Wisdom in fragments

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.

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. What we envision is humans using informal reasoning and intuition as a powerful guide, with formal systems then verifying those ideas. That interplay across layers is, I think, the real magic of combining multiple levels of abstraction.

We try to make decisions through a kind of committee-based approach around what species we're going to bring back, where we're going to invest our dollars, and what fits our core ethos. If it's not going to be beneficial to humans, if we can't do it at arm's length from humans, and if we can't do it in a way that's good for animals, then we just don't touch it.

At Colossal, we are not going to work in humans or non-human primates because we felt like we're already going to have an uphill battle with transparency and education, and we don't want people to be like, if a hair-loss treatment comes out of Colossal, 'are they selling a gene from a woolly mammoth?'

From day one, we said that we wanted to create value, create impact, and create inspiration. We're not going to build a business that just creates impact—we're not a nonprofit. We're not going to just create a company that inspires the next generation. We have to create value.

Data is showing that the amount of information we're sending them without hope, and the ratio of hope versus negativity, is shutting down the next generation, which is terrifying. And so it's not like, 'Oh my gosh, there's a fire—they're going to go put the fire out.' They're like, 'Oh my gosh, what's the point? The fire is too big.'

I think that we must move to a model where we value nature differently and work by integrating with nature. And that's one of the things I'm very, very excited about—leveraging synthetic biology. Because 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.

What the 1% of SMEs in a region that can really grow desperately need is local risk finance. We can think of this as venture capital. But we need venture capital that will work for SMEs, and that means it's got to be locally based. We've got a lot of venture capital, but it's nearly all in London and the Southeast.

Economist specializing in poverty, conflict, and development in Africa

So Manchester and Sheffield had to apply to Transport for London for money to allocate to their bus routes. This is so comically bizarre that if you put it in a novel it would seem too silly. But that's how it is. The power is amazingly concentrated in Whitehall with a few very clever technocrats who go into it as their first job.

Economist specializing in poverty, conflict, and development in Africa

What happened was that people saw their place going down while London was booming. It's not countrywide; it's not that everyone in the country is poorer. London's doing fine. So people ask: why are they not doing fine when London's doing fine? They start to blame each other.

Economist specializing in poverty, conflict, and development in Africa

When the Sheffield steel industry collapsed, incomes collapsed, and so demand in the region collapsed—businesses of all sorts, not just steel but shops and businesses that sold things to consumers. The consumers were poorer, and so businesses started to fail. Investment didn't flow into this region saying 'oh good, a depressed region.' It flowed out to the places which were booming.

Economist specializing in poverty, conflict, and development in Africa

Milton Friedman had a postulate about this. He got a Nobel Prize and all the rest of it, very fancy. But as soon as you interrogate this postulate, it's manifestly rubbish. The postulate was that capital would move into a region hit by an adverse shock. So, Sheffield's steel industry collapses. 'Oh well,' says Milton Friedman, 'that means wages are cheaper, property is cheaper, and so capital will move in.' Which sounds fine until you think about it for more than two minutes.

Economist specializing in poverty, conflict, and development in Africa

My entry into this space came in 2019 when I read a paper by Demis Hassabis of DeepMind advocating for machine learning and AI researchers to go back to their roots in neuroscience. I took that literally — I went back to the neuroscience department at my alma mater, the University of Melbourne, and asked what was exciting them.

You cannot expend more energy than you can consume. That's a fundamental law of physics. If you do, you starve, you die, and you remove yourself from the gene pool. Biological systems have therefore been under enormous selective pressure to develop highly efficient intelligence.