Venture capital is the lifeblood of technology startups, including young companies deploying advanced AI. John Cassidy is a Partner at Kindred Capital, a UK-based venture capital firm. Before he became an investment professional, he co-founded CCG.ai, a precision oncology company which he sold to Dante Labs in 2019. He joined the London Futurists Podcast to discuss how venture capital firms are approaching AI today.
Kindred Capital was founded in 2015 by Mark Evans, Russell Buckley, and Leila Zegna. It has raised three funds, each of around $100 million, and is focused on early-stage investments, known in the industry as pre-seed and seed rounds. It likes to invest in platforms, and picks and shovels, which means businesses which can become part of the essential infrastructure for many larger companies. Its preferred sectors are ‘techbio’ (by which he means tech-focused biotech businesses), software (especially software as a service, or SAAS), energy and fintech. Its main geographies are the Europe, the UK, and Israel.
Among its recent AI investments is Scarlet, which is building a continuous compliance infrastructure for companies operating in the highly regulated medical software industry. Another is Cradle Bio, a generative AI tool which allows protein engineers to use deep learning AI systems and models like AlphaFold to identify new and better proteins for medicines and industrial enzymes.
Bubbles and reality
The venture capital industry is highly cyclical, and notoriously prone to excess. In recent years it has applied over-exuberant valuations to blockchain companies, and in companies offering ten-minute delivery services, but the dotcom bubble at the turn of the century was perhaps the most infamous example. Cassidy hopes the current wave of excitement about AI is different from those situations. There is some exaggeration of the capabilities of transformer AIs, and some people argue breathlessly that they are virtually artificial general intelligence (AGI) systems, which is not true. But underlying that hubris, large language models and generative AI are starting to demonstrate the transformational capabilities that will ensure this is no bubble, because they can create real efficiencies, and generate real money.
It is often said that an economic boom is like a gold rush, and in a gold rush you are better off selling picks and shovels to the miners, than digging or panning for gold yourself. Nvidia is a great example of a company doing the equivalent of selling picks and shovels to miners, and its valuation is exuberant. Cradle Bio, Kindred’s portfolio company that helps protein engineers use generative AI to design molecules for medicines and industrial enzymes, is also in the picks and shovels business. Cassidy says the number of proteins which scientists have been studied so far is vanishingly small compared to the number of all possible proteins: it’s like the ratio between a single grain of sand and all the sand in the world. So there is a lot to go for.
The trick for investors, of course, is to identify which of the companies operating in the new value chains will be successful, and which are built on castles of sand. Some of the biggest companies in the world today, like Amazon and Google, were formed during the dotcom bubble, but a great many more disappeared without trace, taking large pools of capital with them.
At the pre-seed stage, the factor which matters most is the capability of the founder or founders. During the journey from startup to successful exit (stock market flotation, or sale to a bigger company), everything about the company will change, including its technology, its product, and its business model. Pretty much the only thing that can remain constant is the founder. Cassidy spends his time trying to identify and develop relationships with founders and potential founders who have the spark, (“the creative destruction in their being”), which means they have an outside chance of starting a company and guiding it through all the enormous changes and challenges that lie between the start point and the finish point.
These founders are extraordinarily talented and driven, but that is not enough. They have to be irrational enough to believe that they can change the world – that they can lift themselves by tugging on their own shoelaces – while also having great judgement, which tells them which strategies and tactics will work in a given situation, and which ones won’t.
Cassidy suggests it is useful here to apply the model of fluid and crystallised intelligence, which was first suggested in 1963 by the psychologist Raymond Cattell. Crystallised intelligence is the trump card of older people, who have seen many of the possible strategies deployed, and learned from experience what works and what doesn’t. They also know the written and unwritten rules which guide organisations. Fluid intelligence is the ability – more evident in younger people – to solve problems from first principles, and to ask “why do we do it this way?” when everyone else takes a sub-optimal approach for granted. The best founders possess both these types of intelligence.
Lessons from Silicon Valley
Cambridge is where Cassidy went to do his PhD, and he was enchanted by the geeky conversations he overheard in pubs, where people talked about how to engineer new proteins. As he was growing his precision oncology company business, CCG.ai, he also spent a lot of time in Silicon Valley, where the conversation in bars was all about how to create new types of company, and how to be successful in new and creative ways. He thinks Cambridge (and indeed, Europe as a whole) has a lot to learn from Silicon Valley, and there is much to do in order to build the availability of growth capital, and a helpful institutional environment. But he is confident it can be done, because of the exceptional talent emerging all the time from universities there.
There is still a fear of failure in Europe, whereas in Silicon Valley if you start a company, raise some money, but fold the company again six months or a year later, nobody holds that against you. This should be second nature to scientists, who make progress by disproving one hypothesis in order to develop a better one.
Europe is also in danger of hobbling its tech industry by regulating both the products and services it develops, and also the mergers and acquisitions that enable it to reward success. If the only way to exit a successful high-growth business is to float it on the NASDAQ, then Europe cannot expect to build a cluster of home-grown tech giants.
Another factor often cited to explain why Europe has no tech giants is that its single market remains a work in progress, with Brexit being a big step backwards. Cassidy argues that the US’ single market is also imperfect, at least in his area of healthcare, as individual states have different regulatory frameworks. He also argues that any company that wants to scale must learn how to work in different environments, and starting a company in Europe can mean you simply acquire the skills to do that sooner.
Focusing AI on clinical trials
Cassidy is excited about the future of AI in biotechnology. Much of the current action in healthcare AI is devoted to designing new molecules, but the biggest hurdles to getting new drugs to market lie in the clinical trial process that lies downstream of protein engineering. This is where pharmaceutical companies spend the vast majority of their budgets – and their time. AI could enable efficiency improvements – large and small – which would collectively get drugs to patients much faster and much more cheaply.