Startups & Tech

By Andrew R. Jacobs, Esq. | Founder & Managing Attorney, Jacobs Counsel LLC | Director, Sports, Entertainment & Gaming Initiatives, Seton Hall University School of Law | Super Lawyers Rising Star 2026

You're building an AI company in New York. You have a product, a small team, and either a pre-seed check or a Series A on the horizon. Legal is the last thing you want to think about — and the first thing that can quietly wreck you if you ignore it.

That's not a scare tactic. It's just the reality of building at the intersection of AI, data, and commercial software in 2026, where the regulatory surface area is larger than it was two years ago and investors are asking harder questions earlier.

Here's how to think about your legal structure at each stage, which surface areas matter most for AI startups specifically, and why the fractional general counsel model beats the alternatives from seed through Series A.

Legal Structure Looks Different at Seed vs. Series A

At seed, your legal needs are narrow but high-stakes. You need a clean Delaware C-corp, a founder equity structure that won't create problems later, a basic IP assignment agreement, and commercial terms you can actually ship with your product. That's it. You don't need a 12-partner firm billing $800 an hour to set up a standard SAFE round.

Series A is a different situation. By then, investors are asking for representations and warranties about your data practices, your IP chain of title, and your AI model governance. Enterprise customers want SOC 2 readiness before they sign. You have employment agreements, vendor contracts, and potentially a first trademark filing. The legal surface area multiplies fast.

The mistake most founders make is treating both stages the same way — either over-lawyering at seed and burning cash on BigLaw for work that doesn't require it, or under-lawyering at Series A and relying on the same generalist who set up their LLC and has never read an API terms of service in their life.

The Legal Surface Areas Unique to AI Startups

General business attorneys miss this. A startup building a SaaS analytics tool and a startup building an AI product on top of OpenAI or Anthropic APIs are not the same legal animal.

Here are the surface areas that actually matter for AI founders:

SaaS Terms and AI-Specific Commercial Contracts

Your terms of service and master service agreements need to address AI-specific risk: model output disclaimers, data processing obligations, liability caps tied to AI errors, and customer data use rights. Standard SaaS templates don't cover this. If your product ingests customer data to fine-tune or improve a model, that needs to be explicit in your agreements — or you're creating liability.

AI Product and Data Counsel

How you collect, store, and use training data is a legal question, not just an engineering one. New York's AI bias disclosure requirements, federal FTC guidance on AI claims, and the EU AI Act's extraterritorial reach all create obligations that need to be mapped before you sign an enterprise deal. Getting this right before Series A diligence is far easier than cleaning it up after.

API Terms of Service and Third-Party Model Agreements

If your product runs on OpenAI, Anthropic, Google, or any other model provider's API, their terms govern what you can and cannot build. Usage policies, output restrictions, and prohibited use cases are legally binding. Founders frequently build features that violate these terms without realizing it — and discover the problem when a customer asks for an indemnification clause you can't give them.

IP Strategy and Chain of Title

Who owns the IP your AI generates? Who owns the model weights if you fine-tune a base model? Did every contractor who touched your codebase sign a proper IP assignment? These questions surface at Series A diligence. If the answers are unclear, the deal slows down or the valuation takes a hit.

Financing Documents

SAFEs, convertible notes, and Series A preferred stock documents are not interchangeable. Terms that look standard — pro-rata rights, information rights, MFN clauses — carry real downstream consequences. You need counsel who reads these documents every week, not once a quarter.

Why the Fractional GC Model Beats BigLaw at This Stage

BigLaw is built for public companies and late-stage deals. Their billing model, their staffing model, and their risk tolerance are calibrated for clients with $50M+ in revenue. At seed or Series A, you're not that client — and you won't get their best people. You'll get a third-year associate supervised by a partner who bills you for every email.

A general business attorney is the other common mistake. Cheaper, yes — but they haven't read an AI model API agreement, they don't know what a SOC 2 Type II report means for your customer contracts, and they won't catch the data use clause that creates a problem in your Series A data room.

The fractional GC model — a senior attorney working across a defined set of matters at a flat monthly fee — solves both problems. You get direct access to someone who handles AI and tech company work exclusively, with no associate layer between you and the judgment calls. You know what you're paying each month. And because the attorney is embedded in your company's context, they move faster than outside counsel who learns your business from scratch on every call.

At Jacobs Counsel, every engagement works exactly this way. I handle every matter directly — no associates, no hand-offs, no billing surprises. The firm runs on AI-native internal systems for research and drafting, which means the time I spend with you is on strategy and judgment, not on tasks that can be systematized.

What to Look for in Outside Counsel for an AI Startup

When you're evaluating attorneys, ask these specific questions:

1. Have you reviewed API terms of service for AI model providers and advised a client on compliance with them? 2. Can you draft SaaS terms that address AI output liability and customer data use for model improvement? 3. Have you worked on a Series A data room where AI IP chain of title was a diligence issue? 4. Do you work on a flat-fee or retainer basis, or do you bill hourly? 5. Will I work directly with you, or will my matters be handled by associates?

If the answers to the first three are vague — or if the answer to the last question is "primarily associates" — keep looking. At seed and Series A, you need a senior attorney who knows this specific terrain.

The Right Time to Get This Right

The best time to structure your legal team was when you incorporated. The second best time is before your next financing round or your first enterprise customer conversation.

Investors and enterprise buyers both run diligence. They will find the unsigned IP assignments, the missing data processing addendum, and the API terms violation. Fixing these things proactively costs far less than negotiating around them on a deal timeline.

If you're an AI or tech startup at seed through Series A in New York and you want outside counsel or a fractional GC who works directly on your matters at a flat fee, book a 15-minute strategy call at jacobscounsellaw.com.

FAQs

What does a fractional general counsel actually do for an AI startup?

A fractional GC acts as your outside legal department on a retainer basis — handling commercial contracts, financing documents, IP strategy, and product counsel. It's the same work an in-house GC would do, without the full-time salary. For seed and Series A companies, it's usually the right structure before you have the revenue to justify a full-time hire.

When should an AI startup in New York hire outside counsel?

Before you close your first financing round. Before you sign your first enterprise customer. Before you launch a product that processes user data. Those three moments create legal obligations that are far easier to address before the fact.

Do AI startups need different legal terms than standard SaaS companies?

Yes. AI products create specific risks around output accuracy, data use for model training, and third-party model provider compliance that standard SaaS templates don't address. Your commercial terms need to reflect how your product actually works.

What is AI IP chain of title and why does it matter at Series A?

Chain of title is the documented ownership of your intellectual property — your code, your models, and any AI-generated outputs. Investors will verify that your company owns what it says it owns. Gaps in IP assignment agreements from contractors or co-founders are a common diligence problem that can delay or complicate a round.

Why does it matter which API terms of service your product relies on?

Model providers like OpenAI and Anthropic impose usage restrictions on what you can build with their APIs. Violating those terms creates legal exposure and can affect your ability to offer certain product features or indemnify enterprise customers. This needs to be reviewed before you build, not after.

What is the difference between a SAFE and a convertible note for an AI startup?

Both instruments convert to equity at a future priced round. SAFEs are simpler — no interest, no maturity date. Convertible notes are debt instruments with interest and a maturity date, which creates repayment obligations if a qualifying round doesn't happen. The right choice depends on your investor relationships and your timeline to a priced round.

How does Jacobs Counsel price its work for AI startups?

The firm works on a fixed-fee per matter or monthly retainer basis — no hourly billing. You know your legal costs in advance and can budget accordingly, which matters when you're managing a seed or Series A runway.

*This article is for informational purposes only and does not constitute legal advice. Reading this post does not create an attorney-client relationship.*

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