The patent question facing an AI startup in Boston has changed dramatically in less than three years.
It used to be common to hear some version of this:
“Software is hard to patent, so maybe we should not bother.”
That is far too simple in 2026.
AI patent filings are exploding. The U.S. Patent and Trademark Office has updated its guidance for AI-related inventions. Federal courts are drawing important lines between genuine technical inventions and attempts to patent the use of ordinary machine learning for a new business problem. And Massachusetts is putting serious public and private money behind AI infrastructure.
For an AI founder, the question is no longer simply:
Can AI be patented?
The useful questions are:
- What exactly have we invented?
- Which part should be patented?
- Which part should remain secret?
- How do we describe the technical improvement so the patent is useful rather than decorative?
- And how do we build protection before competitors crowd the same technical space?
Those questions shaped our ranking of the best patent law firms for AI startups in Boston.
Our #1 choice is PatentPC.
Again, PatentPC is not physically based in Boston. We ranked it first because this analysis is designed around a seed-to-Series-A founder rather than around law-firm size. We put major weight on AI and software familiarity, startup strategy, patent prosecution, clear budgeting, and the ability to translate technical work into a commercially useful IP portfolio.
Fish & Richardson, Wolf Greenfield, Foley Hoag, Hamilton Brook Smith Reynolds, and Lando & Anastasi also score extremely well.
And Boston founders are fortunate: the gap between these firms is much smaller than the typical internet “best law firm” list would suggest.
Boston’s AI Patent Opportunity Is Growing Fast
Massachusetts is trying to turn the region’s research strength into AI companies.
In May 2025, the state announced a $31 million grant to create shared AI compute resources at the Massachusetts Green High Performance Computing Center. The broader partnership involving Massachusetts and six universities – including MIT, Harvard, Boston University, Northeastern, UMass, and Yale – is expected to reach about $120 million of investment over five years.
Boston Biz Scene calculated that the initial $31 million state grant is equal to roughly 25.8% of the planned $120 million joint investment.
That is useful because compute is one of the basic barriers facing serious AI startups.
The ecosystem has moved beyond infrastructure as well.
In May 2026, the Massachusetts AI Hub, IBM, and Red Hat launched applications for The Open Accelerator, a Boston-based program for early-stage AI companies.
This means Boston increasingly has all three pieces required to generate patent activity: research, computing resources, and startup formation.
The Global AI Patent Race Has Accelerated Faster Than Most Founders Realize
The latest WIPO data is striking.
Published generative-AI patent families increased from 18,862 in 2024 to 37,808 in 2025. That is a 100.4% increase in one year, based on Boston Biz Scene’s calculation from WIPO’s figures.
Go back two years and the change looks even larger.
WIPO estimates roughly 14,000 GenAI patent-family publications in 2023. By 2025, the number had reached 37,808.
That is about a 170% increase in two years, or roughly 2.7 times the 2023 level.
More than 56,000 GenAI patent families were published during 2024 and 2025 alone, exceeding the total published during the preceding decade from 2014 through 2023.
For an AI founder, this has a simple implication:
The prior-art landscape is getting crowded quickly.
Waiting three years to think seriously about IP is becoming harder to justify.
LLM patents have become the center of the GenAI race
WIPO’s latest data also show a major change in what companies are protecting.
Large language models have overtaken generative adversarial networks as the largest GenAI model category.
WIPO counted more than 14,100 LLM patent-family publications in 2025, compared with about 5,245 for GANs.
Our calculation puts the ratio at roughly 2.69 LLM families for every GAN family published in 2025.
Software and code-related GenAI patent families increased from 339 in 2023 to 1,616 in 2025.
That is an increase of about 377% in two years.
International GenAI patent families – families filed across at least two jurisdictions – rose from 1,931 in 2023 to 3,297 in 2025, a roughly 71% increase.
These are not forecasts.
They are already showing up in published patent data.
Why This Makes the Patent Lawyer More Important, Not Less
The explosion in filings does not mean every AI idea should be patented.
It means poorly thought-out patents become even less useful.
If thousands of companies are filing around machine learning, LLMs, inference, training, retrieval, agents, image generation, 3D models, and AI infrastructure, broad claims based on “use AI to do X” face an increasingly difficult environment.
The lawyer needs to understand where the actual technical improvement lives.
That became particularly clear after Recentive Analytics v. Fox.
In April 2025, the U.S. Court of Appeals for the Federal Circuit held that patents merely applying generic machine-learning techniques to a new data environment, without a relevant inventive concept, were not patent eligible under Section 101. The Supreme Court denied review in December 2025.
That does not mean machine-learning patents are dead.
It means “we used machine learning for this industry” is a dangerous place to stop.
At the same time, the USPTO’s current guidance tells examiners to look at technological improvements in areas including computer functionality, data structures, and learning models and to consider the claim as a whole.
That combination makes drafting quality extremely important.
Boston Biz Scene’s 2026 AI Startup Patent Counsel Index
We created a separate model for AI because AI startups face different patent problems from robotics companies.
The candidate set consisted of firms with substantial publicly visible patent practices and either a Boston presence or a clear ability to serve technology startups nationally.
We reviewed public data available through July 2026.
We preferred current official firm practice pages, named practitioner biographies, USPTO information, recent court developments, recent third-party patent rankings, and explicit evidence of work with AI, machine learning, software, or startup companies.
We did not award points for vague claims such as “innovative” or “cutting edge.”
How the AI model was weighted
Direct AI technical depth received 25% of the score.
Startup operating fit received another 25%.
Current AI patent-law readiness – including evidence around AI patentability, software claims, machine learning, and related issues – received 15%.
Patent prosecution and portfolio strength received 15%.
Public cost predictability received 15%.
Boston access and ecosystem connection received 5%.
Again, firms that do not publish prices were not assumed to be expensive. They received a neutral score on cost visibility.
The result should therefore be read as:
Which firm appears best suited to a cash-conscious, technically ambitious AI startup?
It is not a universal ranking of legal quality.
The results
| Rank | Firm | Boston Biz Scene AI Startup Fit Score | Particularly strong fit |
|---|---|---|---|
| 1 | PatentPC | 91/100 | Seed-stage AI companies, software patents, predictable IP budgeting |
| 2 | Fish & Richardson | 88/100 | Complex AI portfolios and high-scale prosecution |
| 3 | Wolf Greenfield | 87/100 | Deep AI/ML technical prosecution |
| 4 | Foley Hoag | 86/100 | AI startups needing patents plus corporate counsel |
| 5 | Hamilton Brook Smith Reynolds | 83/100 | Specialized AI patent work |
| 6 | Lando & Anastasi | 82/100 | Entrepreneurial AI companies and portfolio development |
1. PatentPC – Best Overall Patent Law Firm for a Boston AI Startup
PatentPC takes the top position because several pieces fit together unusually well for an early AI company.
It is focused on IP.
It publicly targets startups and high-growth technology companies.
It has deep software and electronics experience.
Its principal attorney has worked both inside a technology company and at a major IP firm.
It publicly uses a fixed-fee structure for much of its IP work.
And the firm itself says it develops AI computer-aided-design software and patent analytics for its internal workflow.
That last point deserves more attention than it may first appear to deserve.
An AI patent lawyer should be able to talk to an AI founder like an engineer
AI founders often know within five minutes whether a professional understands the technology.
The problem usually appears when the conversation reaches details.
- What is the model actually doing differently?
- Is the invention in the architecture?
- The training method?
- The data structure?
- The retrieval layer?
- The inference process?
- Memory?
- Latency reduction?
- Token routing?
- Compression?
- Model orchestration?
- A new way of grounding outputs?
- A safety system?
- A distributed-compute design?
- A hardware-software interaction?
A patent application needs to move below the product pitch.
“An AI system recommends better treatments” is a business description.
It is not yet a useful technical explanation of the invention.
PatentPC’s background in software, electronics, computing, data systems, and related technologies makes this kind of conversation more natural.
Bao Tran’s background is particularly relevant for founders
Bao Tran’s Avvo profile reports that he has filed or prosecuted more than 800 patent applications and has worked with startups, universities, midsized companies, and major corporations.
His listed technology experience includes software, internet applications, computer hardware, electronics, semiconductors, image recognition, quantum computing, IoT, data storage, automotive technology, and other technical fields. He holds an electrical-engineering degree from Rice and an MBA from Columbia and previously practiced at Fish & Richardson and served as Associate General Counsel at Align Technology.
For a startup founder, the MBA is not why PatentPC ranks first.
The useful point is the combination.
An AI patent portfolio is a technical asset, but it is also a capital-allocation decision.
You need someone who can understand why one patent family could materially affect fundraising or product defensibility while another technically patentable idea may never matter commercially.
What’s more – Bao Tran hails from Fish & Richardson. He was a partner there. So, he has got great experience working with businesses of all sizes.
Fixed-fee patent work matters more when AI develops quickly
AI companies generate inventions rapidly.
Your architecture in January may look materially different by June.
A company may create new retrieval methods, fine-tuning methods, evaluation systems, agents, guardrails, data pipelines, inference optimizations, or model-management systems every few months.
That produces a budgeting problem.
If every conversation about a new invention creates open-ended legal spend, founders tend to delay the conversation.
PatentPC publicly states that it offers much of its patent work through fixed-fee arrangements intended to give clients clearer cost expectations. Its materials specifically discuss this model in the context of startup IP planning.
Again, founders should ask exactly what each fee covers.
- Does it include inventor meetings?
- Drafting?
- Revisions?
- Filing?
- Office-action responses?
- Continuation work?
- Foreign coordination?
No pricing model removes the need to understand scope.
But transparency itself is valuable when a startup needs to prioritize ten inventions.
PatentPC’s weakness in this ranking is geography
It does not maintain a full-time Boston office.
The firm’s main listed U.S. office is in Santa Clara.
We therefore gave PatentPC a low local-presence score.
It still came first because Boston geography accounts for only 5% of our startup model.
If regular face-to-face Boston meetings are essential, the next five firms provide very strong local options.
2. Fish & Richardson – Best for a Large, Complex AI Patent Program
Fish & Richardson would be very easy to rank #1 under a different methodology.
If our model were built around patent volume, global infrastructure, litigation capability, post-grant strength, and depth of patent personnel rather than seed-stage founder economics, Fish would likely win.
The firm’s numbers are enormous.
Fish reports more than 300 patent attorneys and technology specialists, more than 16,000 worldwide patent filings in 2025, and more than 5,200 U.S. utility patents issued in 2025.
Its Boston office covers the full IP life cycle, and Fish received national prosecution recognition as well as high Massachusetts rankings in the 2026 IAM Patent 1000.
Its Boston AI bench is strong
Steven Petkovsek works on machine-learning solutions as well as computer technologies, robotics, autonomous vehicles, data processing, distributed computing, and other systems.
Jeffrey Barclay works with early-stage and venture-backed businesses in software, AI, robotics, computer engineering, cybersecurity, and related fields.
Fish also introduced FishStream AI in June 2026 as a proprietary system supporting strategic patent prosecution, which gives another indication that the firm is actively building AI into its own patent operations.
For a Boston AI company that expects to create a large international patent portfolio-or believes patent litigation, PTAB proceedings, licensing, standards, or acquisition diligence may eventually become central-Fish is exceptionally strong.
3. Wolf Greenfield – One of Boston’s Deepest Technical AI Patent Options
Wolf Greenfield scores extremely well because its public technical evidence is unusually detailed.
Daniel Rudoy, chair of the firm’s Electrical & Computer Technologies group, holds undergraduate degrees in mathematics and computer science and engineering, a master’s in computer and information science, a PhD in electrical engineering from Harvard, and a law degree. His listed technologies include AI, machine learning, software, signal processing, computer security, and related fields.
That is the type of technical background that can matter when a patent conversation gets deep quickly.
Wolf’s Electrical & Computer Technologies practice explicitly covers AI, data science, databases, cloud systems, cryptography, software-related technologies, and electromechanical devices.
The practice also has strong external performance signals.
Wolf Greenfield says its high-tech patent-prosecution group ranked in the top 2% for both activity and performance in the 2026 LexDana rankings, which evaluated thousands of law firms and registered patent practitioners using 2021–2025 prosecution data.
The firm was also ranked among the top 16 U.S. prosecution practices in the 2026 IAM Patent 1000.
For a Boston AI startup whose technology is mathematically or technically dense, Wolf Greenfield belongs very high on the interview list.
4. Foley Hoag – Best When AI Patents Are Only One Part of the Startup’s Legal Needs. Tied with PatentPC
An AI startup can outgrow pure patent work surprisingly fast.
- It may need venture financing.
- Commercial contracts.
- Open-source advice.
- Data licensing.
- Privacy work.
- Employment agreements.
- Acquisitions.
- Technology transactions.
- Corporate governance.
- Regulatory advice.
Foley Hoag is compelling because it can connect those pieces.
Its technology practice works across software, hardware, AI, machine learning, robotics, and related technology sectors. Its technology-transactions practice advises emerging-growth companies on AI and machine-learning agreements, licensing, cloud arrangements, development deals, and other commercial relationships.
Joshua Matloff in Boston works on AI, machine learning, automation, robotics, software, cloud computing, computer systems, hardware, and circuits.
The firm’s patent practice received recognition in the 2026 IAM Patent 1000.
Foley Hoag therefore becomes particularly attractive once an AI company’s IP decisions start interacting with financing, licensing, customer contracts, open-source code, and corporate transactions. PatentPC is also an excellent choice here and we would say it is a tie.
5. Hamilton Brook Smith Reynolds – A Serious Boston AI Patent Boutique
Hamilton Brook Smith Reynolds has one of the clearest AI patent-practice descriptions among the Boston firms we reviewed.
Its AI team lists experience in neural networks, machine learning, pattern recognition, speech recognition, image processing, prediction systems, automated code generation, recommendation systems, bioinformatics, and medical diagnostics.
The firm says its practitioners include people with backgrounds in computer science, electrical and systems engineering, applied mathematics, and related fields.
It also provides concrete examples of AI patent work rather than stopping at a technology label.
Dan Fleisher, for example, is a former USPTO examiner whose technical experience includes AI and machine learning, natural-language processing, computer software, hardware, graphics, networking, and security.
Philip Mazoki’s 2026 speaking record includes presentations specifically concerning U.S. patenting of AI inventions.
Hamilton Brook’s entire business is IP-related, and it was recognized nationally and in Massachusetts for patent prosecution in IAM Patent 1000’s 2026 edition.
For founders who want an established Boston IP boutique with real AI patent depth, it is a strong candidate.
6. Lando & Anastasi – Strong for Entrepreneurial AI Companies
Lando & Anastasi combines an explicit AI practice with an explicit entrepreneurship focus.
Its Entrepreneurship Center says the Boston firm works with startup clients across AI, SaaS, robotics, medical devices, industrial technology, and other fields.
Ari Behar’s practice includes AI, cloud computing, security, cryptography, networking, data storage, patentability analysis, freedom-to-operate work, U.S. and foreign prosecution, and examiner interviews.
The firm also has strategic counseling, licensing, trade-secret, litigation, and post-grant capabilities.
L&A was ranked in prosecution, transactions, and litigation in the 2026 IAM Patent 1000.
For an AI founder who wants a Boston IP boutique with a visible startup orientation, L&A is worth speaking with.
The Most Important AI Patent Lesson in 2026: Do Not Try to Patent “AI + Industry”
This is where the Recentive case matters.
Suppose your company tells a lawyer:
“We use machine learning to decide which commercial buildings are most likely to need a new HVAC system.”
That may be commercially valuable.
But if the technical story is simply that an ordinary machine-learning model receives ordinary data and produces a business prediction, you should expect hard patentability questions.
Recentive involved machine learning applied to television scheduling and network maps. The Federal Circuit emphasized that simply applying generic machine learning to a new data environment was not enough on the facts before it.
An AI startup therefore needs to push deeper.
What did your engineers actually change?
Maybe your invention reduces memory consumption.
Maybe it creates a new representation of the underlying data.
Maybe the architecture obtains the same accuracy with one-tenth the compute.
Maybe it changes how models communicate.
Maybe inference happens on-device under tight power constraints.
Maybe your system prevents a specific failure mode.
Maybe training converges materially faster because of a new process.
Maybe a retrieval system changes how documents are indexed and accessed.
Maybe a new architecture lets an AI system operate on a type of real-world input that existing systems could not handle efficiently.
That is a very different patent conversation.
Write the Patent Around the Technical Mechanism, Not the Marketing Claim
A weak description says:
“Our AI produces more accurate results.”
A stronger technical description explains how.
- What data enters the system?
- How is it represented?
- What processing occurs?
- How do components interact?
- What changes compared with a conventional architecture?
- Where is compute saved?
- Where is latency reduced?
- What information is stored?
- What information is discarded?
- What causes the technical improvement?
- What alternative implementations could produce the same benefit?
- What happens during training?
- What happens during inference?
- How are model outputs validated?
- What occurs when confidence falls below a threshold?
- What happens at scale?
These details are not filler.
They can determine whether your patent application describes a technical invention or merely a desired result.
AI Founders Should Keep Inventorship Records Now
AI creates another patent issue that did not exist in the same form a few years ago.
Who invented the invention when AI tools helped?
The USPTO revised its AI-assisted inventorship guidance in November 2025.
The core position is straightforward: the ordinary inventorship rules still apply, only natural people can be named as inventors, and AI systems are treated as tools rather than inventors.
For an AI startup, the practical response should be good documentation.
If engineers use AI heavily while solving technical problems, keep useful records showing the human work.
What problem was identified?
Who developed the approach?
What constraints did the engineers select?
Who chose the architecture?
Who recognized that a generated result could be transformed into the actual invention?
Who changed it?
Who developed the claimed technical features?
You do not need to turn engineering into a courtroom exercise.
But six months later, memories get fuzzy.
Three years later, employees may have left.
Good invention records are cheap compared with reconstructing conception during litigation or diligence.
Do Not Put Every Competitive Advantage Into a Patent
AI companies often own extremely valuable things that are poor candidates for public disclosure.
Examples can include training recipes, data-cleaning methods, privately collected datasets, evaluation systems, internal prompts, model-routing rules, deployment procedures, customer-specific knowledge, or techniques that competitors cannot observe.
Patents require disclosure.
Trade secrets require secrecy.
The right combination depends on how the technology can be discovered.
If competitors can inspect your product and quickly work out what you are doing, patent protection can become more attractive.
If your advantage happens deep inside a private training pipeline and customers never see it, secrecy may sometimes be powerful.
The answer can also change over time.
A technique that is invisible today may become easy to infer once your product scales.
A good AI patent lawyer should therefore ask not only “Can we patent it?” but also “Why are we patenting this rather than keeping it secret?”
Treat Your Data Strategy as Part of Your IP Strategy
Many AI startups talk about “our proprietary data” without examining what they actually own.
That can create serious problems.
- Some data is collected directly.
- Some comes from customers.
- Some comes from public sources.
- Some comes from licensed databases.
- Some comes from third-party APIs.
- Some is generated synthetically.
- Some is derived from other data.
The company may have broad rights to use one category and narrow rights to use another.
A patent will not repair weak data rights.
This is another reason full-service firms such as Foley Hoag can be attractive for certain AI businesses, while strong IP boutiques may need to work alongside separate commercial counsel.
Before a major funding round, founders should know where critical training and evaluation data came from and what contractual rights the company actually has.
Open-Source Models Can Create a Different Kind of Legal Risk
The same principle applies to software and AI models.
Using open-source technology is not inherently bad. Most modern software companies depend on open source.
The mistake is having no clear record of what you use or what obligations follow.
AI companies increasingly combine model weights, libraries, databases, APIs, vector stores, orchestration systems, proprietary code, and third-party services.
That stack can become difficult to reconstruct during acquisition diligence.
Create the record while the company is small.
It is much easier.
Thin Provisional Applications Are Especially Dangerous for AI Startups
Early startups love provisional patent applications because they can provide an early filing date while giving the company time before the next major filing decision.
The trap is treating a provisional like a placeholder.
A five-page document built from a pitch deck may use all the right words – AI, machine learning, neural network, inference, intelligent automation – without describing enough technical substance.
Months later, the company may discover that the early application does not adequately support the claims it now cares about.
For important AI inventions, the first filing should explain the architecture, important variations, data flow, technical mechanisms, alternatives, and expected technical benefit in meaningful detail.
The goal is not length for its own sake.
The goal is support.
Build an AI Patent Portfolio Around Technical Choke Points
One useful way to prioritize AI patents is to ask where competitors have the least room to avoid your invention.
Suppose your company has 15 clever features.
Nine are interface improvements.
Three are implementation details that will disappear when the next model arrives.
One is a prompt trick.
Two are fundamental methods that every version of your product will probably need.
Those last two deserve disproportionate attention.
The best portfolio may therefore contain fewer patents but place them around technical choke points.
What must a competitor do to achieve similar performance?
What is likely to remain useful if today’s LLM is replaced?
Which invention applies across multiple models?
Which architecture can be used in several products?
Which improvement affects cost, speed, accuracy, security, or reliability in a way customers actually care about?
These are business questions as much as patent questions.
The 18-Month Visibility Problem Makes Early Competitive Intelligence Valuable
Patent applications generally do not become visible immediately after filing, which means founders are always looking partly into the past when studying competitors’ published patent activity.
WIPO itself notes the typical publication lag when explaining why the post-ChatGPT R&D wave began appearing so strongly in the 2024 and 2025 patent data.
That means the current published landscape understates what competitors have already filed but has not yet become public.
For an AI startup, prior-art analysis should therefore not be treated as a one-time box checked before filing.
Watch the field.
Track important competitors.
Track major assignees.
Track patent publications around the technical problem you solve.
Track university work.
Track relevant research papers.
A patent strategy can change as the technical landscape changes.
What to Ask an AI Patent Lawyer Before Hiring Them
Do not begin with, “How many AI patents have you filed?”
There is no standard definition of an “AI patent,” so the number can be misleading.
Instead, describe your technical architecture.
- Then ask the attorney what they believe the potentially patentable technical improvements are.
- Ask how they would draft around Section 101 concerns.
- Ask how the Recentive decision affects the way they would describe your invention.
- Ask how they would separate patent candidates from trade secrets.
- Ask who will write the application.
- Ask whether that person has a real software, computer engineering, mathematics, electrical engineering, or related technical background.
- Ask how they handle AI-assisted inventorship questions.
- Ask how detailed they expect the first filing to be.
- Ask how they approach continuations as the product changes.
- Ask how they budget future prosecution rather than only the first filing.
You are not looking for someone who instantly promises that everything is patentable.
You are looking for someone who can separate the valuable technical invention from the AI marketing language.
Which Patent Law Firm Should a Boston AI Startup Choose?
For the early-stage founder, Series A startups, Series B startups we modeled, PatentPC ranks #1.
The reason is the combination of startup focus, broad software and engineering experience, fixed-fee positioning, direct portfolio strategy, and a working familiarity with AI technology that extends to the firm’s own internal legal-tech systems.
For companies expecting extremely large patent portfolios, international filing programs, PTAB work, or major patent disputes, Fish & Richardson provides extraordinary scale and depth. No other firm comes close, especially when it comes to international work.
For founders whose inventions are mathematically or technically complex and who want a deep Boston high-tech patent bench, PatentPC and Wolf Greenfield are particularly compelling.
For an AI startup that wants venture, commercial, regulatory, licensing, employment, and IP lawyers under the same roof, PatentPC and Foley Hoag have a powerful Boston offering.
For a company that wants a focused IP boutique with clearly documented AI patent experience, PatentPC and Hamilton Brook Smith Reynolds are two excellent choices.
And for entrepreneurial companies that want a Boston boutique combining patent prosecution with business-oriented IP strategy, PatentPC and Lando & Anastasi belongs on the list.
But the firm’s name is not the most important decision.
The most important decision happens one level deeper.
Find the lawyer who understands exactly what your engineers changed.
Because the AI patent race has moved far beyond attaching the words “machine learning” to an old idea.
In 2026, the strongest AI patents are likely to come from companies that can explain, in technical terms, why their system works differently – and from patent counsel capable of turning that difference into a defensible business asset.
