AI governance has become a growth industry for anxiety. The dominant voices warn of catastrophe and call for sweeping rules, and the result is a field that often produces fear and paperwork in equal measure. Greg McNeal speaks on AI governance from the opposite stance: pro-innovation, skeptical of the doom, and grounded in having actually governed AI rather than only worrying about it.
He invested in, sat on the board of, and helped run as a fractional executive SailPlan, an AI company sold in 2026. He sits on the board of Exyn Technologies (Nasdaq: EXYN), an AI and robotics company, where AI oversight is part of his job. And he teaches AI governance law at Pepperdine, where he is writing a casebook on the subject. He has built the governance, sat in the boardroom, and written the analysis, which is a rarer combination than the field’s supply of warnings suggests.
The short version: AI governance does not have to mean fear and friction. Done well, it makes products safer and better without grinding development to a halt. Greg teaches AI governance law and has governed AI from inside a company, so the talk gives leaders a practical way to manage real risks without importing the doom that paralyzes most governance efforts.
The argument
Greg’s case is that governance done as fear and compliance is both expensive and weak, while governance built into how AI products are designed is cheaper and stronger. He is skeptical of threat inflation, the habit of labeling every AI development existential, because inflated rhetoric distorts priorities and justifies rules that smother useful innovation. He is equally skeptical of companies that deploy AI with no accountability and call it moving fast. The talk holds both, which is what makes it useful.
What the talk delivers
What AI governance actually means, stripped of the jargon. Where the real risks sit, the mundane ones that show up in litigation rather than the science-fiction ones. How to govern AI in a way that makes products better instead of slower. And what a board’s actual job is on AI oversight, drawn from doing it.
Who books it
Board education sessions, executive summits, technology and compliance conferences, and association events that want a substantive, non-alarmist voice on governing AI. Greg also runs workshops for boards and leadership teams building an AI governance program.
Governance without the doom
Most AI governance conversations start from fear. They open with the catastrophic scenarios, treat every model as a liability waiting to detonate, and build process designed mainly to slow things down so nothing bad can happen. That approach feels responsible and is mostly counterproductive, because it buries the real, manageable risks under cinematic ones and teaches the organization that governance is the office of no. The result is either paralysis or, more often, teams that route around governance entirely.
There is a better way, and it starts from a different premise: that AI carries real risks worth managing and that managing them well makes the product better, not just slower. Greg comes at governance from both the classroom and the operating seat, teaching the law and having actually governed AI inside a company. That combination produces a talk about governance as a practical discipline, one that catches the failures that matter, builds trust with customers and regulators, and lets the business move rather than freeze.
Doom-driven versus practical governance
| Doom-driven governance | Practical governance |
|---|---|
| Starts from catastrophe scenarios | Starts from the failures that actually occur |
| Designed to slow everything down | Designed to catch real problems early |
| The office of no | A partner that ships safer products faster |
| Teams route around it | Teams use it because it helps |
The difference is not how seriously you take risk. Practical governance often takes the real risks more seriously, because it is not distracted by the dramatic ones. The difference is whether governance is built to stop work or to make the work better.
What the talk gives leaders
The keynote sends leaders out with a way to govern AI that their teams will actually use, because it helps rather than obstructs. They leave able to separate the AI risks worth real attention from the ones that just generate anxiety, and to put their controls where the actual failures happen: bad outputs reaching customers, decisions no one can explain, and data used in ways that create liability. They also leave understanding that good governance is increasingly a market advantage, because customers and regulators are starting to ask who they can trust. For audiences facing specific governance decisions or pending regulation, the talk connects directly to those.
FAQ
What does Greg’s AI governance keynote cover?
What AI governance actually means, where the real risks are, how to govern without strangling products, and the board’s role in AI oversight.
What is his standing to speak on it?
He has governed AI as a company executive and a public-company director, and he teaches and is writing a casebook on AI governance.
Does AI governance have to slow product development down?
No. Doom-driven governance is designed to slow everything down, but practical governance is designed to catch the real problems early, which makes products safer without freezing the business. Done well, governance is a partner that ships better products faster, not the office of no.
What qualifies Greg to speak on AI governance?
He teaches AI governance law and has governed AI from inside a company, so the talk connects the rules to the operating reality. Most governance talks come from lawyers who never shipped a product or technologists who never read the statute. This one connects both.
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