This month marks my tenth year at Balderton, and my 12th year investing in and supporting UK & European technology companies.
I've been fortunate to have met and worked with Europe's best technology entrepreneurs, and their teams, in an exceptionally broad range of industries. I've worked with businesses as they have gone from a handful of customers to hundreds of millions in revenue, and on to IPO or M&A in sectors ranging from VR to 3D Printing, mobile marketplaces to bioinformatics.
I've loved this breadth of experiences, and Balderton is, and remains, a sector agnostic fund - we support the best founders using technology to change the world for the better in every sector where software provides leverage. However in 2023 I'm trying to narrow my focus to those areas where I believe I and Balderton can be great partners, and on the three biggest problems I believe will face the world in the decades to come: AI for All, the Great Energy Transition and Scaleable Healthcare.
As a fund, we invest anywhere from seed to pre-IPO, although I spend more time on Series A-B investments. If you have built your founding team, have early validation of your product and a clear vision of what you want to achieve over the years to come in the sectors below, please don't hesitate to get in touch.
AI for All
If “Any sufficiently advanced technology is indistinguishable from magic.” then it's fair to say that advances in 'A.I' are currently making people feel like magicians. Increasing access to LLMs, actionable-LLMs and breakthroughs in the performance of other models in fields from computer vision to transcription have opened a pandora's box of new opportunities for entrepreneurs.
We've already published a few thoughts on the areas of A.I, and use cases for LLMs we are interested in, in particular on the future of neural search and demand for vector DBs. And we have made some investments as part of this thesis in Levity.ai, Photoroom and Supernormal.
These are just a few examples of where we are focussed, but we have a much broader view of where advances in machine learning are going. From accelerating workflows and performance, to robotics and cancer diagnosis - the need for new interfaces and infrastructure to develop, deploy and maintain these models is clear.
If you share this view, have some initial proof points that your product can outperform industry benchmarks and a theory on how it becomes more effective and defensible at scale, we're always keen to talk.
The Great Energy Transition
In the next few decades, the world is going to undo almost 200 years of industrial and commercial carbonisation. To achieve this our homes, cars, offices, factories, city infrastructure and huge parts of the supply chain for the goods we consume are going to have to change the way they use energy and many other resources.
Much of this shift will require significant investment in energy infrastructure, which other funds and types of financing such as debt are better suited to serve. There are still a huge number of technical breakthroughs and software solutions required to make this a reality, and it's in those areas that taking venture capital makes sense.
We have already made a significant number of investments along this thesis - from using advances in hydroponics, machine learning and robotics to shrink the food-supply chain at Infarm, to reducing your home's use of energy with Tibber. We are keen to talk to any companies who have evidence that their product, whether it's in the lab, robot or smart device, can improve our world's sustainability at scale and in particular reduce or remove carbon emissions.
Our health is the most important thing we have, and the global mismatch in supply and demand of healthcare is only going to get worse as the global population ages and chronic diseases become more prevalent.
There are, however, a number of converging trends in health and biotech that we believe can have a meaningful impact on millions, if not billions of people's health - miniaturisation, personalisation and automation. We have invested in many different areas of innovation in health and biotech for decades, in bioinformatics companies like Sophia Genetics and A.I-first companies like Digital Surgery. Suffice to say if you are building a software solution in healthcare with a clear and scalable go-to-market we believe we are amongst the best firms in the world to work with already.
As our own expertise expands, and as software becomes a more important part of the biotech workflow, we are increasingly looking at companies building medical devices and personalised therapeutics as well. From applying machine-learning for de novo antibody designs, to automating wet-lab processes, if you have initial proof of effectiveness, a CE mark or clinical trial level data ( if not a full clinical trial yet ) and a handful of early adopters within the profession, we are always keen to learn more.
Above is as simplified a version of my thinking I could get too, and this will not be the only areas I end up investing in this year - despite my efforts the best entrepreneurs have often convinced me to explore topics outside of focus. However I hope sharing these thoughts will encourage those working in the space to reach out, I'm always online at firstname.lastname@example.org.