Second-Level Thinking
Howard Marks is a well respected investor and the founder of Oaktree Capital Management. In a recent letter to investors, he introduced a concept that he calls 'Second-Level Thinking'. In his words:
This is a crucial subject that has to be understood by everyone who aspires to be a superior investor. Remember your goal in investing isn’t to earn average returns; you want to do better than average. Thus your thinking has to be better than that of others – both more powerful and at a higher level. Since others may be smart, well-informed and highly computerized, you must find an edge they don’t have. You must think of something they haven’t thought of, see things they miss, or bring insight they don’t possess. You have to react differently and behave differently. In short, being right may be a necessary condition for investment success, but it won’t be sufficient. You must be more right than others . . . which by definition means your thinking has to be different. . .
For your performance to diverge from the norm, your expectations have to diverge from the norm, and you have to be more right than the consensus. Different and better: that’s a pretty good description of second-level thinking.
Second-level thinking is deep, complex and convoluted.
Certainly, he sets a high mark for how to stretch our thinking.
In the context of the technology industry, I would use the following examples to contrast first-level and second-level thinking around building products:
First-level thinking says, “Clients are asking for this; this functionality will fill a need.” Second-level thinking says, “It’s something that our clients are asking for, but everyone is asking for that. Therefore, every competitor is pursuing that and its just a race to the finish and will quickly commoditize; let’s go in a different direction.”
First-level thinking says, “The IT analyst firms say this market will have low growth and most companies already have the capability. Let’s focus on a different market.” Second-level thinking says, “The outlook stinks, but everyone else is abandoning it. We could reinvent how clients are consuming in this critical area. Double down!”
These are rudimentary and simple, but hopefully sufficient examples for how Second-Level Thinking may apply in the technology industry.
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Market Forces at Work
We are in an unprecedented business cycle. Protracted low interest rates have discouraged saving, and therefore money is put to work. At the same time, the rise of activist investors has altered traditional approaches to capital allocation. Public companies are being pushed to monetize their shareholders investments, either in the form of dividends or buybacks (and most often both). Because of this non-relenting pressure on public companies, investment has begun to flow more drastically towards private enterprises (at later and later stages), leading to the 'unicorn' phenomena. These 'unicorn' companies, which have the time and resources in their current form, are doing 3 things:
1) Paying anything for talent, causing wage inflation for engineers and some other roles.
2) Attempting to re-invent many industries, by applying technology and in many cases, shifting them to a pay-as-you-go (or as-a-service) model.
3) Spending aggressively, in any form necessary, to drive growth.
Public companies, in some cases, are crowded-out of the investments they would normally make, given this landscape. But, a central truth remains: at some point, an enterprise must make money. That timeline is typically compressed when capital begins to dry up. The term 'unicorn' was first used to connote something that is rarely seen. The fact that they are now on every street corner is perhaps an indication that time is short.
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The Impact
1) "Winter is coming" for the engineering wage cycle. Currently, this inflation is driven in part by supply/demand but more so by the cult of "free money" and nothing else better to do with it. At some point, when 'hire at any cost' dissipates, we will know who has truly built differentiated skills.
2) The rise of cloud and data science will eliminate 50% of traditional IT jobs over the next decade. Read more here. The great re-skilling must start now, for companies that want to lead in the data era. Try this.
3) As-a-service is a cyclical change (not secular). The length of the cycle is anyones guess. And, as with most cycles, it will probably last longer and end faster, than most people believe. Much of this cycle is driven by the market forces described above (less money for capex, since all of it is being spent on buybacks/dividends). At some point, companies will realize that 'paying more in perpetuity' is not a good idea, and there will be a Reversion to the Mean.
4) Centralized computing architectures (cloud) will eventually diminish in importance. Right now, we are in a datacenter capital arms race, much like the Telco's were in 1999. But, as edge devices (smartphones, IoT, etc.) continue to advance and the world is blanketed with super computers, there will be less of a need for a centralized processing center.
5) Machine Learning is the new 'Intel inside'. This will become a default capability in every product/device, instrumenting business processes and decision making. This will put even more pressure on the traditional definition of roles in an organization.
6)There is now general agreement that data is a strategic asset. Because of this, many IT and Cloud providers are seeking to capture data, under the notion that 'data has gravity'. Once it is captured, the belief goes, it is hard to move, and therefore can be monetized. While I understand that in concept, its not very user centric. Who likes having their data trapped? No one. Therefore, I believe the real winners in this next cycle will be those that can enable open and decentralized data access. This is effectively the opposite of capturing it. It's enabling a transparent and open architecture, with the ability to analyze and drive insights from anywhere. Yet another reason to believe in Spark.
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It's debatable if the 6 impacts above represent Second-Level Thinking. While they may to some extent, the real thinking would be to flesh out the implications of each, and place bets on the implications. These are bets that could be made in the form of financial investments, product investments, or "start a new company" investments.