already built on the wrong foundation.
Where a DIY filing becomes a liability
- Incomplete provisional applications. A provisional's early filing date only helps if its disclosure is strong. Later claims inherit that date only when the provisional adequately describes and enables what gets claimed. A DIY draft often leaves out technical details, alternatives and variations that future claims will need. Adding them later can't fix the original disclosure retroactively.
- Premature disclosure. Pitching investors, demoing a product or announcing an innovation before filing can jeopardize patent rights, especially in foreign markets. Public disclosure can also erase trade secret protection. Founders who paste confidential invention details into AI tools without checking those tools' data practices add another exposure.
- Unclear ownership. Paying an employee or contractor doesn't automatically transfer rights to their inventions. Invention assignment agreements need to be signed when people join, not chased down during diligence. Founders should also confirm that relevant work belongs to the company and that no prior employer or university has a competing claim. Ownership gaps can stall a fundraising round or an acquisition.
The cheapest filing isn't always the most economical one. A weak application can create false confidence while valuable work sits exposed.
What counsel actually decides
Counsel's value shows up in the choices that come before drafting. Which innovations belong in a patent and which should stay a trade secret? Which filings get priority within a fixed budget? What deadlines bind before a disclosure or filing cuts off options? An internal process that competitors can't observe or reverse engineer may be worth more as a trade secret than as a published patent.
Claim scope is where judgment matters most. A fraud detection claim tied to one specific algorithm may leave a competitor free to deliver the same functionality another way. Claims that are too broad can run into existing technology and get rejected or challenged. The valuable claims target what competitors must do to compete, and ideally a single identifiable rival would infringe them in a way the company could detect and prove.
The specification has to do its part too. It needs to describe alternatives and variations that can support broader claims later, without boxing in their scope today. AI can speed up that work, but someone with strategic judgment has to steer it.
What investors check beyond 'patent pending'
A long list of pending applications says little about what they're worth. Investors and acquirers tend to ask three questions:
- Do you own it? They want signed assignments from relevant founders, employees and contractors, with no competing claim from a prior employer or university.
- Does it protect what the business depends on? The claims should cover the core of the product, not a feature the company plans to drop next year, and the application has to contain enough detail to back them up.
- Can you enforce it? The claims should be hard to design around, and infringement should be something the company can realistically detect and prove.
Those questions are where a DIY filing tends to fail. A draft can pass a quick read and still collapse under this kind of scrutiny.
Making protection affordable without cutting corners
The real promise of AI here isn't just producing more applications for less money. It's making meaningful protection affordable for companies that couldn't justify a traditional process, without giving up the legal judgment that makes a patent worth owning.
Founders evaluating help should ask how a firm combines its AI tools with legal expertise, rather than simply whether it uses AI. A good answer explains how the process improves both efficiency and substance, and who is accountable for each decision.
The most expensive patent mistake may be learning too late that a DIY application never protected the innovation the business was built on.
Michael Drapkin is a founding partner of Landfall IP, an AI-native patent law firm based in Boulder, Colorado. He previously founded one of the largest electronics and computer patent prosecution practices in the U.S. at an Am Law 100 firm and was named U.S. Patent Practitioner of the Year by Managing IP in 2022.
Nathan Mutter is managing partner and founder of Landfall IP. He has served as outside counsel to startups and large public companies, and he has pioneered automation systems that improve efficiency across legal services.
