The title is true. But, the arguments don't hold water for me. 12 years ago, I started a big data company. It looked similar for big data companies when Cloudera raised almost $1B in 2014. Too many people building data warehouses, especially in the cloud. I exited. Who knew that Snowflake and Databricks would emerge against the incumbents. Similarly, there will be winners in the AI infrastructure space. To win, you need to focus on your customers and delight them. Narrowing focus makes a lot of sense. Don't pay attention to the doom and gloom, or you'll never do a startup.
I've worked with hortonworks, cloudera, and databricks. It's no surprise at all that databricks is killing the competition. Those other companies products were embarrassingly terrible. Not stable, slow, and worst of all, I had instances of wrong results. The software just wasn't good.
Databricks is different, it's fast, it's robust, I trust the results. They just built a good quality product.
They are bundling open source stuff (Spark and Delta and so on), sure, they have their fancy IDE and whatnot on top, but they can let the community maintain things, scale back R&D, focus on things that matter to existing clients.
They have 1.6B revenue, a 50% YoY growth. And still not profitable. Hm, okay, recent acquisition on ML stuff, and of course probably burning hundreds of millions on cloud-GPU-AI shit.
Well, I guess as long as they have so big growth it makes sense to invest and raise ... and yeah that probably completely obscures the actual profitability of their core business. (Not to mention that they are probably spending all that money to try to expand their core business. To upgrade their value prop from cloud version of less-dumb-data-pipelines to 1-800-data-4-AI.)
They are becoming like SAP though where initially a company buys one service and it soon finds itself buying every adjacent service from them. If they manage to do that successfully they will be quite profitable.
