Mental Models and Technology Investing

As a lower risk operator in the technology investment markets, I operate well behind the venture capital frontlines, where hype and momentum reign supreme (because that’s just how that game works). Yet, it is still helpful to consider where the Seed-Stage and Early-Stage VC technology investment horizon is heading. What trends are real versus hype? Now that hype has worn off and the opportunists have moved on to the next buzz word, who is left doing real work? Which companies are emerging as winners from overfunded early-stage areas? What is realistically going to occur over the next 5-7 years with a winning company? What market segments are perhaps cheap due to investor biases? (AdTech?) Even though I might not make a move for years, pondering these questions is helpful to form a prepared mind.

HBR Transformative TechnologyConstantly questioning technology’s evolution is why I found the mental model shown at left from this HBR article interesting. This model highlights the differences in proliferation between foundational versus transformative technologies, comparing Blockchain today to TCP/IP (i.e. the Internet) in 1972.

While this model is helpful to compartmentalize things, the reality is that investing in technology is sloppy business. Sometimes it is hard to see where technology is heading, like driving in the fog. Other times, it’s obvious – less a matter of if, but when. Yet, even if a technology trend is obvious and the timing of its proliferation is roughly predictable, determining the winning company for investment is difficult, especially when squared with entry valuation and the number of “horses in the race” on a hyped technology trend.

As a simple illustration of this process at work, consider, for example, the succession of Tech buzzwords over the past 18 years: .com -> Web 2.0 -> Mobile-First -> Big Data -> Predictive Analytics -> Internet of Things -> Self-Driving Cars -> Virtual Reality -> Blockchain -> Machine Learning / Artificial Intelligence. There has clearly been no shortage of innovation, corresponding buzzwords and companies chasing each trend. Literally, thousands of companies have been formed to capitalize on technology’s new horizons, many failing, some creating massive amounts of value for investors early on a trend.

The funny part, though, is that investing “early” or “late” depends on an investor’s point of view, which itself is informed by his technology investing mandate. A buyout investor (who seeks return of capital investments) thinks quite differently and goes much later on trends than public stock, growth equity and venture investors (generally return on capital investors). In this latter group, the tricky part is that due to the constantly evolving nature of technology, the lines between the varying strategies can blur. Sometimes, you even see buyout investors cross over into the growth buyout arena as we saw with Marketo and Qlik. Therefore, no matter your strategy, it is an art to craft a balanced risk/reward outcome when seeking alpha via growth, not to mention the holy grail of asymmetric returns.

While there are no silver bullets, just hard work, I have found that mental models and research help with the challenge of going “late enough, but early enough” on technology trends. Over the years, from macro to micro, a mixture of the following has proven helpful: Michael Mauboussin, Carlota Perez, Gartner Hype Cycles, Wall Street Primers (e.g. Goldman on Virtual Reality), Gartner Magic Quadrants, 451 Research, Gartner Research, Forrester Research, obscure blogs and research papers, conferences, field research (aka constant meetings in San Francisco and down in Silicon Valley), comparing notes with Tech bankers and Tech investors (from LBO down to pre-Seed), capital markets data, SEC filings, valuation frameworks, and Excel spreadsheets. While spreadsheets come last, that says nothing of their relative importance. Valuation matters too much (the investing equivalent of gravity) and hype is just too easy in this business, which is why the sobering effect of numbers is helpful.

My recent Blockchain research is what spurred this post. But what I find most interesting is squaring this model with the key consideration as a technology investor: Where, how and when should you allocate innovation capital? And how does this decision differ based on your strategy: public stocks, buyouts, growth equity, late-stage VC, early-stage VC, post-Seed, Seed, pre-Seed or Angel? Confusion here creates some pretty interesting investment opportunities, irrespective of technology’s proliferation through society.

More on that to follow.