Reining in Complexity: Data Science & Future of AI/ML Businesses
There is no spoon. Or rather, “There is no such thing as ‘data’, there’s just frozen models”, argues Peter Wang, the co-founder and CEO of Anaconda — who also created the PyData conferences and grew the early data science community there, while on the frontlines of trying to make Python useful for business analytics. He views both models and data as fluid, more like metaphysics than typical data management… Or perhaps it’s that when it comes to data, those with a physics background just better appreciate the mind-bending complexity and challenges of reining in the natural world, and therefore get the unique challenges of AI/ML development, observes a16z general partner Martin Casado — whose first job after college involved computational physics simulation and high-performance computing in Python at Lawrence Livermore National Laboratory. (Wang, meanwhile, graduated in physics.)
But this not just a philosophical question — the answer has real implications for the margins, organizational structures, and building of AI/ML businesses. Especially as we’re in a tricky time of transition, where customers don’t even know what they’re asking for, yet are looking for AI/ML help or know it’s the future. So what does this all mean for the software value chain; for open source collaboration and commodification; and for the future of software businesses? After all, it’s not written in stone that “All information systems must be deconstructed into hardware, and software, and data” and that “software must have these margins”… Will there be a new type of company?