No Priors Live: Building Durable Software in the AI Age with MongoDB President & CEO CJ Desai
Episode
36 min
Read time
2 min
Topics
Productivity, Fundraising & VC, Leadership
AI-Generated Summary
Key Takeaways
- ✓Platform versus Product Strategy: Products get replaced easily, but platforms become sticky when customers use two or more integrated products working together with existing systems. A London bank runs 300 applications on MongoDB out of 9,000 total applications, demonstrating deep infrastructure integration that prevents switching. This stickiness comes from security checks, governance approvals, and system integrations that take years to build.
- ✓Speed During Technology Transitions: Companies must build and pivot rapidly during platform shifts like cloud or AI to avoid obsolescence. ServiceNow succeeded by moving fast on mobile in 2010, while Nokia and Blackberry failed despite initial success because they delayed transitions. MongoDB navigated the Atlas cloud transition successfully and now faces the AI transition with architectural advantages that require customer trust and execution to capture.
- ✓Enterprise AI Adoption Patterns: Fortune 500 companies report strong positive feedback on coding assistants in 2024, marking a breakthrough year for developer productivity. Office productivity copilots delivered unclear value, and customer support AI remains incomplete for end-to-end experiences. Large enterprises ask whether AI-native vendors represent an "and" or "or" decision versus existing systems of record like Salesforce.
- ✓Customer Intimacy for Product Leaders: Product managers must speak with at least 10 customers weekly to understand pain points and see around corners, not just ask how to serve better. One European retailer abandoned expensive ERP implementations to build their own system on MongoDB after failed deployments. This customer engagement reveals deployment timelines, value expectations, and how organizations make technology bets through specific individuals.
- ✓Replacing Systems of Record: Leaders show openness to wholesale replacement of existing SaaS platforms if AI-native companies offer cheaper, faster, better solutions with disrupted pricing models where payment ties to delivered value. This represents risk-taking that ignores sunk implementation costs. One retailer builds their entire ERP system including supply chain and financials on MongoDB rather than using traditional vendors, demonstrating willingness to disrupt from within.
What It Covers
MongoDB CEO CJ Desai discusses software durability in the AI era, explaining why platforms outlast products, how Fortune 500 companies approach AI adoption, and why only single-digit software companies exceed $10 billion in revenue. He shares customer insights on coding assistants versus productivity tools and MongoDB's strategy for AI-native applications.
Key Questions Answered
- •Platform versus Product Strategy: Products get replaced easily, but platforms become sticky when customers use two or more integrated products working together with existing systems. A London bank runs 300 applications on MongoDB out of 9,000 total applications, demonstrating deep infrastructure integration that prevents switching. This stickiness comes from security checks, governance approvals, and system integrations that take years to build.
- •Speed During Technology Transitions: Companies must build and pivot rapidly during platform shifts like cloud or AI to avoid obsolescence. ServiceNow succeeded by moving fast on mobile in 2010, while Nokia and Blackberry failed despite initial success because they delayed transitions. MongoDB navigated the Atlas cloud transition successfully and now faces the AI transition with architectural advantages that require customer trust and execution to capture.
- •Enterprise AI Adoption Patterns: Fortune 500 companies report strong positive feedback on coding assistants in 2024, marking a breakthrough year for developer productivity. Office productivity copilots delivered unclear value, and customer support AI remains incomplete for end-to-end experiences. Large enterprises ask whether AI-native vendors represent an "and" or "or" decision versus existing systems of record like Salesforce.
- •Customer Intimacy for Product Leaders: Product managers must speak with at least 10 customers weekly to understand pain points and see around corners, not just ask how to serve better. One European retailer abandoned expensive ERP implementations to build their own system on MongoDB after failed deployments. This customer engagement reveals deployment timelines, value expectations, and how organizations make technology bets through specific individuals.
- •Replacing Systems of Record: Leaders show openness to wholesale replacement of existing SaaS platforms if AI-native companies offer cheaper, faster, better solutions with disrupted pricing models where payment ties to delivered value. This represents risk-taking that ignores sunk implementation costs. One retailer builds their entire ERP system including supply chain and financials on MongoDB rather than using traditional vendors, demonstrating willingness to disrupt from within.
Notable Moment
Desai reveals his intellectual honesty test when MongoDB reported quarterly results. Analysts repeatedly asked if growth came from AI, but he insisted the core data platform drives results, not AI-native companies. Only about 10 AI companies have reached meaningful scale today, so claiming AI revenue would optimize for the wrong metric and create false expectations.
Episode Transcript
Since 2022, the future of software is in question. This is from the investor community, but also customers. It is a very pivotal moment on the software stack. And then you look at the software stack and you say, okay, what is the one thing that will always be there? How many companies today that are there that are more than 10,000,000,000 in just pure play software revenue? It's single digits. Why is that? The software industry has been around for a long time created by many, many smart people like yourself. Why is it only single digit companies are more than 10,000,000,000 in revenue? Because Platforms are rare. Platforms are rare. Speed matters. When technology transitions happen, are you building as fast as you can? And then are you learning on the technology shift, whether it's the Internet age or AI age or mobile? Are you pivoting fast? It's just that you have to stay ahead of that game. If you fall behind that game, investors or customers will always ask you that question, what is the future of your company? Welcome to the very first live recording of the No Priors podcast with host, Sarah Guo, and MongoDB president and CEO, CJ Desai. Hey, everyone. I am so happy to be here with you guys and my, long time friend, CJ. I know you guys have had a great day of announcements and and learnings here at the conference, but I I'm really excited personally to have the opportunity to zoom out with CJ to talk about, the future of software, what's happening in SAS and and where the value is going to be. These are important questions to me in my, you know, day job as a as a venture investor. So, CJ, you have worked at these platform enterprise software and infrastructure companies, became CEO of MongoDB recently. I feel like the one question that we were just talking about that every investor asks you and then everybody in the technology ecosystem has the back of their mind is, what is the value of software when you can generate a bunch of software? And and so I I'd love to just get your thoughts on this. It's a very spicy question to start with. I like it. I'm making sure everybody's awake. Yeah. Yeah. First, thank you for doing no priors live for the first time. It's Maiden, and we have a really good crowd here. So, it's always exciting. You know, when you think about technology transition software, whether you look at Internet age or mainframe all the way to AI, you have to really think through what is the mode here. Right? Whichever applications you create, you know, SaaS applications got created late nineties. Right? Late nineties. I think Salesforce had the twenty five years anniversary recently. And so SaaS has been around for at least twenty five years, from a transition perspective. And now with AI, the question is, just in general, what …
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