Navigating the E-Discovery Landscape

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.

The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.

This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/.

Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.