Fetcher: Why We Invested
New technology applied to a big problem by a great team.
The secret to success in venture capital is asking good questions. I’ve come to appreciate the value of one in particular: “Why now?”
Why Now forces you to look at a business in context, to understand its external drivers of success. No startup wins big solely on the strength of variables it controls, meaning great entrepreneurs spot and exploit shifts in the technological, cultural, and regulatory environment better than the rest of us. Timing those changes correctly is often the difference between greatness and oblivion.
One such change is the growing impact of artificial intelligence. As data processing begat “big data,” and big data demanded “machine learning,” machine learning has advanced to the point of training itself to spot patterns humans can’t see in dots created mostly by machines. While the limited capabilities of AI are easily oversold, there’s little doubt we’re on the cusp of a new era of enterprise technology. New winners will attack worthy problems with AI powered by proprietary data, while new losers build solutions in search of problems, or bolt AI onto the margins of old models.
Candidate sourcing is one such worthy problem, and it’s the problem Fetcher chose to attack.
A Big Problem: Candidate Sourcing
Every company in our portfolio and every company we talk to is desperately searching for great people.
Even in the context of our current employment crisis, skilled people in engineering, sales, marketing, customer success and operations have never been more in demand, or — it seems — in shorter supply.
Finding those people has become a valued skill in its own right, with armies of savvy and detail-oriented recruiters scouring LinkedIn profiles for whatever cocktail of job titles, educational credentials, keywords, associations, and status updates seems to have worked for them in the past. Candidate sourcing is by far the most labor intensive aspect of recruiting these days, and the truth is it’s hopelessly broken.
The problem is fundamental. While modern recruiting emerged from the scarcity of information — on the strength of singular super-networkers connected to “all the best people” — today it operates in an excess of information, awash in a sea of online profiles and intermediaries loyal to none and available to all.
Can AI solve that problem? Eventually, it will. But today? Not alone.
For all its promise, AI technology just isn’t good enough to replace people altogether. Success, as revealed through a 5-year study published in HBR last year, means transforming the way you do business so that “human resources can be augmented with machine powers” rather than approaching AI as a “‘plug and play’ incremental technological investment.”
The big winners in this first stage of the AI revolution, in other words, will be those that let people deal with people while building machines that deal with data.
Which brings us back to candidate sourcing. LinkedIn for recruiters is 90% people dealing with data — people with biases, distractions, and time pressure dealing with data that’s always incomplete, often incorrect, and sometimes inaccessible. It’s no wonder the system is so challenging, why it’s become as much a bottleneck as an enabler of finding great people online.
Fetcher solves that problem using AI, scalable processes, and smart people to automate repetitive, top-of-funnel recruiting tasks so clients can focus more on candidate engagement & team collaboration.
Fetcher brings the right candidates to you by combining AI with a human touch. It uses great customer service to feed powerful underlying technology, learning not only the candidate attributes that predict job success but establishing a feedback loop with its customers to learn what each recruiter really wants for a given role at a given company. This two-sided approach makes Fetcher special, and — as they deepen their understanding of company preferences by role, level, job function, and geography — it makes them smarter about what other recruiters will be looking for, better at finding those needles in the LinkedIn haystack and elsewhere.
Having found a “race” we believed in, it came down to picking a “jockey.”
The Power of A Road-Hardened Band
Bruce Springsteen, explaining why the E Street Band was so legendary, once said they’d played for years in no-name clubs all over the country before they ever became a national sensation, and that — when they finally did hit the big stage —they’d hardened into the “heart-stopping, pants-dropping, hard-rocking, booty-shaking, love-making, earth-quaking, Viagra-taking” rock and roll band loved by fans around the world.
Like a lot of teams, the Fetcher team went through some shit early in the pandemic. CEO Andres Blank and I had built a relationship back in 2019 after an introduction by my friend and frequent co-conspirator tjmahony, and Andres was unusually frank when we talked in April last year as COVID-19 was shutting down the economy. “We’re in trouble,” he said, hunkered down at his parents in Miami with a unique perspective on the pandemic afforded by the service of his physician father.
I checked in on Andres again in September, just as the first wave ebbed in Massachusetts. He was still in Florida, but everything had changed. He’d made some hard choices to get more runway from available cash, and maintained the strong support of his seed investors. Like the best of teams, under the best of leaders, his had come together under the stress. Senior people had made financial sacrifices to minimize the damage, and good people at every level were stepping up to get things done. They were actually growing stronger as they leaned out, using their own technology to upgrade in key areas as their business came into sharper focus.
Talking with them, it struck me how much they believed in what they were doing.
These folks were stars… people with plenty of professional options, smart and experienced enough not to hang on too long to something that wasn’t working. Their level of commitment was just striking to me, and remained so as we took a closer look.
Having survived the storm, by June they were growing again at a healthy clip, winning back customers they’d lost and benefiting from the dramatic reduction in expenses. I saw an opportunity to invest and took it, and after the usual cave diving we came to terms at the end of January, and got a deal done in the month just passed.
This is my second investment in the AI-enabled sourcing automation space, with RippleMatch similarly well-positioned in early career hiring and hitting a similar stride. Andres already knew RM founders Andrew Myers and Eric Ho, adding to the G20 mafia in lower Manhattan (GO SOX.)
I’m genuinely excited to be a small part of the Fetcher story going forward, and thankful to be working with Andres and the whole team there to help scale their go to market model. If you’re looking for ways to make finding great people easier please check out Fetcher today, you can learn more and schedule a quick demo right here.
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