This VC just closed on $60 million to fund “technical risk,” saying other VCs rarely do the same – TechCrunch

Ashmeet Sidana, a longtime VC who struck out on his own in 2015 to form Engineering Capital, just closed his third and newest fund with $60 million in capital commitments from a university endowment, a fund of funds, and three foundations.

Sidana — who previously spent nearly nine years with Foundation Capital and received one of his first limited partner agreements  afterward from Foundation’s legendary founder, Kathryn Gould — says the fund came together despite the pandemic without too much pain.

That’s thanks in part to Sidana’s track record, including the sale of the cloud monitoring startup SignalFx to Splunk for $1 billion after it raised $179 million from VCs, and the sale of the cloud application monitoring startup Netsil by Nutanix for up for $74 million in stock after it raised just $5.7 million. (Engineering Capital was the first investor in both.)

Sidana’s day-to-day work in Palo Alto, Calif. –which centers on working with teams “that you can feed with two pizzas,” yet whose narrow technical insights can have broad applicability — was also an apparent draw. To learn more, we talked earlier today with Sidana, a self-described engineering nerd who studied computer science at Stanford about what “technical insights” have caught his attention most recently.

TC: You talk about pursuing founders with technical insights. Is that not true of most venture capitalists?

AS: No. Silicon Valley is a tech investing ecosystem, but most of its participants aren’t solving hard technical problems. They have market insights or consumer insights. It’s the difference between Google and Facebook. Google figured out how to index better, how to better prioritize a sorting problem. Facebook was started with the consumer insight that people want to be connected with each other. I focus on companies based on technical insights. Most VCs don’t.

TC: What are you looking for exactly?

AS: A team that’s using software or tech to solve a known problem that exists but for which there does not exist a solution. Many such problems exist. For example, we now the future will be multi cloud. Amazon has succeeded wildly with AWS. Microsoft is doing well with its cloud business. Google is catching up to them. Then you have the seven dwarves, including Digital Ocean. It’s a difficult way for enterprises to engage with infrastructure. Another technical problem is rooted in all of us wanting to give our infrastructure over to the cloud but not our data. How do we solve this? Some are solving it legally, some with publicity. But really, it’s a technical problem.

TC: What’s a recent bet you’ve made that has solved a technical problem?

AS: I’m the first investor in Baffle, which is a really interesting company that enables 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 newer investment that’s solving the problem of data contamination in artificial intelligence.

TC: How so?

AS: When you run models and do machine learning, you do cybersecurity and protect them, but what about the data that the AI is working on? They have a killer demo that shows that when you deposit a check with your iPhone, your bank is of course using AI to recognize check and ensure the right amount goes into the proper account. [But a nefarious actor could] procure a small number of pixels that are invisible to the human eye in the photo of check and change the numbers and the routing number. What Robust does is protecting [both the bank and its customers] from that kind of data contamination.

TC: I know you tend to invest very early — often writing the first check. Are you hovering around Stanford all day? How do you find these nascent teams?

AS: I have good relationships with many schools, including [the University of] Michigan, Stanford, I’m involved with the University of Toronto’s Creative Destruction Lab; I keep active relationships with [schools in India]… I spend a lot of time with engineers in academia or industry.

TC: What size checks are you writing to get them started, and how much of their companies do you expect in return?

AS: Most people think investing in technical insights is expensive, but it can be very capital efficient if you are working with software. I’m also looking at companies where you can get to revenue with $1 million and $3 million and funding. That typically takes a small team of five to eight people who you can feed with two people.  Linux was ultimately written by one person. VMWare was started by a technical insight addressed by two people. Google had its earlier stuff working with just Larry and Sergey.

As for ownership, my job is to buy low and sell high. I’m as greedy as the next VC and would love to have as much ownership as I can, but there is no formula.

TC: What’s a mistake you tend to see with new teams?

AS: Gluttony. Most think they have to go after a big market and solve a big problem, but the magic of doing a startup is to focus on an incredibly narrow problem that has broad application. As Steve Jobs used to say it is difficult to throw away features, not to add them.