Build the AI product without the regret later.

Counsel informed by running production AI

AI startups are usually advised by lawyers who understand startups or lawyers who understand technology, rarely both, and almost never by someone who has architected and runs a production AI system of their own. Adam is the architect of a production multi-agent AI operating system he governs daily, and a former chief legal officer and in-house private-equity counsel who has taken companies from formation through financings and M&A. For an AI founder, that combination is unusually well-matched: counsel who understands the product you are building because he architects and governs AI himself, and who understands the corporate and financing path because he has lived it on the operating side. The result is advice that anticipates where an AI product's legal problems actually arise, rather than generic startup counsel with an AI label.

IP: who owns the outputs, and do you own your stack

Intellectual property is where AI products most often have a hidden defect. Two questions matter enormously and are easy to get wrong at the start. First, ownership of AI outputs: the legal status of AI-generated content is unsettled and evolving, and your terms need to address what you claim to own, what your users own, and what you can actually promise — getting this wrong creates problems that surface when someone tries to enforce or transfer rights. Second, ownership of your own stack: as with any startup, founders and early contributors must formally assign their work to the company, but AI startups add layers — model weights, training pipelines, prompts, and fine-tuning work whose ownership must be clear. A diligence team in a Series A or an acquisition will find these gaps, and an unassigned core technology or a muddy output-ownership position can stall or kill a deal.

Training data: the risk that is easiest to ignore and hardest to fix

The data an AI product is trained on is one of its largest and least-examined legal exposures. Where did it come from? Was it licensed, scraped, user-contributed, or purchased — and did the rights to use it for training actually transfer? Does it contain personal data that triggers privacy obligations, or copyrighted material whose use is contested? Training-data provenance is difficult to reconstruct after the fact, which is why building data-rights discipline in early — documenting what the model was trained on and on what legal basis — is far easier than untangling it during diligence or a dispute. This is also a fast-moving area of law, so the analysis has to be current.

Terms of service, disclaimers, and the compliance map

An AI product's terms of service and disclaimers are load-bearing in ways an ordinary app's are not, because the product can produce outputs neither you nor the user fully predicts. The terms need to allocate risk for inaccurate or harmful outputs, set acceptable-use boundaries, address data rights, and disclaim what the product cannot guarantee — without overpromising in a way that itself creates liability. Alongside the terms sits the compliance map: depending on what the product does, it may implicate BIPA (if it touches faces or voices), HB 3773 (if it's used in employment), HB 4875 (if it generates likenesses), privacy laws, and others. Knowing which laws your specific product triggers — and building for them before launch — is the difference between a clean scaling path and a series of expensive surprises.

Built for the founder's whole trajectory

Because Adam has been in-house counsel through financings and M&A, the product-counsel work is done with the later stages in view: the IP assignments, data discipline, terms, and compliance posture are built so they survive diligence and support a raise or an exit, not just so they work today. Engagements suit a startup's budget — hourly, flat-fee for defined deliverables like a terms-of-service draft or an IP-and-data review, or a fractional arrangement for ongoing product counsel. The aim is an AI company whose legal foundation is an asset in the next round, not a liability discovered in it.

A national product, coordinated with dedicated IP counsel

An AI product launched to the public is exposed to the privacy and AI rules of every state and country its users sit in — California's automated-decision privacy obligations (among the most stringent in the country, phasing in over time), other states' regimes, and, for international users, the GDPR and the EU AI Act, whose phased deadlines should be verified against the current official text. As a practical risk posture, a founder is usually better served building the product's compliance to the most demanding standard in its realistic footprint than retrofitting state by state after launch. And because the hardest IP and training-data questions — patent strategy, copyright, trade-secret protection, prosecution and enforcement — belong to dedicated intellectual property counsel, the firm leads the product's governance, contracts, data, and compliance strategy and coordinates with IP counsel and with local counsel in other jurisdictions as the matter requires, rather than overstating what one practice should handle alone. (The AI, IP & training-data page covers that coordination in depth.)

What usually goes wrong

The defining failure is the training-data gap: building a product on data whose rights were never cleared or documented, then being unable to prove provenance when an investor's or acquirer's diligence team asks — which can stall or sink a deal. A close second is the IP-assignment hole, where founders, contractors, or the muddy ownership of model weights and pipelines leave the company not clearly owning its own core technology. The third is the overconfident or absent terms of service — either no real allocation of risk for AI outputs, or marketing-driven promises about the product that become liability when the AI inevitably produces something unexpected.

Related

• AI Governance Frameworks & Policy →
• AI, IP & Training-Data Rights →
• HB 4875 — Digital Replica Rights →
• AI Governance Board & Advisory Roles →