A while ago I was invited to join a podcast called Geeks of the Valley where we chatted about my background, the framework of AI automation investment, what makes a great VC from the good ones, and some other things I’ve been thinking about.
Here is the audio transcript generated by AI (surprise!) - I edited slightly for clarity. The audio is about 40 min long but the discussion on AI starts at 8 min so you can skip right into it if you want.
Cheers,
Jay
Kunal Thakur: Hi, my name is Kunal, and welcome to the geeks of the valley podcast, which connects with some of the brightest minds globally, who are leading their respective industries today to discuss the hottest upcoming industry trends and how their work is affecting the global economy. This morning from the San Francisco Bay Area, we have a very special guest joining us, the founder of pure land, and the health Nast fund. Please welcome the current partner of TCL ventures. Joe. Jay, thank you so much for joining us today.
Jay Zhao: Thanks for having me.
And how are things with you in light of COVID?
things being good, like most people, we just live on Zoom nowadays and hopping on different calls. It’s definitely taking longer than everybody expected on but we just trying to make the best out of it. So things are going good.
So let's jump into the first question here, shall we? Yeah. Tell me about yourself and your background, and how they did the path of becoming a CVC. For cross border China US fund?
Yeah, um, so it's interesting, because I get asked this question quite a lot, which is how do you become a VC, I've been fortunate to be to be a career VC over the past 10 years in, in tech investment at two different venture funds in the valley. One is at granite ventures. And the other one is Walden, both are very well-established fund in the US with presence in other parts of the world like China, like Israel. Over there, I kind of just learn and really step up to partner with founders, with companies like Anaplan, Marqeta and HireVue. Some of those are more well will recognized in the enterprise space.
So these two-fund experience really taught me two things; one is how to be a true partner for early stage companies. And two is how to identify and be analytical about the big market trends, which apply to some of the things that we're doing at T Fund nowadays. So it is kind of like not by plan in a way, but definitely enjoyed every moment spent those different funds.
What a background Jay, and as the current managing partner of T fund, aka TCL ventures, what is the fun size, investment philosophy and ticket size?
So T fund was set up by TCL, which is a consumer electronics company in China. So about two years ago, TCL was really thinking about how they can establish and expand the global investment practice in a more systematic way. So at that time, I was coming out of Walden, and I was actually thinking about starting my own fund. So we kind of just clicked, because we share the same passion for investment focus, we like some of the philosophy (idea) in terms of helping entrepreneurs by partnering them with, with corporate resources, and really to help these companies succeed. And we can talk a little more about that, like, how do you balance the CVC program and the startups. So the fund is a 100 million dollar fund, and we really set out to invest in AI, Enterprise, and Deep tech companies. So for each investment that we do, we typically write a check size between $1million to $5 million. And that's, there's a sweet spot that we target. And we don't just limit ourselves in the US. We also invest outside of the US, for example, in Israel, and also in China. So T Fund has been really great platform for us and for me personally because it really provides a global perspective in seeing different innovations happening at different ecosystems. So yeah, so that's kind of like the backdrop of the T fund.
Right, and to dive a bit deeper into this, Jay, what are some of the verticals the fund really focuses around? What is it that you're proud of? With this fund?
Yeah, so it's pretty much continuity of my previous investment thesis. So when I was at granite, and at Walden, one of the big focuses is on enterprise software. So it's very interesting to see how, how enterprise software has specifically evolved with different underlying technologies, right? So if you look at the history of technology evolution: we’re seeing from the 1970s 1980s, we’ve come from IT infrastructure, that's kind of where the enterprise companies most got built on, and then comes cloud, and then mobile, and now we're seeing this new emerging trend of AI automation. So the enterprise workflow, or enterprise productivity software is one of the major things that we spend a lot of time in.
So break that down, what does it mean?
That means how do you generate more leads and more sales lead for modern enterprise? And how do you cut costs in terms of unnecessary spending or expenditures within enterprise operation? And how do you make better decisions, but with better business analytics. So from the time at granite, the early enterprise software company called Siebel system was one of the more well-recognized names back in the 1990s. And then there came Salesforce, and then now we have Slack. And now we have Zoom. So each generation of enterprise software companies evolved with underlying technologies. So to answer your question, that's one of the big areas that we spend a lot of time on. And it's also one of the areas that we think we can be helpful to startups because we can pair them with a corporate TCL in terms of synergy, in terms of getting the startup to become a pilot partner to the enterprise, to the big companies.
And then there was a, there are other two verticals that we still spend time on as well. One is smart manufacturing or industrial applications. So that evolved a lot of, again, a lot of AI and a lot of computer vision as underlying tech. But more importantly, when it manifests itself into voting vertical applications, then we'll look at robotic companies, we'll look at Warehouse Solutions, we'll look at supply chain logistics solutions: how does the product really benefit modern companies, in terms of saving costs, in terms of increasing productivity. The only difference is that the second category such as manufacturing will have tighter integration with hardware with physical product form, versus the pure software form.
And the third part is digital media. Because TCL has a lot of screens including mobile phones, and TVs and all that. So we also look at a lot of digital media companies that can be loaded into those, those screens. So in that, we provide additional distributions to start up into companies. And, you probably noticed the news right lately, in the digital media space, there is AI impact on this space as well. I mean, the biggest example, in the high-profile example is by Bytedance. So, they really disrupt the whole media space by using AI algorithm to increase the efficiency, matching people and information, which is super interesting. And will obviously we look for companies in that category, with a similar characteristic, matching to the AI automation theme as well.
Jay, that's a very good point that you bring up in regards to now jumping deeply into this thesis you wrote on AI automation, and how it could be the new industrial revolution or how it's already happening, right? You spoke about in your thesis, how AI automation can materialize itself into multiple forms of products, right? new product category that you mentioned, scalability, and efficiency and efficacy. Can you speak a bit more to this?
So I recently wrote an essay, a post about ai automation. I think it's interesting, because as an investor, we in a way, kind of like historians because you really have to study the past to understand the present and the future. So if we look at the past, right, there are a few major tech breakthroughs that happen, right? One of them is the Industrial Revolution, where humans know how to how to use the steam engines, and convert one form of energy into productivity. So right now, with what we're experiencing, I mean, that's, we've experienced the different megatrends before in the tech space. we talk about the internet, mobile cloud. And now I think, looking over for the next 10 years, what's going to happen, what's the major theme that's going to take place and drive innovation. And I think the conclusion is AI automation.
AI is not a new term, that means something been around since the 1960s. But a few things that happened over the past five to 10 years that really contribute to the tipping point of where we're standing now. The cloud, the drastic low cost of a cloud computing, and the abundance of data and also the sensors and algorithms that make the AI automation or AI accuracies is much more, much more accurate at the production level that can be applied to commercial use. So, in a way that we try to understand… this investment trend or investment theme, we tend to draw a different framework to help us better understand how a new company can fit into those frameworks, or how new opportunities can emerge.
So, from the tech side, or enabling tech side, it really breaks down to three categories, as you said, one is what we call the new innovation or the new product. So these are - some examples are - self-driving cars, self-driving trucks, autonomous vehicles, or things that you've seen in the news, right? A voice AI, these are things that that was not possible without the breakthrough of AI automation. And we're seeing it and we're funding it. And we're hoping that when you come to the mainstream level, it will create a tremendous amount of opportunity, we're talking about billions and even trillions of opportunities. So that's one category, that's fairly exciting;
And the second category is scalability. So examples will be like chatbots, or virtual assistants, or human facial recognition. These are the software tools that existed before, as programmers were able to hard code, or teach computers tell computers what to do when they face or when they recognize certain things. But computer is never, never able to get to a spontaneous level, to teach themselves, or use one rule and apply to others to, to the scale to really scale it up. So now with machine learning, you're able to have a commercialized product that can be sold to different enterprise customers, and generate value in that category. So that's a scalability pitch or scalability selling points.
Now, the last part is the AI automation will do or enable new software will do is to increase efficiency and efficacy. So this is the part I think is very exciting, because it's going to have the major impact in different software players or existing software companies, because we're at the very, very beginning of it. Before we talk about different evolution and evolution paths of enterprise software, from Siebel system to Salesforce to slack, to zoom. And nowadays, we see major funding in the RPA, RPA space, like automation anywhere and UiPath. But these companies started, not like last year, not two years ago, but roughly 10 years ago. So they accumulate a lot of data, they'll come in a lot of customer base, but it came it took about 10 years for them to get to the brick, sort of get tipping point to where that now is where you can use RPA and inject that into enterprise workflow, so that the whole enterprise productivity will increase five-fold and ten-fold. And that's super powerful. But the bullish side of me looking at this from an investment angle is that we're just at the beginning of it, because there are so many different pain points and different conjunction of enterprise workflow can be increased in such as leadgen such as CRM, such as customer support, and such as financial planning, like what's the next next version on next AI first version of Anaplan? Or what's the next first version of Salesforce? Um, I think we don't have the answer yet. But it's, if anything, if any disruption could happen, that took away the value from the incumbents like Salesforce, like even Microsoft, then it will come from this new trend, will come from AI automation.
Now, from these three major categories, we kind of break it down into different verticals. we look at, obviously, manufacturing, that's one area is going to have a profound impact. And also look at FinTech and payment, and also IoT. We'll call that AI IoT or AI times IoT. Smart speakers kind of fall into that category. But anyway, so those that's kind of like the lens or the framework that we use when we think about AI automation companies.
Right and what I want to do Jays I want to take a deep dive and deeper dive here. into the Gartner hype cycle right and for listeners out there quickly, the Gartner hype cycle is a pattern that arises. With each new technology innovation. It's somewhat of a graphical depiction. And right with this Gartner's hype cycle, you have right Jade, the five phases, right, the innovation trigger a peak of inflated expectations, and the trough of this illusion meant the slope of enlightenment and the plateau of productivity with artificial intelligence. What are some of the, the unique expectations you're kind of seeing throughout those five stages?
Yeah, Gartner's hype cycle is a really useful framework to think about different life stage of technology development. And the other framework, I will speak quickly, which is very similar to the hype cycle is his Perez, Perez technology revolution in the financial capital framework. So, basically, it's a book that kind of talks about how the two parts, the tech evolution, and financial capital are correlated in a way. The book talks about, which is similar to different phases, right, you're going to go through for each new technology goes through different phases, including eruption frenzy, and then turning point and passe turning point, go to synergy and maturity. So the last to represent the deployment period, that kind of a…the general mainstream market will start to accept the new technology and it was seeing more and more applications that's driven and enabled by this new technology.
So going back to Gartner's hype cycle, and interestingly, if anyone's interested in taking a deeper dive, you can read my posts, it's very interesting to comparing these two frameworks together, because they kind of talk about the same thing. And the same timeline framework for AI technologies. So over the past five, five to 10 years, I think we all agree that we have seen a lot of capital going to autonomous driving investments, a lot of sensor investments, and frankly, just anything that can be coated with AI, whether it's real or not. Um, and I think that create the present a big problem, there's a, I'll be more worried if, when I’m a investor.. seeing a big frenzy of capital that just rushed into one space. Um, you can argue that, it, it was a similar dynamic, when VR first appeared, or the first become hot after the Facebook acquisition, a lot of capital went into the space without thinking too much, how long it will take a how big the things will be or can companies survive long enough to get to that the mainstream stage or the deployment stage. Um, so, looking at the hype cycle, you can you can see, that's, that's obviously that’s Gartner's viewpoint, so they kind of putting different color coded in terms of the speed of each movement. But you can see, like, a lot of a lot of these AI technology or application that we'll talk about, like chatbots, and NLP, and edge AI, all those things, either are at the peak of the inflated expectations, or on the way to get there.
Do I think investment to AI now is a perfect timing? I do. Because we are not investing these AI companies on the way up when a drive up hype cycle curve, but actually, we're going to invest this year and over the next five years, and be patient, about how the technology moves through this types of moves through the hype cycle, because there's going to be downtime, there's going to be a time when the capital is not as passionate as excited, as in some of the categories that we're in, but that's okay. That's basically the rule of venture capital.
So anyway, so to the answer the question, I think every new technology went through this similar face on it as an investor, we are very mindful about not to chase every shiny things, but to really understand, what’s the ultimate value AI automation can create to the end users. So in this sense, we talk about the enterprise productivity, we talk about how robotics can increase the throughput of manufacturing, and the efficiency of warehouse and logistics. And those are real values. And those are real business use case that has concrete ROI. And those are the things that can get us most excited, rather than just some sort of a buzzword according to the hype cycle.
And Jay, going off on this tangent, when looking at being a US-China cross border fund, and looking at investment, is AI automation, really playing a big role in China today, and how?
Yeah, so this is something I, I would love to talk about because….
Geeks of the Valley