Jerry Chen | How to Break Up Big Cloud's Dominance

Hi everyone. Welcome to Grey Matter, the podcast from Greylock, where we share stories from company builders and business leaders. I'm Heather Mack, Head of Editorial at Greylock. This is an audio version of Greylock General partner Jerry Chen's essay entitled how to Break Up Big Cloud. This is part two of our essay series based on our Castles in the Cloud project. Part one in this series provides an introduction to the project, which is a comprehensive, interactive data compilation and analysis to map the cloud ecosystem, identify trends, and find new opportunities for startups to compete with the big cloud providers. You can listen to Greylock marketing partner Elisa Schreiber read that essay on the Grey Matter podcast and you can find the entire project on the Greylock website. Greylock.com castles how to break up Big Cloud's dominance strategies to identify where startups can thrive in the cloud computing world Big Cloud's dominance is a given when it comes to the most foundational and capital intensive services, but as I proposed in my previous essay, aws, Google and Microsoft have not conquered the entirety of the cloud market and Castles in the Cloud Greylock's new interactive data project will aid us in identifying and understanding the areas where startups can and may thrive. Of course, the sheer number and range of business divisions operated by cloud castles means it's just not possible to avoid all head to head competition. Nor is it realistic to expect to avoid the competition from the countless other startups battling the Big three. This is why the challenger companies, emerging as the forerunners of the new era of cloud innovation are implementing the following strategies. To start, we'll examine how some challenger companies have managed to achieve relevance in spite of the cloud castles. First, avoid the castles the best way to attack the big clouds is simply to avoid them. This is a well worn first principles strategy of many challenger companies, and most do not compete directly with any of the cloud castles. Instead, go up the stack or identify different users for whom APIs, tools and applications can be developed on top of the foundational Big Cloud offerings like storage and compute. This is how cloud API based businesses like Shopify, Stripe and Twilio have been able to thrive. All were able to get a firm hold on E commerce, payments and communications respectively by targeting developers and use cases to build an atomic unit of almost every application. Most of the apps we use every day need to handle payments, subscriptions or other financial transactions. Almost every other app needs to email, text or call their customers. Developers should identify the other atomic units that underlie other apps. If the Big three cloud vendors have already won the hearts and souls of the infrastructure developer, then the next battle will be for the hearts and Souls of different APIs. If Shopify, Stripe and Twilio combined are over 300 billion in market capitalization, this includes private and public, then there should be another generation of great API companies. APIs and higher level developer abstractions are the spiritual successors to the platform as a service market. Outside of Heroku, which was acquired by Salesforce, the popularity of infrastructure as a service has outgained platform as a service. But as the cloud and infrastructure as a service ecosystem reaches a new level of maturity, we are seeing another generation of companies moving up the stack. For example, one emerging market that may produce large companies is the new Jamstack to produce content sites. Startups like Netlify and Vercel could be significant platforms in the next few years. In particular, the E commerce market as proven by Shopify has become a popular area for developer centric companies in the headless commerce space like Builder, Contentful, Webflow and many others. To date, the big cloud companies have instead prioritized releasing sets of API tools to make the production and consumption of APIs easier and safer. A few startups have similarly focused their efforts on API tools, but not as many as we would have expected. From the data we can see that roughly 400 million in venture capital financing have been directed at new API tools, which may seem like a lot, but that's still underfunded compared to the billions going to AI and ML market which is 4 billion, data analytics which is 3 billion and security which is 3 billion. In this analysis we are not tracking the investment in API businesses like Stripe because by and large the big three cloud providers aren't investing as much effort into these areas but potentially could be in the future and therefore do not present an opportunity to do a side by side comparison with challenger startups. Another strategy is to own the community. This often overlaps with all the previously mentioned strategies and is of paramount importance if you are building an open source project, which I will get into at length in the next essay. The big three and open source projects are co opting the developer community faster than startups can hope to. As I mentioned earlier, going up the stack will help you identify the community of product users who inform your go to market strategy by narrowing the Persona you sell to. Increasingly, the Persona of the cloud user is not a developer but a data scientist, a revenue ops manager, a business analyst and others. Startups are no longer building products to sell to an enterprise CIO but for the actual practitioner within the company, whereas in the past the CIO was the gatekeeper, a monopoly provider of technology to companies. The cloud has enabled companies to reach users directly with low friction to investors and founders. This product led growth strategy has become the de facto go to market selling motion. The key for startups in this selling motion is to create awareness of their solution and then reduce friction to trial and usage of the product. One way to analyze market need is to disaggregate applications into atomic parts like messaging, identity and other sections. Another potential analysis is to look at the different potential users of the cloud. Every enterprise is full of departments, lines of businesses and functions that are underserved by technology today. Software historically represented the digitization of a business process like order to cash or hire to fire or a system of record like CRM or served a distinct user like a knowledge worker toiling over a spreadsheet. Cloud has reduced the cost to build new applications and expand the number of departments, lobs and Personas it can serve. For example, startups like Figma, Airtable, Notion and Asana have thrived in the productivity space during the pandemic induced shift to working from home. But that's because they were ahead of the game before remote work was a thing. They developed tools for knowledge workers coupled with prescriptive templates and workflows. Figma's initial focus was on the designer Persona and the company later expanded to product and other knowledge workers. This is a strong example of owning the community and owning the practitioner. Let's think about the other practitioners inside a company that are underserved and products that can help them. Today we are seeing this in the form of product clouds for product managers such as Productboard or engineering productivity tools like Jellyfish, which enables engineering leaders to measure productivity of their remote teams. There are also SRE tools like Geli, Transposite and Blameless, and there are opportunities for revenue ops products. As you can see, the cloud has enabled startups to focus on increasingly more distinctive roles inside a company. Another strategy is to find the white space. The demands of an increasingly cloud based world means there are lots of different startups offering very similar services such as databases, DevOps and storage, and still thriving thanks to the sheer size of the market. As a note, success in these areas is dependent on the strength of your modes such as deep ip, ownership of a user community and and more that I'll get into later. Within whitespace there are niche or focused markets. Big cloud is focused on these common infrastructure services like storage, but that leaves an opportunity for startups to focus on niche areas like vertical markets or more focused technical or business problems. This has led to a few successful companies like Samsara and Keep Truckin in IOT for fleet Management or a company like Pragma in gaming. Serving the demands of a focused market requires a careful evaluation before plunging into these markets to understand why. What are the vertical specific channels, buying behaviors and needs? As my partner Reid Hoffman is cautioned about such cases, while it is possible that a highly valuable opportunity exists but just hasn't yet been recognized by serious players, it is also possible that the opportunity just isn't that valuable. Security, almost by definition is an eternal white space for new companies as startups continue to innovate to keep pace with hackers and new exploits. The recent spate of ransomware attacks on our energy and food supply chains, combined with sovereign cyber hacking like the Solar Winds hack have only highlighted the need for new security tools. While security startups received over 2 billion of venture capital financing in the past two years, newer adjacent markets like Compliance and government only received 700 million. Compliance and governance is a market that we think is still under the radar, but will quickly move to the front of our awareness as more data compliance laws like GDPR and CCPA are passed. Another area that we have been tracking is the intersection of the API market with management and governments, such as how SALT Security has used API analysis to prevent sensitive data exposure and overall simplify compliance. Another strategy to attack the big clouds is to build deep IP Perhaps the most notable example of a recent individual company succeeding against big cloud is Snowflake, which built a $70 billion market cap company not with open source, but by building deep IP combined with operational excellence. Snowflake didn't try to replicate what AWS's redshift was doing. They rethought the entire data warehousing process and separated storage from computing. Twenty years ago, the three fundamental building blocks to any computer was storage, compute and networking. Snowflake was able to exploit the fundamental advantage of cloud computing by turning storage, compute and networking into an elastic resource to build a better database. This begs the question of whether there will be a next generation storage company built today or if those categories have already been killed by the cloud. We've seen a lot of innovation in IP move to adjacent markets like databases and machine learning that take advantage of the cloud like Snowflake does. As I mentioned in my Evolution of Cloud essay, many of today's leading cloud companies were built by people who either experienced the cloud transition painfully while working at the Dinosaur Legacy enterprise companies or who worked at tech companies that rose to prominence specifically because they were born in the cloud. Like the major consumer app companies Uber and Facebook. Their firsthand exposure to issues that arose from using hybrid infrastructure allowed them to identify not only new problems to work on, but also gave them the opportunity to develop proprietary methods to solve novel and existing problems. We wonder where are the next areas where Deep IP is being built by startups? First, the same database market that created Snowflake will continue to be a market disrupted by Deep ip. The predecessors owning this space like Oracle and Teradata gave way to Cloudera, which gave way to Snowflake and Databricks. The next evolution of IP in the data market is the move to real time data and streaming. In the streaming and stream processing markets, each of the cloud providers have created solutions like aws, Kinesis and Google dataproc. As discussed in the essay on VC funding trends, these markets have created multi billion dollar startups like Confluent, AKA Kafka, fivetran, Fishtown analytics and and more recently formed startups like Rockset, Starttree, imply and RealData. All of these startups are building real time databases. For example, Rockset, which was built for the cloud only world, looks like a database to the developer, that is someone who speaks SQL but looks like a search engine under the hood because it uses a serverless indexing that delivers analytics at an unprecedented speed and scale. Another strategy is to build companies with AI and machine learning. These are buzzwords we all hear every day and there is a huge amount of investment from both big cloud and venture capital. The big three cloud providers have 74 machine learning services between them and in the past two years the 35 or so machine learning powered startups which are all valued at over 500 million, have raised more venture capital than any other market. Despite all this investment, the number of multi billion dollar machine learning companies is still relatively few. C3, AI, scale AI and DataRobot are three of the larger public and private companies in this sector. The machine learning tools category is still in the early innings, but aws, Microsoft Azure and Google Cloud are all trying to win the category. AWS has SageMaker, Microsoft has Azure ML and Google has Google AI platform. This is one market where two different strategies may help startups win. The first is to own the end to end experience for the ML user like SageMaker or DataRobot attempts to do. The second strategy is to go deep in areas and build deep ip. For example, Snorkel is leveraging its open source project and technology around data labeling to differentiate itself versus the market, and Truera is using its IP around explainable AI to build tools for model quality and monitoring. While billions of dollars are going towards winning the ML tool market, even more money can be said to be chasing the returns of applied AI and ML. As every company today uses some form of ML to build a system of intelligence, applying ML deeply in one focused market is potentially the best way to build a deep IP mode. The recent IPO of UiPath, which has a $35 billion market cap, is illustrative of how a company can build a large business in robotic platform automation. The next generation of applied ML companies will use automation to solve the most complex business problems. For example, Instabase is using ML to transform how enterprises work with documents and complex business workflows. Instabase leverages ML techniques like OCR and data transformation to create a set of tools that let customers build custom apps to automate previously human intensive workflows. Examples of applied ML exist In all major SaaS applications like Salesforce, Einstein or Gong in Sales Operation. Technology Security is another market where applied ML has been transformative. Abnormal Security employs various ML techniques to prevent business fraud in email and other communication channels. In conclusion, as we go through the various strategies companies have employed to compete or at least coexist with the Big Three, it's important to remember that not all tactics that have worked in the past will necessarily work in the future. Furthermore, each strategy may unfold in different ways as each market we have analyzed matures or in some cases, declines. Additionally, we expect to discover new strategies and tactics deployed as the cloud ecosystem continues to evolve. We are already beginning to see surprising tends in our data analysis of venture capital funding, which is which we will discuss in an accompanying essay in Grey Matter Podcast. As we gather more information through the course of this project, we are eager to learn all the ways entrepreneurs are working to break up the dominance of the Big Three cloud providers. This concludes this episode of Gray Matter. Stay tuned for the next episode in our Castles in the Cloud series, which features Greylock investor Corrine Riley reading the essay about venture capital funding trends in the cloud ecosystem. I'm Heather Mack and thanks for listening.