The Fire-In-The-Belly Cycle

Growing up as a sports fan, you learned that players enter the league, playing for the love of the game, and some, after a few flush years, stop caring as much, feeling entitled, distracted by their lifestyles and their paycheck.

Growing up, you realize this works in many professions,  You encounter those partners, a decade when they last gave good advice, much too worried about the size of their paycheck and their mortgage for their upstate log mansion and Jackson Hole ranch to care about their clients or show up on calls.  As one hedge fund GC recently told me, he never hires the senior partner, because he knows that person does not care, but the hungry junior partner might care.

Here is how this works

One chief investment officer at a $5 billion institution breaks down the typical hedge fund life cycle into four evolutionary stages. During the early period, when a fund is starting out, its managers are hungry, motivated, and often humble enough to know what they don’t know. This tends to be the best time to put money in, but also the hardest, as the funds tend to be very small. Stage two occurs once the fund has achieved some success, when those making the decisions have gained some confidence but they aren’t yet so well-known that the fund is too big or impossible to get into.

Then comes stage three—the sort of plateau before the fall—when the fund gets “hot” and suddenly has to beat back investors, who tend to be drawn to flashy success stories like lightning bugs to an electric fence. Stage four occurs when the fund manager’s name is spotted as a bidder for baseball teams or buyer of zillion-dollar Hamptons mansions. Most funds stop generating the returns they once did by this stage, as the manager becomes overconfident in his abilities and the fund too large to make anything that could be described as a nimble investing move.

You maximize your chance at success by understanding where folks fall in that cycle.

Hiring Humility

It’s refreshing to see Google being so upfront about their past mistakes in hiring philosophy:

On the hiring side, we found that brainteasers are a complete waste of time. How many golf balls can you fit into an airplane? How many gas stations in Manhattan? A complete waste of time. They don’t predict anything. They serve primarily to make the interviewer feel smart.

One of the things we’ve seen from all our data crunching is that G.P.A.’s are worthless as a criteria for hiring, and test scores are worthless — no correlation at all except for brand-new college grads, where there’s a slight correlation. Google famously used to ask everyone for a transcript and G.P.A.’s and test scores, but we don’t anymore, unless you’re just a few years out of school. We found that they don’t predict anything.

What’s interesting is the proportion of people without any college education at Google has increased over time as well. So we have teams where you have 14 percent of the team made up of people who’ve never gone to college.

After two or three years, your ability to perform at Google is completely unrelated to how you performed when you were in school, because the skills you required in college are very different. You’re also fundamentally a different person. You learn and grow, you think about things differently.

Another reason is that I think academic environments are artificial environments. People who succeed there are sort of finely trained, they’re conditioned to succeed in that environment. One of my own frustrations when I was in college and grad school is that you knew the professor was looking for a specific answer. You could figure that out, but it’s much more interesting to solve problems where there isn’t an obvious answer. You want people who like figuring out stuff where there is no obvious answer.

Career Shorts

This has been sitting in my draft folder for months now, having been forgotten. Appropriately, I found this today and am posting.

This is the economist, Tyler Cowen, on career choice as distortion by short-term signaling issues.

“I think about this a lot: you’re young, you come from a smart, wealthy family, you’re somehow supposed to show that you’re successful quite quickly. Banking, law, consultancy allow you to do this; engineering, science and entrepreneurship less so. Your friends expect it, your parents, your potential mates do … So we see so many talented people very quickly having to signal how smart they are but that may not be the longest-term social productivity.”

Build, Buy, or Prize

I have discussed innovation prizes and Kaggle before as a source of algorithmic or other innovation.

But what about a vivid example in its fully glory.  How about about offering a prize and succeeding to gain publicity, get multiple self-selected teams working on a problem and coming up with a creative solution, and becoming, in the process, known as a place to do cool engineering, making it easier to attract talent?

This is what Netflix did, as described in a recent Businessweek profile on Reed Hastings and Netflix.

His geeky side became fully apparent in December 2005. He was convinced that the star rating system provided all the information Netflix needed to predict accurately what people want to watch. Others at the company argued that more indicators—whether people started playing something and then stopped, or searched for a particular actor, etc.—were needed as well. Hastings spent two weeks over his Christmas vacation pounding away on an Excel spreadsheet with millions of customer ratings to build an algorithm that could beat the prediction system designed by his engineers.

He failed. Still, the attempt sparked the creation of the Netflix Prize, a $1 million bounty to the person or group that could improve its ratings-based algorithm the most. It was the rare meaningful publicity stunt: The winning team, a collection of independent engineers from around the world, built Netflix a better prediction engine. And a company that was famous for red DVD mailers and outmaneuvering Blockbuster started gaining attention as a place for creativity.

Netflix can now hire just about any engineer it wants. That’s a function of the computer science the company does and its reputation as the highest payer in Silicon Valley. Managers routinely survey salary trends in Silicon Valley and pay their employees 10 percent to 20 percent more than the going rate for a given skill. Fired employees also get ultragenerous severance packages; the idea is to remove guilt as an obstacle to management parting ways with subpar performers.

The Talent Combine

Forbes reports on the accelerating trend of using programming contests to smoke out the best talent even when it is not looking for a job:

A few years ago such out-of-the-way stars were invisible to U.S. recruiters. Today it’s much easier to spot them. Thanks to a flurry of online programming contests that attract entrants worldwide, it’s possible to identify coders who do Caltech-quality work, even if they live halfway around the world and earned their degrees at Ural State University…

InterviewStreet was the ticket out of Siberia for Yakunin, the programmer from Ekaterinburg. He wowed the hiring engineers at Quora, a knowledge-sharing website in Mountain View, Calif., by being the only person out of more than 700 respondents to win a perfect score on a CodeSprint challenge it sponsored. Often the best coders aren’t eager to apply for a job. They just want to prove their mettle against all comers. Mindful of this dynamic, InterviewStreet moved the bulk of its contests to a website called HackerRank, where most entrants log in with pseudonymous user names. Job hunters authorize the site to reveal their real names to potential employers.

Blunt Tools

While data is a game-changer, it’s also important to remember that half-assed data tools are not better than no data tools.  A good example are the horrible software algorithms used to sort through resumes over the last decade:

Algorithms and big data are powerful tools. Wisely used, they can help match the right people with the right jobs. But they must be designed and used by humans, so they can go horribly wrong. Peter Cappelli of the University of Pennsylvania’s Wharton School of Business recalls a case where the software rejected every one of many good applicants for a job because the firm in question had specified that they must have held a particular job title—one that existed at no other company.

The Village Genius

This story — reaffirming everything good about the potential of MOOCs — was on the front page of the Financial Times recently:

Teenage applicants from as far afield as India and Mongolia are catching western colleges’ attention by taking so-called “massive online open courses” designed for older students.

Schoolchildren taking courses on their own initiative already account for about 5 per cent of the 800,000 students at edX, the non-profit online venture founded by Harvard and the Massachusetts Institute of Technology. Some have used their results to apply to the colleges that pioneered MOOCs.

Amol Bhave, a 17-year-old from Jabalpur, India, learnt last week that he had been accepted to MIT after scoring 97 per cent on edX’s circuits and electronics course. He received the good news on March 14 – or “pi day”, as he put it in a Skype conversation with the FT.

“I am like the first person in my city to get into MIT ever so I have become sort of pretty famous,” he said. “I was so motivated by how we were taught [by edX] that I decided that maybe I belong to MIT after all.”

Mr Bhave came to the attention of MIT faculty members after joining two students he met in the course’s discussion forum to create a follow-up course. Anant Agarwal, edX president and an MIT professor of electrical engineering and computer science, wrote him a letter of recommendation.

The lesson: learning that’s available to all helps surface talent that may otherwise gotten undiscovered.

You Green-Light Your Fucking Self

I have long said Kickstarter centrally fit into the talent elevation thesis.  Fast Company puts it well:

“”If you can raise money on Kickstarter, you don’t have to wait around to be green-lighted,” says Matt Porterfield, a Baltimore filmmaker whose Kickstarter-backed movie, I Used to Be Darker, premiered at Sundance. “You green-light your fucking self.”

You green-light yourself–the idea that creative people don’t need intermediaries–has long been one of the Internet’s great promises, and Kickstarter appears to finally deliver on it.”

Doing What You Love May Be A Question of Where

Today on AVC, in response to “doing things you don’t like to do should be an outlier case. not the majority case”:

Yes, doesn’t have to be that way. a lot of times we love what we do but hate the structure in which we do it.

for example, i loved my geeky law practice, but just hated what the law firm structure had become.

talent elevation platforms like kickstarter in the arts, but others in other areas, have the potential to bring that enjoyment back by changing the existing revenue flows and the stagnating institutions that mediate them.

Sniffing Out Talent

Second great articulation in the Keith Rabois regards finding hidden talent, a recurring topic on this blog. If you can find unrealized talent, you can have a winning attainable team, by looking forward and making forward-looking bets rather than chasing past success.

These are some characteristics for identifying that talent, per Keith:

  • The candidate can relay incredibly complex ideas in simple terms.

  • The candidate can see things you don’t see. Even within topics you’re fluent in, they’re able to convince you of new points of view or make you realize you’re missing something.

  • They’re relentlessly resourceful. There should be things in their history, whether it’s on or off the résumé, which conveys that they’re able to make things happen, against all odds. If there is a wall in their way, they’ll go over it, under it or become friends with it. They just make things happen and leave you wowed. Any time you have that “Wow!” kind of feeling you need to just hire the person.

  • They’re often contrarian. Peter Thiel now has a popularized way of figuring this out. He asks, “Explain something that you believe, that everybody else believes is wrong.”