Fetcher is a Slow portfolio company that combines artificial intelligence with human expertise to reimagine the recruiting process. Fetcher provides an outbound recruiting platform to automate both candidate sourcing and email outreach so that recruiters can refocus their time on the higher value aspects of recruiting. As our readers know, we at Slow do a lot of thinking about 'artificial artificial intelligence' (AAI) and how to provide leverage to human operations. Recently, Fetcher's CEO, Andres Blank, caught up with Alex to dive deeper.
Alex: At Slow, we think about 'artificial artificial intelligence' (AAI) as a way to give employees superpowers. What superpowers does Fetcher give recruiters?
Andres: We help recruiters fill open positions at a fraction of the time and cost of traditional tools. There are lots of great techniques and solutions for finding candidates online, but it takes a tremendous amount of time and domain expertise to do so successfully. To recruit effectively you have to understand how to search, which is neither easy nor efficient. We are the solution for recruiters who lack a pipeline of qualified candidates and don't have the time to create one. We create that pipeline - according to specific criteria - and then manage the outreach for you.
AM: How would you say recruiting has evolved over time due to advances in technology and platforms like LinkedIn?
AB: Traditionally, recruiters had to be extremely personable so as to get candidates excited about a potential role. Now, recruiters must also be competent at repetitive tasks like writing boolean search strings and sequencing personalized messages. In many ways recruiting has become more like programming as there is less of an emphasis on that traditional human element.
Most people get into recruiting because they love building relationships with people, not because they love scouring the web for talent. That's what gets recruiters most excited about Fetcher. We help them get back to the uniquely human elements of recruiting, allowing them to spend more time talking to candidates and less time pulling together lists and managing email outreach.
AM: Taking a step back, what is the single most important metric Fetcher helps companies optimize for?
AB: Internally the most effective way to measure a client's success is by tracking how many emails are being sent to Fetcher candidates. Every outreach message is a potential new opportunity that we have unlocked. Most of the people we reach out to would not normally have been found otherwise. It's not because they are necessarily obscure or unusual candidates, but rather, it's because humans just don't have the time to source the sheer volume of candidates Fetcher can. The more candidates Fetcher reaches out to on behalf of our customers, the more time employees are saving - and the more opportunities for companies we are creating!
Now, obviously, the real satisfaction comes when you hire a candidate but it's harder for us to optimize for hires, which relate to a company's brand or their hiring processes as opposed to the number of unlocked opportunities.
AM: What is your response to recruiters and sourcers who are scared Fetcher is going to take their jobs?
AB: We think of ourselves as a superpower for recruiters, not their replacement. We help recruiters move 10x faster. For many recruiters, who suffer from extreme burnout at high-growth companies, this speed advantage is a game changer. Last week one of our recruiters told us that he loves Fetcher because he finally has time to take care of himself. His job was causing him so much anxiety that he was on the verge of quitting. He calls Fetcher his "self-care."
There is also a human element to recruiting that AI simply cannot replicate. For technical sourcers, perhaps, this is less of an argument for job security. But, I still don't think that Fetcher will ever completely replace sourcers.
For example, let's say that you want to hire an engineer. Computers are really good at pattern recognition so they can identify - within a certain pool of ideal candidates - that 20% come from Company A, 15% come from Company B and 10% come from Company C. Then, based on these percentages, generate a list of 100s of other companies that look similar to those initial three and create a significantly larger pipeline of potential candidates.
Computers, however, are not good at thinking outside the box for hiring. For example, let's say that you find out that a competitor is shutting down. As a human sourcer, it takes 2 minutes to make a decision to map out all the people at that company, but, it will take a full day to execute. That time between decision and execution is what we are selling. We are enabling people to do more critical thinking by spending less time on repetitive tasks.
AM: When you started Fetcher, how did you analyze the sourcing process to decide what was worth automating and what should remain a 'human' task?
AB: Nobody is going to accept a job by just talking to a robot or some software. A final conversation with a human has to happen. And during the recruiting process, you have to sell the position, you have to demonstrate passion for the company's mission and you have to show empathy towards the candidate. These are the uniquely human elements of the recruiting process. Convincing a person to join you is not always a logical progression that can be replicated algorithmically.
It's also extremely hard to replicate those elements of human intelligence needed to assess a candidate's skills, to sell the company and to personalize the pitch based on candidate profiles. That depth of understanding is very hard to automate and apprehend - at least for now...and maybe forever. But you have to find and engage the candidate first. So, Fetcher handles everything up until that point. Fetcher enables you to spend more time in conversations and less time finding them.
AM: What is your best success story?
AB: One of our earliest customers was a powerhouse entrepreneur named Mary Lou Jepsen whose startup builds opto-electronic and holographic systems to look inside the human body. Complicated stuff. Mary came to us desperate to find a "Senior Hologram Architect". I didn't even know that position existed! Yet, we were still able to unearth five candidates with the highly specialized criteria she was looking for. Ultimately, she hired one of our candidates. Finding that needle-in-the-haystack candidate at scale is what makes us happy. Helping mission-driven companies build their teams with incredible, diverse talent is what keeps us motivated.
Last week, Slow hosted a Life Capital Conference focused on Income Share Agreements (ISAs) in SF with The Information, Village Global and Cooley. It was great to get a diverse group of passionate people together thinking about how to create more options for personal finance, especially as it pertains to education.
Domino Data Lab and Paco Nathan are hosting Rev, the Data Science Leadership Summit, in NYC May 23-24. Speakers include Daniel Kahneman, Nobel Prize winning economist and author of "Thinking, Fast & Slow", plus leaders from Netflix, Nike, Slack, Red Hat, Stitch Fix, Lloyds, Workday, TBS, Allstate and more.