Over the past five years I have seen data of over eighty VC-backed tech companies while working with their various teams. That is close to 100 teams and 300 individual people. I thought long and hard about what is the right way to summarize this experience in an essay without disclosing sensitive information. I’ve collected 565 evernotes of various best practices, scribbles to self, observations (no IP data), and actions to avoid.
For most of the world, Sergey Brin’s story was elevated to myth after Google’s 2004 IPO. By 2008, the Economist had proclaimed Brin to be the “Enlightenment Man” of our era. His ascent after that was the stuff of classic Silicon Valley legend: A visionary drops out of school, spends time in a garage and emerges enlightened. Investors throughout the Valley lined up at his feet to offer patronage. As another Soviet émigré named Sergey at birth, I found myself awestruck when I passed by Brin as he hot-tubbed between diving sessions at Stanford’s swimming pool in 2002. So when in 2005, I too dropped out of college as Brin had, I imagined myself launching the next big thing.
Back in 2016, I left a very successful SaaS data company to become a consultant. The company was backed by many of the leading funds (Kleiner, Google, Goldman Sachs, Tomasz’s Redpoint, Rob Ward’s Meritech), so it seemed like a good asset to use as an investment capital in my new firm. This was the first such transaction for this company, so I’ve learned a lot in the process of selling these shares.
When I talk to friends of my father who have read Robert Pirsig’s Zen and the Art of Motorcycle Maintenance, they all say it had a profound effect on them back in the 1970s. Looking around, sometimes it seems the early tech of the 20th century was built primarily by people influenced by Pirsig’s writing. But it has been more than 40 years since the book was published—where are we now?
I recently watched a YouTube video of a technical manager from Criteo, Justin Coffey, demoing the in-house business intelligence tool his team had built. It’s a great presentation. Justin actually lists reasons why his team decided to build its own tool; all are good reasons, except none answer why they would have reinvented something when a better tool already exists. (see footnotes) Since I work across vendors and indirectly benefit from the complexity of new data tools—open source included—I also frequently hear reasonable arguments against buying a new tool.
Recently I left my best job thus far, the job at Looker, to found a consulting company. Naturally there were a lot of factors at play, but Looker’s gravitational pull on me was due to the technology itself. Looker is one of those technologies that everyone wants to build on top of because: