The New Moats: Why Systems of Intelligence Are the Next Defensible Business Model

Jerry Chen’s argument that the defensible layer of software is moving from the system of record to the system of intelligence — the models trained on data that crosses several records at once.

Kyle read this on July 13th, 2020 while working on a Gong deck and model; the article’s own example of a virtuous data cycle is the category Gong sits in.

As Kyle filed it: Read The New Moats. Why Systems of Intelligence are the Next Defensible Business Model by Jerry Chen at Greylock Systems of Record Systems of Intelligence Competitive Moats Competition

Kyle’s highlights

• To build a sustainable and profitable business, you need strong defensive moats around your company. This rings especially true today as we undergo one of the largest platform shifts in a generation as applications move to the cloud, are consumed on iPhones, Echoes, and Teslas, are built on open source, and are fueled by AI and data.
• I believe that startups today need to build systems of intelligence — AI powered applications — “the new moats.”
• Traditional Economic Competitive Moats
  • **Economies of Scale: **The bigger you are the more operating leverage you have which lowers your costs. SaaS and cloud services can have strong economies of scales: you can scale your revenue and customer base while keeping the core engineering of your product relatively flat.
  • **Network Effects: **Best described by Metcalf’s law, your product or service has “network effects” if each additional user of your product accrues more value to every other user. Messaging apps like Slack and WhatsApp, and Social Networks like Facebook are good examples of strong network effects. Operating systems like iOS, Android, and Windows have strong network effects because as more customers use the OS, more applications are built on top of it. #look-it-up
  • **Deep tech / IP / trade secrets: **Proprietary software or methods is where most technology companies start. These trade secrets can include novel solutions to hard technical problems, new inventions, new processes, new techniques, and later, patents that protects the developed intellectual property (IP). Over time, a company’s IP may evolve from a specific engineering solution to accumulated operating knowledge or insight into a problem or process.
  • **High Switching Costs: **Once a customer is using your product, you want it to become as difficult as possible for them to switch to a competitor. You can build this stickiness through standardization, from a lack of substitutes, through integrations to other apps and data sources, or because you have built an entrenched and valuable workflow that your customers depend on. Any of these can act as a form of lock-in that will make it difficult for customers to leave.
  • Brand and Customer Loyalty: A strong brand can be a moat. With each positive interaction between your product and your customers, your brand advantage gets stronger over time, but brand strength can quickly evaporate if your customers lose trust in your product.
• Companies need to recognize and react when they are in the midst of an industry wide transformation, lest they become victims of their own success.
• Startup founders who succeed tend to execute a dual-pronged strategy: 1) Attack legacy player moats and 2) simultaneously build their own defensible moats that ride the new wave.
  • Arguably, Gong is attacking Salesforce’s legacy moats as the Systems of Record by abstracting away the data in sales calls before it gets to Salesforce ($CRM)
  • Gong’s own defensible moat is also the volume of data they collect from every call. With ever call their analytics become more and more powerful.
• These platform shifts can also change the buyer and end user. Within the enterprise, the buyer has moved from a central IT team to an office knowledge worker, to someone with an iPhone, to any developer with a GitHub account.
• Open source tools and cloud have pushed power to the “new incumbents,’ — the current generation of companies that are at massive scale, have strong distribution networks, high switching cost, and strong brands working for them. These are companies like Apple, Facebook, Google, Amazon, and Salesforce.
• Companies that focus too much on technology without putting it in context of a customer problem will be caught between a rock and a hard place — or as I like to say, “between open source and a cloud place.”
• On the other hand, companies that build great customer experiences may find defensibility through the workflow of their software.
• Generations of companies have been built around owning a system of record and every wave produced a new winner. In CRM we saw Salesforce replace Siebel as the system of record for customer data, and Workday replace Oracle PeopleSoft for employee data. Workday has also expanded into financial data.
• Other applications can be built around a system of record but are usually not as valuable as the actual system of record. For example, marketing automation companies like Marketo and Responsys built big businesses around CRM, but never became as strategic or as valuable as Salesforce.
• Systems of engagement are the interfaces between users and the systems of record and can be powerful businesses because they control the end user interactions.
• The current generation of companies vying to own the system of engagement include Slack, Amazon Alexa, and every other speech / text/ conversational UI startup.
• In a multi-channel world, owning the system of engagement is most valuable if you control most of the end user engagement or are a cross channel system that reaches users wherever they are. **Perhaps the most strategic advantage of being a system of engagement is that you can coexist with several systems of record and collect all the data that passes through your product. Over time you can evolve your engagement position into an actual system of record using all the data you have accumulated. **Disintermediation
• What makes a system of intelligence valuable is that it typically crosses multiple data sets, multiple systems of record. One example is an application that combines web analytics with customer data and social data to predict end user behavior, churn, LTV, or just serve more timely content.
• For a startup to thrive around incumbents like Oracle and SAP, you need to combine their data with other data sources (public or private) to create value for your customer. Incumbents will be advantaged on their own data. For example, Salesforce ($CRM) is building a system of intelligence, Einstein, starting with their own system of record, CRM.
• In all of these markets, the battle is moving from the old moats, the sources of the data, to the new moats, what you do with the data. Using a company’s data, you can upsell customers, automatically respond to support tickets, prevent employee attrition, and identify security anomalies.
• Products that use data specific to an industry (i.e. healthcare, financial services), or unique to a company (customer data, machine logs, etc.) to solve a strategic problem begin to look like a pretty deep moat, especially if you can replace or automate an entire enterprise workflow or create a new value-added workflow that was made possible by this intelligence.
• These AI-driven Systems of Intelligence present a huge opportunity for new startups. Successful companies here can build a virtuous cycle of data because the more data you generate and train on with your product, the better your models become and the better your product becomes. e.g. Gong
• In summary, you can build a defensible business model as a system of engagement, intelligence, or record, but with the advent of AI, intelligent applications will be the fountain of the next generation of great software companies because they will be the new moats.

How it connects

• Systems of Intelligence
• Competitive Moats
• Jerry Chen
• Greylock
• Gong
• Salesforce
• Artificial Intelligence