Making sense of Mineral.ai
Would Mineral.ai do to Agriculture what Palantir did to defence?
01/ Before we talk about Palantir, Mineral.ai and the future of agricultural data, allow me to warm up with Conway’s Law. If you’ve had a loving affair with technology, products and data, you might be familiar with Conway’s Law.
“Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization's communication structure.”
02/One of the existential joys of being an agritech analyst is to observe Conway’s Law play out in rubber-meets-road market conditions - How market contexts of a particular business domain (agriculture in this case) create particular organisations which end up building particular products that are suited to address peculiar workings of a domain like agriculture.
As you can see, this process of seeing is cyclical in nature. If you want to see the complete picture when you put Conway’s Law into perspective, it looks like this.
And therefore, to make sense of Mineral.ai, we must answer two questions
How do we characterize the current evolutionary stage of agricultural innovation systems that necessitates an organisation like Mineral.ai to solve computational agriculture at scale?
What is the best mental model to think of Mineral.ai’s approach to solving computational agriculture at scale? (Hint: Palantir)
We will start with 2 before we get to 1. Shall we get started?
04/ In an interview with Tim Hammerich on the Future of Agriculture podcast, Mineral.ai’s CEO, Elliott Grant said something interesting that caught my eye.