The Compression of the Hype Cycle

I spend a lot of time thinking about hype cycles, across industries (Big Data/AI, IoT) and ecosystems (New York).

Whether you use the Carlota Perez surge cycle (see this great Fred Wilson post) or the Gartner version, hype cycles convey the fundamental idea that technology markets don’t develop linearly, but instead go through phases of boom and bust before they reach wide adoption.

Hype cycles are a great framework for investors (and founders), because entering the market at the right time is both crucial and very hard.

Everything else being equal, you’d want to invest after the crash, early in the “deployment cycle” (Perez) or the “slope of enlightenment” (Gartner), when competition is comparatively limited but the market shows early signs of actual adoption. Easier said than done of course, because it is exactly the moment when things look the most uncertain. Investing right before the crash, in comparison, may seem foolish with 20/20 hindsight, but feels a lot better at the time, as one gets a lot of external validation (press, markups).

Recently, hype cycles have become even harder to decipher and “time” correctly. They’re much faster, with the time between boom and bust going from years to mere months. They’re also more pronounced, with sharper spikes that feel like instant bubbles. As soon as a category shows early signs of promise, founding activity accelerates dramatically and investor money flows in very rapidly. After just a few months, every investor interested in the space has “made their bet” in it. The category goes cold quickly, and everyone moves on to the next shiny object. Occasionally, you see a category going through a series of mini-hype cycles, with quick ups and downs.

If you think about what has got VCs (and founders) excited about the last few years, all cycles have been very short. On-demand, online lending, food, consumer IoT, VR, drones, bots, AR: they were all the rage for a bit. Fast forward to today, and it is generally difficult to get companies in those spaces financed. Deep learning, vertical farming and autonomous vehicles could very well be next in line.

As to crypto, the current industry darling, it seems to have already gone through several booms and busts (just blips as part of a broader hype cycle, true believers will say). Naval Ravikant nicely captured the velocity of the latest gyrations (for cryptocurrencies and the underlying blockchain ecosystem) in this recent tweet:

The reasons for this compression of the hype cycle are not hard to figure out. We live in a hyperconnected world, where everyone around the globe now reads the same press and social feeds in real time. Previous waves of innovation (social networking, mobile) have subsided, and investors are anxious to find the next big thing. The system is flush with money, with billion dollar early stage funds and multi-billion dollar late stage funds with “kingmaker” strategies. A lot of it is related to a very favorable macro environment with low interest rates and a long bull cycle over the last few years (if not the last couple of weeks).

From a founder perspective, the compression of the hype cycle reinforces the temptation to raise as much money as possible during the boom phase of the cycle, in part because the bust may follow pretty quickly. Raising more money at a higher valuation will certainly expose you to the post money trap, but it may be rational behavior from entrepreneurs if you think about it as “insurance” against a quick cooling down of the market.

From an investor perspective, a shorter cycle compresses the window during which one can be “contrarian and right”. A great playbook would involve showing up very early (but not too early) in a space before it is too obvious; picking some early winners and working hand-in-hand with the founders to find the right balance between growing and conserving cash; reducing new investment pace as too much fast-follower money pours into the space; waiting for the inevitable bust; then slowly but decisively re-engaging early after the crash. Not easy!

From a system perspective, quick booms and busts are probably not the best environment for long term technological progress. As a lot of the current emerging areas involve a “deep tech” component, there’s a brutal asymmetry between how quickly markets move on and the time it takes to actually build a great company.

Photo by Charlotte Coneybeer on Unsplash