20VC Why Machine Intelligence Will Eat The World Of Software with Roy Bahat, Head of Bloomberg Beta

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.

• Roy Bahat is the head of Bloomberg Beta, a venture fund backed by Bloomberg LP.
• Prior to Bloomberg Beta, Roy was the chairman and first investor at the games console company Ouya.
• Roy has experience leading News Corporation's IGN Entertainment and has served on the board for companies acquired by Discovery and Warner Bros.
• He has also worked in the public sector for New York City Mayor Michael Bloomberg and New York's 2012 Olympic bid.
• The 20 minutes vc podcast is sponsored by Lisa, an online mattress company, and Matamart, a data and analysis provider.

"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.

• Roy initially was a skeptic of technology and had no intention of becoming a venture capitalist.
• His experiences with venture capitalists were mostly negative until he was approached by Bloomberg to start a corporate VC.
• Roy's approach to venture capital was to address the issues with corporate VC by focusing on making money to maintain transparency with founders.

"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.

• Bloomberg Beta does not require unanimous agreement to make an investment decision.
• The firm has a unique model where any one person on the team can say yes to a deal.
• This model is designed to harness the polarizing nature of the best ideas, allowing for quick and passionate investment decisions.

"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.

• Bloomberg Beta focuses on investing in startups that aim to improve the future of work.
• The discussion around AI is compared to teenage curiosity about sex: much talked about but not well understood.
• Roy prefers to avoid the term AI due to its broad and unclear usage, instead focusing on specific techniques like deep learning and machine learning.

"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

• AI is often conflated with humanoid intelligence and malevolent intentions due to popular culture.
• Machine intelligence is a preferred term, as it is less loaded and emphasizes the computational aspect of making judgments.
• Computers are making judgments that humans might or might not be able to make themselves.

"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.

• Machine intelligence is similar to software in its potential to impact every industry.
• It involves the question of whether we are discussing pure AI, like DeepMind and Watson, or the next wave of software-style applications.
• The term "machine intelligence" encompasses both the intellectual property-heavy aspects of AI and broader applications in software.

"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.

• The explosion of data volume is the biggest recent advance in software functionality.
• Big data has produced valuable technologies but fewer valuable businesses on its own.
• Machine intelligence applies big data to useful tasks, potentially "eating" software by adding value to the data.
• The utility of machine intelligence is more important than its ability to mimic human behavior.

"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.

• Large companies like Google and Facebook have an advantage due to their massive data sets.
• Startups face challenges due to limited access to data but can still find opportunities.
• Startups should focus on applying AI techniques to new use cases where they can access data and create virtuous cycles of improvement.

"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.

• Startups with a generic AI approach face difficulties without a specific use case.
• Success for startups lies in applying AI to new areas with data access, creating a feedback loop of data and user experience.
• Textio is cited as an example of a startup successfully utilizing AI to predict the outcomes of business documents.

"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.

• Competitions like Imagenet and platforms like Kaggle provide public data sets for use.
• Building a startup requires having unique data, as competing with common data sets necessitates outperforming others in algorithm development.

"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.

• Open source is a critical method in the future of work, not just in software or machine intelligence.
• The speaker's VC firm practices transparency and has published its operational manual on GitHub under an open source license.
• Open source can improve the quality of software quickly when developers focus on a particular issue.

"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.

• There is debate over whether large standalone AI companies can be built.
• The analogy is drawn to whether a company like Amazon is a commerce company using software or a software company.
• The potential for AI companies depends on applying technology to useful situations.

"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

• Companies are increasingly replacing 'data scientist' with 'machine learning' in job requisitions.
• Machine learning is a sought-after skill due to its importance and the scarcity of experts.
• There's a shortage of individuals who can apply, let alone invent, new machine learning techniques.

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 rise of machine intelligence brings both danger and power.
• More individuals are exploring machine learning, increasing the pool of potential startup founders.
• Intense competition can be overwhelming but is also indicative of significant opportunities.
• Roy credits partners Siobhan and James Cham for his early awareness of machine intelligence.
• Despite competition, the focus should be on maintaining personal investment beliefs amidst fluctuating startup trends.

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.

• Predicting the first successful Slackbot use case is challenging and uncertain.
• Early market leaders aren't always the eventual best in their field.
• Different kinds of developers, focused on human-oriented design, are entering the bot space.
• Diverse backgrounds among founders can lead to a variety of new company ideas.
• Roy is excited about the potential for elderly-focused services once older tech founders emerge.

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.

• AI and machine intelligence may disrupt a significant percentage of white-collar jobs.
• The pace of AI development could lead to societal crises if job displacement happens too quickly.
• Roy studies policies like universal basic income to address potential work and subsistence issues caused by technology advancements.
• The relationship between work and subsistence might need reevaluation in the near future.
• Roy proposes the 'human corner theory' where humans will do jobs that are valued specifically for being human-made.

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.

• The future might see humans occupying jobs that are valued for being human-made ('handmade').
• The intrinsic value of human-made products and services could define future work.
• The economy could be largely run by computers, with humans serving each other in areas where the human touch is preferred.

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.'

• Roy's favorite book, "Watership Down," is cherished for its themes of unexpected leadership and judgment.
• The book resonates with Roy due to its portrayal of leadership not by the strongest or smartest, but by those with the best judgment and concern for the right outcomes.

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

• The speaker reflects on the universal nature of human experiences across different settings.
• The idea that people are more similar than different is highlighted as an inspiring message.

"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.

• Speaker B criticizes the typical venture capitalist (VC) approach to early-stage startups.
• The concept of "quantum startup physics" is introduced to describe the unpredictable nature of pre-product market fit startups.
• The speaker advocates for a greater respect for the unknown and curiosity in the VC industry.

"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.

• The importance of trust and authenticity in the relationship between VCs and founders is highlighted.
• Speaker B emphasizes the role of VCs in supporting founders rather than imposing their expertise.

"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.

• Speaker B views founders as customers and stresses the importance of supporting them through difficulties.
• The approach to founders who do not meet expectations is discussed, with a focus on honesty and backing the founder's vision.

"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.

• The challenges of trust breaches between VCs and founders are acknowledged.
• Speaker B discusses the complex nature of VC-founder relationships, likening them to a "shotgun wedding."

"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.

• Speaker B shares his favorite sources of media and technology insights, emphasizing the human element in content curation.
• The value of newsletters that provide unique perspectives and cover exponential technologies is highlighted.

"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.

• The approach to investment criteria at Bloomberg Beta is discussed, with a focus on looking for reasons to believe in a startup's potential.
• Speaker B shares the thought process behind their most recent investment decision.

"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.

• Speaker B expresses gratitude for being part of the show and appreciation for the host's work.
• The host concludes by encouraging listeners to engage with the show and thanking a sponsor.

"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.