What we do have is machine learning, which is a subcategory of AI.

Another issue is low tech defensibility. In the AI world tech defensibility is harder to achieve because new/revolutionary models are being developed mostly in open, academic settings and so are available in open source.

We also tend to be more excited by enterprise focused solutions. Big companies like Facebook, Amazon and Google are concentrated on solving large consumer problems and have all the data in the world to do that really well, so it’s harder for startups to compete.

When it comes to platform vs tools, we prefer companies that start as a tool and then develop as a platform. These are startups that solve a complete business problem. They figure out how to get the data and then how to build a loop to learn from that data and create a user experience that solves a specific business problem.

We are more excited by startups that go vertical. We think ML techniques are built to go vertical. The model needs to recognise a very specific set of signal data in a massive, noisy dataset. To get accuracy, you need the same type of data that is relevant to the problem you are trying to solve.

The first thing to realise is that ML businesses just don’t have the same units economics of software businesses. ‘Sometimes, they look more like traditional service companies’.

“Programmes that can perform a defined task based on training data”.

There are a lot of different definitions out there, but the one that I like to use internally is:

The one I’m personally most excited about is companies which are extending capabilities to new market participants. AI massively reduces the cost of serving a mass market, allowing everybody to enjoy a premium service. A very good example of this is 24/7 security guards. It would cost you a lot of money to hire a security guard to control your... See more