What does the Midas List tell us about success in venture capital? Reply

Basically nothing, other than it’s not founder-CEOs who make the best investors.

Everyone tries to find the best practices that ensure success in a given line of work. This is no less true for venture capital, where success is easy to measure (investment returns) yet impossible to stack rank (venture firms let alone individual investors do not publish their results).

So, every year, the industry opines on what makes for a great investor — at least when it comes to the criteria of the Forbes Midas List and ‘hits’ over the past five years, making some big assumptions regarding the actual financial returns.

There have been many prognostications over the last 25 years since I got started in VC:

First, it was all about having a financial background and understanding venture deals and putting capital to work (Arthur Rock).

No, wait, operators make better investors: they know how to manage people, and people are the most important part of any startup (Don Valentine).

Actually, it’s the product execs who really know how to identify product-market fit — they do the best job creating the right product and understanding customer needs. Can’t have a hit without a killer product, right?

Nope! Engineers are the ones who understand how hard it is to build something from the ground up and then scale it, so they’re the ones who can pick scalable companies (Eugene Kleiner).

And, the best one of all: founder CEOs really know what it’s like to start a company, so they will identify and help build the best ones. And then, finally, there are the stars that don’t fall into any easy bucket (Mike Moritz, Jim Breyer, and John Doerr, to name a few).

But what does the actual data tell us?

I looked at the 2017 and 2018 Midas Lists to find that CEO experience is not a requirement and, in fact, seems to be the exception: There was only one previous CEO in 2017 (Peter Thiel) across the top 30 investors and only two previous CEOs in 2018 (Eric Paley, Byron Deeter) across the top 30 investors.

Having an engineering background is also not necessary to be a successful investor. Mary Meeker, for example, has a BA in Psychology, while Peter Fenton has a BA in Philosophy. Countless others have non-engineering, and even non-technical degrees. Across the top 20 ranked investors in 2017, only 20% had engineering degrees and only 25% had engineering degrees in 2018.

For fun, here are the backgrounds of the Top 10 Midas list investors in 2018:

  • Equity research analyst (3)
  • Mid-level operating execs (3)
  • Investment banker turned CFO turned VC (2)
  • Start-up co-founder (1)
  • Career VC (1)

Then, to the disappointment of many who deride degrees, an MBA is shockingly common: 60% of the top 20 investors, across both the 2017 and 2018 Midas Lists, hold an MBA. Moreover, in general, post-graduate education is very high among the List’s top performers. 80% of top 20 performers in 2017 and 95% of top 20 performers in 2018 have a post-graduate degree.

So what can we learn from all this? It’s pretty simple:

Like just about anything, there are multiple paths that lead to the top.

Edging Out the Cloud (and why we should thank Justin Bieber for edge computing) Reply

MIT panel 1

Edge computing has found its way into our tech lexicon over the last few years, and we’re getting a good look of how the edge is emerging with autonomous vehicles and robots in daily headlines. The edge certainly has huge potential, but ask four savvy technologists to define ‘the edge’ and what it means for our computing infrastructure, and you’ll get four very different answers.

So that’s exactly what I did at an MIT NorCal event late last year, where I moderated a panel of founders and product leaders working on the ‘cutting edge,’ including AB Periasamy of Minio, Edward Hsu of Mesosphere, Dana Wolf of Fastly, and Eugene Kuznetsov of Abine and a stealth company (he recently led product at Salesforce).

We dove into an hour-long conversation with these four fantastic participants. The discussion ranged from definitions to predictions, and I think both I and the audience learned a great deal about the possibilities and uncertainties that remain in edge computing.

Below is a condensed version of our talk.

How do you define ‘the edge’?

AB Periasamy: The edge is simply data centers sitting outside the public cloud and located closer to the remote site where you need some computing storage and intelligence. It’s helpful to think of it as a very large cache that can interface with a master data center, where things are constantly backed up to the cloud so you can’t lose anything, but can still provide far lower latency for applications.

Edward Hsu: The edge is a mental construct more than anything. Anytime you are processing data that is not in your core data center and doing it closer to your client, that can be thought of as edge computing. At Mesosphere, though, we don’t really think of it as cloud or edge, instead it’s more about a data center operating system that can handle a global infrastructure mixed with data locality when needed.

One interested anecdote is around how Mesosphere got its start. As Twitter was scaling, one of their first power users was Justin Bieber, who accounted for something like 5 to 10 percent of Twitter’s traffic. Twitter was one of the first companies to offer a real-time service, and every time Justin Bieber tweeted, Twitter would need to deliver the message about 10,000,000 times concurrently. Of course, Twitter would then crash. In order to keep up with that scale they started to look at distributed technologies like Apache Mesos, and those infrastructure and architecture needs spawned a lot of the thinking at Mesosphere and edge computing.

Dana Wolf: Fastly generally looks at the edge as optimizing non-static content. If the user expects a real-time stream of data that is constantly changing, you want that processing done without needing to ping your central cloud and without keeping it overly intensive that it can’t be done close to the user.

Eugene Kuznetsov: I’ve seen two definitions of edge, which is why I think there is this ongoing debate. First, there is the traditional networking definition of edge, known as an edge aggregator or router, which sits outside of the core Internet but also outside your own data center or on-premise farm. It’s essentially a very particular place in between the core network and the LAN. Nowadays, however, everything that is not public cloud is known as the edge. So computers in your car are edge computing. We keep flipping between these those two concepts, but both are very important.


What are some of the current challenges in edge computing?

Edward Hsu: One challenge I see is a lot of companies increasingly trying to take advantage of this “always connected” world we are living in. I will give you a challenge to count how many network devices are in your house. I guarantee it’s more than you thought. I read this report that the average North American home has 15 network devices. I thought that was kind of high and went home and I counted 25. What does that mean? It means that a whole bunch of companies that had no business building software now have to be software companies — but not just any software company with a website, but personalized edge-experienced type software companies.

Dana Wolf: Security. The edge provides more vectors for attack because there are so many instances. The edge must be built with security first in mind at the architecture level. We won’t be able to solve security issues with traditional a firewall mentality. There’s also the connected issue of rolling out updates and maintenance as things move from cloud-central to edge-distributed. It’s an operational and architecture nightmare.

Eugene Kuznetsov: Manufacturing and edge/cloud is very complicated. Stacks and security protocols are outdated. Software and engineering don’t really understand each other. I have a funny story. We were working with a very large European manufacturer – they stood up a data science team very proud of themselves and installed all the latest stuff from Silicon Valley and hired all the data scientists and started analyzing a lot IoT data from industrial equipment. Two years later, the Data Science teams say, “This is amazing! Machine Learning has made a breakthrough! We see almost a linear correlation between the current and voltage going into the motor.” This is what happens in these industrial companies when the software teams are completely separate from building the stuff and undermine their credibility because they don’t understand things like Ohm’s law.


What are some of the use cases of edge computing that people might not know about?

AB Periasamy: Well I find the range of possible edge deployments to be really interesting. Some just started by putting Minio on a Raspberry Pi 64 – these are ARM Processors ($35 computers). Minio ran fine on these ARM computers and had local storage and captured data. Raspberry Pi is not really industrial IoT, it’s just a hobbyist project, but it worked. We’ve seen automotive companies want a central data center of 100 terabytes and then have processing data centers spread across different geographies with 2 petabytes of data in cache. And that’s just an extension of a Raspberry Pi flash as a cache.

Edward Hsu: We have six or seven connected car companies that use us because they are trying to build an infrastructure that can allow cars to talk to each other and do machine learning or machine vision, so they can do autonomous driving. In these driving conditions you don’t have time to ask if that thing I’m approaching at 50 miles an hour is a stop sign or not, and then wait for the data center to tell you. Ideally there has been some machine learning that has been applied and internalized in the car so they can approximate and make decisions on the fly. Those are all examples of edge computing – how much compute power is required to call it ‘edge’ is just philosophical. Most advanced cars today have 100+ microprocessors in them. Cars have more processing power than fighter planes had only a few years ago. Compare all that to Raspberry Pi as a lower range. All of this can be considered ‘edge’.

Dana Wolf: The idea of real-time feedback on applications that have to be so incredibly quick – think of dynamic use cases like customers selecting seats on Ticketmaster while other customers are doing the same. Or, for example, Fastly actually uses edge computing in its messaging, which means we provide an edge cloud platform for our customers to optimize their web experiences.


What are each of your predictions for edge computing in a year and in five years?

Eugene Kuznetsov: In one year we are going to see the major cloud vendors continue to push to expanding their APIs and the software interfaces into what is essentially on-premise hardware. Five years out, we are going to see a lot of friction around lock-in to the public cloud APIs – and a lot of customers are going to find out they are in a IBM mainframes relationship and there are going to be lots of interesting issues arising, possibly even regulatory in nature.

Dana Wolf: In one year we’ll continue to see IoT grow and the network demands from that growth. Five years out, all of that growth will be extraordinarily difficult to maintain and update due to security issues.

Edward Hsu: There are two extreme views of the future. One scenario where open source software is largely not around anymore and all you have are cloud service providers and you have to navigate issues of cloud provider lock-in. The reason why AWS is taking off right now is not because it is super cheap – in fact, there is a lot of data that proves the opposite. What Amazon does is win on making things super easy. If you are a developer who wants a message queue and an analytics engine with a distributed database, one click away and it is there. If I ask my IT department, I have to wait forever. Of course I am going to do this, and my ops guy has to run it. Now our company is stuck on this cloud.

The other extreme is a whole world where people shy away from cloud lock-in and focus on having a platform or architecture that is conducive to open source. For that to happen, though, it means a lot of open source-based startups come together and build solutions that are just as easy as AWS. If people think ahead about the benefits of open source, and find an ecosystem for distributed data technologies and think ahead and use multi-cloud approach we will see a very different future in five years.

AB Periasamy: One year from now, nothing exciting. Everything happening today, just with at greater scale. In five to ten years, though, we’ll see fundamental shift in compute, where machines can finally mimic biological behavior. I’m not talking about consciousness, but the ability to sense things like skin, or for devices to act as eyes and — sensing and learning and acting on those signals in real time.

Alex’s Predictions: Five years from now, Ridge Ventures will have backed multiple founders building successful edge computing companies!

Welcome Ben Metcalfe to Ridge Ventures Reply

We are extremely excited to welcome Ben Metcalfe as the first addition to our team since we rebranded from IDG Ventures USA just a month ago. Ben has joined Ridge Ventures as our first ever Principal, and brings broad expertise as an entrepreneur, angel investor, software engineer, product developer, and operator that directly complements our existing investor team.

Ben’s track record is impressive. He was an early technical team member at the BBC newsite, co-founded and scaled WP Engine — a successful SaaS startup that 70,000 customers and over 500,000 websites rely on — and he’s built accessibility products for Uber.

Ben’s breadth of experience, however, goes beyond building companies, websites, and products; his expertise extends to details, from properly configuring DNS entries, to creative and strategic marketing plans, and to evaluating both technical solutions and business models as an investor. At WP Engine, he helped grow the company to tens of millions of dollars in revenue, while contributing to product, sales, marketing, and partnerships. What makes Ben impressive, is that he works tirelessly to achieve mastery across so many disciplines: from public policy, to yoga, to building tech products.

At Ridge Ventures, we believe in flexibility and a deep founder focus, and Ben’s addition strengthens that ethos. Ben provides a background and fresh perspective that we welcome and value: he understands the needs of today’s founders better than most investors, drawing from his time learning about technology and scaling businesses with no formal university background. He also helps expand Ridge’s network among young, ambitious founders so we can continue to work with the best and brightest.

We look forward to having Ben on the team. You can reach him at ben@ridge.vc.

– Alex

AdTech, MarTech, and RampUp Reply

I had an enjoyable panel discussion with several highly accomplished industry operators and investors at last month’s Ramp2017 event. One discussion topic was of particular interest to me: the adversity in the AdTech industry from an investment perspective. While I recognize the challenges, I personally remain deeply interested in this area.

While MarTech has become the more popular nomenclature, in reality, most companies in both AdTech and MarTech are addressing similar problems. As Shakespeare once said, “a rose by any other name would smell just as sweet.” So what I’m discussing encompasses both AdTech and MarTech. More…

Podcast With Harry Stebbings on SaaS Startups and More Reply

I was recently a guest on The Official SaaStr Podcast hosted by Harry Stebbing. Harry and I had a great discussion that ranged from the (un)importance of early-stage SaaS metrics, to the intricacies of software pricing, to the ways in which term sheets can be simplified.

Before we delved into all that good stuff, Harry had me talk about my background and the formative experiences that led me to a career in venture capital. The quick history is that I first encountered––and fell in love with––computers when I was 13. To fund the purchase of my first computer, an Apple IIe, I taught coding classes at the local Apple dealer. Coding work also paid the bills for my college education at MIT, where I majored in electrical engineering and economics. My foray into venture happened before business school, when I landed a job at a venture firm in New York. More…

Big Welcome to Autumn Manning and the YouEarnedIt Team Reply

youearnedit-logo-28129I’m delighted to announce a new addition to our portfolio: YouEarnedIt, an Austin-based SaaS HR company that helps drive employee engagement and company performance metrics. IDG Ventures co-led the $6.5M Series A round with Silverton Partners.

We often get asked what we look for in our investments. In many ways YouEarnedIt perfectly exemplifies our investment criteria: Series A, founder-led team, capital efficient, technology solving a real problem with a SaaS model, large and growing market, and great co-investor. More…