YouSaid · the spoken record

James Cham

lines on the record
26
first
2016-10-19
most recent
2016-10-19
sittings or episodes
1
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podcast

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  1. “The most recent public investment is in a company called Netlify. In that case, they're a set of designers and programmers who realize that the way that we make websites and deploy them right now are insecure and too slow.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  2. “It's magic. One way to think about machine learning is that it is basically really, really smart statistics that end up constantly improving. And I think that as a frame for thinking about machine learning, rather than machine learning as magic is much more useful.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  3. “So there are three, 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. Those are spectacular. And then I read Tyler Cowan, The Economist quite a bit, and I think those are probably the things I look forward to the most every week.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  4. “There's so many. 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. And I think that everything from redoing logistics over to HR, you know, sort of rethinking the way that contracts are being written, all those are huge, huge opportunities. And then there are a whole set of opportunities around rather than selling products into specific companies for one specific function. They're hosting companies that can be remade because that one core function will be so valuable. And so we look a lot of that part of the world, I'm certainly quite excited about.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  5. “So I think that that's the wrong way to think about it because machine is just a metaphor we use right now for technology. The truth is that 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. And then so I think that that can be inevitable progression of how different technologies end up influencing us. And in some ways you and I are incredibly augmented by a whole set of things, sort of everything from living inside buildings to like using light. To be honest, I don't know that AI is going to be necessarily that different. We'll just 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 marvel and be confused, but we will still feel like we're human.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  6. “Yes, the paperclip. It was incredibly unpopular. Cliff ended up doing a few studies near the end of the life of it in which he changed some error messages. And he changed the error message from saying, oh, I'm sorry that I can't find a result you're looking for to something along the lines of, oh, you know, that stupid developer, I can't believe he couldn't get a good result for you. That act of blaming someone else or scapegoating made Clipley incredibly popular, right? And that's because we as people have a whole set of common patterns for how we interact with the world. Those common patterns are well understood and predictable and they could actually be simulated very, very well by machines. I think Cliff would very much be interested in the world that we live in right now as we are now thinking more and more about how do we anthropomorphize machines and how do we create deeper relationships with them.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  7. “My favorite book, or at least the book that I give out the most often, is The Man Who Lie to His Laptop, which was by a Stanford professor named Cliff Nass. But he wrote about how we as people treat machines as personalities. 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, right? And so his most famous example is with Clippy. It's that horrible little thing in Microsoft Office.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  8. “Sure. You know, I think that I do take my faith and my Christianity fairly seriously. And so it was important for me to do work that I thought reflected that. The thing that I love about entrepreneurship is that it is the act of creation. And I think that the active 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. You know, one way to think about investing is that you're doing some straightforward money transfer and you've got exchange of skills. The other way to think about it is to think about it as a tangible example of by putting my faith in someone and saying, yes, I believe that you can create something from nothing. And I think that that's as close as we're going to get to doing that act of creation and doing that with God. And I find that very congruent and very exciting.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  9. “You know, I'm always glad to have more company. You're right that certainly this is an area that lots of people care about and talk about. To be honest, wanting to be early, one of the reasons why I wanted to be part of a seed fund was so that we can invest, you know, two to four years before everyone thought something was interesting. So I, for one, am very, very glad to have everyone else join the party. And I think that part of that means that we'll all get smarter about it. The truth, though, is that there still remains a relatively small set of folks who've thought about this type of business model, building these types of applications, and we're lucky to know a lot of them.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  10. “Well, remember, if I get to yes, then I am accountable, and also critically, when I go to my partner, I'm going to my partner because I respect his or her opinion and his or her insight, right? And so I'm going so that I can actually figure out what the best decision is to be made rather than trying to reposition information or to push something as a great investment, even though I have lots of questions.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  11. “I think we now know enough about what groups are good and bad at, and we know now that groups are really, I mean, there's been enough theory about this. And we now know that 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.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  12. “Knowledge work that we've ever seen. 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, you know, those sets of tools now apply to every single other type of knowledge worker. And when everyone else is a knowledge worker, it applies to every other vertical. And so I will often actually, I've often built this grid, which shows every single software development tool. And then I'll apply it to some industry and I'll say something like, well, gosh, I wonder if we had bug management for the construction industry. What would it look like if we had version control for legal contracts? And I think that frame of thinking is a helpful frame and is going to lead to a huge number of startups.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  13. “Okay, so I think we live now in a world where everyone is a knowledge worker. And so everyone from the construction worker to the line chef will have a Android phone and will be able to digitize their work. When that happens, when you see that sort of transformation of the workforce, what ends up happening is you need to look to see what are the tools that the best knowledge workers in the world use and then try to figure out whether there are lessons to be applied from those types of knowledge workers. And it turns out that the best knowledge workers in the world are these group of kind of lazy antisocial people who like love to procrastinate and work on meta systems 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. I think if you look in the last 40 years software developers have built the best set of tools for thinking about”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  14. “And don't entirely make sense. And so I think all of our best companies are trying to figure this out right now, and it's really exciting.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  15. “Or what we do with that is one of those big questions. If you think about the early days of Salesforce or SAS, one of the things they talked a lot about was this idea of multi-tenancy tenancy. That was mind-blowing and was one of those examples that they would say that this is one of the reasons why we'll be able to scale unlike our other SaaS competitors. And I think there's going to be 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. And I think exactly what that looks like is entirely unclear. Let me just highlight a couple of the differences between machine learning related SaaS companies and normal SaaS companies. One of them is the fact that 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 don't really apply.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  16. “I think that's the great question. So the reason why investing in machine learning right now and the reason why I'm so excited about investing in machine learning right now is because we don't know the answers to these questions and people are groping around to try to figure it out. And I think that there are, let me take a step back and just sort of offer one easy insight that I think all the folks inside the world of machine learning know, but I think is poorly understood outside of the machine learning world, which is that building machine learning tools and models is very different than normal software development. So the normal software development, you kind of know how to QA things and you sort of understand the clear separation between the program and data. And what's interesting in machine learning is now you've got the program and you've got data and then you've got a model that you're constantly refining and rebuilding and knowing whether the model is good enough or what that even means is still something we haven't figured out. And then knowing how we share the model across different organizations.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  17. “Always been the dream of making programming so easy that everyone will be a great programmer. And I think similarly there's a dream that it'll be really easy to build models. And I think what actually happens is when everyone's able to program, you end up with really bad programs. When you have these little black boxes that are not thoughtful about what data they're taking in and how they're using it, you end up with basically bad analysis. My own bet is that you'll see a lot of that happening over the next few years as a number of high profile projects inside corporations and not yielding fruit in part because the problem was not defined well enough or there was an assumption that if we just trust this black box without being thoughtful about processes around it we'll end up with sort of terrible outcomes.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  18. “Yet, right? There are still huge questions around how to think about machine learning, about what it even means to build these models. That's the challenge in front of many of us.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  19. “The biggest challenge out there right now is that there's not really a good metaphor for thinking about how to apply machine learning. I think that we watch movies that will tell us about falling in love with the robot or will read economists telling us that things are falling apart. But the real opportunity, I think, is 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. That's the actual interesting opportunity out there right now, in part because 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 things on our behalf. And I think that's identifying that and also figuring out both where the low hanging fruit, but also critically for a lot of corporations, what's actually possible, figuring that out is a hard challenge in front of us. And I think nobody knows what they're doing.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  20. “That's a great question. You know, 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. And what that means is that people are able to worry about higher level problems. So that's one thing. And then the other thing is that in part thanks to a set of guys who hit out and hit out in Canada for a little while diligently working on a set of techniques that continue the work of folks from 40 years ago, we've got cases where we can look at deep learning and say that neural nets actually work and produce really, really impressive results. Those two things, one slow and one sort of kind of dramatic because the results were so impressive meant that people started paying attention again and suddenly you would look and you'd say, wow, the work that Google has been doing for kind of quietly for the last 15 years.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  21. “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 think in both those cases, those are not the immediate challenge and the immediate opportunity for most organizations. I think what you're seeing now is the creation of 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. They know that somehow their organization needs to change. That opportunity and that question is the interesting question out there.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  22. “When we first started the fund, the mandate was and the promise to Bloomberg was that we were going to invest in 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. What that meant at the time was a mix of empowering people, more machine learning. And I have to admit, when we first started the fund, the thought was, and the general reaction was that that was further out there. And I've got to credit Siobhan a lot for pushing our thinking around how to use machine learning and the increasing adoption of machine learning. 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. The CEO of a Fortune 50 company now has the question of what machine learning means is one of the top questions in his or her agenda. The danger, though, is that a lot of the conversation focuses on”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  23. “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. Unfortunately, David was also part of that second camp. And so as I saw what he did and I saw the opportunities to work with, honestly, the most interesting, stubborn, determined people that I'd ever met before. It was something that was really hard to pass up.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  24. “Point of transition, and we really got along. So for a number of years, I apprenticed under David and learned to love the world adventure.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  25. “You know, I think that I have a number of friends who knew that they wanted to do venture investing from the point that they were 14. And, you know, I have one friend who systematically went about similar to you to talk to every single venture capitalist they knew that they could reach and establish themselves and then find a great job at a great firm. I came about it through a much more roundabout way. 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. But I threw a variety of circumstances and through a couple of failed startups ended up going to business school, which is where I think one escapes occasionally to retreat a little bit thanks to a good friend of mine I was introduced to David Cowan at Besssemer and he caught me at a”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source

  26. “It's my pleasure. I've listened to a number of the podcasts recently, and I have to admit I'm a little intimidated. You've got some great interviews.”

    2016-10-19 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source