Wisdom in fragments

Trust is a confident relationship with the unknown. 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.

Author & Leading Expert on the Sharing Economy & Collaborative Consumption

They were outnumbered twelve to one north of Kyiv, and completely outmatched in terms of tanks, planes, artillery—all of it. Outmatched, yet they pushed back those Russian forces that colossally outnumbered them.

What we need now is the George C. Marshall of our era to help us train better than the Chinese and the Russians.

We need fear, but fear has to be harnessed, and a crucial way we harness it is through training. Training helps you turn fear from something that paralyses you 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.

We have to recognise that if we ignore or downplay the human side, we could lose. That, I think, is really the key insight for why and how things should be done differently.

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 research effort and resources.

The third and deepest reason this matters—why it's not just commercially meaningful but potentially world-changing—is the ability to bridge different levels of abstraction.

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.

What we envision is humans using informal reasoning and intuition as a powerful guide, with formal systems then verifying those ideas. In this way, the formal system grounds high-level intuition.

We see math as code and code as math. The real magic, and the key transition, comes from combining AI, programming languages, and mathematics—bringing all three pillars together.

This tool is truly the best thing that the science has come up with for getting two people to like each other. A little bit of optimisation can be effective—you should be eating healthy and exercising and doing the basics. But I tend to think in terms of relational optimisation: how can I put two people in the best position to share things about themselves that allow them to find conversational pathways that happen to go well?

You feel like a product when you're on these things. You feel like you're selling yourself. But what's amazing is that when you get people in person, actually interacting, much of the time they don't do that same optimisation, especially if you give them just a few nudges.

When you tell people these things, they get a little fatalistic about it. They tend to think, 'Well, I guess there's not much I can do about that.' I would call that a bias. It is wrong just as often as it's likely to be right. We really need to interrogate and deconstruct that assumption. It's rarely true, and it can lead people to internalise some of these effects in a way that can be pretty damaging and totally unnecessary.

What we see in the science is something very different. We often see that romantic partners basically see the best in each other. They're wearing rose-coloured glasses. And the primary thing that brings people together is what we would call attachment bonds or pair bonds: the idea that people are looking for somebody who they feel has their back, somebody who's going to celebrate their successes, and somebody who's going to be there for them.

If we can create excitement, wonder, and solutions to problems like loss of biodiversity, then I think that not only can we inspire the next generation, but we can give them hope.