Where the money is going & where AI is succeeding

Recently, CB Insights published their annual review of what they call the “AI 100,” a list of the most promising AI companies. The ranking is a product of their blackbox assessment over several factors such as patent activity, investor profiles, market potential and competitive landscape, so keep in mind that there are many more companies out there that didn’t make their selection. You can read the ranking details and download the data from their summary page.

As for this writeup, I wanted to take a look at the broader industry trends, specifically where success was being found with applications of contemporary machine learning, deep learning and, to use loose language, AI1.

Given the available data segmentation from CB Insights I had two primary questions and assumptions:

  • Where is the money going?
  • Where are AI solutions succeeding?

The first question, as a crude proxy, appeals to me as a measure of venture capitalist knowledge. VCs throw their money where their expertise, analysis and speculation leads them to believe the future of business is headed. You can think of their investments as a partial revelation of their knowledge.

If you consider that the people building new business, namely entrepreneurs in charge of innovative endeavors and applications, operate from a different set of knowledge and value analysis than VCs, the number of businesses in a given sector can be thought of as another indicator of market value from a second, distinct perspective. In traditional terms, you can think of this competition as a proxy for market validation. If you’ve read Moore’s Crossing the Chasm you might recall the discussion around the importance of competition in driving later-stage adoption of new solutions.

In sum, the pragmatists are loath to buy until they can compare. Competition, therefore, becomes a fundamental condition of purchase. — Geoffrey Moore, Crossing the Chasm

With these points in mind, I was curious if the number of businesses that are finding success in different sectors was proportional to investment from the VC side in those sectors.

Where is the VC Money Going?

Below is a chart of the aggregate VC funding grouped by sector. Remember, this is VC funding into “top” companies applying “AI” in their respective sectors. Clearly, enterprise tech stands out, which isn’t too surprising given how encompassing that sector is. But Government? It’s obvious in hindsight, but for a fun little test why don’t you go ahead and ask yourself where you think the government investments are going (we’ll come back to that shortly).

VC funding by sector

Where are the Entrepreneurs going?

Next up we have the number of distinct businesses per sector. Given the preamble above about VC knowledge and entrepreneur knowledge, if they were roughly identical you might expect to find a commensurate number of active competitors per sector, regardless of the estimated total addressable market (TAM) per sector.

Company count per sector

If you look at the shape of this chart, you’ll see it doesn’t quite match the shape of the VC investments chart. The dynamics at play here involve not only CB Insight’s selection process, but also the interaction between market size, technology, and funding availability. Let’s explore this a little bit more.

Who is winning in what sectors?

Considering the VC investment into AI applications in various sectors combined with the number of active competitors in each sector, another interesting question to ask is what is the average funding per org per sector?

Company count per sector

Looking at this chart, we see something that possibly suggests a competitive activity to VC knowledge correlation, but there are a few clear outliers.

  • Media: What’s really driving this down is the lack of VC money. There are a few potential interpretations: perhaps AI applications to the media industry are still developing in their early stages (technology as a barrier), or that VC awareness of the potential is lagging, thus investments are low (knowledge distillation as a barrier). Another possibility is that the CB “top” ranking is dubious at best (garbage in, garbage out).
  • Industrials: I don’t have much to say here, but it seems that this sector is getting both a decent chunk of money while possessing only a few leading companies. Most likely, this is a capital intensive sector, or perhaps this is a sector that could use more competition given the willingness of VCs to invest. I’d be curious to know more about the solutions and cost of entry, and if there are any network effects2 in this sector that might be feeding into a winner-take-all outcome.
  • Government: In this case, there is both an abundance of funding and few successful competitors, resulting in a very large average funding per business. As hard as it may be to provide solutions for the government, the bounty looks to be well-worth the effort. (You have been pondering what areas these solutions might focus on, right?)

How many paths into a given sector?

As noted above, sectors like “Enterprise Tech” are quite broad, so much so that they lose meaning. The areas of focus within sectors, provided by the CB Insights data, help reveal these dynamics. For example, the unique number of focus areas listed can be thought of as the number of entry points to a sector (or possibly the number of sub-markets within a sector, depending how you want to view it and how the focus areas relate). Furthermore, this is likely a lower bound: there are at least this many focus areas to compete in, and entrepreneurs are certainly developing more methods to enter any given market.

Business diversity within sectors

If you want to frame your product and business thinking around needs and “jobs to be done,” you can think of this as the primary number of “jobs” per sector that have managed to find traction with AI-based solutions. Taking a look at the government sector in detail reveals that out of the six companies operating in that space, there are only two focus areas: security and disaster management (Alas, revelation! I’m guessing you guessed security, but not disaster management). It turns out that five of the six companies in the government sector are focusing on security. Considering the advancements in computer vision tasks such as facial and object recognition, this isn’t too surprising. But it is interesting to recognize, just to throw an idea out there, that there’s the whole realm of disaster management that might not be getting adequate attention.

This chart serves to remind us of the obvious: almost all sectors have multiple problems worthy of undivided attention. Despite the temptation to say “company X does AI for Y,” there are often multiple, specific problems to be solved within each of those sectors, and those problems can define entire businesses. Don’t forget to look into the different angles of approach.

So, what next?

Although there are some caveats involved in examining a subset of companies like this, the overall trends and features serve to motivate a few interesting questions. From the CB Insights data it appears there is success being found in all the major sectors, so the question of interest might not be “where” AI is succeeding, but “how” and to what extent are AI applications succeeding? There appears to be a mad rush in security applications, the enterprise sector appears to be attracting the most entrepreneurs, and other massive sectors like media and telecom are awaiting grand success. These are each worthy of a deep dive.

The interplay between the availability of funding, the state of incumbents in a given market, and, most excitingly (to me), the role of breakout technologies in the creation of new markets, is fascinating. For the sake of brevity, I didn’t get into the regional data and a few other dimensions of interest, but there is plenty more to be said. If any of these topics interest you, feel free to contact me. I’d be delighted to share notes, references or have a casual chat. And if you’ve read this far, thanks for your time!

Footnotes

  1. The use of the term “AI” within the research and engineering community is a bit of a hot topic. There’s been some clear mismanagement of expectations, overpromises, and blatant abuses of the term. However, it does have its appeal in being only two letters and conceptually symbolic of “all of those various engineering & research efforts,” so I use it is used here with full respect for the nuances therein. ↩

  2. For one of the most accessible introductions network effects and their role in business, I highly recommend Anu Hariharan’s (Andreessen Horowitz) presentation. Back when that presentation came out, I read all the references and collected them, should you be interested:

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