A lot of machine learning startups initially feel a bit of “impostor syndrome” around competing with big companies, because (the argument goes), those companies have all the data; surely we can’t beat that! Yet there are many ways startups can, and d...
A lot of machine learning startups initially feel a bit of “impostor syndrome” around competing with big companies, because (the argument goes), those companies have all the data; surely we can’t beat that! Yet there are many ways startups can, and do, successfully compete with big companies. You can actually achieve great results in a lot of areas even with a relatively small data set, argue the guests on this podcast, if you build the right product on top of it.
So how do you go about building the right product (beyond machine-learning algorithms in academic papers)? It’s about the whole system, the user experience, transparency, domain expertise, choosing the right tools. But what do you build, what do you buy, and do you bother to customize? Jensen Harris, CTO and co-founder of Textio, and AJ Shankar, CEO and co-founder of Everlaw, share their lessons learned here in this episode of the a16z Podcast — including what they wish they’d known early on.
Because, observes moderator (and a16z board partner) Steven Sinofsky, “To achieve product market fit, there’s a whole bunch of stuff beyond a giant corpus of data, and the latest deep learning algorithm.” Machine learning is an ingredient, part of a modern software-as-a-service company; going beyond the hype, it’s really about figuring out the problem you’re trying to solve… and then figuring out where machine learning fits in (as opposed to the other way around). Customers are paying you to help solve a problem for them, after all.
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