Why AI Companies Can’t Be Lean Startups – Machine Learnings

Here’s a discussion of the way machine learning-first startups are built, which ones are pushing the ecosystem forward, and why they look so different than the SaaS startups that came before them...
you can do very interesting things with IBM Watson... if you spend a few months and a significant amount of money training it... their marketing claims have gotten a bit too far ahead...
machine learning talent... is really the gating factor at this stage. Salesforce has much better connectivity to the startup ecosystem, which matters a lot.... startups are figuring out all sorts of ways to get access to the large datasets they need... getting the data is only part of the challenge, you also need to label it, for deep learning to work. And here as well, startups have been really resourceful in figuring it out....,
any machine learning company that can pool enough data from multiple users, run algorithms on pooled data set, and send back that learning to each individual customer, can benefit from data network effects...
B2B ... harder to get data network effects going because corporations are ... protective of their data ... Federated Learning .... enable collaborative machine learning without actually pooling the data...
the complexity ... requires a lot of R&D, and training the algorithms requires a lot of time, effort, technical resources... data... it’s much harder for a machine learning company to be a “lean” startup.... important implications on go to market strategy... going after larger customers with larger budgets... follow a partnership strategy where they build a lot of the early product in close collaboration with a handful of customers.... probably just a temporary phase of the market