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Why Systems Of Intelligence Are The New Defensible Moats

Josh Elman, VP of Product at Robinhood and Venture Partner at Greylock, takes a look at the disruption of old moats and the development of new moats around systems of intelligence.

Key Quote

"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."

Our Takeaways

  • Old moats struggle against disruption. Traditional moats like economies of scale, trade secrets, high switching costs, and branding have helped established some of the most successful products. However, new successful products were able to join or even unseat them by riding the wave of disruption that came along with the mobile, cloud, and platform shifts of the last decade.
  • There will be new moats. It always feels like we've discovered everything, right up until something new is unearthed, and often in retrospective, it seems obvious. It might feel like there will be no new moats, but changes in the product stack will open the door for a new set of moats based around systems of intelligence.
  • Old moats owned the systems of record. The products that have owned the systems of record, like CRMs, HCMs, and ERPs over each generation, from Siebel to Salesforce, have established strong moats.
  • Moats of co-existence. Sitting atop of those systems of records are the systems of engagement, like a product like Slack, which is able to co-exist with multiple systems. This isn't as vital but finds its moat in its providing value based on their integration into the system of record.
  • Tomorrow's moats will be based on systems of intelligence. The systems and products that made up yesterday's tech stack were based around sources of and storage for data. The next moats will be built on what you intelligently do with the data. Machine learning and artificial intelligence products will leverage existing systems to create new systems that turn raw collected data into intelligence data and insights.

Why We Think This Matters

Increasingly, products from enterprise to consumer are being built with the fundamentals of machine learning and AI in the foundation. Products that become the components of companies’ intelligence stack will take on more and more importance in powering products.

Previously, we looked at Clemens Mewald, Product Manager @ Google, and the fundamentals that all PMs should understand about building products and ML.

“I argue that PMs do NOT need to know the nitty-gritty details of ML. They don’t need to know exactly which algorithm to apply when, or how ML models are deployed to billions of users. Rather, they should rely on their software engineering (SWE) teams and focus on how they can contribute to ML-driven product definition and strategy.”

Summarized by Reforge. Original article by Josh Elman • VP of Product @ Robinhood