Founder of BEA Systems, Race Capital
AI Startups vs. Incumbents that have Abundant Data_Silicon Valley Asian Business Talk_Alfred_Chuang
Alfred Chuang, GP of Race Capital, Co-Founder/CEO of BEA Systems in conversation with Professor Roger Chen, University of San Francisco on AI Startups vs. Incumbents that have Abundant Data.
This interview is a part of the series, entitled “Silicon Valley Asian Business Talk: Conversation with U.S & Asia Business Leaders/Entrepreneurs”, sponsored by Centre for Business Studies & Innovation in Asia Pacific, University of San Francisco.
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About Alfred Chuang
Alfred Chuang, founder of BEA Systems and managing partner at Race Capital, appeared on CNBC's Squawk Box Asia on April 29, 2026, to discuss the impact of artificial intelligence on legacy software companies and the venture capital landscape. Chuang described the current AI shift as "the most profound technological shift" he has experienced in his career, stating that AI will embed a "brain" inside applications that can think and complete tasks. He said that legacy SaaS companies must either fully re-architect their software to embed AI or risk being left behind, and he pointed to Anthropic's Claude Opus 4.6 as a "watershed moment" for AI's ability to understand and replicate old code. Chuang also commented on market dynamics, stating that "tech is not an industry that can revolutionize itself without large bubbles" and that the current AI-driven bubble is "pretty big" and necessary to propel the industry forward. He noted that while some legacy SaaS companies' valuations have dropped 40 to 50 percent despite stable earnings, this creates a favorable environment for startups and predicted a wave of M&A beginning in the second half of 2026. Regarding cryptocurrency, Chuang suggested that crypto may find utility in AI for transactions between autonomous agents, where small, fast payments are needed.
Transcript (17 segments)
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Roger Chen0:00
Welcome to Silicon Valuation Business Talk. Hello everyone, I'm Roger Chen from University of San Francisco and also the Center for Business Studies and Innovation in Asia Pacific, short for USF CBSI Asia Pacific. So Alfred, would you please, first of all, I want to thank you for supporting us to join this program. And would you please briefly introduce yourself?
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Alfred Chuang0:27
Thank you, Roger. Thank you for inviting me. This is my honor and really it's a privilege to be here today. And thanks to Heer and Tha for having me speak today. I think the last time I actually spoke at a Heist event was maybe 22 years ago, which someone just found. I think I went to a dinner in Catino and I spoke at the dinner. It was very well attended, and I have extremely fond memories. It's been a long time, so it's great to be back.
I was born in Hong Kong and I graduated from University of San Francisco with a computer science degree. Then I went to UC Davis for my graduate studies and got my master's degree from them. Halfway through my PhD program, I quit and went to work for Sun. I was at Sun for just under nine years, and then I founded my own company called BEA Systems. BEA was a very successful venture. We went public in four years and pivoted the company into the web space in the late '90s, reaching a $58 billion market cap and eventually just under $2 billion in revenue. We sold to Oracle for just under $9 billion during the financial crisis.
I've done other companies and been very active in investing in early-stage companies for the past decade plus. About five years ago, I started a firm with two other partners focusing on seed-stage investment in infrastructure technology. That's what I've been very focused on in the past few years. I'm having a grand time. This is like 2000 all over again for this AI era, so I'm very thankful to be here.
AI startups versus incumbents that have abundant data. I think this is a very good point. A corporation has a very distinct way of dealing with data. I used to call it an American garage. You see people, they open up the garage, packed with stuff inside. You often wonder, for sure the car cannot get inside, but they're not receiving anything from the garage either. I think that explains how corporate data actually works. We hoarded everything for compliance reasons, for fear that the data will be useful in some way, but we hardly ever use anything from that.
I'm not saying that if you were able to take all of Salesforce's data and train it, it won't be helpful. It will be. But we also remember the error. Remember how much data Facebook started with—nothing. You just look at Facebook, started in 2008. In just the last short 16 years, Facebook has accumulated 1.9 billion users and an enormous amount of data. It's because people want to put the data in. So if there's a will, there's a way. I think if the application is so useful, people will find a way to put their records back in.
There are tons of people who will be building interfaces. By the way, that data in Salesforce, when you subscribe to it, is owned by the user, it's not owned by Salesforce. They have a distinct advantage that they could have built inferences using this. I'm sure they're doing it. But if there's a new application incumbent that's going to be providing a level of efficiency that's going to be so great, I think they can overcome the data issue. Despite my belief that data is really the most important thing at the end of the day, obviously they're in the best position to reinvent themselves. But if the productivity is increased so much, I think people will forego some of the historical data or do some retraining and relearning.
We look at the training itself. The A6 all the way up is getting cheaper by the day. The competitions are all rolling in very quickly. Intel just made a claim that their new chip is going to be three times faster than H1. I'm sure Nvidia will have a new thing that's going to be 10 times faster. That's all good news for us, because that means the training time is going to continue to be compressed. So the amount of data we can feed into the thing to train will take a shorter time. So I'm going to bet on the new invention anytime over the legacy advantage.
I tell you, I'm almost in shock the number of companies that are being created per day. I'm just looking at the amount of my top of the funnel, which is the most crucial part of the venture business: how many companies are you seeing? The only way to be able to pick the gems out of an ocean of great ideas is to see as many companies as humanly possible. Right now, it's basically infinite. I won't be able to see them all. I will say, I think the week before last, we saw 82 companies in a single week.
And this is a small firm, so I can only imagine how many companies Theo is seeing, or Andreessen is seeing, Greylock is seeing, Lightspeed is seeing, B is seeing. I think it's enormous. This is like nothing compared to anything, by the way, my entire journey in computer science that I've ever seen. Because we now can do things we didn't imagine were doable.
There may be people that are skeptical about AI and say this may be a fad and it's temporary phenomenon. We have a big bubble, the bubble will burst and go away. It is not going to go away. The clock will never be turned back. Now, the bubble may burst, right? Very similar to the dot-com bubble, it burst, but then you see survivors. I don't know, maybe some of you are not old enough. I'm certainly old enough that I have customers looking at my face saying, 'Alfred, don't waste your time because this is JP Morgan, we never ever would do business on the web.' Look what happened.
So now this time, they're not saying that. They say, 'We definitely will be using AI.' That's the thing that they'll be saying. So I think, without a doubt, this is not going to stop. But the low-hanging fruit, if you're a little startup, you're not going to say, 'I'm going to build a full CRM suite to replace this huge set of software that Salesforce has or any of the large company has.' You'll be building things where it's going to make the most impact and people will be most interested. You chip it away, but that's normally how it works. So slowly you chip it away enough, they will die. That's how mainframe software died. In the beginning, when people first had client-server software, they didn't think that was going to go away. Anyone using a mainframe and a dumb terminal is all gone.
So everything takes longer than we think, but I'm very confident this chipping is like, I'm seeing almost like a woodpecker. All I can hear in every corner is packing on that wood constantly. It's packing away pretty good. So I think this will be a very exciting time.
One other thing I didn't mention. Sometimes people ask me, 'Why do you think...' This is actually going to be one of the questions you'll be asking me. I'm going to jump ahead, Roger, for a second. Why do you think BEA was successful? I said, well, I would say actually the biggest contributor was timing. If it wasn't because we had the web era and we had sufficient foresight to look at this is going to be very impactful, and we took advantage of what we had and then rolled into it and pivoted into it, we wouldn't be the company we were. We grew revenue from 100 million to a billion in six quarters. That was not because of us. That was because there was a sucking sound in the marketplace. They wanted it.
I think the same thing is exactly happening. We want it. I'm already seeing applications. We have one company that's doing voice AI. They trained their own model called Play. It's a remarkable software. What they have done is now they can actually not just train the voice to replicate somebody in a call center, they can replicate the emotion, they can read the emotion, they can read the accent, and answer back in a language native to the person. They call it, they were looking in the vast context because it looked up the FAQ and give a proper answer back to the person. They understand what customer satisfaction is. Actually, it turned out the human didn't, that they were supposed to know, they didn't.
They inserted them into a roomful of real agents, actual call center agents, and those agents could not tell that it's not human. And you see one, two, three full replacements soon. This is all going to be replaced. What does that mean? Direct cash to the bottom line. This is EBIT that you know, change for every one of these call centers that you make it more efficient. I don't think humans need to do those jobs. Doesn't mean that those people don't need jobs, they will find other jobs. But this is how impactful this will be. So to me, it's chipping away very, very good.
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Roger Chen9:31
Okay, thank you. Thank you for watching.