Quick research on AI (part 1 of 3): Becoming an AI entrepreneur to achieve high returns
Last week we saw that AI and ML startups had seen some of the highest returns across Pitchbook’s analyst notes. Figure on left.
I mentioned wanting to dive deeper into AI, this is going to be a three part series.
This week: where are the opportunities for AI startups.
Week 2: Quick analysis of the top 10 AI acquisitions (by deal size) + Element AI’s recent acquisition.
Week 3: Research permitting, who are the investors, acquirers and what do the portfolio returns look like for select VCs.
Quick Primer: Platform Vs Vertical
Quick topic introduction:
There are two types of AI companies: Platform(Horizontal) and Vertical AI companies.
Platform AI would be tools such as Orange, Datarobot, IBM Watson and C3.ai, which would allow the consumer to plug in data and get an answer. Real world example: I am an SME that is looking to hire younger employees and want to understand how to get the best culture score on Glassdoor. Using Orange, I can build the nomogram in the left and get an understanding of what is valuable according to the reviews, and thus what the recruiters should be stressing when they put out new recruitment ads. In theory, Platfrom AI could be used to build a number of Vertical AI startups.
Vertical AI would be companies such as Mobileye, Quanergy, Cruise all focused on using AI/ML for one application (in this example autonomous vehicles). The AI being built only has one use case.
Which approach has a higher likelihood of exit?
As seen on the left, using M&A activities, platform AI transactions are in the minority compared to vertical AI applications. From own observations, this is likely because platform plays take longer to validate as the product market fit is often unclear (Think Element AI).
In general, for the platform plays you need to focus most your cash on salary in the form of consultants and teaching your customer how to use the tool before you can move them to a SAAS model. As a result, you would see that companies such as C3.ai list “professional services” on their financial statements.
A quick look at C3.ai’s financials to get an understanding of what a successful Platform AI’s financials look like. More interestingly, sales and marketing spend for 2020 and subscription revenue increased by a ratio of 1:1. In general, within the startup space, most startups are looking for “scale”, meaning a decrease in revenue relative to sales. It will be interesting to see what their churn rate looks like over the next two years. Further note, other SAAS players (e.g. Salesforce) tend to have a sales and marketing spend to subscription revenue of about 1:2.
Opportunities for AI companies
From McKinsey, we see that there are still many opportunities for startups to develop within a number of different industries. The industries still ripe for disruption would have low equity raise, strong industry size and high willingness to pay. In this case, the industries to target would be: Basic Materials, Telecom, and Pharmaceuticals. Now, just because these look interesting and show promise, the bigger question “is there data for AI startups?”
Is there data for AI startups?
Looking at information from GP Bullhound, we have the amount of data being produced every year for eight different industries. This helps to give us an idea that AI could be useful for these targets. For a bit more granularity, we use the McKinsey image to give us an understanding of the exact pain points faced within AI and what those use cases look like.
Now, with the amount of data, we see that the data needed for pharmaceutical and telecom is still not available (majority of use cases are ≤3). This would mean, if you are planning to start a company in one of the two industries, you should first work to make sure you have access to the data you would need. Some ways I have seen this done: pilots with target companies, partnerships/licensing with data providers, or just looking to see what information is available and figure out how to gather it.
For entrepreneurs: Where to find data for your AI models?
Kaggle: https://www.kaggle.com/datasets
US Gov: https://catalog.data.gov/dataset
Some information from Tableau https://www.tableau.com/learn/articles/free-public-data-sets
Github: https://github.com/awesomedata/awesome-public-datasets
and https://github.com/sindresorhus/awesome
Google: https://datasetsearch.research.google.com/
credit to vidder911 for the google datasets
If you are in the construction, finance or pharmaceutical industry, I am interested in talking with you. Please find my calendly here: calendly.com/muieen
Next week:
Quick analysis of the top 10 AI acquisitions (by deal size) + Element AI’s recent acquisition. We will be going a bit deeper into something pointed out by Reddit user u/zelappen that we would still need to divide vertical AI based on “three examples that put the first company as the one acquired for its dataset, the second — for its patents/algorithms, the third one — just an acquihire that in many cases boils down to outstanding data science expertise and capability of building cool ML models using open source DL frameworks.” I will be going into more detail on these comments next week :).
All images can be found here: https://docs.google.com/presentation/d/1BGx2HosOplD77mkh0916zg1AV-m_L0ArF94-EHWABdA/edit?usp=sharing
