In this episode of "The 20 Minute VC," host Harry Stebbings interviews Roy Bahat, the head of Bloomberg Beta. Bahat shares insights from his diverse background, including his tenure at IGN Entertainment and his public sector work with Mayor Michael Bloomberg. They discuss the future of work, the impact of AI and machine intelligence, and the importance of data in developing AI technologies. Bahat emphasizes the significance of startups focusing on applying AI to unique use cases rather than creating general-purpose AI technologies. He also addresses the competitive advantage of companies like Google and Facebook due to their extensive data sets and the role of open source in AI development. Bahat highlights the necessity for startups to have access to unique data to succeed and the potential societal implications of AI displacing jobs. Throughout the conversation, Bahat underscores the importance of curiosity, transparency, and founder support in venture capital.
"Joining me today is Roy Bahat, head of Bloomberg Beta, a new venture fund backed by Bloomberg LP."
"Roy also served on the board of Revision Three, acquired by Discovery, and was a board observer at Flickster, acquired by Warner Bros."
These quotes introduce Roy Bahat as a seasoned professional with a diverse background in technology, investment, and the public sector, establishing his credibility as a guest on the podcast.
"I had zero intention of becoming a vc. In fact, most of my own experiences with vcs were fairly negative."
"We invented what became Bloomberg Beta, which is to say, fix the issues with corporate VC by investing just to make money."
These quotes highlight Roy's unconventional path into venture capital and the philosophy behind Bloomberg Beta, which aims to correct the perceived flaws in the traditional VC model.
"Any one person says yes on our team, we do a deal."
"We want to say yes when there is one person on our team who is very excited about it."
These quotes describe the unique investment decision-making process at Bloomberg Beta, emphasizing the power of individual conviction over consensus.
"We invest in startups that try to make the future of work better."
"AI has unfortunately come to mean a hodgepodge of things. So we actually try to avoid talking about AI."
These quotes reflect Bloomberg Beta's investment focus on the future of work and Roy's pragmatic approach to the often-hyped and misunderstood field of artificial intelligence.## Definition of AI
"has come to mean both the cluster of all those techniques and replicating human intelligence with something that feels humanoid and is out to get you, like in ex machina or something like that. And so we prefer to describe it as machine intelligence, which a number of people have used that phrase as well. It's less loaded, and it signals the fact that what we're really talking about here is computers making judgments that people might be able to make themselves or might not be able to make, but computers are making judgments."
This quote emphasizes the distinction between the stereotypical portrayal of AI and the more practical concept of machine intelligence, which focuses on computational decision-making rather than humanoid attributes or intentions.
"And so with that theme in mind of computers making judgments, it can most definitely be seen as an enabling technology there. So very much like software with its ability to impact every industry when it first arose."
The quote draws a parallel between the transformative potential of machine intelligence and the historical impact of software on various industries, suggesting that machine intelligence could be similarly pervasive and influential.
"Machine intelligence is the layer that takes all of that data and applies it to things that are useful for us."
This quote explains the role of machine intelligence as a value-adding layer that transforms large volumes of data into practical applications, highlighting its significance beyond the ability to replicate human-like interactions.
"Yeah, so I think that's true. My partner Siobhan Zillis, who two and a half years ago was the first one on our team who said, hey guys, machine intelligence is a big deal, let's start paying attention."
The quote acknowledges the advantage that large, established companies have in the field of machine intelligence due to their access to extensive data sets, as observed by the speaker's partner, Siobhan Zillis.
"That said, if your startup is let me take some pretty well understood AI techniques and apply them to a new user use case where I can get access to the data and then create a virtuous cycles where you get more data, which gives you a better experience, which gets you more users, which gets you more data, then it can work."
The quote provides a strategy for startups to succeed in the field of machine intelligence by focusing on specific use cases and leveraging the cycle of data acquisition and user experience improvement.
"Well, sure there are. I mean, there's the competitions like Imagenet, and then Kaggle, which is another of our portfolio companies, publishes some of its data sets in public."
The quote identifies public sources of data that startups can access, such as Imagenet and Kaggle, but also implies the challenge of using the same data as everyone else in developing competitive algorithms.
"I think that when you think about how the future of work is done generally, not just in software, open source is probably the single most important new method that we as a working culture have invented, period."
This quote highlights the importance of open source as a transformative method in the working culture, suggesting its potential to significantly influence the development and application of machine intelligence.
"Well, it's sort of like asking, can you build a big standalone software company in the sense that the software has to be applied to something useful in order to be, you know, is Amazon a commerce company that uses software or is it a software company?"
The quote compares the challenge of building a large standalone AI company to the broader question of defining companies like Amazon, emphasizing the need for practical applications of technology to achieve success.## Aqua Hire Terrain
It's just this talent area where everybody knows it's important and is gasping for expertise.
This quote highlights the high demand and critical need for machine learning expertise in the current job market.
The competition isn't bad, it's a signal of opportunity there.
Roy explains that competition in the startup space, while challenging, signals that there is substantial opportunity for growth and success.
The first iOS app companies were not the best iOS app companies, platform after platform.
Roy emphasizes that being first to market doesn't guarantee long-term success, as seen in the history of technology platforms.
The only question with machine intelligence is, because it is happening more quickly, will it happen so fast that it will be disruptive to the point of creating a crisis?
Roy expresses concern about the speed of AI development potentially leading to a crisis due to rapid job displacement and societal disruption.
And we might imagine a world where the short story written by a person is intrinsically more valuable to us because it was written by a person than the short story written by a machine.
Roy speculates on a future where human-crafted work, such as storytelling, is more valued than machine-generated content, reflecting the 'human corner theory.'
Watership down? It's the story of these rabbits who go on a long journey to save themselves.
Roy shares his favorite book, "Watership Down," and implies that its themes have personal significance and possibly influence his perspective on leadership and decision-making.## Human Similarity and Inspiration
"But sometimes you transpose it into this silly tale about bunnies and you realize it's all kind of the same, and we're fundamentally much more similar to each other than we are different. And that, to me, is a really inspiring message."
This quote underscores the speaker's belief in the fundamental similarities between people, which can be revealed through simple stories, such as those about bunnies.
"What has yet to change is that we still believe at the very earliest stages that we know more than we can possibly know."
Speaker B points out the overconfidence of VCs in their ability to understand and predict the success of nascent businesses.
"I think respect for the unknown is the biggest thing that I think we're missing as an industry, and curiosity is the way that I think you build more respect for the unknown."
The quote suggests that the VC industry lacks a necessary appreciation for the unpredictable elements of startups and that fostering curiosity could bridge this gap.
"Because when you fake expertise, you don't have or assume you know something you don't know. That's what puts distance between you and a founder, because the founders are the ones who actually have to figure this out."
The speaker criticizes the pretense of knowledge in VCs, which can harm the trust-based relationship with founders who are the true problem-solvers.
"And so we're very straightforward about our views, but ultimately the founder is our customer and we work for the founder."
This quote encapsulates the speaker's philosophy of serving the founder's interests and providing candid feedback while respecting the founder's decisions.
"And so the trust breaks are very difficult for us. And candidly, we're still trying to figure out what you do in those situations."
Speaker B admits the difficulty in dealing with situations where trust is broken and the ongoing process of determining the best course of action.
"And it is all about technologies that are having exponential effects and obviously focuses on machine intelligence."
The speaker appreciates newsletters like the exponential view for their coverage of rapidly advancing technologies, particularly in the field of machine intelligence.
"And what we look for is there are a bunch of things that are tick boxes. Do we trust the founders? Are the deal terms fair? Is it in scope for us? But then really, what you're looking for is one reason to believe the startup could potentially be an outlier."
The speaker describes the investment criteria used by Bloomberg Beta, emphasizing the search for a singular compelling reason to invest in a startup.
"Thank you. It's great that you're doing."
Speaker B thanks the host, showing appreciation for the opportunity to share insights on the podcast.