Wisdom in fragments

Vulnerability isn't oversharing; it isn't necessarily personal. And that's where some of the fear has come from—this sense that leaders might need to role-model less sharing about their personal lives. Yet vulnerability can look very different. Trust benefits from clearer boundaries, because trust needs clear expectations and clear limits.

Author & Leading Expert on the Sharing Economy & Collaborative Consumption

Children, because they take fewer physical childhood risks in the real world, are experiencing a decline in trust in themselves and in others. Their ability to take those risks in the world is decreasing, and that is a huge problem.

Author & Leading Expert on the Sharing Economy & Collaborative Consumption

AI tools are incredibly good at replicating that. If I share something, they know how to give empathy and validation immediately; they know how to close the loop. And here's the tension: humans aren't practicing the art of trust with each other, or even with themselves, enough. Yet they're outsourcing those trust loops to digital tools almost instantly.

Author & Leading Expert on the Sharing Economy & Collaborative Consumption

Older generations give their trust to experts and influencers based on who and what: credibility, qualifications, and institutional affiliations. Younger generations, by contrast, trust based on how someone makes them feel. And you can manipulate that incredibly effectively in 15 seconds of video: the music, the mood, the atmosphere.

Author & Leading Expert on the Sharing Economy & Collaborative Consumption

The number one thing is misread intentions. You assume someone has ill intent toward you—maybe to hurt you, harm you, or make you feel uncomfortable—and you don't check in on those intentions. Then a narrative forms in your head, and that's when you start spiralling. You can apply this to your children, to relationships, and it happens constantly at work.

Author & Leading Expert on the Sharing Economy & Collaborative Consumption

Trust is a confident relationship with the unknown. And the reason this is so important is that trust isn't, at its essence, an asset or an attribute or a currency. It's a belief. It's what we believe about someone or something. That definition is slightly counterintuitive because, often, when people think about trust, they talk about knowing what to expect from someone or knowing what the outcomes will be. So, they're actually thinking about trust through the language of risk, thinking about it in terms of certainty. And that's not what trust is.

Author & Leading Expert on the Sharing Economy & Collaborative Consumption

We must always remember, in the West as democracies, that we have to do this within the character of a free society. That will hopefully always be what separates us from them: that we defend ourselves, but we do so within the character of a free society.

The idea that Google or Meta weren't American companies was a fiction they liked to tell themselves. It just simply wasn't true. And I'll tell you why: physically, their servers are in actual places.

Our brains are constantly making predictions about the world, and we learn when those predictions turn out to be wrong. So what Marshall did was deliberately redesign training so that people learned how to cope with a wide range of unpredictable challenges—turning the unpredictable into something more predictable.

Training helps you turn fear from something that paralyses you or makes you flee into something that spurs you to take the right decisions in very difficult situations.

You can have the best AI in the world and the best robots in the world, but if they aren't integrated well with the humans, then you will lose.

I think this is something that would have been a lot less controversial just a few decades ago, when people still remembered that, yes, the material side of war is obviously important, but it's only ever one aspect of it. And so, I'd say the big difference here is that we have to recognise that if we ignore or downplay the human side, we could lose.

There's a compelling mission—both because of the enormous economic opportunity it represents, if it succeeds, and because of the broader impact it can have on the world. The mission is defined by a stubborn technical challenge. It has to be hard. No one wants to work on something that isn't difficult, and in a sense, choosing the hardest problems becomes a moat.

Everyone on the team strongly believes in the mission. This is the one thing we want to work on in our lives, and multiple people share that same sentiment. We've fallen in love with a very stubborn technical challenge. Convincing others is always hard—it requires conviction on both sides. What we've demonstrated is small-team speed: speed in hiring, speed in getting strong results, and speed in refining our intellectual understanding.

At Axiom we've raised $64 million—we're a small startup—and we recently won the Putnam competition. We scored 90 out of 120, which would have placed us above all ~4,000 human contestants last year and at the level of a Putnam Fellow, meaning top five in the world. If you tried to achieve that purely through informal methods—where hallucination is a persistent risk—getting to the same level of consistent correctness would likely require a lot more resources.

A model that's really strong at mathematical reasoning is likely to be strong at coding. And a model that's excellent at both math and code is often very good at analysing the nuts and bolts of legal reasoning as well. The third and deepest reason this matters is the ability to bridge different levels of abstraction. All of these domains involve multiple layers of abstraction, and the ability to move fluidly between those layers is likely to be extremely commercially valuable.