theCUBE + NYSE Wired: Physical AI & Robotics Leaders | AI Unicorn Hunters

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More from theCUBE + NYSE Wired: Physical AI & Robotics Leaders

Aileen Lee

Founder & Managing Partner

Cowboy Ventures

Lake Dai

Founder, Managing Partner

Sancus Ventures

Sue Xu

Managing Partner

Amino Capital

play_circle_outline Overview of the major AI and robotics event in Palo Alto featuring industry leaders.

play_circle_outline Discussion on AI infrastructure and venture investment dynamics in the current market climate.

play_circle_outline Lake Dai highlights the influx of young founders and AI talent from academic institutions.

play_circle_outline Empowering Young Founders: How Generative AI and Shifting Mindsets Disrupt Traditional Industries and Revolutionize Entrepreneurship

play_circle_outline Insights into changing investment strategies and challenges of high seed round valuations.

play_circle_outline Discussion on the opportunities for startups amidst competition from larger established companies.

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AI Unicorn Hunters

Aileen Lee

Founder & Managing Partner Cowboy Ventures

Lake Dai

Founder, Managing Partner Sancus Ventures

Sue Xu

Managing Partner Amino Capital

Aileen Lee, founder & managing partner at Cowboy Capital LLC dba Cowboy Ventures; Lake Dai, founder and managing partner at Sancus Ventures; and Sue Xu, managing partner at Amino Capital, join theCUBE’s John Furrier at theCUBE + NYSE Wired: Robotics & AI Infrastructure Leaders 2025 event. The trio examines the seismic impact of AI on venture capital and how emerging tech trends are shaping the next wave of startups.

The discussion covers investor strategies for identifying breakout companies in a high-speed, infrastructure-driven AI landscape. From f... Read more

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>> Welcome back everyone to theCUBE's coverage here. For three days, robotics and AI leaders all coming together for three days here in our Palo Alto Cube Studios. Of course, we've got a big event tonight with NYC Wired community, theCUBE plus NYC, bringing all the leaders together halfway through the year. Lots happened in AI this year and we've got a great power panel of investors who are going to break down what's going on in the market, where the money's being deployed, and where the values created and extracted. Got a great guest, Aileen Lees, founder and Managing Partner at Cowboy Ventures, famous for quoting unicorn, decacorn and really identifying the wave of startups as it came onto the market. Aileen, thanks for coming on. Appreciate it.

Aileen Lee

>> Happy to be here.

>> We have Lake Dai, who's the founder and Managing Partner at Sancus Ventures, and Sue Xu, Managing Partner, Amino Capital, CUBE alumni.

Sue Xu

>> Thanks for having me.

>> All right, let's get into, as venture investors. The world has changed. We're seeing a once in a generation shift with AI infrastructure. I just interviewed the head of Amazon Web Services who runs all the CapEx. They just dropped 30 billion, I think 50 billion. They added another 20 billion to the mix. Large scale data centers. You're seeing enterprise AI booming. A lot of the data's locked in databases, that unlocking's happening. The young startups are coming in. Seasoned systems people are coming out of the woodwork. So you have the old school operating systems, systems thinkers, as well as the young guns. So the market's changed. So Aileen talk about the dynamics because writing checks now. We're hearing more and more it's difficult to identify that next big company.

Aileen Lee

>> It is. It's really exciting. We are, I think probably in the first or second inning of a massive platform shift, but for those of us who've been around for a couple cycles, you also know that it's not a one-year shift, right? It's kind of a five to 10 year shift. And if you look at kind of dot com or mobile, for example, right? There are many early winners who did not wind up being the long-term winners. And so it's really important to be in the market, to be spending a ton of time with founders, to be following as the technology's evolving, but also you have to be quite picky because it's a gold rush and so things are, and there's a lot of money chasing momentum. So it's challenging, but keeps us on our toes.

>> Lake, we were talking before you came on that also you're also at Carnegie Mellon as well. I've forgot to mention that you see a lot of talent coming in. You mentioned the comment about younger founders.

Lake Dai

>> Yeah.

>> You and Aileen were talking about kind the season playbook and then also now high schoolers are looking at doing, and it's also the talk we hear all. I think solo founders will happen, but I don't think it's going to be as prominent as people think, but the ability to create value is so fast. You could be in high school and have a unicorn.

Lake Dai

>> I think it's a very exciting time. So actually to start with, so my founder, Sanctus Ventures, in the past, we invest a lot of experienced founders. Actually over 70% of founders build companies and we have over 20% of founders have built unicorns. So the founder profile I used to look at are actually more the experienced ones. But what's interesting is now not only the infrastructure building has really enabled a lot of new founders to build a company at much lower cost, much higher speed, and faster to revenue. But also I think because the new tools and gen AI highly change our lives, the young founders have a blank canvas so they can think very differently. So when we look in the future of AI applications, there are two schools of, I would say potential winners. One the one deeply understand industry, but then the other type I think is well because there is no baggage, they can think really fresh. So yeah, if we think about Uber, if we just size the market as a tech market, you never think about how you're going to size the market for Uber. And same as Airbnb.

>> They don't have the dogma.

Aileen Lee

>> I won't be surprised. I mean, actually one thing that I think a lot of us are waiting for is AI native consumer companies. We haven't, I mean obviously ChatGPT and Open AI is largely a consumer company, but oftentimes when you have these platform shifts, and I think younger founders who don't have all that kind of knowledge of legacy are going to be incredibly well positioned to build some really exciting new consumer products.

Lake Dai

>> Yeah.

>> It's interesting. That's a great point. I was riffing on my podcast a couple of episodes ago around today, the hot areas enterprise, and if you look at all the young guns coming in, they're doing enterprise startups, and we see that in New York. I asked one of them, "Hey, you worked at Palantir, Facebook, and what are you working on?" "I'm working on this data pipeline." I'm like, "What? Well, who's your customer?" Well, it's like 20 customers in one block radius from here. They're into the enterprise, right?

Lake Dai

>> Yes.

>> It's like, okay. But then it's like, "Okay, what's the consumer hit? Who's going to reinvent a user experience at scale?"

Aileen Lee

>> Yes, I think it's going to come. It's going to happen for sure, and it's probably going to be college students and high schoolers.

Sue Xu

>> So I think there is a reason. If you look at, so take YC Demo Day as example. Out of 140 something company last bench, there's only six of them for consumer and majority of them are actually enterprise. And then I think it makes sense because oftentimes you actually have SAP and then there's $50 million contract and you have to hire Accenture, and that's another two million. So it's really hard for SMB to take advantage of ERP or even CRM. But today with MCP and a lot of agents, and you can actually just spend $5,000 and you can use different agents because for ERP, every stakeholder can actually jump in and then to actually disrupt this workflow. So it is not like Zoom. So called I think in 2022, everybody says, "If this company is not PLG SaaS, I'm not going to invest, right?" But right now, ERP, it's kind of like PLG SaaS, so it's just $5,000. And even just small startups or even small groceries, they're able to use ERP. It's amazing.

>> It's interesting. I was at a venture event, I won't name the name, but they were saying to the founders, young founders, "Go out there and replace Salesforce." Now I was the position of Salesforce is hard to get rid of because they're nested in there, but that's the mindset of the younger generation. Why do we need Salesforce? I can just build that. And you're starting to see that kind of mindset creeping in. This generational shift because their value is, I don't need the newly old playbook of the picks and shovels. I don't need to sell software to taxi companies. I'll just be Uber. So why would I do that? Or if I can change the user experience. So the question to you guys is, as you look at startups, we're now getting into the business transformation conversation, not digital transformation, which is kind of a tech thing. Yeah, go big or go home. That's still in play. I would agree with that. And so I want to ask you guys, what is that view? Because there is business value that you can serve a transformation project at an enterprise or an existing company or full transformation for our lifestyle. So these are the kinds of things. What's your reaction to that?

Lake Dai

>> Oh, it's about time to change. Because there are so many different industry where we, for example, like the buy and sell house agency process where we can talk about even experiencing DMV. Those are perfect enterprise use cases exist today, but they're up to complete new change.

>> So you see the opportunity there.

Lake Dai

>> We see the opportunity, and it's interesting, many are coming from young kids too. Not saying they are going to be the perfect founder to execute it, but the way they think about things are very different. Ask a different question. Why can't be this way or that way? So for example, I had three-hour conversations with a high schooler this weekend, so he was talking about how terrible the experience is for college application. So college application again. So it's so much information, very different choices, very difficult for parents and kids to collaborate. And most of all, all the career directions are changing. We don't know what we're going to be working on. The kids don't know what the career path will be. So it's a massive headache for everybody. And then for someone actually deeply understand the process, and then they actually come out with completely new ideas and they can execute it.

>> Are the check sizes changing on the earlier stages? I mean, some say it's cheaper to start a startup, do a startup, but now you're seeing seed rounds at a hundred million.

Lake Dai

>> Yeah.

>> So what is going on here?

Aileen Lee

>> It's both. You've got, you can do so much because you've got access to the models. And a lot of the AI companies, because of the amount of money that's gone into them, they're basically letting us use their products at negative margin for them. And so you can build something. I mean, there's a lot of talk about how many unicorns there'll be in the future that are just 10 people unicorns or five people unicorns. I think on the one hand you've got that, but because there aren't a lot of barriers to entry and that there's so much potential competitive risk, you do want to build a bit of a war chest so that you can run fast, you can recruit great team members.

>> So I want to ask you guys a question because there's an old school saying in the venture world of there's a pony in there somewhere, or that's a feature, not a company. And now in the world where one feature could be the company. So thoughts on that. This is now you mentioned the ability to start a company with 10 people could have been a hundred a decade ago or five years ago. Now the go-to markets are changed, but you still have to go to market. You still have to sell it.

Lake Dai

>> Yes.

Aileen Lee

>> Of course.

>> Thoughts on there's a feature, company, dynamic or there's a pony in there. There's a lot of startups out right now in AI, hundreds and hundreds of new companies being formed this year alone. Thoughts on survival rate slash is there a pony in there somewhere?

Sue Xu

>> Yeah, that's why as VC, we started 2013 and then, so back then Meta has this rule, DDD data, driven, decision making. So that's why back then we were thinking we have to build our own tool. So as you mentioned, there is a lot of startup, they have very high valuation and then they raise maybe like two million, three million, but they only have 18 months. After 18 months they have to produce something, right? But what if they launch the product? Nobody cares. But there is also breakouts. They already have one million dollar ARR, five million dollar ARR, but nobody knows about them. So I think it's perfect for actually investors to build our own system to track the performance. So out of our investments, we actually build this so-called arm system, we can track them. So we are already looking at hundreds of companies and then that we invested and then there were 20 became unicorn. There is like 40 valuation between 100,000 to one billion. There is already there is also 60 of them.

>> In valuation or ...

Sue Xu

>> I mean in valuation, there are 60 of them we're tracking. They haven't raised their series A, but their ARR is already over one million, but nobody else knows about them. So that's why we want to invest in YC Demo Day, post-YC Demo Day. We really wanted to actually have their data. Yeah.

>> Ron Conway used to have a strategy spray and pray.

Aileen Lee

>> It was very effective at the time.

>> Yeah, I mean optionality wise why has produced a lot of good outcomes. Yeah.

Sue Xu

>> Exactly.

Lake Dai

>> We like to invest in company before they went to YC. That being said, I think there's interesting, what Sue just said is tracking the revenue, tracking the progress is really important. So I think today talk about the multiple some valuation, but we watch really closely where their revenue is going. So for us, the company we are having invested, we see over a hundred X of revenue increase in just two years. So what we talk about before is how much money going in the company, the speed to revenue is completely changing today. Yeah.

>> So what are you guys' investment thesis? Because one of the things we're tracking with this event here, Robotics and AI leaders, is looking at the structural change. Because if you look at what said, you mentioned Meta, one of the things we've been saying. A little bit of a haymaker a little bit over the top is that the rich get richer and the poor become rich. The middle kind of might get squeezed. You can punch above your pay grade with AI if you're a fast-growing company. But if you're a big company, you get richer because you have more compute, you have more GPU's. So are we looking at a structural change where the rich gets so big where they control the GPU's, which controls the AI? Do they foreclose the upside to the startups and then startups, they're not going to sit around and taking it. They're not going to sit there and say, I'm going to let the big, I won't say proprietary, but back in the old days, if you were IBM, you had a proprietary system. The startups just would eat in their lunch left and right? So the question is what's your investment thesis, each of you, and how do you see some of the key structural changes that you're watching? I mean, that might flip the bid a little bit one way or the other.

Lake Dai

>> Yeah, for us, oh, go ahead.

Aileen Lee

>> No, go ahead. Go ahead.

Lake Dai

>> Oh, okay. So I think for us, because in the last three years we foresee there's going to be a gold rush to building AI. So we believe in pick and shovels. So the last three years we invest heavily in the software infrastructure to support that data infrastructure, compute infrastructure, orchestration. And then from there, I think the whole wave of development is now moving towards from building infrastructure, more of now enabling AI builders. But what we're looking for in addition to strengthen our leadership in AI infrastructure investment is looking to what are the new AI native apps can make generational change. As Aileen pointed out, it's difficult because some are able to create value, but very few can capture the value for long term.

Aileen Lee

>> I mean, I think the big companies like Microsoft, Meta, they've got incredible talent, incredible resources, low cost to capital, incredible access to GPU's. But historically big companies, they've got people who are not going to be AI native, they're learning it. But I do think small companies who have a lot more at stake, they just historically are much better at seizing new opportunities and executing on very specific things versus a company that's got a very large revenue base, it's got a lot of existing customers, you've got a lot of legacy products that you may not want to disintermediate so quickly. And so I think it's wide open because if you think about security, you think about all the different things that are going to come up in terms of infrastructure needs as this ecosystem continues to proliferate. There is so much open ocean for startups to really, I think crush it when it comes to competing with big companies.

Sue Xu

>> Totally. I feel like a second-rate culture has always been here. It never changed actually. So taking Meta as example, scale.ai was a startup before, so in the very beginning it was a YC company, so it's not Meta and Meta spent 143 billion, right?

Aileen Lee

>> Yeah, right.

Sue Xu

>> 14.3, 14.3 billion to actually ...

>> By the way, that's a good argument for what Eileen was saying, Meta needs scale.ai's founder because they're struggling internally with their models.

Sue Xu

>> Exactly.

>> So opportunities to come in and re-change the landscape because they look big on paper.

Sue Xu

>> So I think second-rate culture is still the same. So Google became AI first because of the acquisition of Android and became now is AI first because acquisition of DeepMind. So I think for me, we felt like what's the investment thing for us from day one has always been data.

>> Speed matters too. Right now, speed game, Aileen, it's a whole other dimension. The pace of play on all fronts. Capital markets, startup, entrepreneurship.

Aileen Lee

>> That's one of the interesting, I'd be curious to see your take, and I think this has always been the case, but this quality and velocity of execution is critical. And so when you're meeting with these startup teams, you really want to understand what's their mentality around velocity and quality because there's so much new information, there's new models, there's new infrastructure, there's new ways to do things, and you need to back teams that are going to be constantly synthesizing that information and iterating on what they do at a very rapid pace. You can't ship once a quarter or once a month even. It's not fast enough anymore.

>> I think that's a great point. In fact, last week at the Databricks AI Summit, one of the things that came out of that was across all generations was the speed. That the quality and the evaluation of models at the picks and shovels levels are coming super fast because the agents are disruptively enabling the value, not just search and doing retrieval augmentation generation, vector embeds. That's great, but the value's going to come in for where the top line is. So that's a validation of that. So I have to ask you guys, valuation comes up a lot, not just startup valuation. I was talking to a customer. They said, "How do we value the project? Is it payback, discounted cash flows, net present value? Is it TCO?" So the old school mechanisms of valuation come in to play both on the customer side and the venture side. So since you guys see both sides, how do you value a hundred million dollar GPU purchase at a seed level or I'm making that up.

Aileen Lee

>> Yeah, those things don't go together generally.

>> But I mean that could be a future where it's like, "Hey, I'm going to go all in." And you'd have to justify that investment.

Sue Xu

>> I feel like for Amino, we felt because our investment same has been data. So data as mode and network effect as growth hacking strategy. So for us, we felt like the growth stage investors, most of them, majority of them, they haven't actually changed their criteria. It's still 200% year-over-year growth. So actually, so coming back to the early stage, if this company is overvalued and then 18 months later they don't actually have any revenue, and then I think the growth stage investor, they normally will not pick this up even though the demo is magical. So to us it's very easy. Whether this company can collect data, meaning that whether this company can have some real revenue rather than vibe revenue.

Aileen Lee

>> A vibe revenue.

>> Vibe revenue. That was 2024, zero. Vibe is moving to engineering. And that was the conversation I had with Jonathan Frankel at Databricks, Chief Scientist from Mosaic. He and I were talking about that the vibe coding was great for the front end, but the engineering, getting back to engineering solutions that a lot of the AI's about this year is about going into next year, what are you going to engineer? And he used that word intentionally.

Lake Dai

>> Yeah, interesting. So vibe coding actually is pretty close to my field as a professor at Carnegie Mellon. At CMU I teach massive engineering programs and in the past the students will be snapped away before graduation. But now I'm seeing more and more students are struggling to get internship because we don't need junior developers anymore. So there's a whole argument is about is vibe coding has completely replaced junior engineers. Is vibe coding continue to get smarter, then go into the field intentional engineering later. I think that's inevitable trend.

>> Vibe coding is when we used to go to the computer lab and hang out and socialize. It's a fun thing to do. And it creates propensity though. I mean this is where I love vibe coding as that translates into ventures, how do you guys squint through that and saying, "Okay, natural talent, we're seeing some action there." Is it ...

Aileen Lee

>> It is really amazing. So there's a application that we're thinking about buying at Cowboy Ventures to help us model and manage our portfolio data and it's like a 30, 40K annual ACV kind of thing, but it's not a need to have. It's a nice to have. And so we said, "Could we vibe code it?" So we basically went into ChatGPT and said, "Write us prompts that we would put into Vercel or Cursor to build this application. Here are what the screens are going to look like. Here's the kind of data we're going to put in. Here are the logins, the authorizations, the level of permissions that we want to have." And then we cut and pasted the prompts into Vercel. We had an application in about an hour, and so basically cost us zero, maybe cost us 40 bucks versus $40,000. I think that's kind of what's at stake for the software business in a lot of ways where your software really has to add defensible value, you have to at least in the application layer, but also in the infrastructure layer. You need to understand your buyer. You need to understand their pain points. You need to understand how they're going to be re-evaluating the ROI and the re-up every year. They really have to be delivering true value because right now we're in this, I think the folks at Altimeter coined this term ERR, experimental run rate revenue. A lot of AI companies right now, whether they're in infrastructure-

>> One year contract.

Aileen Lee

>> Yeah. Whether they're infrastructure, application, application layer, it's experimental. Everybody wants to try even in the most conservative corners of your enterprise, like accounting, they want to try AI products. They've all gotten the memo from the C-suite. We have to be embracing AI. They've read the Duolingo and the Shopify memos, what are we doing? So you're having a ton of startups getting four to 10 logos from very interesting customers that are all going to be up for grabs next year and re-evaluated, "Should I stay with this thing that I tried or should I run to a new shining object?" And so I think for a lot of the early stage companies, it's really critical that they're delivering that kind of value that's defensible.

>> When you guys see companies do that, I think that's a great entrepreneurial thing. A lot of them don't have the experience to understand how to manage the customer and go to market. Back in the old days, you'd have to hire a seasoned pro to come in, help with the go-to-market, the classic hire a Salesforce. Now it's like, okay, you got Beachhead. Do you understand what that means? Do the young companies understand that they're sitting on a gold mine?

Lake Dai

>> Yeah.

>> I mean that's because they're like, "Okay, we've got a customer, let's get 10 more." Do they lose sight of that or they actually have a intentional mechanism to extract and do the product work because the smart founders are going to the customers, they are getting them and then doing the product work with the customer and working backwards.

Sue Xu

>> So taking Rapid as an example, which we founded after YC Demo Day and it was a great company. And then I think it's totally the beauty of a lot of giant companies that they are a little bit PLG. So like Rapid, you can just onboard and then without any kind of like in-person selling. So that's the beauty of founders. The founder actually comes from tech giants, that kind of training actually equipped them with PLG SaaS. So the founder of Rapid , so he used to work at the Meta. So definitely have a little bit experience. Definitely help.

>> Go ahead.

Lake Dai

>> I think how we define enterprise customers are also changing as well. So at the end of the day, can the young founder speak the language of customers? Really depends who the customers are. So I think for example, the young gen I've seen so far talk about the high schoolers and college students.

>> Young guns. .

Lake Dai

>> High schoolers do enterprise sales. They can actually, they're already sold.

>> Just go to ChatGPT.

Lake Dai

>> Yeah, they're already selling.

>> sales rep.

Lake Dai

>> The question is whether the parents allow them to actually drop off of college. That's actually due diligence question. But it was interesting is they deeply understand the problem, but what they're selling to today, also, there's a prosumer approach as well. So you can actually get market traction, have consumer to use it, and then for example, when enough students are actually using it, 50,000, 80,000 and you go to college, it's a much easier conversation.

>> My final question for you guys is disruptive enablement always is a discussion in these big waves. You see either a protocol or a shift. What would you see as a disruptive enablement? You mentioned MCP and is a big surprise this year is great. Organically, community-grown ecosystem development. That's kind of like ...

Sue Xu

>> Open source.

Lake Dai

>> Yeah.

>> It's kind of like a consensus-

Lake Dai

>> Open source.

>> Let's just get behind this. We all salute the flag, we can do more.

Lake Dai

>> Correct.

>> Tooling is getting much easier to build and we're going beyond API's. Is there a disruptive enablement that you see that's kind of creating gravity around the AI stack? I mean, platform engineering that's Kubernetes is great. We cover that. That's come out of the organic community as well. Is there something that you see around this enablement that will create a step function trajectory change in the market for value creation?

Sue Xu

>> I think there's a key thing when I see an infrastructure layer is that we are going to continue to see the bottleneck compute. So compute demand and consumption has went up over a 100X in the last three years. And that's just because large language model. So we're talking about modality, we talk about physical AI. So we're going to see is a continued pressure on how much compute that we need, how efficient we can to scale the AI applications, but also how we can be energy efficient because we are hitting the bottleneck of energy. So I think what we have seen is some tons of research and experiments so far in those completely new foundational infrastructure will be quite interesting to watch.

>> Cool.

Aileen Lee

>> No, I think just the pace of evolution in the ecosystem. MCP is a really big one. I mean DeepSeek is a big one. There will be more and more models and more and more models tuned for specific, whether it's pharmaceuticals, for physics, for geospatial, and there will also be edge-based models. So you'll be able to have an on-prem model, a smaller model on your device if you don't have access to great wifi or if you have data that needs to be sequestered. I think kind of in a way how we don't think about how the cell phone networks switch us from tower to tower, like the call just works. Probably three to five years from now, the infrastructure will be switching you between whether it's cloud service providers, models, resources. So the efficiency of the cost and the leverage of the resources is going to be incredible and we won't even notice it.

>> Awesome.

Sue Xu

>> For us, we're saying the real data flywheel, all this. It is beautiful that the ChatGPT already OpenAI already makes over 10 billion AR just by all those subscription models. They haven't even unlocked their so-called iOS app store moment yet. So it's already making so much money, it actually helped. So who is actually using those tokens? Startups. Startups in all this. There are two already very defined, making totally real money. One is Cursor, Rapid, that kind of a code generation. And then second is marketing, martech. I went to this market conferences. Everybody's selling that martech, marketing people, they're losing their jobs. So it's got replaced, but in the meantime there are new jobs for them. A lot of them, they actually have their own YouTube channels, all those . It's amazing. They can work with nobody, work for themselves than they're making two million dollar a year.

>> They could be doing seed investments too. They'd creep into the structure.

Sue Xu

>> And the laboratories, pharmaceutical company, they're using this online agents to help them writing patents and writing papers. It's just all this numerous applications there. So with MCP one person unicorn, right?

>> What do you call that person? .

Aileen Lee

>> One person.

>> We need a name for that.

Sue Xu

>> One person hundred agent.

Aileen Lee

>> I guess it really is a uni-unicorn.

Sue Xu

>> Thousands of agents.

>> It's a uni-unicorn.

Lake Dai

>> Uni-unicorn.

>> Uni .

Lake Dai

>> Management skill sets is actually the key. So we have this youth program as well for all this Stanford students we're teaching, so we manage, we help them rather than coding. We help them how to actually manage MCP agents.

>> Yeah, I mean the entrepreneurial action's hot, the structural change is coming. We've kind of seen this movie before, but it looks different. The ending will be value creation. Value extraction. Great insight. Thank you so much.

Aileen Lee

>> Yeah, hopefully it's not going to be like Wall-E where we're just floating around on lounge chairs when AI's are doing all the work.

>> I already see hosts that could be replaced too. I mean my job's hopefully won't, is at risk. Thanks so much for sharing the insights.

Aileen Lee

>> Thank you.

Sue Xu

>> Thank you.

>> The investor perspective as investments in the hot startups, it's coming down the pipe. You're seeing more and more action. The startups are out there. There's hundreds and hundreds of new startups being formed every year and of course we're trying to track that on Silicon angle on theCUBE. I'm John Furrier, thanks for watching.