Oct 19, 2016 · 25m · 20vc
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
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In this episode of The 20 Minute VC, host Harry Stebbings interviews James Cham, Partner at Bloomberg Beta, to examine the rising enterprise adoption of machine learning, evolving SaaS business models, and unconventional decision-making structures in venture capital. Cham shares actionable insights on how AI transforms traditional industries and why venture firms must rethink consensus investing to empower visionary founders.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Harry holds 32.8% of the talking time here. How this is scored →
Harry as informed peer 2.7 Guest teaching 4.7 Guest disagreement 1.9 Harry pushing back 1.7
speaking balance: gold is Harry, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 20:25 Dismissing existential AI fear framing
When the host brings up fear surrounding man-machine integration and references Elon Musk, the guest directly rejects the premise, arguing that viewing machine integration as a scary new phenomenon is the wrong way to look at human technology progression.
Hardest push from Harry ▶ 16:17 Challenging single-partner decision postureThe host presses the guest on Bloomberg Beta's single-partner 'yes' rule, asking whether it forces a posture of unwavering accountability and complete confidence, requiring the guest to clarify the collaborative intention behind the rule.
Biggest teaching moment ▶ 11:20 Distinguishing ML SaaS from standard SaaSThe guest clearly educates the host on how machine learning software development differs from standard SaaS, explaining how dynamic data refinement and model separation fundamentally break traditional SaaS pricing metrics.
Harry holds his own ▶ 11:00 Referencing portfolio founder insightThe host demonstrates preparation and industry context by directly quoting portfolio founder Kieran Schneider at Textio regarding enterprise adoption to lead into business model dynamics.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Harry as informed peer | Guest teaching | Guest disagreement | Harry pushing back | Why |
|---|---|---|---|---|---|---|
| James Cham's Path into Venture Capital | 1 | 2 | 1 | 0 | The conversation begins cordially as the host asks standard background questions about the guest's transition into venture capital. The guest shares his roundabout path from software development and business school to Bessemer Venture Partners, noting lightheartedly that he initially avoided VC because he thought investors became worse people. | |
| The Rise of Machine Learning in Enterprise | 2 | 5 | 2 | 1 | The host introduces machine learning enterprise adoption. The guest educates the host on how corporate perception shifted from viewing ML as far-off tech hype to top Fortune 50 CEO agenda items, while cautioning against existential panic or simple labor replacement framing. | |
| Technical Drivers Behind the ML Explosion | 3 | 6 | 2 | 2 | The host asks what drove the 18-month surge in ML interest. The guest details key technical drivers including cheaper compute, data storage, and early Canadian neural network research, while reframing near-term AI as many small specialized intelligences rather than one monolithic system. | |
| Evolving Business Models for Machine Learning SaaS | 3 | 6 | 1 | 1 | The host cites portfolio founder Kieran Schneider to prompt a discussion on ML business models. The guest explains why ML companies differ from traditional SaaS, highlighting how evolving models and data flywheels disrupt standard SaaS unit economics. | |
| Software Development Frameworks Applied to Other Verticals | 4 | 5 | 2 | 4 | The guest explains how developer tools can be applied to other industries and details Bloomberg Beta's single-partner decision-making model. The host pushes back to clarify whether single-partner voting stems from posture and complete confidence or accountability, leading the guest to clarify his view on partner collaboration. | |
| Herd Mentality and Faith in Venture Investing | 3 | 4 | 2 | 2 | The host asks if the arrival of the VC herd in ML irritates early believers. The guest rejects the premise, stating he welcomes the company, and goes on to explain how his Christian faith informs his view of startup creation as reflecting divine work. | |
| Quick-Fire Round with James Cham | 3 | 5 | 3 | 2 | In the quick-fire round, the host prompts the guest on AI fears, greenfield opportunities, and misconceptions. The guest explicitly pushes back on the Elon Musk existential dread premise, framing technology as continuous human augmentation and ML as smart statistics rather than magic. |
Statements from this episode (16)
Cham: VCs enter the industry for returns or science fiction
“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.”
Machine learning is now a top agenda item for Fortune 50 CEOs
“Now, what's interesting, when we first started talking about machine learning three years ago, I think it was still seen as something that only tech companies were interested in, and what's changed over the last three years is that now everyone's interested. T…”
James Cham: Doomsday and labor replacement debates miss real AI opportunities
“The danger, though, is that a lot of the conversation focuses on what I would call either Theological questions about the end of the world, which is interesting, but I think oftentimes not relevant, or questions around straightforward labor replacement, and I …”
Cham: Google's 15-year quiet ML work is yielding major opportunities
“The work that Google has been doing for, you know, kind of quietly for the last 15 years is now yielding an incredible amount of fruit and creates all these opportunities.”
James Cham: Near-term AI will consist of many small specialized systems
“I think machine intelligence is not going to be, at least in the near term, you know, one big intelligence, but as it is for our own brains, many small intelligences and many, many small systems that help us make Good decisions or make decisions for us and do …”
Cham: Many corporate ML projects will fail due to poorly defined problems
“My own bet is that you'll see a lot of that happening over the next few years as the number of high profile projects inside corporations end up not yielding fruit in part because the problem was not defined well enough, or there was an assumption that If we ju…”
James Cham: ML SaaS will enable cross-company insights without sharing raw data
“There's going to be something, some similar insight and some similar buzzword about the separation of models and the ability to share insights of models between companies while at the same time not actually sharing data.”
James Cham: Standard SaaS pricing metrics fail for machine learning software
“The more data you get, the more valuable it is actually for the SaaS company, right? And so all the normal units of measure around how much you should charge are, don't really apply and don't entirely make sense.”
Cham: Smartphone adoption turns all workers into knowledge workers
“I think we live now in a world where everyone is a knowledge worker, and so everyone from the construction worker to the chef, the line chef, will have a Android phone and will be able to digitize their work.”
Cham: Applying developer concepts to vertical industries will generate many startups
“And I think that frame of thinking is a helpful frame and has, is going to lead to A huge number of startups.”
James Cham: Groups are uniquely bad at making interesting decisions
“Groups are really good at Getting new diverse points of view and new bits of information. And groups are actually also really good at doing complex coordinated tasks. Groups are uniquely bad at making interesting decisions.”
Cham: The best venture investment decisions are always highly controversial
“The best investment decisions were always very, very controversial.”
Bloomberg Beta allows any single partner to individually approve investments
“In our firm at least, we've made the decision that anyone can say yes.”
Cham: Entrepreneurship is the closest humans get to God's work
“Is that it is the act of creation, and I think that the act of creation, that is to say making something from nothing or something from very, very little, is as close as we'll really get to doing the work of God.”
James Cham: AI will transform society without altering fundamental human identity
“To be honest, I don't know that AI is gonna be necessarily that different. Which is say all those other technologies were incredibly transformative, and this will be another example. And people who come from a thousand years ago will look at us and sort of mar…”
James Cham: Machine learning should be viewed as smart statistics, not magic
“One way to think about machine learning is that it is basically Really, really smart statistics that, that ends, that end up constantly improving. And I think that as a frame for thinking about machine learning rather than machine learning as magic is, is, is …”