An Interview with James Cham, Partner, Bloomberg Beta


James Cham is a venture capital investor with Bloomberg Beta, a firm focused on investing in the future of work. James invests in companies working on applying machine intelligence to businesses and society. Prior to Bloomberg Beta, James was a Principal at Trinity Ventures and a VP at Bessemer Venture Partners, where he focused on consumer services, enterprise software, digital media; and served on the boards of CrowdFlower, Open Candy, LifeLock, ReputationDefender, Sonic Mule, and BillShrink. He was previously a management consultant at The Boston Consulting Group and a software developer. James received an MBA from MIT's Sloan School of Management and Computer Science degree from Harvard.

How did you get into the world of Venture Capital?

After the startup that I was a part of got acquired, I went to business school. A good friend of mine introduced me to a firm called Bessemer, where I got my introduction to venture capital and how I ended up investing in startups.

And before Venture Capital you were a software developer?

That's right, I was a software developer in the late 90s to early 2000s. I was part of that transition from client-server over to web-based, enterprise applications, and I wrote a bunch of mediocre code and made a bunch of bad design decisions that other people suffered for as a result. So I’ve been through enough cycles to least understand what that feels like from a potential customer perspective.

Why did you invest in LaunchDarkly?

Let me take a step back. When we raised money from Bloomberg to start the fund nearly five years ago, one of the core claims was that we are living in a world where everyone's a knowledge worker. In that world, we should look at the best knowledge workers around. We should copy their techniques to find ways for them to scale what they're doing. And of course, the best knowledge workers in the world are software developers. This is in part because some of the best software developers are a mix of lazy and smart -- they spend all their time avoiding working on applications and instead work on frameworks and systems infrastructure. So broadly, that is what we’re excited about.

LaunchDarkly is exciting for two core reasons: One, there was an immediate sense of recognition of a problem. When I first heard Edith pitch the idea, I thought “Oh my goodness! I wish this existed when I was being yelled at as a software developer or when I was managing projects.” There's a sense that this should exist and this is the right way to do something. I think most software developers do this. You build your own bad bug-tracking system or slightly lame issues-tracking system. And I had done something like a features flag product for some other project, but I didn't call it that. There was a sense that Edith understood this and saw this more clearly than I did. That’s one excitement.

And then there's the other reality which is the excitement of seeing a leader like we did. There's a point when you meet her and say, “Oh, she's not just someone who has built something interesting, but she’s someone you can see leading something important.” That’s another important part of what made it exciting for me. As I've gotten to know her better, that’s only been validated more and more.

You met LaunchDarkly through Alchemist. What are your thoughts of Alchemist in general?

The thing that is most helpful about Alchemist is that it’s more systems driven. The people around it are quite credible and thoughtful. You look at the set of advisors: These are people who aren’t really famous and lightly involved, but rather accomplished and very deeply involved. From my perspective, that makes the process of diligence and validating people much easier.

There’s always a sense about Alchemist that you’re being as positive as possible about the opportunity, but at the same time you don’t lie. That's an important thing for an investor and really helpful.

What is the approximate size of your fund? How does that compare to other funds in a similar stage?

As the markets are fragmented, even in the earlier stage, judging how we compare to other funds does become more complicated. But the core physics of our first fund was $75M, and the second fund is also $75M. Our first check sizes range between $100K to $1M, and we participate anywhere from friends and family rounds to right before the Series A.

Does your fund have a specific vision or focus? I know you've touched on the future of work prior, but is there more to that?

We talk about the future of work, in part, because historians of science would say that it takes two generations of managers for any new technology to really make an impact on the economy. At the start of our fund, we were twenty years into the Web -- networked computers, which is another way to think about it. We were convinced that it is only now we’ll see massive changes in the way people work, because now you have a bunch of people creating businesses that are suited for the Web.

Within that vision, we have a focus both on productivity for knowledge workers -- we see a lot of opportunities to integrate and learn from developers -- and the way software ends up changing the way that people do business. New tools will be required to support this new kind of business, which include developer tools up to enterprise software.

We also believe that machine learning, model building, and AI in general are different than normal software development. I think they have profound implications that we haven’t understood yet, not just on all the cutting edge research we’ve done, but especially around the way that people make good software and machine learning models. Machine learning model building is different than software development. The economic characteristics are different, meaning machine learning will give rise to new business models. So somewhere out there, there’s going to be a person that is the Bill Gates or Marc Benioff of machine learning. They are going to do a mix of marketing, technical, and product insights and come up with a different way of providing machine learning or AI-driven businesses in a different light. They are going to charge in a different way or sell it in a different way. That’s the innovation or change in the way that people do business that we’re most excited about, and where we spend a lot of time.

How does your fund differentiate itself from other funds?

On the one hand, the money is a commodity. The money is the same, and so the way you differentiate is you bundle different services along with it. Some of that is the personality of the partners and the way that they relate to other people. A part of that is also a set of things that we focus on. I think, we think through more than other firms ways that founders can make a dent in the universe through the way they talk about themselves. On that side, we’ve thought a lot out. And we work with our companies a lot around that.

So much of it depends on the specific relationship that each partner has with the founder that that investor has invested in, especially at the seed stage. There aren’t magic formulas.

How do you individually differentiate yourself from other individual VC’s?

The right way to compete along those lines is not to compete. Instead, I’m most interested in angles that people aren’t thinking about yet. And I’m most interested in thinking through angles that are poorly understood.

So if someone has just another generic SaaS company that’s growing at a certain percentage, then I'm probably not the right person for them. An old friend of mind would say that there’s two types of VCs. There are VCs that if they weren’t VCs, they’d be bankers, and others who are VCs because they spent too much time pitching. I’m definitely part of the second camp. There are a whole set of ideas that should be enabled and would be if someone stuck their neck out and said they believed this founder could create something special and make the world better. And that’s what I try to do.

​What makes an investment compelling for you? Is there something in particular that makes an investment more compelling than not?

There are all the things that people talk about: traction, the team’s experience, potential, etc. I think those things are all really important, but the thing that might be under appreciated is that core insight. Sometimes the founders don't understand what the core insight is. There is nothing quite as exciting as sitting with a founder and discovering together what actually makes them special. And oftentimes that core insight can be communicated in a paragraph or it could take a lifetime to get there. For me, that’s what I'm looking for that. It’s going to be in areas where I have enough preconceived notions that someone could surprise me.

What is the number one red flag for you that would make you pass on an investment?

The moment I feel like I can’t trust someone is probably the number one reason why. When it’s close or we thought we should have invested, that tends to be the number one surprising thing about most folks that we pass on. Investing in a company is not something you take lightly. We take it very seriously and it’s a relationship we take very seriously as well.

What separates the great founders who get an investment from you vs. the good founders who don't quite make the cut?

There’s a way in which the best founders help you believe. Whether it’s helping the investors believe or first customers or the first employee or the co-founders. And that way of getting someone to believe, it comes in all sorts of ways. It’s not generic. It comes in many sizes and forms, but that ability to impose your will on the universe. It only works if you can convince other people.

Would you be more likely to fund a very experienced team with a mediocre idea or a team of novices with an amazing idea?

Nuance matters a lot here. I think that there are plenty of times when the very smart, experienced team can take a mediocre, initial idea and because they are so customer-oriented or technically visionary that they end up building something better, smarter, or more interesting. However, generically, I hunt for people who have extraordinary insight and how they get there. The insights do not have to manifest themselves with the first product, but they manifest themselves somehow that makes them extraordinary.

Is there any piece of advice you would give founders who are fundraising that you think does not get shared enough?

I think founders forget how much power they have in a situation. There are cycles that founders get in where they end up feeling like this is just another boring sales call. But what the founders are doing is they're sharing their most precious things. They're sharing things that they probably care more about than almost anything else in the universe. When they pitch, they should treat it that way. That investors are lucky to get a view into this. The moment the founder forgets that, humans can smell it. You have to continue to be resilient and continue to believe because investors, although we do it through a financial instrument, at the core, we’re declaring we have faith in someone and we have enough faith that we’re putting our money and our goodwill behind it.

If you think about Edith and the way that they were together and the way that they communicated and seemed to take what they do seriously, even when things are difficult, that’s the sort of thing that an investor is looking for.

What areas are you excited about now and in the future?​

I’m excited for when things that we call AI-related start being machine learning-related and get boring. When everyone understands how to engineer a bunch of problems, things get boring, and that's when you end up with a lot of product innovation. I’m very excited about that!

Not BI, AI

A product business can double its revenue and quadruple its margins by moving to a service business. What is service? It's information, personal and relevant to you.  

Amazon delivers information that is personal and relevant to you, for example, with its recommendations: customers like you bought this book, or customers like you like this music. Now think about your favorite banking site and log in. I will contend that there’s very little personal and relevant information. The only reason you’re being asked to log in is for security reasons. After that you are really looking at a big shopping cart to move money from savings to checking, buy stocks, sell a bond, etc. 

Could the bank deliver information that’s personal or relevant to you? Could they say that people like you bought this stock, or people like you re-financed their mortgage? Yes, they could, so why don’t they? Well, you probably never thought about this, but the consumer Internet that Google and Bing let you see through search is believed to only be about 100 or 200 terabytes. That’s it. Now, I’ll guarantee your current IT systems have 10, 100, or 1,000 times that amount of information; so why can’t they deliver information that is personal and relevant to you? Well, I say they are held hostage by the SQL monster. So let’s just have a little fun here.

It’s the late ‘90s and I have several SQL engineers in the room. I come in with a brilliant business idea. My idea is that we are going to index the consumer Internet and we’re going to monetize it with ads. We’re going to be billionaires! Just guess what the SQL engineers would do?

The first thing they’re going to do is design a master, global-data schema to index all information on the planet. The second thing they’re going to do is write ETL and data cleansing tools to import all that information into this master, global-data schema. And the last thing they are going to do is write reports, for instance, the best place to camp in France or great places to eat in San Francisco.

Any of you who are technical are probably laughing right now thinking, “Well that’s a completely stupid thing to do.” But if you try and attack the problem using SQL and BI tools, you’re also going to fail.  

Furthermore, as you connect your machines, you have the opportunity to bring in large amounts of time-series data. Modern wind turbines have 500 sensors and the ability to transmit those sensor readings once a second. Most analytic techniques depend on the idea that the data scientist can try and visualize the data, but how is that possible if I have a 1,000 wind turbines and data for 12, 24 or 36 months?  How can we learn from that?

Artificial Intelligence (AI) has been increasingly in the news. Google’s DeepMind made headlines when the machine, AlphaGo, programmed to play Go, defeated Lee Sedol, one of the best players in the world, by 4 - 1. Amazon’s Echo and voice assistant Alexa is being widely praised for its voice recognition capabilities, and many people remember how Watson handily beat the best Jeopardy players in the world.

Things have been changing quickly and here is a great example. ImageNet is a database of millions of images. Beginning in 2010 the ImageNet Challenge was established to see how well a machine would do at object recognition. As a point of reference an average person will be able to achieve 95% accuracy. In 2010, the winning machine could correctly label an image 72% of the time. By 2012, accuracy had improved to 85%, and in 2015 the machine achieved 96% accuracy.

So why have things been changing so quickly?

First, we’re continuing to get more computing and more storage for lower and lower prices. Next generation compute and storage cloud services can provide thousands of computers for an hour or a day. AI and machine learning software require lots of computing during the learning phase. The second reason is the emergence of neural network algorithms. Third, it’s not possible to apply these advanced AI technologies without data, and lots of it. Consumer Internet companies like Facebook are able to use billions of photos to train facial recognition systems. AlphaGo learned from millions of games of Go and Alexa learned from millions of voice patterns.

While we’ll continue to see progress in replicating what humans do, we have the opportunity to apply these AI technologies to even more important challenges. Today, many of the machines that generate electricity, transport goods, farm food, or sequence genes have large amounts of data. If we were able to connect these machines and collect the sensor data from them, we would have the opportunity to use AI and machine learning technologies to operate a more precise planet. Imagine a future farm that can use fewer pesticides, which not only reduces the cost of the food, but also makes it healthier. A future power utility could be based on a vast array of solar panels, wind turbines, small hydro generators and batteries to generate more power, much more efficiently. A pediatric hospital could share the results of millions of MRI scans and diagnose patients far faster.

Next-generation machine companies could not only double their revenues and quadruple their margins, but build a better planet in the process.

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Timothy Chou, Ph.D.

Timothy Chou has lectured at Stanford University for over twenty-five years and is the Alchemist Accelerator IoT Chair.  Not only does he have academic credentials, but also he's served as President of Oracle's cloud business and today is a board member at both Blackbaud and Teradata. He began his career at one of the first Kleiner Perkins startups, Tandem Computers, and today is working with several Silicon Valley startups including as the Executive Chairman of Lecida, which is building precision assistants for the IoT using AI technologies. Timothy has published a few landmark books including, The End of Software, and Precision: Principals, Practices and Solutions for the Internet of Things, which was recently named one of the top ten books for CIOs.  He's lectured at over twenty universities and delivered keynotes on all six continents.

PRESS RELEASE: ALCHEMIST ACCELERATOR ADDS JUNIPER NETWORKS AS BACKER

Contact: Danielle D’Agostaro                                                                                                   RELEASE: May 23, 2017

Email: danielle@alchemistaccelerator.com


                                     ALCHEMIST ACCELERATOR ADDS JUNIPER NETWORKS AS BACKER

                             Juniper Networks Joins Alchemist Accelerator’s Second Round Fund as Backer


San Francisco, May 23, 2017 – Alchemist Accelerator, an accelerator dedicated to enterprise start-ups, today announced that Juniper Networks has joined Analog Devices, Cisco, Ericsson, GE, and Johnson Controls as a backer in the accelerator’s second fund. This brings the total fund to $6.5 million.

Alchemist Accelerator is a six-month program, accepting about 20 companies every four months. On average, accepted companies receive $36,000 in seed funding. Alchemist structures the program around mentorship, sales and fundraising to help early-stage companies raise their seed or series A round and secure their first few customers.

Many founders who have gone through the program would agree that a major perk of joining Alchemist comes from the large network of high-caliber experts and coaches who mentor Alchemist founders.

“We are thrilled to have Juniper Networks join as a backer of Alchemist. Few companies think as deeply about next gen trends in AI, cloud, analytics, and networking – all core areas to Alchemist – as Juniper does. We are excited to have Juniper join the Alchemist family,” said Ravi Belani, Founder and Managing Director of Alchemist.

Since the debut of Alchemist’s first class in January 2013, 14 Alchemist companies have been acquired (including Cisco’s acquisition of Assemblage and Dropbox’s acquisition of Mobilespan). More than 50 percent of its graduates have gone on to raise significant seed or institutional funding rounds. The average raise of these companies is $2.6 million. Many of these are from the top venture capital firms in the valley, including Andreessen Horowitz, Bessemer Venture Partners, Draper Fisher Jurvetson, Foundation Capital, Founders Fund, Greylock Ventures, Menlo Ventures, Redpoint Ventures, Social + Capital Partnership and True Ventures. The complete list is provided here.

“At Juniper Networks, we believe that venture investment is an integral part of our innovation engine. Alchemist fills a gap in our portfolio strategy, acting as a vehicle to invest in seed-stage companies, a stage we are eager to participate in,” said Rita Waite, Investment Manager at Juniper Networks. “We are thrilled to be joining Alchemist Accelerator as a backer and look forward to working with Alchemist start-ups and its network.”

Today, Alchemist held its 15th Demo Day at Juniper Networks in Sunnyvale in conjunction with the announcement. They were joined by more than 200 customers, partners and investors. The event debuted 18 companies.

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Learn More

Anyone interested in getting involved as a mentor, investor or customer or members of the press, should fill out this form: https://vault.alchemistaccelerator.com/register-profile.

For more information on the accelerator, please visit http://www.alchemistaccelerator.com/.

About Alchemist Accelerator
The Alchemist Accelerator is a new venture-backed initiative focused on accelerating the development of seed-stage ventures that monetize from enterprises (not consumers). The accelerator’s primary screening criteria is on teams, with primacy placed on having distinctive technical co-founders. The accelerator seeds around 60 enterprise-monetizing ventures / year. Over 50% close institutional rounds within 12 months of their Alchemist Demo Day[LM1] . 


 [LM1]I removed our boiler plate and media contacts. This is a third party release distributed by Alchemist without our ticker or a classified joint release.