Ashmeet Sidana, a longtime VC who launched himself in 2015 to form Engineering Capital, has just closed his third and last fund with $ 60 million in capital commitments from an university endowment, a fund funds and three foundations.
Sidana - who previously spent almost nine years with Foundation Capital and then received one of his first sponsor agreements from legendary Foundation founder Kathryn Gould - says the fund was built despite the pandemic without too much pain.
This is partly due to Sidana's track record, including the sale of cloud monitoring startup SignalFx to Splunk for $ 1 billion after raising $ 179 million from VCs, and the sale of application monitoring startup Netsil cloud by Nutanix for $ 74 million in stock. after raising only $ 5.7 million. (Engineering capital was the first investor in both.)
Sidana's daily work in Palo Alto, California - which focuses on working with teams "which you can feed with two pizzas", but whose narrow technical knowledge can have broad applicability - was also an apparent attraction. To find out more, we spoke earlier today with Sidana, an engineering nerd who described himself and who studied computer science at Stanford on the "technical knowledge" that caught his eye. most recently.
TC: You talk about chasing founders with technical knowledge. Isn't this true of most venture capitalists?AS: No. Silicon Valley is an ecosystem of technological investment, but most of its participants do not solve difficult technical problems. They have information about the market or the consumers. This is the difference between Google and Facebook. Google has understood how to better index, how to better prioritize a sorting problem. Facebook started with the consumer idea that people want to be connected to each other. I focus on companies based on technical knowledge. Most VCs do not.
TC: What are you looking for exactly?AS: A team that uses software or technology to solve a known problem that exists but for which there is no solution. Many of these problems exist. For example, we now have the future will be multi-cloud. Amazon has had resounding success with AWS. Microsoft is doing well with its cloud activity. Google catches them. Then you have the seven dwarfs, including Digital Ocean. It is a difficult way for companies to engage in infrastructure. Another technical problem is that we all want to transfer our infrastructure to the cloud but not our data. How do we solve this? Some solve it legally, some with advertising. But really, it's a technical problem.
TC: What is your recent bet that solved a technical problem?AS: I am the first investor in Baffle, which is a really interesting company that allows the user of a traditional relational database to see the data but not an administrator. [Editor's note: the company says it enables the field level protection of data without requiring any application code changes.] Or Robust Intelligence is an even more recent investment that solves the problem of data contamination in artificial intelligence.
TC: How is that?AS: When you run models and do machine learning, you're doing cybersecurity and protecting it, but what about the data that AI is working on? They have a killer demo which shows that when you deposit a check with your iPhone, your bank of course uses AI to recognize the check and make sure the right amount goes into the right account. [But a nefarious actor could] provide a small number of pixels invisible to the human eye on the check photo and change the numbers and routing number. What Robust does is protect [both the bank and its customers] of this type of data contamination.
TC: I know you tend to invest very early - often by writing the first check. Do you hover around Stanford all day? How do you find these emerging teams?AS: I have good relationships with many schools, including [the University of] Michigan, Stanford, I'm involved in the Creative Destruction Lab at the University of Toronto; I keep active relationships with [schools in India]... I spend a lot of time with engineers from academia or industry.
TC: What size check do you do to launch them and how many of their businesses do you expect in return?AS: Most people think that investing in technical knowledge is expensive, but it can be very capital efficient if you work with software. I am also looking at companies where you can get income with $ 1 million and $ 3 million and financing. It usually takes a small team of five to eight people that you can feed with two people. Linux was finally written by one person. VMWare was launched with a technical overview by two people. Google had its previous stuff working only with Larry and Sergey.
As for property, my job is to buy low and sell high. I am as greedy as the next VC and I would like to have as much property as I can, but there is no formula.
TC: What mistake do you tend to see with new teams?AS: Gluttony. Most think they need to tackle a large market and solve a big problem, but the magic of a startup is to focus on an incredibly narrow problem that has broad application. As Steve Jobs said, it's hard to throw out features, not add them.
