Liv Boeree
Senior teams know the AI race rewards speed and punishes caution, even when caution is what their own risk function is asking for. Coordination across competitors looks naive; unilateral restraint looks like ceding ground. The question is how to operate, and govern, inside that pressure without sleepwalking into outcomes no one in the room actually wants.
Liv Boeree is a physicist and former professional poker champion who helps leaders see the game-theoretic forces behind AI competition, climate, and high-stakes decision-making, and what it takes to escape them.
Full Profile
Why organisations work with Liv Boeree
- She gives boards a vocabulary, “Moloch traps,” for the race-to-the-bottom dynamics they already feel in AI deployment, pricing, and risk-taking, and a framework for naming when their organisation is inside one.
- Her decision-making content is grounded in nine years as a Team PokerStars Pro and a European Poker Tour championship, not theory. Senior leaders take her seriously on probability, expected value, and tilt because she has earned the right to teach it.
- She is one of the few speakers who can connect operator-level judgement under uncertainty to the systemic question of how the AI industry coordinates. That range matters for boards trying to hold both perspectives at once.
- Through Raising for Effective Giving, which she co-founded, she has moved more than twelve million dollars into evidence-weighted causes including AI safety. Her stance on responsible technology is backed by her own capital allocation, not just rhetoric.
- Her TED main-stage talk on competition in AI gives buyers a known reference point and an audience already primed to engage with the argument.
Biography highlights
- First-class degree in Physics with Astrophysics, University of Manchester.
- European Poker Tour Sanremo champion and World Series of Poker bracelet winner; nine years as a Team PokerStars Pro.
- TED main-stage speaker, “The Dark Side of Competition in AI.”
- Co-founder, Raising for Effective Giving (REG), which has directed over twelve million dollars to high-impact causes.
- Host of the Win-Win Podcast, applying game theory to AI, climate, and coordination problems.
- Featured guest on The Jim Rutt Show and Ground Truths with Eric Topol, among other named outlets.
Biography
The AI industry is in a race that few of its participants would, on reflection, choose to be running. Speed is rewarded, caution is punished, and the incentive landscape pulls each lab away from the safety posture its own people quietly believe in. Liv Boeree calls this a Moloch trap, after the game-theoretic force that turns competition between rational actors into outcomes none of them want.
Her route into that argument is unusual. She trained as a physicist at the University of Manchester, then spent nine years as a Team PokerStars Pro, winning a European Poker Tour championship and a World Series of Poker bracelet. Poker taught her a vocabulary for thinking under uncertainty: expected value, calibration, tilt, the difference between a bad outcome and a bad decision. That vocabulary now sits at the centre of how she talks to leadership audiences about judgement.
In 2014 she co-founded Raising for Effective Giving, which has channelled over twelve million dollars from the poker community into causes including AI safety, disease prevention, and farm animal welfare. Her TED main-stage talk, “The Dark Side of Competition in AI,” extended the same argument to the largest technology firms: industries can be steered into outcomes the individual players inside them do not endorse, and the only way out is deliberate coordination.
Her current work runs through the Win-Win Podcast and a steady cadence of writing and broadcast appearances on AI risk, governance, and how serious organisations should act inside competitive races. For boards and executive teams trying to hold both the operator’s question, what is the right next decision, and the systemic question, what game are we actually in, she offers a rare combined view.
Key speaking topics
- AI race dynamics and Moloch traps
- Decision-making under uncertainty
- Game theory for leaders
- AI safety and alignment
- Effective philanthropy and capital allocation
- Probability, calibration, and forecasting
Ideal for
- Boards and executive committees setting AI strategy and risk posture
- Chief Risk Officers, Chief AI Officers, and heads of AI governance
- Senior leadership offsites focused on judgement and decision-making under uncertainty
- Investor and philanthropic audiences weighing high-uncertainty capital allocation
Audience outcomes
- A working definition of Moloch traps and a way to spot them inside their own competitive context.
- A sharper distinction between a bad decision and a bad outcome, drawn from professional poker practice.
- A more honest read of where AI industry incentives are pushing their organisation, and where governance can credibly intervene.
- A vocabulary for talking about expected value, calibration, and risk that travels between the boardroom and the operating team.
Talks
A TED main-stage argument that the AI industry is caught in a Moloch trap, where the race for capability pushes safety down the priority order even when individual leaders would prefer otherwise.
Key takeaways:
- Why the AI race is structurally similar to past coordination failures including arms races and the ozone crisis.
- What historical precedents like the Montreal Protocol suggest about practical paths out of a Moloch trap.
- What boards and senior leaders can do to keep safety on the agenda inside a competitive race.
A talk on how nine years at the top of professional poker reshaped her thinking on probability, calibration, and acting well under uncertainty.
Key takeaways:
- The difference between a bad decision and a bad outcome, and why senior leaders confuse them.
- How expected value thinking changes which decisions feel acceptable to make.
- What “tilt” looks like in executive teams, and how to design against it.