After earning degrees in mathematics and physics, Ewan Kirk pivoted into entrepreneurship.Credit: Isaac Raymond
In 2023, technology entrepreneur and early-stage investor Ewan Kirk joined the Centre for Mathematical Sciences at the University of Cambridge, UK, as its first Royal Society entrepreneur in residence.
Kirk helps staff and students to better understand business and entrepreneurship, offering them practical advice and support in launching companies, attracting funding and investment and collaborating with industry partners.
He talks about the role, the history of his entrepreneurial career and why the United Kingdom needs to do more to retain talent in science, technology, engineering and mathematics (STEM) if it is to remain a science superpower.
Tell us about your entrepreneur-in-residence role.
One of the first things I did after being appointed was to plan and deliver a lecture series that included practical advice on how to turn an idea into a business, give a presentation pitch and fund and grow a new enterprise.
I also ran a series of drop-in sessions, during which people could discuss their ideas, ask for career or interview advice or talk about their next steps after earning their PhD.
Sometimes, faculty members will connect me with a postdoc who wants to talk about a piece of technology they have developed, and whether there is a commercial angle. They might be an international specialist in an incredibly complex area of mathematics or physics. These conversations are always intellectually demanding for me — but the real challenge is in working out how to turn an interesting technology into a commercial enterprise.
What types of question do you get asked by budding maths entrepreneurs?
Some things are relatively simple, such as, “How do you start a company?” Some aspects of entrepreneurship are domain-specific, but there’s also a lot of commonality — forecasting your cash flows, hiring employees, understanding your customers, forward planning, raising money and selling your product to a customer or to an investor.
I advise people to refine their message. The fundamental question for every potential investor is, “How and when do you expect to make money?”
A big challenge for early-stage entrepreneurs is raising money. I see an enormous number of company pitch decks — slide-format presentations made to pitch ideas to investors — both in my entrepreneur-in-residence role and through DeepTech Labs, which I started in 2020 in conjunction with Cambridge Innovation Capital, a venture-capital company, and the university.
Why postdocs need entrepreneurship training
DeepTech Labs invests in early-stage technology companies and runs a 10-week programme to help them bridge the gap between early-stage pre-seed funding (which gets the company off the ground) and a larger series A funding round. We select five or six at a time, and the programme includes sessions on developing a strategic roadmap, value proposition and fundraising strategies, and building high-performing teams.
People generally don’t think about how to pitch their business. They’re often excited about their own technology, but an investor has to see some kind of exit, or an opportunity to realize a return on their investment.
What specific skills do mathematicians have that can help to nurture entrepreneurial instincts?
Mathematics is fundamental to absolutely everything, from particle physics to economics. The current interest in artificial intelligence (AI) — which is fundamentally just a branch of statistics — ensures that maths is at the cutting edge of the economy. Not only is maths a fundamental part of science, but mathematical training produces smart thinkers who have a lot to offer academia, businesses and society in general.
I explain to mathematicians, statisticians and theoretical physicists that being able to control, program and understand computer networks is an incredibly valuable skill. ‘Data are the new oil’ is a cliché, but it’s true. And in most commercial jobs, you have to weigh up uncertainty or find ways to prove things, which is precisely what mathematicians and statisticians are trained to do.
Presenting your ideas and convincing the audience of your conclusions are crucial skills. One of the most common mistakes made by people transitioning from academia to the commercial world is to present their work as if they are presenting an academic paper.
In fact, most people, including investors and customers, just want to hear the conclusion. Presenting is an essential skill that start-up candidates need.
Tell us about your own entrepreneurial career journey.
I grew up in Cumbernauld, a town in west Scotland. I couldn’t play football, so my only option was to go down the academic route.
After earning a bachelor’s degree in natural philosophy and astronomy at the University of Glasgow, UK, I was admitted to the University of Cambridge to do Part III of the Mathematical Tripos, a one-year master’s-level taught course, followed by a PhD in general relativity at the University of Southampton, UK.
The business of science
I was always very interested in learning to program, and while earning my PhD, a friend and I created a software package, called MicroDraft, for Amstrad, which was a UK consumer-electronics company. To commercialize it, we formed a company called DaLEK Software.
Back then, there was little to no support for entrepreneurs, so we pretty much had to work out everything ourselves, such as company formation, accounting and marketing.
It was a good but frustrating experience. We finally sold the rights to the software, and I think that experience really sparked my interest in entrepreneurship.
I’d realized after earning my PhD that academic research wasn’t for me: I was good, but definitely not good enough to make a career as a researcher in the general theory of relativity.
In 1989, I went to work for a consultancy called Scientific Generics in Cambridge, as its director of innovation. The company, now called Sagentia Innovation, provided a great introduction to entrepreneurship and technology in a commercial environment.
Three years later, I got a call from the investment bank Goldman Sachs, inviting me to interview for a position. During my interview, I was asked about programming for quantitative analysis, which involves using complex mathematical and statistical modelling to analyse financial markets. They offered me a job as a strategist on the commodities desk, based in London, despite my lack of knowledge about the industry.
After a discussion with a colleague in the London currencies department, I started building software and creating models to price commodity derivatives, financial tools that allow an investor to profit from a commodity without owning it.
After a few years there was a group of nearly 30 mathematicians and programmers who were writing the software and the models for the commodity-trading group, and eventually we gained a dominant position in that market. I was promoted to partner at Goldman in 2000; in 2005, I retired from the firm and I travelled around the world with my family.
I then started a systematic hedge fund, which uses algorithms to trade financial instruments on the basis of quantitative data, called Cantab Capital Partners.
The company initially employed just a few people in a small office in Cambridge. We wanted to be the geekiest, most technologically advanced trading firm we could be — and five years later, we were managing US$5 billion.
