Lead-Lag Live

AI Today: From Chatbots to Humanoid Robots – Investing in the Full Stack

• Michael A. Gayed, CFA

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Imagine the internet’s 20-year curve compressed into a few years: costs plummet, capabilities skyrocket, and adoption leaps from novelty to necessity. That’s where AI stands today. In this episode, we go deep into the evolution of reasoning models—how chatbots are transforming into agents that plan, code, and act across real workflows. Then, we explore the next frontier: physical AI, humanoid robots designed to operate in human spaces, where the “final mile” of automation requires hands, balance, and judgment.

We break down the full AI stack so you can invest with intent:

  • Digital AI: From semiconductors and data centers to secure data infrastructure, code generation, and multimodal systems connecting perception to action.
  • Physical AI: Humanoid ecosystems, including integrators, brains, and bodies. We explain why actuators, harmonic drives, tactile sensors, and manufacturing clusters are today’s critical picks-and-shovels plays.
  • Investment insights: U.S. leadership in intelligence and commercialization vs. Asia’s manufacturing depth, hype vs. investable opportunities, and a practical portfolio approach balancing concentrated and globally distributed AI exposure.

 #AI #ArtificialIntelligence #HumanoidRobots #MachineLearning #AgenticAI 

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SPEAKER_00:

So you can see like uh this company is already starting like a mass um production. There's some like a facial um recognition and facial like expression. Their motion control is really strong. Like you if you kick it, if you like move it, it's very stable. Uh it's like they're running my marathon. And this is another interesting like company. You can actually use an app to do like autonomous, like just like fully autonomous like retail experiments.

SPEAKER_01:

This should be a good conversation with uh Derek Jan. We're gonna be talking about uh some really interesting thematic ideas, which are getting some really big traction in today's environment. For those advisors that happen to be in a physical office location, do me a favor, tell your other fellow advisors that this webinar is taking place. Uh Crane Chair is one of the uh great issuers out there, a number of unique and innovative funds, or as Finra likes to say, distinctive, because unique can never be said from compliance perspective for whatever reason, but distinctive is okay. Uh so they've got a distinctive lineup. Um, and uh for those that are here for the CE credit, I will email you all after this webinar. Uh just stay to the end of the webinar, I'll get your information and submit it to the CFP board, and we'll go from there. So appreciate the uh support for those that keep attending these webinars. Uh, it's quite a bit of effort and work, and my clients uh like Crane Shares, obviously, are very appreciative as I am as well. So, with all that said, my name is Michael Guyad. Uh, this webinar is sponsored by Crane Shares. Uh, let's get started with Mr. Uh Derek Jan and uh talk about uh a whole bunch of interesting ideas. Go ahead, Derek.

SPEAKER_00:

Yeah, thank you, Michael. I mean, um my name is Derek Young, for people who don't know about us. Um Crane Shares, uh, we are asset manager based in New York, uh company founded in 2013, uh, and we manage about uh 13 billion right now. So let me uh share my presentation today. Um, just for I think people like been talking a lot about uh both AI and um robotic investment. So at CraneShares, we have done a lot of research um on this. Me personally, I've been working uh on with CraneShares for like 10 years, uh covering global technology companies. Um so starting from, I think like like previously a lot of investment is really on the semiconductors, on the SaaS and cloud. Uh and now like all of a sudden, like everybody's talking about the AI robotics. Um there's a lot of nuances happening, and there's a lot of like debate, like whether it's too expensive right now, like isn't more opportunity out there? What's the next big thing? So with that, like we're like we just want to have like a little conversation today, just like show you what's our research on this topic. Um just before we dive into the presentation, probably I think like um a lot of people may have experienced this um before. Like when we're like super young, like we have this like doub like connection experience, like when the internet speed was super low, right? If you want to download a picture, you're gonna wait for like five minutes. Like, as like a young boy, I was like, yeah, I'm like super impatient. Like, I like definitely want to see like pictures the full pictures by loading the half. So I'm like screaming, like smashing the keyboard. Um, but like just fast forward to the day, like the internet speed is like crazy. Like we can do anything. Um, like streaming and we can like social media, like doing this webinar, but like like the AI now just give everybody the capability to like you can generate essay, you can generate like a weekend plan, recommend the restaurants, you can now I'm like addicted into also like generating pictures like from AI. Um, but it just like I'm now still waiting for like a few minutes to to get a picture generated. It's like so funny how my life experience is like a loop. Um but like is but like showing you the reality is like the internet speed is almost went up like 100 times over the last 20 years. And in the meanwhile, the cost of data dropped 100x. So that means like if you look at like 2000s, you have probably experienced like the Yahoo time. Um the internet speed and it's more expensive to go into net to the internet, and then the applications is only available in those formats. Then fast forward to 2010, you're gonna start to see a lot of like mobile use cases as like data become cheaper, the speed speed becomes like higher than YouTube, you have like Facebook, it's booming, then we have now 5G, everybody's streaming like YouTube, um, and like social media, TikTok, like like probably hate like your kids like scrolling like TikTok every day. But like it just imagine like that really just happened over 20 years. Like how fast the internet industry has really evolved over the last 20 years and created so much new applications that's really um paving the way for the next generations of applications and adoptions of the internet. Um so that just like get like great similarity to what's happening today. Um the AI, we have seen over the last few years a very similar growth trajectory, uh, like what happened over the last 20 years on the internet. Uh, if you look at the intelligence level of the AI models, it's getting better and better um almost every month. While the cost of the output from the AI is dropping dramatically. So starting from the pre-trained models, right, you can see from 2024 to early 2025, the cost of those models really dropping to like 100x, more than 100x. The intelligence level is increasing um at like 2 to 3x. Well, then I think starting from like earlier this year, you more and more reasoning models coming out. The O Y model, deep C RY models showing you that the intelligence level is now nip-rocking to a level that's like 10x better. And the adoption is getting crazy because of the reasoning capabilities, while the cost is dropping, like what happened to the pre-trained model. So we start to see this similar things happen. I think like now going back to the internet era, the AI era is gonna follow a very similar pattern where the cost could be probably 100x cheaper, while the intelligence level could be 1,000x better. So if that's gonna happen, that's gonna drive a lot of the I think future adoptions of AI. So already we have seen a lot of apps like in the AI, like ChatGPT is probably the fast-growing app out there getting like 100 million act monthly active users within two months. We have never seen that in the internet era. We have the enterprise revenues coming from like anthropic. That is like with by like the end of 2024, their revenue is only like one billion, but just like on August this year, it's already five billion. So five X in seven months. We have never seen a company growing like that. So decommeralized commercialization of AI and the adoption of AI, the user growth, is actually outpa outpacing the internet era. So with that, we think like we have to really invest uh in a way that like you have to position your investment around the current level or the evolution of intelligence. If you think of previously, the most AI is really like chatbot, right? Like think about like the first generations of ChatGPT, GPT-4, is really understanding human language and then doing like one prompt and give you an answer. Um then starting from like this year, you're gonna see the AI become like a reasoner. You can solve problems, you can understand context, you can do some like a chain of thoughts. That's getting smarter, and that's unlock a lot of options. And also with multimodality, like the images, the videos coming out, AI, that's really like creating tons of new applications out there. So we're currently at the stage between um AI being the reasoner to AI being uh agent. I think a lot of people are talking about like may heard or may not heard of the agenda AI. Like AI agent is definitely the hottest keyword this year uh for most companies. So if you talk to enterprise owners, talk to the talk to the like technology um officers of each company, um, they're all like crazily figuring out how to really onboard the agent solution uh to the company. So, what is the urgentic AI? It's really autonomously planning and make decisions and take actions with the environment. So you actually can gather data, you can um understand your environment, you understand your workflow. So you deploy that, you can make autonomous decisions. That is like uh I think like a lot of people question about it, the AI adoption previously on the enterprise level, because oh, it's it's the end of the day, it's just like a taboa. Then now with those agenda capabilities, people start to believe this has like a real economic impact uh in a lot of enterprises. So with um, I think the current stage, a lot of applications really around AI coding, because coding is um, I think the best way to integrate it um with the AI agents, because um you probably if you try the latest models, um uh either from the GPT model or the NSA big cloud models, though the coding capabilities for those the latest models is quite amazing. It's achieving a level that's almost like a junior to mid-level software software engineer. So with those capabilities um in place, you can actually integrate it into a lot of workflow that within your organization. Um, and going from here, we're gonna see the intelligence level increase. So you can actually create knowledge, it can actually interact with the physical world, it can bring the action data uh into the training model or the inference model that can actually bring the robot to AI, right? So that's something I think is gonna happen for the next stage. As probably heard like Jensen Huang mentioned on GTC conference, that robotics bigger opportunity or the next big thing after AI. Uh, because we have seen the same framework that people are putting intelligence on the digital world, now are training the AI in the physical world. Um then after that, we're gonna see potentially AGI is gonna happen, artificial general intelligence. And the AI could potentially be an organizer for our society, for our economy, uh just automate all the workflow and organize all the decision making uh to the maximum at this effective way. So that's kind of like the roadmap for the AI revolution. Um, and we're just just started, I think, in the level two, between level two and level three. So it makes like a lot of sense. If you look like uh the workflow for enterprises, um like we have this data to showing what percentage of each industry now uh have paid subscriptions or in models, platforms, and tools. Um, so actually, it's not really surprised that the technology sector uh take a far lead compared to other industries. The technology sectors uh has like 72% of uh uh companies actually now using AI, uh followed by like finance, uh a lot of like I think both banks, insurance, and uh fintech companies. Uh and then the rest is really lagging. Uh I think manufacturing, retail, healthcare, construction, those mostly happen in the physical world. It's lagging, just lacking for a reason. As I said, the digital world is easier to be automated, to be trained using AI, because the digital world data is so available. There's so many taxes, text and images, the videos available for people to train a model, to deploy it, and train live transaction model. So most of the AI models nowadays is really focusing on the um digital world. So I think going forward, then these adoptions are gonna gradually migrate to the manufacturing side, retail, healthcare, and construction, and our service sector in the end, maybe our home. So that um give you an example. I think like um Anthropic, um, you may not heard of. Anthropic is funded by the former OpenAI um like executive. Um they have been really focused on the enterprise market um because they are pursuing more uh safe, ethical, uh controlled AI. And they're, I think one of the key reasons is their code generation capabilities very strong. Um, so the user experience from software engineer enterprises to really get work done in in a more like stable way, that's a game changer. So that's why their API is um uh very popular among like each enterprise like um agenda AI adoption. So we as I said, like their revenue jumped from like 1 billion last year to now 5 billion uh August. So um their market share of enterprise solutions is really increasing, um taking the max years from OpenAI. So we have seen like a company like that is really a critical company. Think about like the next stage of investing in AI. Um and there's a lot of nuances happening, right? So you think about like the AI opportunity. Um, so currently there's three-layer or four layer if you're including the model companies. There's hardware, I think a lot of people already focused a lot of uh their investment on the hardware still, it's like the most obvious names, like the semis and data center companies. Like um, then you have the AI infrastructure providing the cloud, um, providing the data preparation, the data monitoring, cybersecurity. Um, so that's the infrastructure for the model to be trained, to be deployed, to be inferenced. Then you have the model company themselves, like Anthropix, XCI, that is being the core intelligence uh of the whole ecosystem. Um, then upon that, then you have all kinds of applications, either to the enterprises, um, then you have those in the middle ground, like context layer provider, those just enterprise like service provider that's really helping enterprises using AI. Or you have um consumer-facing applications that's using the AI to transform their business model um uh in the in either in the healthcare sector, in the education sector. So this ecosystem, we realize, is so dynamic. Um, just like I think everybody's saying, oh, model is really commodity, like it's commoditized, it's like commodities, like there's no sense to invest in the model. But we have we do see OpenAI, Anthropic X AI, they've been growing, they've been expanding, they've been at like the kind of like the core for all those opportunities. And uh people abandoned some of the hardware, I think, previously, because they think like it's overinvesting, like then we don't need more chips. Um, so focus on applications, then people realize I think this year, applications not it's really lagging. Um, so we still need a lot of like chips, still need a lot of infrastructure. So our focus is back to infrastructure. And within each layer, there's a lot of dynamic as well. You think about just among the applications, the SaaS companies, there will be a SaaS company that's ready for AI. And there will be definitely like losers. Um, everyone's gonna, so I think like just in the next two to three years, every company is gonna claim their AI company. So that's a challenging thing because like you have to understand like how the AI model is gonna be integrated into each company's business model. So that's challenging. Um, as that's like a challenge, like I think every investor, including professional portfolio managers, is facing. Um, so we believe the probably the best way or the only way to gain the edge um in the AI investing is through partners and knowledge uh sharing with the AI native researchers. Um so we launched a fund uh called AGIX. Uh it's supervised by the AI native researchers and venture capital list, the depth investing, like Anthropic SAI Problesty at an early stage. So those people they talk to the model companies, they talk to the AI researchers uh among each firm. So they understand what's the status for the AI deployment, what's their model, what's which what's kind of like latest dynamic with within the firm. So those knowledge are so valuable. So as when we talk to them, so why not just let's partner um with those knowledge? Maybe you can do it quarterly, we can put it into the index ETF. So that's why we launched uh AGIX, uh kind of like a first ETF partnering with like AI native researchers uh last year. And um then the following discussions like what if we miss those most critical companies, the foundational model companies like Anthropic, like XAI. Um, so the answer is like, why not just put them all together um to complete the ecosystem? So together with them, we we we've been able to talk to those uh companies directly. So we talked to uh Anthropic and XAI, so we being able to really participate in their run, like fundraising. Um we sit on the cap table uh as a shareholder. So AGX on behalf of Clainships Trust, sit on CAP table of both Anthropic and XAI. We participated the Series E round of Anthropic on March this year, and the tender offer round of XAI on July. Um so we're happy to offer investors a complete solution um to really navigate this dynamic of the AI development. Um so that's kind of like a very innovative way, one of the first um kind of like ETF um to have private exposure uh within the ETF. Um so that's kind of like we have more than I think like um uh 85% into the uh public, then you have less than 15% into the private to really capture the the full opportunities that uh within the AI ecosystem. Um and as I said, um maybe going to the few years later, we're gonna see the AI is gonna deploy to our physical world. Um so this is so early, but like everybody's like now getting attention because uh as I said, like um big tech companies, Tesla, Nvidia, Amazon, they'll be embedding on this category. So like Elon definitely the biggest fan of humanoid, been talking about like their optimus um um plan like on every every earning call. So that's like um kind of like the the they're gonna like the Elon thinks a humanoid is gonna transform the business. It's not gonna be like a smart car business, it's gonna be a humanoid business going forward for Tesla. And um Jensen, as I said, um, is really developing models and foundations for um the model trainers, um, and to create this generalized uh robotic models that can really bring the action data to the model so that people can train that and people can really um navigate the humanoid with like human demonstration, then synthetic data in the omniverse. So you can train the physical model, like they train it in the digital model. Um, so like the Google, Gemini, they're they're doing like a vision language action model uh with their very advanced multimodality capabilities. Um so they can actually bring the vision, the images um to the model so they can really understand what's happening. So there's all kinds of efforts around humanoid uh because there's there's a real demand, right? So um the factories want it. Um, as I said, like um the last 10 years, there's a lot of automations already happening, but still it's very labor-intensive for a lot of factories. Uh, to finish that like last mile of automation, um, humanoid becomes a natural solution because those working environments is designed for human. Uh opening doors to navigating stairs to kind of like um like you work, you have to work with human actually at the first stage. So humanoid in a human shape makes a lot of sense um to that way. Amazon is now, I think, like testing uh using humanoid as kind of like the delivery guys. Um they already have like a lot of robotics on their logistics and um like sorting on the packages and the warehouses, but like you still have to facing very complicated scenario when delivering the box. Um, for example, like the store, like there's um the stock there, there's like some stairs they have to navigate. Um so we have seen like some like autonomous food delivery, but like that's something I think like gonna be uh more um scalable if we have like humanoid that can really uh take over a lot of delivery job. So Morgan Stanley uh actually they projected um the humanoid robotic market can be as large as five trillion dollars by 2050. Um and we could see like a billion unit um of humanoid that's like a mass produced um by then. And I think Elon has another projection is like they're gonna have millions of units uh started to be produced, uh starting from next year. Um I don't know, Elon's timeline is always like very aggressive, but like he always got a direction right. So uh give him the credit, um, that the EVs kind of like he sees this as an early stage, and even though everybody's questioning, questioning after years, he finally delivered. So we see like humanoid is probably a similar development, like what we've seen in a smartphone, in the smart cars, now it's the smart robotics. So at this stage, what we can do to invest in humanoid? Um, we think there's a three component. Um, one is the integrity. Um maybe everybody already knows some of the companies, uh, but interpreters is like companies putting the things together. Um, so um, like Tesla is one of them, like Newbie Tech, like uh Rainbow Technology. So those companies are integrators. And then there's um intelligence. The company is providing um the foundations for model training, um, the oldest like newer technologies and the motion, like the really um from the the models to motion control, there's a connection, there's um chips available. So that's the brain. Um, but most importantly, I think is the body, um, because um the body uh needs to be really ready. The supply chain needs to be ready to make it scalable, so the humanoid can be cheap enough, so everybody can use it, and everybody's gonna use it like a phone, like a car, then people can keep upgrading with new models. And that that's the only way to make it where it's scalable and sustainable. Um, so far, um, there's a whole ecosystem out there. I think nobody really understands or not like owns a lot of those positions, because a lot of those companies are really listed on the international market or emerging market, even. So there's accuration system that's really allowed each component to move, uh the control. Um, there's a mechanical system that's really uh functioning uh with also those very sophisticated move. Um the hand is very expensive, probably the most expensive part. So there's a lot of companies just focused on the hand. Um then there's like a sensing and perception companies providing sensors, providing all those like touching technologies, uh, and there's critical materials. Um so that's really um, I think like an ecosystem out there. Um we actually did a trip to China recently to see, I think the as people know, they have been doing this um um the first world humanoid robotic game. Um they also have like a the world uh humanoid uh conference there. So it's a lot of things happening. Like people can see like the racing competition, like boxing, or just like sorting competitions like all over the place. So the humanoid uh is at another level. Um then this um, as I said, it is very expensive at this stage. Um the supply chain is not there, it's not ready. Uh you have to really keep putting a lot of capital to work to make it ready, to make it scalable, so that those billion units of humanoid can be really um produced in a very cost-efficient way, so it's really consumer-friendly, work manufacturing friendly. Um, so we look at the cost of like the humanoid manufacturing, the body takes the most apart. Um, those like actuators, ammonic reducers, bearings, encoders, um, those are key components uh as a hand is is not a very key component. So we we identified um like a suite of companies that's really the supply chains um from the for the for the startups where the figure of electronic optimist sanctuary, unit tree in China, like UbiTech in China. Um there's some So many companies now are providing those components. So they have like a vantage in each of their own category. So we think this ecosystem approach is kind of like the picks and shoulders. As this is already, and a lot of key companies are still in private. So that's why a lot of companies, they're gonna need to invest in the supply chain to make their humanoid um mass producing. So we think at this stage, it's so already so investor can position in very diversified way to capture the ecosystem. And as we see the ecosystem, it's really defined as the three bucket. As we find a methodology to identify the relevance and their supply chain strategic partnership with the humanoid companies. And also like they have a lot of integrators that's coming. And China and uh other emerging markets like Japan and Germany, they're good at manufacturing. Um, they have a good like ecosystem out there too, providing the supply chain uh solutions. Uh their motion control is like far beyond the imagination now. Um so we actually bring one of the humanoids to ring the bell. Um so uh for for our ETF uh coit. So uh as I mentioned, uh we have uh now two ETFs here actually like um uh offering you like both solutions. Uh on the AI side, there's AGIX. So it's a public and private hybrid ETF capturing the ecosystem for I think from Gen AI to AGI. Um but COI is really focused on the physical AI, where the humanoid um and embodied intelligence is kind of like a next generation of the investors focus. Um those are really like structural growth opportunities, I think. They're gonna take like a decade to really play out. Um and we believe um taking a more like ecosystem and basket approach um could be a better way to uh invest in the long run compared to betting on like single companies or single product. So that's kind of like the um kind of like presentation today. Um so AGX uh currently about like um 97 million of we launched on July last year, um, and is uh listed as listed ETF on Nasdaq. So anybody can can see it on on their uh on the website um of crane shares.com slash AGIX. Um so the for the performance is um it's been very strong, um, driven both by the public and the private. Uh for the COID um is um uh another ETF we launched on June this year. It's about like um uh 67 million, I think now. Um so it's about like 59 equally weighted. Uh um and yeah, since inception, we have been doing very impressive return uh as well. Um so the we uh I want to show like a quick clip actually. Like we did a trip to China recently, and we have been really filming some like great footage. So just want to share with everybody. So you can see like uh this company is already started like a mass um production. There's some like facial um recognition and facial like expression. Their motion control is really strong. Like you if you kick it, if you like move it, it's very stable. Uh it's like they're running marathon. And this is another interesting like company. You can actually, using the app to do like autonomous, like just like fully autonomous like retail experience, your app order, then humanoid is gonna grab the stains or make coffee. There's another humanoid actually beside it to making coffee, uh, then it's gonna be delivered to you um with uh online payment. So that's something like quite unique. Um and we within like the hand company, there's like a detector's hand, they're like um doing the sensing technology that you can feel the pressure, proximity, and um the kind of like the um it's a touching technology. So you think like if humanoid is gonna do very sophisticated tasks going forward, like grabbing the cell phone or like a water or something like that's really fragile. You don't want like if you want a humanoid to do laundry or um clean the dishes, you don't want like it to really break the dishes. Um so you need those technology. Uh we find a lot of those supply chain and um component companies in China is really really interesting. Um they've been doing great. I mean, like growing business. So it's a it's an interesting ecosystem out there. Um so that's why like I think like if you think about like uh Chat GPT is kind of like the moment where uh investor realizes like AI is here. Um we can see like some humanoid um actually like probably gonna walk in by you and running on Times Square, like someday then you're gonna see, oh, this is this is happening. Um so um, yeah, so that's kind of like my presentation. We're gonna see if there's any questions from investors. Um happy to answer any questions you've had.

SPEAKER_01:

The uh somebody asking about uh thing, great presentation. Uh is it possible to get the slides?

SPEAKER_00:

Uh yeah, I can if you can uh send me an email um at uh info at cranchairs.com. Uh we can definitely send you the presentations. Um yeah, and for there's also like a lot of articles and uh uh for the font presentation font uh deck factories uh it's available also on the uh crane shares.com uh slash Ajax or Cranechars.com slash uh K O I D. So for more information you can check there.

SPEAKER_01:

So the big talking point now in the media is that um the bottleneck for all the AI is uh is the actual energy, right? That's the the sheer amount of energy is needed for these data centers is enormous. Um what's the bottleneck for humanoids? Like what is there is there an equivalence?

SPEAKER_00:

I think we don't have that same problem as the AI data center right now, because if you think about like the AI models, it's on cloud, right? So it's really have the you have to solve the problem that the now the bigger the model is, the bigger the data center is. Uh and you need like tons of energy. And for the humanoid models, so far as I know, most of the models are running on the edge. So those edge computing is very different in terms of energy consumption. And as you can see, like those humanoids really run on battery. So uh we don't, I think like we have an oversupply of batteries so far. Uh who knows if there's a mass production of humanoid, maybe there's a battery shortage. Um, but like so far the energy part is easy to fix. The hurdle is really on the brain. Uh you can see the motion control, the the hardware, like the action that the humanoid can do, can move, can run, can see, can flip, can do dances. It's crazy. Like, so the hardware is ready. Um, so you just need AI. Uh you need physical AI to be good enough um to operate the humanoid. So I think that's still lagging, but like that's starting. Um, just as I said, um the the vision language action model or the generalized robotic model is gonna change this. It's good, then you're gonna unlock the unlimited list of the possibility for humanoid because the hardware is so ready for this disrupt disruption.

SPEAKER_01:

What do you think is more upside potential? I mean, is that even a fair question to ask between uh the AI side and the and the humanoid side? I mean, the one needs the other, obviously.

SPEAKER_00:

Yeah, so I think a lot of people already have a position to AI names to their standard. Um, so but like not a lot of companies from the COID, the humanoid ecosystem, are really exposed. Um so that's kind of like the I think opportunity for investors to really think about uh a company if you already own AI, what's like complement, right? So most of the AI currently is really US focused, it's like big tech companies. Uh, then there's a lot of uh, I think innovative companies also in the US, um, that's not in Nasdaq, um, and in the private side. But if you think about a humanoid, uh, as I said, it's very international. Uh, it's very hardware-driven at this stage because a lot of hardware is so good, um, and the model is not ready. And so that makes a whole different set of investment opportunities in terms of sectors, in terms of geographic, in terms of correlation. Um, so sometimes like you can see like AI is up, humanoids down. Sometimes you can see, well, actually, humanoid outperformed uh NASDAQ this year. So that's kind of where different set opportunities then, and I think, and in in terms of um asset location perspective, like the AI is probably not core because those like trillion dollar companies are a lot of them, right? So those are kind of like the the drier for the economy, enterprises, that's gonna be mainstream. But humanoid is so under-owned, and it's not really discovered by a loting master yet. So at the early stage, this is kind of like it's like quantum computing in a way, because I think like I think today, just like HSBC Seth can use IBM's quantum computing. Like so people don't realize, oh, it's actually come to our real life. So maybe like Am's gonna ask, we're gonna replace all the human delivery guys with humanoid. So then people can ask, oh, this is such a big opportunity. Um so when that comes, I think like those humanoid companies are gonna definitely get a lot of attention um in a way like AI did today.

SPEAKER_01:

It's not just the US that's in the race, it's also China. Uh so the question here, who do you think is gonna win the physical AI race, US or China? You just came back from Asia.

SPEAKER_00:

I just came back from Asia. I was traveling. Um, so I think is you are a good question. Because I don't think there's a way to each one gonna win themselves. Um so China, they have very good hardware capability, manufacturing capability. Um, they have a good supply chain ecosystem. From each component is very sophisticated. Um, there's tons of supply chain you need to figure out from material to the component to the the accuration system together. Um so that's uh advantage so far for China. Um they have a lot of companies that's together in the same province that they can just like being very scalable. So if there's um humiliary companies like Unitree or uh anything like Ajiba, there's like tons of those companies now ready to they're they're pretty profitable because they've been able to source the components in a very cost-effective way. So that making them uh kind of like advantage in the the body. But if you look at intelligence, so far, without a doubt, US is leading in intelligence, in the model developing a lot of talents around um creating the best models in the world. China's catching up, but like uh I think the US is still leading in the space, uh, especially with its ecosystem in the Silicon Valley, with leading by like media's platform, and all the AI researchers is here. Um that gives US a key advantage. Uh, and also on the commercialization part, I think US is also leading because think about the labor cost. Here it's very expensive, right? So hiring someone um at a factory, warehouses, service sector, um, it's so expensive. There's labor shortage, aging population here. Uh in China, they're starting to see that in manufacturing, but the labor cost is still very low. So the early time commercialization makes a lot of sense here. If you think about nanny price, in China's so cheap for the nanny, but here it's crazy. So the price gap um is is gonna be different. So uh like I would think like when those humanoid companies, they starting mass production of humanoid, figure out the supply chain, then that's a problem for commercialization. Who's gonna win the commercialization? Um, that's probably very far. Like when we're gonna see AI, we're we're still wondering like what's the commercialization of AI at this stage now, enterprise coming out for the solution. But for humanoid, I think that's a long way to go.

SPEAKER_01:

You hit on a little bit, sort of the the it's not core, right? Although you can argue SP NASDAQ is now core AI, uh, because that's what's driving the returns. But um, how should one think about sort of position sizing for these two mega trends? Because if these are mega trends, then they probably shouldn't be small. But then again, they're also not as diversified.

SPEAKER_00:

Yeah, I think like for the AI is kind of like um so far as the driver for the for the happy market for Nasdaq. Uh if you think about like uh overlap this year, uh SP was up like, I don't know, like um low double digit, and AI is probably like 70% of that contribution. Without AI, uh you're probably gonna be flat on SP 500. So we have seen that before with same as PC, internet, cloud, SaaS. Um, so on the next five to ten years, you're gonna see AI gonna play a similar role um to those AI winners. Um, they're gonna dominate the performance. You think about like Dell in the early times, gonna like thousand times and Apple, like those opportunities kind of happening in AI, uh, kind of transform the weight. I think it can even be more concentrated uh to AI names within the even ST 100 or SP 500. So AI concentration can just lead people to overweight AI naturally. Um, but you have to play in a way that you have to identify winner out of losers because losers are gonna drag the performance. Um, so that's one thing. But humanoid, I think it is really on the owned. It's like nobody's has much of the position, especially talking about international emerging marketing. Nobody's like US actually has been doing so well over the last 10 years. Why bother? Why should I diversify? Especially on something like humanoid. Um there's a US company, but like, yeah, they're China's leading, so uh um I don't have access. So that's why like I I cannot trade for like those single single stock. I don't think a lot a lot of people can open an accounting in Hong Kong and start buying these companies. Um so that's why like that's we feel like we have to create a solution to really bring all the companies relevant to um the investors here um so people can get access.

SPEAKER_01:

Folks, for those that are here for the C credits, I will email you after this webinar, get your information for the CFP board. Appreciate those that are here. Um for those who want to learn more about uh the humanoid side, right? Aside from this webinar, and you know, I'll have this as a replay. Any other good sources uh either on the clean share side or maybe even outside the crane share side that are worth paying attention to?

SPEAKER_00:

Yeah, actually we have seen a lot of research on humanoid ashi across the major banks and uh brokers and research providers. Um Moving Stand is definitely the biggest one. I don't know if anyone has access to their research, but like they have done a lot of good research on the humanoid side. Um their analyst, um Adam Jonas, um at I was talking with him, he's definitely a genius, uh, who has the vision for Tesla, I think like 12 years ago or something. It's quite quite like focused on the like emerging technologies. And humanoid is their latest focus. Actually, he now called himself humanoid analyst. So um that's something I realized that this is happening. Um, same as for other uh firm, I think Goldman, UBS, they all have like uh latest research on humanoid. Uh at Credentials, we do publish our research on humanoid as well. Uh as I said, uh, if you check creations.com slash K O I D, uh, you can find a lot of research that is really um we asked what we what we talked today, uh, what is driving this industry, uh, which company going to benefit. Um, so for uh there's a lot of articles about that on our website, so investor can check that out.

SPEAKER_01:

That's a uh good place to wrap up this webinar. Appreciate everybody that joined here. Hopefully you found it very interesting. And uh take a look at the funds. As you can tell, the right space, the right theme, the right issue. Uh thank you, Derek. Appreciate it.

SPEAKER_00:

Thank you, Michael, and thank you, everyone.

SPEAKER_01:

Cheers up.

SPEAKER_00:

Cheers.

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Michael A. Gayed, CFA

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