Ask HN: Why do so many startups claim machine learning is their long game?
Because there is a real moat with data ownership and pipelines.  If you want to do any analysis you quickly find that learning to properly use scikit-learn and tensorflow (or your machine learning algorithm of your choice) is atleast an order of magnitude lower of work than getting the data. For instance, I wanted to build a machine learning algorithm which took simple data from the SEC filed 10-Q and 10-K, which are freely available online and predict whether stocks were likely to outperform the market average for over the next 3 years.<p>Time to setup up scikit learn and tensorflow algorithms to make predictions: 4 hours.  
Time to setup python scripts which could parse through the excel spreadsheets, figure out which row corresponded to gross profit margin, and a few other &quot;standard&quot; metrics: ??? unknown because I gave up at about 80 hours trying to figure out rules to process all the different spreadsheets and how names were determined.<p>I had a professor who was doing machine learning + chemistry.  He was building up his own personal database for machine learning.  He spent ~5 years with about 500 computers building the database so that he would be able to do the actual the machine learning.