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November 15, 2016

3 Factors Critical to Vertical Machine Learning Companies

Over the past year, we’ve had the pleasure of welcoming a number of leading Machine Learning (ML) focused companies to the Notion family. These are startups that are tackling verticals ranging from autonomous vehicles (Five.AI) to behavioural monitoring within the enterprise (Status Today). What’s more, many of our existing portfolio companies are now using ML to power aspects of their products and delivering accelerating returns as they go. As a B2B fund with a strong history in SaaS, we keep an eye out for emerging technologies that have the potential to bring a business’s marginal cost down to near-zero and in turn, enable subscription-based models. Here are a few thoughts on why we are bullish about a new wave of AI startups and the lens through which we are analyzing these opportunities.

The current AI Summer, the third since 1950s, has manifested itself in three waves. The first is that of pure research-driven companies, set up anywhere from 3 to 6 years ago out of top academic institutions. While these had minimal commercialization efforts, they did succeed in capturing the best grey matter and made significant progress in a nascent field, usually resulting in an acquihire. Examples include DeepMind, which was acquired by Google in 2014, or more recently, Jürgen Schmidhuber’s Nnaissense. Then came startups that took core machine learning technology, usually in natural language or machine vision, and packaged them into APIs. Startups like Wit.AI, acquired by Facebook, and Alchemy API, acquired by IBM, have since been added to the API arsenals of large corporates.

IBM Watson APIs (including Alchemy API), delivered through Bluemix

This brings us to the third wave, that of applied ML companies going after a specific vertical. A number of factors explain this shift. First of all, players like IBM, Microsoft and Facebook have managed to commoditize some of the basic building blocks through acquisitions, hiring rockstars and leveraging their massive technology infrastructure. We have also seen AI enablers being more readily available, including training data sets available on the web, the adaptation of GPUs for deep learning, the drop in cost for cloud compute and storage, etc. There’s also a record number of ML PhDs coming out of top universities and joining or starting technology startups instead of professional services companies in order to commercialize this nascent technology. Given these volumes, differentiation is a must and verticalisation is the solution to this end. There’s data to support this: consider that in SaaS we see 38% of vertical companies with ACVs in excess of $100k, versus only 4% for horizontals. This has given us a range of companies going after very specific verticals ranging from credit scoring to optimizing CNC milling machines.

Third wave AI, with its focus on deep specialization, represents a dramatic shift from generalist approaches in both strategy and talent needs. Here are three factors critical to vertical ML companies:

Flexciton: revolutionizing industrial automation in the process systems and energy sector.

Industry expertise: Unlike with horizontal software that requires more functional knowledge in sales, marketing or HR, vertical startups pushing a new service will require deep industry knowledge in order to build the right product and walk the C-suite through longer sell cycles.

Ravelin: fraud detection for a real time world using machine learning.

Data: One of the advantages of going vertical is the prospect of capturing unique data sets that corporates or incumbents would struggle to get their hands on. Doing this early on in the right field can kick off a flywheel that reinforces your model by offering a better service that guarantees further data acquisitions.

Unbabel, the latest member of the Notion family powering the internet’s translation layer.

Models: While a whole range of useful ML APIs are out there, building your core product around publicly available algorithms doesn’t do much for defensibility. While leveraging existing services might make sense for some, the focus for a third waive company should be on having top talent building a differentiated core product.

Paired with the right market, having deep industry knowledge, capturing unique data sets and forging state of the art models are great ways of building a competitive advantage. While being strong in all three isn’t necessarily a must, a combination of these and an understanding of where weaknesses lie and how to address them is critical.

Internet era technologies like the cloud, mobile, and optimization techniques have allowed companies to scale much more efficiently. By bringing marginal costs down, especially for early stage companies that have yet to scale, these technologies have enabled new industries to switch to SaaS models (e.g. Uber testing a subscription model), providing a much better and deeper experience for both users and companies. We are convinced that ML has the potential to continue this trend and make services an order of magnitude better, faster, and cheaper. Europe is punching well above its weight in this field, and we can’t wait to see what industries get challenged by third wave AI.

Thanks to Chrys Chrysanthou, Nathan Benaich, Ernest Oppetit, Nick le Fevre for the suggestions, edits and banter.

Posted by Alex Flamant, Associate at Notion Capital.

This blog was first published on Medium and the original article can be found here

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