All Segments
a16z Podcast | The Product Edge in Machine Learning Startups
16 segments available
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.
Segments Timeline
Welcome to the a16z Podcast
In this episode of the a16z Podcast, host Sonal introduces the topic of machine learning startups and their competitive edge against larger companies. The discussion will explore how startups can leverage data and build effective products in the machine learning space.
"hi everyone welcome to the a6 & Z podcast I am sonal today's episode moderated by a 6 & Z board partner Steven Sinofsky is all about the data edge in machine learning startups the conversation covers ..."
Meet the Guests: Jensen Harris and AJ Shankar
Sonal introduces guests Jensen Harris, CTO of Textio, and AJ Shankar, CEO of Everlaw. They discuss their respective platforms that utilize machine learning to enhance business processes, such as writing job descriptions and managing legal evidence.
"conversation covers everything from machine learning algorithms and academic papers verses and products to how startups can compete with big companies on the data front to machine learning as a servic..."
The Role of Machine Learning in E-Discovery
AJ Shankar explains how Everlaw uses machine learning to help lawyers sift through vast amounts of evidence efficiently. He highlights the contrast between human effort and machine capabilities in handling large data sets in legal contexts.
"quantitative guidance based on evidence that's continually mine from tens of millions of documents and then we also have AJ Schenker CEO and co-founder of everlaw which is an a6 in Z portfolio company..."
Startups vs. Big Companies: The Data Advantage
Steven Sinofsky poses a question about why startups can compete with larger companies that have access to vast amounts of data. The discussion reveals that big companies often overlook niche markets, providing opportunities for startups to thrive.
"involve over 10 million documents and terabytes and terabytes of data machine learning helps sift through all that at both a content and context level to figure out who's saying what what they're talk..."
Finding Opportunity in Niche Markets
The guests discuss how startups can find success in specialized domains that larger companies neglect. They emphasize the importance of focusing on specific areas like legal tech, medical, and HR, where tailored solutions can outperform generic offerings.
"what is it that that really enables a start-up to have an advantage in this machine learning world because a lot of people just believe that that deep learning is a big company thing because they have..."
The Power of Tailored Data Sets
The conversation shifts to the significance of having the right data rather than just a large quantity. Jensen and AJ explain how specific, high-quality data sets can lead to better machine learning outcomes, especially in unique contexts.
"particular data set in a particular litigation context will have a particular set of documents that are actually interesting and that set might differ depending on the context and so we don't even kno..."
Building the Right Product with Machine Learning
Jensen discusses the importance of understanding the problem domain before applying machine learning techniques. He emphasizes that startups should focus on creating a user experience that integrates machine learning effectively rather than just relying on algorithms.
"away from that it's be powerful in the right domain so you're one of the things I hear you're saying a little bit is if you're getting started in your company and you have access to a data source it d..."
Beyond Algorithms: Crafting User Experience
The guests highlight that successful machine learning products require more than just advanced algorithms. They stress the need for a well-designed user experience that enhances the value of the machine learning component.
"size data set to find the interesting wedge that lets you find the smoking gun you know in the in the discovery or lets you find the thing that all of a sudden brings twenty percent more people to app..."
The Importance of Data Quality
AJ and Jensen discuss the critical role of data quality in machine learning. They explain how poor data can lead to ineffective products, emphasizing the need for clean, relevant data to achieve meaningful results.
"a company for the academic papers the sole concern is giving this corporates what can I extract from in we're trying to solve people's problems in the real world and the problems are rarely reduced to..."
Creating a Comprehensive System
The conversation delves into the necessity of building a complete system around machine learning products. Jensen explains how integrating various components, such as user interfaces and data management, is essential for delivering value.
"wants I want to actually then building on this which we're going in an interesting direction belief you share your path to product market fit involves a lot of other code you don't have like a command..."
Machine Learning as an Ingredient
AJ and Jensen reflect on how machine learning should be viewed as an ingredient in a broader software solution. They discuss the importance of focusing on solving customer problems rather than just showcasing machine learning capabilities.
"have the whole monitoring stack so how do you tell whether or not people are using it and how much they're using it and things like that and so the the the heart of it of course is the pieces that you..."
Choosing the Right Technology Stack
The guests share insights on selecting the appropriate technology stack for machine learning applications. They emphasize leveraging existing tools and platforms to streamline development and focus on delivering value to customers.
"value proposition it's just likening it's like an ingredient that's right I think how it should be again maybe I mean my view it seems like there's no there's certainly a ton of hype around machine le..."
Lessons Learned from Early Days
AJ and Jensen recount their experiences in the early stages of their startups, discussing the challenges of imposter syndrome and the importance of starting small with tailored data to build effective machine learning solutions.
"this is the kind of thing we're with domain specificity understanding a user's workflow and providing a tool that's catered to how they like to work again not a big company skill sure sure yeah I woul..."
Building Trust with Users
The discussion highlights the significance of transparency in machine learning products. AJ explains how providing users with insights into the decision-making process fosters trust and enhances user engagement.
"meaningful throw out the data that isn't meaningful that is hugely important we've also found talking about technologies when you're getting your company off the ground you're gonna end up doing a lot..."
The Human-Machine Collaboration
The guests conclude by emphasizing the importance of collaboration between humans and machines. They discuss how effective machine learning solutions should empower users and enhance their capabilities rather than replace them.
"bill versus use versus buy versus contribute yeah the tooling is really good now and not only do they have these services they're actually seemed to be a race for all these big companies to open sourc..."
Final Thoughts and Advice
In the closing segment, Jensen and AJ share their final thoughts on building successful machine learning startups. They encourage entrepreneurs to focus on solving real-world problems and to embrace the iterative process of learning and adapting.
"something you might have done differently than you so one of the one of the things for us that I wish we had known now was we felt a lot of imposter syndrome in the first two or three months especiall..."