Where I'm Hunting for Enterprise Infrastructure Startups in 2020

Copyright: Lucasfilm

Every time January rolls around, venture capitalists bring out their crystal balls on where they believe things are headed in the new year. As an early-stage investor at Mayfield, I suppose I'm no different. But as a product guy at heart, I try to stay out of the ivory tower, keep my ear to the ground, and constantly meet with talented entrepreneurs, engineers, product managers, IT executives, open-source maintainers, and other industry leaders within the enterprise infrastructure community. It's these conversations that have driven my areas of startup investment for 2020. If you're building or thinking of building something in these areas, I'd love to talk with you.

Cloud Native Day 2

The world of cloud-native has changed. Six years ago, I was a fiery (possibly misguided) product manager trying to tell anyone who would listen how this whole container thing was going to revolutionize the way applications are built. Now, Kubernetes has effectively been accepted as the next-generation standard across enterprises and startups for hybrid-cloud applications and infrastructure. 48% of enterprises adopted Kubernetes in 2019, almost doubling from the year before. But as Lyft engineering manager Vicki Cheung stated in her KubeCon keynote, "It's not all unicorns and rainbows." If you spend any amount of time talking to DevOps and IT leaders, you will consistently hear three things:

  • Operationalizing Kubernetes in production is hard.
  • Teaching novice users to work with Kubernetes is hard.
  • Digital transformation, cost reduction, and security are the real top of mind priorities. Kubernetes is just part of the implementation.

Beginning the journey to cloud-native and selecting Kubernetes was Day 1. We need to solve problems for Cloud Native Day 2--operations that it takes to actually keep modernized applications and infrastructure up and running. Examples of potential Day 2 problems include monitoring/observability, troubleshooting and incident management, multi/hybrid-cloud ops automation, security and policy management, and making it easier for developers to build and manage microservices applications. These are complex and difficult issues, but ones that IT and DevOps leaders have significant budget to have solved.

Developer Augmentation

As a former product leader, I spent a lot of time talking with leaders in engineering, product management, and design across both enterprises and the startup world. There are several key evolving issues hitting these product development teams over the next few years:

The cost of talent for engineers, product managers, designers, and similar team members continues to rise. As a result, companies are desperately looking to augment their existing headcount with force multiplier tooling. In addition, product development leadership wants more actionable insights into the effectiveness of their teams. Or, as one CTO put it to me: "Why is it that my marketing and sales counterparts have their pipelines fully instrumented, but I can't even tell if my product is going to ship on time?"

In addition, product development personnel today are being forced to use a dizzying array of tools to get their job done--tracking issues on Jira, code reviews on Github, iterating on designs in Figma and Invision, not to mention having to integrate microservices across a truly vast array of tooling and frameworks. At the time of this writing there were 1,287 development tools listed on the Cloud Native Computing Foundation Landscape. Whatever happened to focusing on building business logic?

Finally, the future of work is remote. Companies like Hashicorp (disclaimer: Mayfield portfolio company), Gitlab, and Automattic have shown that fully distributed companies at the scale of hundreds of employees are manageable, and tools like ZoomSlack, and Github have led the charge. But there are gaps across these workflows. The simple truth is that is still not as easy to work remotely as it is to bring in a product development team in an office and iterate rapidly together. That needs to change.

There are huge opportunities for startups that augment the effectiveness of product development personnel (engineers, PMs, designers, etc.) by automating repetitive workflows, stitching together disparate tooling, facilitating collaboration of remote teams, and providing actionable insights and analytics for product development managers and executives. I call this area Developer Augmentation, and it's only just getting started.

Machine Learning for the Masses

There's a lot of buzz around Machine Learning these days. Most software companies today focus on ML in one of two ways: either leveraging ML capabilities to solve a particular use case (think Netflix's recommendation engine) or providing software services to users of ML (e.g. labeling, training, etc.). One of the unifying points across these companies is the need for data scientists in order to leverage ML at scale. But there is a known shortage of data scientists in the market; as far back as 2018, LinkedIn posited an unfilled demand of 150,000 data science jobs, and that talent shortage continued well into 2019 and beyond. One solution is to train more data scientists (which is happening), but perhaps this is a not quite enough. Thus the real question is, which startups will truly unlock the power of Machine Learning for the Masses?

I would divide this into two categories--unlocking ML for technical folks (e.g. engineers, developers), and ML for non-technical folks (e.g. business analysts). The first technical category opens up a smaller pool of users but with incredible programming flexibility through the use of APIs and integration points to existing tooling and frameworks. The second non-technical category opens up a substantially larger pool of users, but require tightly focused use cases and personas. Think about what Airtable did with spreadsheets--provide people an interface they understand, and then put the power of ML behind it.

In addition to opening up the pool of ML developers, once ML really takes off in the enterprise, there will be a strong need for infrastructure tooling for ML operators--that is, the software that manages, monitors, and secures ML workflows in production environments. This is effectively what DevOps and IT teams do for traditional applications today. The world of ML is no different, and will require strong platforms for managing the infrastructure across multi and hybrid cloud environments with integrations into Kubernetes and other "conventional" cloud-native tooling.

Wrapping It Up

There are a lot of problems out there today in the world of enterprise infrastructure. The good news is, where there are problems, there are opportunities for startups. If you're working on or thinking about these areas I'd love to chat with you. Happy hunting.

Comments

Post a Comment

Popular posts from this blog

The Four T’s: a PM-turned-VC’s framework for evaluating startups, part one

Putting the Product in Product-Led GTM

From Servant Leader to Servant Investor: Year One of a PM Turned VC