20VC Greenfield Opportunities For Machine Learning, Why Massive Corporates Finally See It's Potential & Why VC's Investment Decision Making Process Needs To Change with James Cham, Partner @ Bloomberg Beta



In this episode of "20 minutes VC," host Harry Stebbings interviews James Cham, a partner at Bloomberg Beta with a developer-centric mindset and a background in programming, evidenced by his Harvard CS degree and previous roles at Trinity Ventures and Bessemer Venture Partners. Cham shares his insights on the machine learning landscape, emphasizing its transformation from a niche interest to a CEO-level concern in just three years. He discusses the challenges organizations face in applying machine learning, the potential pitfalls of "black box" solutions, and the unique business models emerging from machine learning applications. Cham also reflects on the parallels between software development tools and knowledge work in various industries, as well as the personal interplay between his venture capital work and his Christian faith. The episode touches on the herd mentality in machine learning investment and concludes with Cham's thoughts on his recent investment in Netlify, a company reimagining web hosting and deployment.

Summary Notes

Introduction to James Cham and Bloomberg Beta

  • James Cham is a partner at Bloomberg Beta, a venture capital firm with a focus on the future of work and machine learning.
  • Prior to Bloomberg Beta, James was a principal at Trinity Ventures and a vice president at Bessemer Venture Partners.
  • James has a background in computer science from Harvard and is known for a developer-centric mindset.
  • The show host, Harry Stebbings, credits James and his colleague Siobhan for their expertise in machine learning.

"Well, I'm delighted to be joined today by Roy's partner James Cham. So James is a partner at Bloomberg Beta. And I have to say at Bloomberg, James and the team have really become masters at the machine learning space."

This quote introduces James Cham as a key figure in the venture capital industry, particularly in the machine learning space, and sets the stage for his expertise to be shared in the podcast.

Venture Capital Journey of James Cham

  • James Cham became a venture capitalist through a roundabout way, starting as a developer and going through business school after a couple of failed startups.
  • He apprenticed under David Cowan at Bessemer and learned to love the venture capital world.
  • James did not initially intend to enter venture capital, as he observed some investors becoming "worse people."

"I was a developer after graduating from school, and to be honest, I had no intention of ever hitting the venture side, in part because my observation was that many of my friends who ended up in investing turned out to be worse people and so had no deep interest in that."

This quote explains James's initial reluctance to enter the venture capital industry due to negative changes he observed in his peers who became investors.

Mindset Shift to Venture Capital

  • James Cham's mindset shift occurred when he saw the potential for fantastic returns and the opportunity to work with interesting and determined entrepreneurs.
  • His interest in science fiction and the future motivated him to become a venture capitalist, aligning with David Cowan's similar interests.

"I think some people become VCs because they like the opportunity for fantastic returns, and some people become VCs because they read too much science fiction. And I clearly was part of the second camp."

The quote highlights the personal motivations behind James Cham's decision to pursue a career in venture capital, particularly his passion for science fiction and the future.

Machine Learning in Organizations

  • James discusses the evolution of machine learning from a niche interest to a top agenda item for CEOs of major companies.
  • The initial mandate for Bloomberg Beta was to invest in the future of work, which included empowering people through machine learning.
  • Siobhan, James's colleague, was instrumental in pushing Bloomberg Beta's thinking around machine learning adoption.
  • The conversation around machine learning has shifted from esoteric concerns to practical applications in labor and organization efficiency.

"When we first started the fund, the mandate was, and the promise to Bloomberg was that we were going to invest into the future of work. That was going to hit two or three different angles. One of the angles was the future of work was going to be smarter."

This quote outlines the foundational investment strategy of Bloomberg Beta, emphasizing the role of machine learning in shaping the future of work.

"The CEO of a Fortune 50 company now has the question of what machine learning means as one of the top questions in his or her agenda."

This quote signifies the widespread interest and importance of machine learning in the strategic planning of major corporations, reflecting its growth from a specialized tech interest to a mainstream concern.

Emergence and Interest in Data Science and Machine Learning

  • The concept of data science teams is new and ill-defined, but there is an awareness of their necessity for organizational change.
  • There has been a surge in interest in machine learning from various sectors, including traditional industries and tech companies.

"what people initially are calling data science teams, and no one really knows what it means, and no one's exactly sure where to apply it, but they know that there's something there."

This quote explains the uncertainty and novelty surrounding data science teams, highlighting the recognition of their potential despite a lack of clear definition or application.

"why is it that everyone from kind of ceos in Detroit to head of Salesforce is interested in machine learning?"

This quote poses a question about the widespread interest in machine learning across different industries and leadership roles.

Drivers of Machine Learning Adoption

  • Data storage has become cheaper and computing more accessible, allowing focus on higher-level problems.
  • Advances in deep learning and neural networks, particularly from researchers in Canada, have shown impressive results.
  • Organizations have invested in large data systems, prompting exploration of machine learning applications.

"One of the things that's been the slower trend is the fact that data is so much cheaper to store and compute is so much easier to do now."

This quote highlights the reduction in cost and complexity of data storage and computation as a factor in the growing interest in machine learning.

"we've got cases where we can look at deep learning and say that neural nets actually work and produce really, really impressive results."

This quote points out the success of deep learning and neural networks, which has captured public and professional attention.

Theoretical and Immediate Challenges in Machine Learning

  • The future of work and apocalyptic concerns are theoretical challenges, whereas practical application of machine learning is an immediate challenge.
  • There is no good metaphor for how to apply machine learning, and many organizations are unsure about building effective models.
  • Machine intelligence will likely consist of many small systems aiding decision-making rather than a singular intelligence.

"The biggest challenge out there right now is that there's not really a good metaphor for thinking about how to apply machine learning."

This quote identifies the lack of a guiding principle or analogy for effectively applying machine learning as a major obstacle.

"figuring out specific processes and specific places where building good bottles to help us make better decisions, or actually oftentimes to make decisions for us."

The quote suggests that the real opportunity lies in identifying processes where machine learning can assist or automate decision-making.

Machine Learning Integration in Corporate Systems

  • There is skepticism about the effectiveness of "machine learning in a box" solutions.
  • Poorly defined problems and lack of thoughtful data analysis can lead to unsuccessful machine learning projects.
  • Corporations may face challenges with off-the-shelf solutions versus personalized approaches.

"There's always been the dream of making programming so easy that everyone will be a great programmer?"

This quote reflects on the aspiration to simplify programming, drawing a parallel to the idea of simplifying machine learning model building.

"you end up with basically bad analysis."

This quote warns that without careful consideration of data and its use, machine learning solutions can produce poor results.

Future of Machine Learning Business Models

  • The business models for machine learning tools are still being explored and are different from traditional software development.
  • Machine learning involves a constant refinement of models, making QA and understanding the separation between program and data challenging.
  • There may be new paradigms, similar to the concept of multi-tenancy in SaaS, that will enable sharing model insights without sharing data.

"building machine learning tools and models is very different than normal software development."

This quote emphasizes the unique challenges in developing machine learning tools compared to traditional software.

"knowing whether the model is good enough or what that even means is still something we haven't figured out."

The quote points out the difficulty in assessing the quality and effectiveness of machine learning models, which is still an area of active exploration.

Transformation of Workforce and Knowledge Work

  • The current world sees everyone as a knowledge worker, from construction workers to line chefs.
  • The proliferation of smartphones allows workers to digitize their work.
  • The tools used by the most advanced knowledge workers, software developers, are applicable across other industries.
  • Software developers have created tools that assist in knowledge work, such as debugging, build systems, and recomposing ideas.
  • These tools can be adapted to other verticals, potentially leading to a wave of startups.

"Okay, so I think we live now in a world where everyone is a knowledge worker." This quote establishes the premise that in the modern era, the term 'knowledge worker' applies broadly across various occupations due to technology.

"And it turns out that the best knowledge workers in the world are these group of kind of lazy, antisocial people who love to procrastinate and work on metasystems rather than actual applications, and love to think in theory rather than actually write the code they're supposed to. And those knowledge workers, and those people, of course, are software developers." This quote characterizes software developers as top-tier knowledge workers, highlighting their tendency to focus on abstract, systemic work over direct application.

"So everything from thinking about debugging in a clear way, or thinking about build systems, or thinking about make files, or thinking about the process for recomposing different ideas, those sets of tools now apply to every single other type of knowledge worker." The speaker provides examples of tools and processes used in software development that have broader applications in knowledge work.

Venture Capital (VC) Decision-Making Process

  • Groups are effective at gathering diverse viewpoints and managing complex tasks but struggle with making unique decisions.
  • Changing the decision-making process to enable interesting decisions is critical.
  • In the speaker's firm, anyone can say 'yes' to a decision, fostering a sense of ownership and accountability.
  • This approach alters the dynamic of seeking partner approval, emphasizing honest feedback over persuasion.

"Groups are uniquely bad at making interesting decisions." This quote suggests that while groups have strengths, they are not optimal for making innovative decisions, which is a significant consideration in venture capital.

"And so how can we create an environment in which my partners will come to me with an honest point of view, try to help me make the best decisions I can make, rather than force me to curry their favor in order to convince them that we should make something." The speaker discusses the importance of creating a VC firm culture where partners provide genuine advice rather than seeking to influence decisions for personal gain.

"As a result of that, in our firm at least we've made the decision that anyone can say yes." The quote explains a policy within the speaker's firm designed to empower individuals in the decision-making process, contrasting with more traditional consensus-driven models.

Machine Learning and Herd Mentality

  • There's a herd mentality in the tech industry, particularly noticeable in machine learning.
  • The speaker is pleased with the increased interest in machine learning, as it leads to a collective increase in understanding.
  • Despite the popularity, only a few have deeply considered the business models and applications of machine learning.

"You're right that certainly this is an area that lots of people care about and talk about." Acknowledges the widespread interest in machine learning, implying both positive and negative ramifications.

"So I, for one, am very, very glad to have everyone else join the party." The speaker expresses enthusiasm for the growing attention toward machine learning, viewing it as beneficial for the field.

Faith and Venture Capital

  • The speaker's Christian faith influences their approach to venture capital.
  • Entrepreneurship is seen as an act of creation, mirroring the work of God.
  • Investing is not just about the exchange of money and skills but also about putting faith in someone's ability to create.

"The thing that I love about entrepreneurship is that it is the act of creation." This quote connects the speaker's religious beliefs with their professional work, emphasizing the creative aspect of entrepreneurship.

"And I think that that's as close as we're going to get to doing that act of creation and doing that work of God." The speaker draws a parallel between the role of an investor in supporting entrepreneurship and the divine act of creation from their faith perspective.

Favorite Book and Its Impact

  • The speaker's favorite book is "The Man Who Lied to His Laptop" by Cliff Nass.
  • The book discusses how humans interact with machines and perceive them as having personalities.
  • Famous studies, including those involving Clippy, the Microsoft Office Assistant, are mentioned.

"My favorite book, or at least the book that I give out the most often, is the man who lied to his laptop, which was by a Stanford professor named Cliff Nass." The speaker introduces their favorite book and author, setting the stage for discussing its influence on their thinking.

"He talked about a whole set of tests that he did and a whole set of studies that he did in order to show that." This quote highlights the empirical approach taken by the author of the book to explore human-machine interactions, which seems to have resonated with the speaker.

Anthropomorphizing Machines and Creating Relationships

  • Humans have common, predictable patterns that machines can simulate effectively.
  • The popularity of Quipley increased when error messages were changed to blame a developer, showing the power of anthropomorphizing machines.
  • There is interest in how we can create deeper relationships with machines.

"That act of blaming someone else or scapegoating made Quipley incredibly popular."

This quote exemplifies how human-like behavior in machines can resonate with people and increase engagement with technology.

Optimal Relationship Between Man and Machine

  • The relationship between humans and technology is constantly evolving, similar to historical advancements such as agriculture and clothing.
  • Technology fundamentally transforms us, and AI is another step in this ongoing progression.
  • AI's impact on humanity should be viewed as a continuation of technological augmentation, not as a separate or unique phenomenon.

"We as people are constantly being transformed by technology, and whether that's agriculture and eating wheat and having that affect our tummy, to deciding to wear clothes, because it keeps us warm."

This quote reflects the idea that technology is an integral part of human evolution and that AI is a natural extension of this process.

Greenfield Opportunities in Machine Learning

  • There are numerous opportunities for machine learning across various organizational processes.
  • Digitization of functions such as logistics and HR, and rethinking contract writing, present significant potential.
  • Machine learning can also lead to the creation of new companies centered around core functions enhanced by AI.

"There's so many. I think that if you were to break down every single process inside an organization and you'd say, which are the ones that are easiest to digitize, that exercise is sort of the exercise that we go through right now."

This quote suggests that there is a wide array of opportunities for machine learning to optimize and innovate within existing organizational structures and processes.

Favorite Sources of Information

  • James Cham enjoys AI-related newsletters by Rob May and Jack Clark.
  • He also reads economist Tyler Cowan for insightful content.
  • These sources are highly anticipated by Cham every week for their content on AI and economics.

"There are two new AI related newsletters that I just love. One is by Rob May, M-A-Y and the other one is by Jack Clark."

The quote indicates the value placed on staying informed about AI developments through specialized newsletters.

Misconceptions About Machine Learning

  • Machine learning is often misunderstood as a form of magic.
  • It should be considered as advanced statistics that improve over time.
  • Understanding machine learning as a statistical tool rather than magic can lead to more practical applications and expectations.

"That it's magic. One way to think about machine learning is that it is basically really smart statistics that end up constantly improving."

This quote clarifies that machine learning is a logical, systematic process and not an inexplicable phenomenon.

Recent Investment in Netlify

  • Netlify was invested in because of its innovative approach to web hosting and deployment.
  • The founders' technical insight and determination were key factors in the decision to invest.
  • Netlify addresses issues of security and performance in current web development practices.

"The most recent public investment is in a company called Netlify... they were going to work at it like crazy and they were not going to be stopped."

The quote emphasizes the importance of the founders' vision and work ethic in the investment decision, highlighting their potential to disrupt and improve the web development industry.

Closing Remarks

  • James Cham is appreciated for his expertise in machine learning and his genuine character.
  • The podcast host encourages listeners to engage with the show through social media and newsletters.
  • The host also mentions sponsors DesignCrowd and Angel Loop, providing offers for listeners.

"Well, James, it was such a pleasure to have you on the show today."

This quote concludes the interview, expressing gratitude for the guest's contributions and insights shared during the podcast.

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