This is an excerpt from Talent: How to Identify Energizers, Creatives, and Winners Around the World by Tyler Cowen and Daniel Gross (St. Martin’s Publishing Group, May 2022).
Cowen is an economics professor at George Mason University, co-author of the Marginal Revolution blog, and co-founder of Marginal Revolution University. Gross is the founder of startup accelerator Pioneer, and was previously a partner at Y Combinator and co-founder of Cue, which Apple acquired in 2013.
Either way, all of us are involved in the search for talent. But when is it better to depend on scouting for talent acquisition instead of doing all the work yourself?
Traditional venture capital uses fairly centralized evaluation methods run by a small set of prestigious, highly paid general partners. But Sequoia has had a scouts program for more than 10 years, and the newer venture capital firms Village Global and AngelList Spearhead rely on scouting models to locate their talent.
In general, the scouting model works best when talent search begins with a very large pool of candidates and must be reduced to a much smaller number of plausible contenders. It also works best when there is no easy way to assess talent from a distance.
To see how these lessons apply more broadly, let’s look at one field where scouts and scouting have been especially effective — fashion supermodels. One strength of the scouting model is that it encourages scouts to search all kinds of hidden corners and unlikely places for talent.
Beautiful people are everywhere, but even in model-rich places like southern Brazil, no talent agency can visit every village and meet everyone. In addition, the school system is not an obvious place to look for fashion model talent, at least not in the same way it can identify mathematical, engineering, or musical talent. Beauty is not part of the curriculum, and schools would find it institutionally awkward to single out certain individuals too directly as better suited to modeling, even if they might do that for tennis or gymnastics.
As a result, the modeling industry has several layers of scouts to help identify the right talent. Some are photographers who may approach prospective models in hopes of landing a successful shoot or two and perhaps building a strong long-term reputation as talent discoverers. But there are also full-time and freelance scouts who try to identify the right talent and link them with a modeling agency or magazine.
It is worth considering why the scouting model works in this setting. First, the relevant talent can come from many different parts of the world, and the number of people to scout is very large. It is difficult to imagine a centralized process doing the job. Second, many scouts likely have a pretty good sense of who might become a good model. Looks are hardly the only thing that matters for modeling success, but they are a kind of “first stop,” and asking scouts to judge looks well from first impressions is more plausible than asking them to use first impressions to judge talent for something like quantum mechanics. Third, a follow-up check on the modeling talent of the selected candidates is not very expensive. You can bring them in for a photo shoot and see how well they perform in the market without having to invest millions of dollars immediately.
The startup world does not match all of those features exactly; in particular, talent may be hard to judge from an initial superficial encounter. Still, as successful entrepreneurship spreads around the world into so many varied locales, it is easy to see why scouting models are drawing more attention. They promise to widen the dragoon for finding more talent, and for finding talents that the initial, more centralized authorities may be missing.
Scouting is also becoming more important as options for self-education rise. With more people trying out various avocations than ever before, the burden on talent search grows and grows. We need to be more open to the achievements of self-taught people without traditional training, and that is even more true in the tech world, where many of the most important founders have avoided the institutions of traditional education.
In gaming, almost everyone is self-taught in one way or another — no one arrives at World of Warcraft with a master’s degree in the subject. That means a greater burden on talent search, because there are more plausible candidates to sample and evaluate. Fortunately, candidates can now send more signals, whether through competitions, online posts, game performance, social media displays, or other markers of quality. You can think of those signals as substitutes for scouts, and you may need to rely on scouts to the extent that such signals are hard for talent to send and also hard for searchers to interpret.
The limits of the scouting model
In most cases, scouts handle only the early stages of the talent-finding and cultivation process. Finding a potential supermodel with the right look is only one step in a very difficult process.
When it comes to the later steps in identifying prospective supermodels, the sector relies more on centralized talent evaluation and less on scouts. A centralized talent agency, modeling agency, magazine, or some other institution makes its own judgments about the skills and work habits of the candidates under review, because looks are only one part of the equation. The supermodel sector therefore shows both the limitations of scouting and its upside. In a startup setting, the general partners in a venture capital fund make the final decision about whether a promising prospect has a viable, executable project.
The key to a good scouting program is incentives. In tech, scouts were initially venture capitalists themselves, searching for the best talent and motivated to grow the partners’ money. This worked: from Google to Apple, rebellious outsiders were brought into the system, and the gatekeepers were richly rewarded. But as scouting expands and becomes a more general idea, the incentives do not always have to be financial. Status rewards can work too; for example, some of Y Combinator’s biggest successes (Airbnb, Dropbox) came from referrals. Daniel Gross’s Pioneer firm keeps a leaderboard of its best referrers, and that is one of Pioneer’s most frequently visited pages. In these environments, most referrals are bad, but the exceptional candidates are often referrals.
That, in turn, brings us to the second incentive dynamic: skin in the game. Venture funds also run scout programs in which various founders are given free money to invest. No downside, all upside. These programs often do badly, but occasionally a scout will identify an extraordinary company, which the VC then doubles down on. This is unlike the proper venture world, where the partner of a fund has both economic and status interests at stake. The founder investing free money is not putting up either dollars or status, and the main avocation of those founders is being a CEO, not a venture capitalist. Still, that may give them a certain freedom of imagination that lets them spot opportunities others would miss.
With Sequoia, there are many independent scouts who have the authority to write checks in the range of $25,000 to $50,000 for potential startup founders. The scouts play their biggest role at the seed stage, when smaller sums of money are often called for, while the general partners might be writing much larger checks ($10 million and up) for more advanced projects. You can think of those seed deals as part of the pipeline for the eventual larger investments, and some of the economic upside is shared with the identifying scouts. The scouts can be given part of the profit from the deal, or a general share in the broader fund, or be paid on a performance basis, depending on how well they invest the funds they are given.
In essence, skinless scouting games increase variety at the cost of precision. You can engage a broader group of people working on other things to send you their deal flow in exchange for value. This is a good idea if your filtering costs are low. On the other hand, if deal costs are high, you may want to build in some element of risk. Either financial risk (a mix of personal capital with outside capital) or status risk (require that your scouts do scouting under their own name and as their main job) can be used to impose extra discipline on the process.
It is possible to imagine worlds where there is so much data on individuals, and at such a young age, that measurement would dominate search. You would not have to “look for” anybody, at least not if you could access the data in the system. But we are still fairly far from such a world.
We do expect there will be more cases where talent is found “by the numbers,” or by AI scouts. If you look at the Houston Astros, one of the most quantitatively advanced teams in professional baseball, they have already eliminated in-person advance scouting, instead preferring videotape and measurement using Statcast, a state-of-the-art tracking technology based on massive amounts of data.
The future will probably make talent search more like the gaming environment, where prospective candidates are invited to step up and be measured. If you think about the very best gamers, there was no scout visiting local high schools trying to persuade kids to give the game a try, spotting them in the shopping mall (“your thumbs look strong and your skin has that basement pallor”), or measuring their IQ, reaction speed, or game stamina. Rather, millions of people want to play the game in the first place, and the gaming process itself measures how good they are.
Scouting is by no means the only way to find creative contributors. But if you want to succeed in the future quest for talent, understanding its strengths and limits is one good place to begin.