TL;DR: it's the McKinsey of this century. From my relatively limited experience in the space: most companies have trouble actually doing anything with their da

TL;DR: it's the McKinsey of this century. From my relatively limited experience in the space: most companies have trouble actually doing anything with their data beyond slicing it a couple of ways [0] and making bad graphs with it; these same companies also have a lot of trouble hiring and retaining [1] people capable of doing it (I know because they end up my clients). Palantir will offer large amounts of money (despite accusations of below market pay; although a good portion is in equity) and a half-decent lifestyle-wise, not too corporate environment to young smart engineers to go and help them outsource that function for the kind of companies that do not care about paying large fees to "get the best", and for whom the consultant status helps cut through the red tape. The "products" are, from my limited understanding, more like a loose collection of accumulated knowledge from clients which can help future deployments not reinvent the wheel (particularly since so many of the engineers are young and inexperienced). This is also a selling point. "We get it right because we did it before with all these prestigious clients". Each client has its own infrastructure and schema, and so most data related projects are naturally oriented towards a lot of manual effort - talking to technical staff, reading through code, understanding the data customers and feedback loops... and whilst many companies in the space pretend to offer "plug and play" BI (or data science, or whatever) in practice without a strong data team behind it, these end up failing pretty badly. So I see Palantir's competitive edge as its staff, not its products, and I don't think much (as a data professional) of pure product data companies. In many ways, Palantir is what McKinsey was half a century ago: it offers job variety and a pick of the best clients to the "top ranked" fresh grads and early professionals; it offers these "guaranteed fresh" minds to clients who have trouble attracting them in-house to solve difficult problems that can't in theory be solved by the internal talent pool; it brands itself as the "top" company globally doing this. If it is successful, it will just keep growing its competitive advantage ("you don't get fired for hiring IBM"). And I think their focus on people instead of software is a great idea in an age of exceptional open source products and ever cheaper hardware. Where might its cash flow come from? I see two sources: a. as you get further into a data project, you've built the infrastructure and it is comparatively easy to provide answers. For example, once I'm done with a data warehouse, I can produce a complex report or query answering just about any question management can throw at me in 10 minutes to 2 hours; this will take them two weeks to use. So the time actually spent working, vs the monthly retainer, just keeps going down with time (and of course experience - if you wrote the query for another client you can just change the variable names and output). b. the value add from getting the data right is extremely high. Cf [0] below. For many modern companies it can be the difference between a 50% increase in profits and not being a going concern the following year. As CEOs realize this, and realize that no, you don't get "80% of the quality for 20% of the price" by going for the lowest bidders, prices track up and stay there. [0] Most classically trained managers that I've met are unable to cope with more than 2 variables at the same time. A graph is basically a 1D or 2D model with your brain and past experience being the curve fitting. This becomes an issue if the business model fits 20 or 100 dimensions better (e.g. many trading strategies and even market making) and most of your competitors are very comfortable with it. As an example, I've witnessed two e-commerce companies in the same country with a 10x difference in conversion rate; one had statisticians to manage its marketing spend, the other preferred going by the traditional way of "if its conversion rate is high I'm going to add more budget". [1] I know an exceptional data scientist who changed jobs 4 times in the last year... each workplace bidding him up!