Why American Investors Should Have a Pulse on China with James Wang

In this episode of Differentiated Understanding, I talk with James Wang, general partner at deep-tech fund Creative Ventures, author of What You Need to Know About AI: A Primer on Being Human in an Artificially Intelligent World, and writer of the newsletter Weighty Thoughts. James has sat on nearly every side of the table — Bridgewater investor, startup founder/CTO in healthcare, engineer at Google — and now backs “real-world AI” from semiconductors and interconnects to diagnostics and industrial systems.

We start with how the AI investing landscape has evolved since 2016: why “AI” used to be a dirty word in pitch decks, how the post–ChatGPT boom funneled capital into a small set of model companies, and why so many AI startups shot up to tens of millions in ARR only to fall back as incumbents absorbed their features. James explains where he still sees real opportunity — especially in vertical AI built on hard-to-replicate proprietary data — and why moats in healthcare and industrial AI look very different from the “GPT wrapper” era.

From there, we zoom out. We compare China vs. the US on AI pragmatism, industrial policy, and consumer vs. enterprise strengths; unpack the open-source vs. closed-source model debate; and talk about how agentic AI is already furthest along in developer tools. James also breaks down the energy reality of AI: why GPUs turn power into intelligence, how much additional load AI really adds to the grid, and what the Inflation Reduction Act and its partial rollback actually changed (and didn’t) for data centers and renewables.

We close with James’s differentiated view: that over time, AI’s gains will be largely socialized — diffused into everyday life via cheap, ubiquitous models (often running at the edge) rather than captured as persistent monopoly profits by a tiny set of firms.

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

Topics we covered:

* What “real-world AI” means: interconnects, power, semis, diagnostics, industrial systems

* How AI investing has changed from “don’t say AI” to “everyone is an AI startup”

* Why many high-flying AI startups lacked moats and saw revenue fall back to earth

* The case for vertical AI built on scarce, proprietary data (e.g., medical imaging, acoustics)

* China’s strength in industrial AI and consumer apps vs. the US edge in enterprise SaaS

* Open-source vs. closed-source models, and what really matters for enterprise buyers

* What “agentic AI” actually is, and why dev tools are still the most advanced real use case

* AI’s power appetite, data centers going “behind the meter,” and the limits of US grid politics

* Why James believes most of AI’s value will show up in broad productivity gains, not just in a few mega-caps

AI-generated transcipt

Grace Shao (00:01)

Hey everyone, welcome back to another episode of Differential Understanding. Today, joining me is James Wong. James is a general partner at Creative Ventures, spearheading investments in AI across the stack. He was previously the co-founder and CTO of Lioness Health, and before that he was on the core investment team at Bridgewater Associates. He founded a nonprofit consulting firm specializing in microfinance and had a short stint at Google. I’m very excited to actually have you on today, James.

James Wang (00:33)

Super excited to be here too. Thanks so much, Grace.

Grace Shao (00:36)

James, it’s really great to actually finally meet you, I guess, in person. We were just kind of laughing about this. We’ve talked on and off on Substack on WhatsApp, on email for quite a while now. I’ve been a really big fan of your writing and you are actually one of the first paid, I think, subscribers to my own newsletter, AI Proem as well. So for listeners, his newsletter is called Weighty Thoughts. He writes everything about the startup space, VC, AI, know, FinTech, I think, and know, bigger pictures as well, right? But ⁓ start with James, why don’t you tell us about your day job? What is it that you do when you say you invest in AI? What are you investing in? And what kind of businesses are you looking at these days?

James Wang (01:19)

Yeah, sounds great. yeah, glad to be one of the early subscribers to AI Proem because yeah, as a VC, you have to catch the good thing early. it’s a part of the job there. Yeah. So Creative Ventures is an early stage deep tech fund. So deep tech has gone through quite a few evolutions in terms of what it is or isn’t to people.

For us, it is things with harder science and IP barriers. So that includes things like battery manufacturing platforms that cost a billion dollars a piece, AI diagnostics that are completely end to end with no clinicians in the loop, things like materials discovery platforms using AI. So a lot of these different areas that have gotten pretty exciting with AI as well. think one of the interesting things is deep tech, especially within say like some of the materials discovery spaces, the bio space, like a lot of these areas have accelerated quite a bit with AI’s ⁓ involvement at this point. And there’s a lot of exciting things coming up in those areas.

Grace Shao (02:27)

I think you know beyond a business background which a lot of investors have you actually have a technical background as well ⁓ What do you think that like does that make you? More understanding of the deep tech that you’re looking into or do you have any unique perspective on technology companies when you look at investing in them?

James Wang (02:47)

Yeah, totally. So for us, for our team, actually, I’m one of the few folks without a PhD. So a of the team does actually have that kind of background, which is needed within deep tech ⁓ in large part because it’s you do need to understand how the technology works in order to understand the market that it goes into. That being said, like like most technology startups, the ultimate challenge is finding the right market and scaling.

But if you don’t understand the technology on a base level in terms of what it does, it’s really, really hard to actually figure out how to scale the thing. So a lot of that technical background, especially within these areas is quite critical. And I guess just my opinion as well, like a lot of different asset management areas undergo evolution. VC historically has been one that has allowed a lot of generalism within it just because of the nature of how a lot of the software boom went came up and went through and everything. But our opinion is actually a lot of the investors in this particular area will get more and more niche, especially with AI, which I think we might jump into as well. AI does actually involve and help enable a lot of vertically integrated industries ⁓ in interesting ways, which means that, you end up with investors who get more and more specialized in their areas.

Grace Shao (04:00)

Mm-hmm. How big are these ticket sizes that we’re talking about when you’re investing in and how early are you looking at?

James Wang (04:13)

Yeah, typically speaking for us, we are often the first institutional investor in that being said, some of our companies have five, 10, even like 20 million dollars in non dilutive government grant funding or research funding before we actually invest. So it’s kind of hard to say when you’re trying to pin that down. That being said, yeah, we’re among the first investors in. Usually we invest around a million in terms of initial check size and sort of ramp from there.

Grace Shao (04:28)

I see.

James Wang (04:42)

⁓ And then our companies obviously like as they get larger and larger later on They can have quite a bit of range in terms of like where they end up

Grace Shao (04:52)

I see. I definitely want to double click on the vertical AI space later. But to start with, another personal question I want to touch on is your book. You’ve just launched a new book. It came out in October, I believe, right? It’s called What You Need to Know About AI, A Primer on Being Human in an Artificially Intelligent World. Can you tell us a bit about the book, a preview of like, I mean, the gist of your book or why we should go read your book?

James Wang (05:19)

Yeah, totally. Well, think the most recent thing I can remember was someone told me the other day, think yesterday actually, that this is a great stocking stuffer for all the boomers in your life. I believe that was a compliment. So ultimate, I think so. So the book is actually a end to end. Here’s what you need to know to kind of get up to speed on AI.

Grace Shao (05:33)

It sounds like a compliment.

James Wang (05:44)

⁓ It goes through the history of it. It goes through some of the technical background. ⁓ Not too deep, but then again, like also doesn’t really pull too many punches in terms of like actually getting into the structure of it. And then finally it goes into how it’s being used today and some implications of it coming up. So it’s meant to take you through end to end. ⁓ You know, we have a lot of interesting endorsements of it. Reid Hoffman actually had read through it and gave a great endorsement of it as well and I’ve had both people within the AI business sector. So basically people trying to market AI and push it out into market as well as engineers, both tell me that they’ve learned something from it. So I think actually a lot of people can get something out of it. It’s just different people will find different parts of the book difficult or not. but it does attempt to like step you through it. so that was the aim that I had writing the book and, ⁓ hopefully I achieved it so far. sounds like it, if it’s a boomer, baby boomer stocking stuffer.

Grace Shao (06:44)

I think I need to get a book for myself. Does it overlap with what you write about on Weedy Thoughts or is it a bit different?

James Wang (06:52)

It does, but it ⁓ gets into more depth and basically takes you through A to Z a little bit more. Since I’m sure you know as well, it’s like your experience as well there. It’s like for a substack post, inevitably you leave a lot of things out. You kind of hit the high level, you hit the points, but yeah, ⁓ you can’t make the article 10,000 words long or something like that. On the other hand with a book, you do need to actually bring it from beginning to end.

Grace Shao (07:21)

What inspired you to write the book? Because I’m sure you have a busy day job already.

James Wang (07:24)

Yeah, I mean, the first part of it, ⁓ which is the real part of the the marketing story that I give is that which is actually true as well. But the marketing story I give is I sort of looked at the landscape and generally speaking, there’s a lot of great technical resources. There are actually a lot of great sub stack newsletters on AI. A lot of other good places to dive into to get a sense of what’s going on. The problem is in general with the book landscape, a lot of the stuff has been like productivity, get rich quick, et cetera, sort of things within AI, or somewhat more alarmist or very thesis specific driven. It’s like, ⁓ AI is going to do this thing. It’s going to kill us all. It’s going to take all our jobs. It’s going to revolutionize this. Like they’re pushing an agenda. I didn’t really see anything out in the landscape that takes you from A to Z for people who didn’t actually know what was going on and wanted to get up to speed. So I got kind of tired not being able to recommend a book for all the friends who are like, hey, you know what’s going on with the say hi stuff. I don’t know what’s going on with this. I stuff. Where can I go read a single book to find that out? So that was like a big impetus on why I decided to write the book. And in terms of like how I came to it, the publisher actually found me through my sub stack and kept bugging me to write a book. And eventually when I decided to go on this direction, they were like- Are you sure you don’t want to write another thing about how it’s going to kill us all or something? We think that might come off and fly off the shelves a little bit more or like draw people in a little bit more than a light textbook. But nonetheless, it has actually got number one in lot of categories on Amazon for a couple of weeks now. So I think it’s working so far, which I’m happy about.

Grace Shao (09:13)

I think that’s a really pragmatic way of approaching it. like, your point, I think I’ve also noticed in just the AI sphere in general, there’s like kind of the, you know, the investors are talking about money. What’s the return? What’s the return? It’s only about monetary return, right? The tech people, when you speak to them, sometimes frankly, they’re a bit ignorant to the societal implications or they’re not, most of them, let’s just say, people are not evil intrinsically. They just think, okay, tech acceleration is a fundamental goal for them.

And I think sometimes to put the blinders on and they forget about the potential implications of society and the environment on, you know, shifts in like, you know, even dynamics between people and countries. And then like you said, unfortunately, I think the people who really try to bring the awareness and be mindful sometimes can go too extreme. And then what happens is like, let’s reject it. But the reality kind of sits somewhere in between where like you can’t really reject a technology once it’s out of the Pandora box, right?

And then you can’t really only look at the value creation in terms of monetary terms. And you can’t really say, OK, let’s only focus on technology, but not think about all the other consequences. So I think to start our interview, let’s really talk about your philosophical view on this. I think your book really ⁓ resonates with me. What I write about also is trying to help people understand, OK, these are the business implications. This is where the return will be. This is what will be.

This is how the technology change your interactions with each other. But it’s not like you can’t approach it mindfully, right? So I’ll throw it back to you. How do we understand the relationships between if you have to simplify the three kind of caps that we see right now?

James Wang (10:54)

Yeah, I mean, in terms of camps, mean, there’s definitely the I mean, it’s been interesting, right? Because for a while you had did have a group that basically said AI is just a fad. It’s not going to actually do anything. Ultimately, it doesn’t matter. Blah, blah. Not a big deal. You have another group that’s basically like, AI is the AI is going to either kill us or AI is going to take off in terms of singularity. And we’ll have a post. ⁓

We’ll have post-scarcity utopia where everyone will have UBI and AI will do everything for us kind of thing. ⁓ So between those two extremes, I mean, you do also have people who are like, yeah, this is a great business opportunity. We’ll go after it. It has some aspect of all of these things. And like you’re saying, from a societal perspective, I think a lot of the people who boost AI quite a bit, ⁓

Don’t take into account. Yes, there’s going to be disruption. mean, even if you look at the Internet boom, which I think at this point, people have ⁓ misremembered certain aspects of it because it came and went and a lot of our economy has been restructured around it. AI is the same way. It’s going to create disruptions. It’s going to create winners and losers, but it’s going to also help accelerate productivity and do a lot of good in the world, too, like most technologies have ⁓ in the entirety of history.

So it’s a big part of just needing to understand what it can do, what it can’t do, and really where we will see the benefits come out. Because I think otherwise, if you’re just very much on the utopian or doomer perspective, you lose the reality of what ⁓ AI actually is, which is a tool, and what the capabilities of that tool is.

Actually, in terms of this, I think China currently in terms of Chinese AI, which we’ll probably get into and Chinese AI companies have generally had a more pragmatic view of this. ⁓ I’ve had a lot of conversations in Silicon Valley, including with different folks at the model companies, where some of the goal and some of the ultimate aim was, hey, we’re going to get to AGI and then we win. And then it’s all it’s all done and that like, we don’t have to worry about anything else.

I think a lot of that particular mindset viewpoint folly has sort of gone away in the past year or two. But even so, like it tells you something about like the way that a lot of people are looking at this, which is almost pseudo religious.

Grace Shao (13:27)

Yeah, I think definitely to your point, the the caps kind of become bit of like cults themselves as well. It’s even when you cover this space, it’s interesting to meet people who are like all or nothing, very much all or nothing, right? They’re either like, let’s go all in and ignore all the noise and all the issues or go like, let’s reject this. This is just ⁓ inherently evil, which to your point, like all technology disrupts what we know, but

Like you can’t reject it, right? Okay. I think moving on from that, I want to talk about investing in AI. You’ve been investing AI since the early 2020s. I wouldn’t say it’s earliest, but you’ve been in space for longer than most people where, you know, they really jumped in after the chat GPT moment in 2022. So how has that relationship between AI companies and capital change? Like, we’re now hearing a lot of buzzwords. Is this a bubble? Is this like, it’s going to flop? Like, where are you seeing the market kind of like

Where is it at right now? And has that relationship between the founders and the investors changed over the last few years?

James Wang (14:35)

Yeah, it’s an interesting question because yeah, when we had started out, and that was 2016, AI was actually kind of a dirty word just because whenever someone tried to throw AI at something, it was like, yeah, this is kind of scammy. It probably isn’t going to actually work. So you literally had different startup pitches pull AI out of their pitch and basically say, no, no, it’s just statistics. Maybe it’s machine learning, but it’s really just statistics and stuff like that.

So, mean, the way and the evolution of it like changed quite a bit. I think around 2020 was when it started to get a little bit more accepted. And we started to see like certain pitches where it’s like, hey, look, we’re going to do AI for movie studios. And really what we want is to do motion capture for this or something like this. Actually, I think I saw two or three startups around that period trying to do this. And what you really want to do is like, you really want to fund us. We’re going to gather a ton of data.

And then we’re going to train a huge model and then we’re going to win the market. So that was actually kind of a popular thing at that particular point in time, which also ended up becoming unpopular because it ended up not working. but it was post yeah, like some getting some towards the chat, GPT moment that suddenly like everyone knew about AI. took off. A lot of people started putting money into the LLM company, model companies, lot of the companies adjacent to it. And at this point now, I mean, it’s.

Interesting, right? Because I think if you look at headlines, would think anything that has AI in its name instantly gets a ton of funding. But I can tell you just being on the ground, that’s actually not hugely the case. Private markets and VC have still been somewhat more sluggish ⁓ since 2022, since interest rates rose, and since there haven’t been super significant exits across the board, which means a lot of that capital market has been frozen.

If you look at the stats, actually for all of the startup funding and AI funding, a huge proportion of it is actually just the giant model companies having mega round after mega round, or some of the second tier model companies who’ve also had a bunch of mega rounds. We haven’t actually seen like tons of AI company like Dragon, a ton of startup financing. ⁓ Even so, like

AI currently still is the hot thing. So if you’re trying to raise money, especially in Silicon Valley, you generally will probably try to put some sort of AI pitch into your thing, whether or not it actually makes sense or not.

Grace Shao (17:07)

Yeah, I was just gonna say it’s really funny when you said like it used to be a dirty word, whereas now you meet any company, like they could be selling chocolate bars and they’d be like, we’re AI, we’re AI company. They’re really trying to use AI as like the kind of buzzword to hook people in. And it’s interesting to hear from your perspective that actually AI startups are not getting a lot of funding, right? Because in China at least, what I cover in this part of the world,

There’s already the jokes about the last round of AI startups dying out already, like phasing out recently. Yeah, so I don’t know. Is that happening in SF as well?

James Wang (17:45)

Yeah, so it’s interesting. maybe I’ll make the amendment that it’s not AI startups are not getting a lot of funding. AI startups are getting more funding than other types of startups. So one of our companies actually just recently was told that, hey, you’re actually an AI startup, not a health care startup. They’re definitely both. But that was their greatest compliment because that meant that the investor was actually interested in putting money into them. So it’s like it tells you.

Grace Shao (18:12)

That’s so funny.

James Wang (18:14)

Yeah, it tells you something about the landscape.

Grace Shao (18:14)

Yeah.

James Wang (18:15)

⁓ yeah, AI companies get more funding. But yeah, it’s definitely not as much of a bonanza ⁓ as you might think from the outside just by just the numbers thrown at the screen because a lot of the big companies are absorbing. But it is definitely the case that I’ve talked with a couple of other investors who’ve told me about some of the revenue numbers for some of their companies. As much as the revenue numbers shot up, ⁓

Grace Shao (18:27)

Yeah, yeah.

Mm-hmm.

James Wang (18:43)

Let’s see, I’ll obfuscate what this specific company is, but there’s one company that I know of that basically was like shot up to 20, 30, 50 million in ARR in a very short period of time. And the investor who I was talking to was saying, yeah, they’re definitely going to get to 100, 120, whatever it is. The last time I checked in, I think they had dropped back down to 20 and maybe stabilizing down towards 10. So in terms of AI startups dying, the interesting thing is like

A lot of these startups don’t actually have moats. ⁓ Whether or not it’s like some random model company that likely isn’t ever going to get off the ground in terms of having enough compute resources or whatever, or a GPT wrapper, which has become a dirty word or had become a dirty, like derogatory, like phrase to say to some of these companies that just wrap their product around like a chat, GPT API. A lot of these companies don’t have any barriers to entry.

So we’re already seeing them shoot very high up because their products are actually useful. And we can get into that. A lot of these are like programming developer tools, agentic ish tools within developer realm. They go up very quickly. They’re actually quite useful, but then everyone else can utilize, everyone else can build the similar kind of thing very quickly or

Say as Codex came out from ChatGBT or as Cloud Code got better and better and added more capabilities, you have a lot of the big model companies themselves end up just incorporating the functionality that these developer tool AI companies tried to put in, but now they’re obsolete.

Grace Shao (20:24)

Yeah, I think that’s something like I’ve been writing about for the Chinese tech space as well, right? The incumbents end of day have a distribution and what they call the flywheel effect, right? I used to hate the word because I think it sounds really silly, but now I think it really makes sense, right? Like in this case of AI, it’s like if it’s not like we have a new device that we’re interacting with it, so whoever already had existing reach on these operating systems can easily basically just swallow another business, like a newcomer.

and just create a function. And to your point on the coding, the agentic side, I know like Alibaba just came out with ⁓ Codar, who I interviewed a while back. ⁓ ByteDance has their similar tool. Obviously Cursor is still the leader globally right now in terms of capability. ultimately, if one day they all reach a similar, I guess, efficiency or user experience, then if you’re already using Alibaba Cloud and you’re already using their like blah, blah, blah service,

you’re already buying your API there, then why wouldn’t you just use your tool, right? I’m sure it’s the same in the US, like the big players just kind of capture all at this point. ⁓ I want to talk about creative ventures specifically. You guys say you invest in real world AI, right? ⁓ That’s really much like you kind of even touched on healthcare, robotics, manufacturing. It’s maybe less about like the consumer side of things, right? What are exciting businesses you’re seeing and you think are being overlooked right now?

James Wang (21:52)

Yeah, I mean, think two areas. One is there are still a lot of interesting things within. For us, real world AI does actually include things like interconnect companies. It includes power management, storage. It includes like different like semiconductor based companies or semiconductor tool companies. So there’s actually a lot of interesting things going on in that realm. It has been a, for example, with like optical interconnects, optical switches, these other things. That’s been a

place where the semiconductor industry has been interested in going for years. And there’s been roadmaps and industry like things talking about like how we need to go that direction. But ultimately, no one actually ever moved because while we have an existing business, it costs time, it costs money to actually move into that. But the interesting thing with the AI boom has been, OK, all of a sudden there is an impetus to actually start adopting a lot of these technologies with some of the optical interconnects and whatnot.

And there’s been actually some large exits within the space even recently. So that’s an interesting area to us still. And it’s an area that most investors have still shied away from because there’s still been a historical wariness, especially within VC towards hardware, which is ironic since that’s actually where Silicon Valley started in terms of VC. But on the other side, too, there’s a lot of stuff within health care that has been something that we’ve been pretty excited about, at least in the US. Medtech and health care has gone through

Quite a few years now actually of kind of a funding winter where a lot of well-known health care companies, digital health companies just didn’t do very well. A lot of them also tanked on the public market. So it’s just been a super unpopular area for investors. But some of the most interesting and exciting things that I’ve seen within AI have been within the healthcare sector. There’s some that are basically like healthcare productivity optimization.

One of our companies, not to talk up our book too much, but one of our companies is currently the only ⁓ and first and only currently end to end AI diagnostic for them. They’re starting in lung disease, but essentially they’re a diagnostic tool that now you press a button. It sends off the scans. It comes back and gives a diagnostic and gets paid reimbursed by Medicare and all the private insurers. Like there’s no clinician, no technician, nothing in the loop, which actually doesn’t just

like increase margins to software like levels is actually insane in the healthcare sector because there’s just so much red tape. There’s so much red, so many regulatory barriers. There’s so many like pieces that can go wrong and thus you need to check and thus you need to have all these other layers that if a piece of software can actually take all that away and be FDA approved, that’s actually a massive productivity improvement in the space. Again, ours is currently the first and only, but I don’t expect it’s going to stay the only one there’s going to be a lot of really interesting things happening, especially within healthcare and biotech.

Grace Shao (24:51)

It’s interesting because I was just listening to a podcast with a 16 C’s podcast. They’re saying that actually a lot of biotech and med tech companies are routing their trials actually in China, just given that there’s less red tape around a lot of these issues. And I met with a company in Singapore just last week. They are one of the leading AI companies actually using AI to do clinical research and trial a clinical trials. And it’s really interesting. Like you said, AI is advanced enough in some ways that they can actually guarantee there are no issues in these kind of like more tedious work or knowledge work that it doesn’t really need that much human judgment and then the efficiency gain is crazy. So that’s an interesting space. think you’re right. And I think it’s going to blow up not just in the US, but also maybe in China space as well. ⁓ I want to ask you on China, on China AI, open source, closed source, that’s the big topic, right? It’s a whole China’s embracing open source.

The US may be still leading on the closed source models. How do these choices really actually affect the products and the margins ⁓ when you’re looking at these companies? there’s a lot of conversation about their performance, about deployment, but what about when it comes to actual nitty-gritty implementation for the companies? What does it really mean?

James Wang (26:11)

Yeah, I mean, the way that the Chinese ecosystem and the US ecosystem, it’s partially just path dependent. They’ve evolved in very different ways. In the US, you still have a lot of API or subscription usage, like specifically, you know, open AI and anthropic and Google sticking Gemini in essentially every single productivity tool that they own. ⁓ So the way that that market works is you have a lot of paid usage go out there like they wrap, they do GPT wrappers and whatnot. And because China ⁓ was later to some of the, to some of this in terms of like big breakout, essentially some of the open weight, open source stuff helped ⁓ spur adoption. So for some of the people who do not want to simply pay for chat, GBT or Anthropics API and just wrap their stuff around it and want to control some of their own destiny.

It’s great to have something like Quen that you can basically fine tune. can locally host. You can locally host DeepSeq. You might not choose to. Ultimately, you might go to a number of different providers who all allow you to have it available there. But nonetheless, you basically have an easier way of saying that you won’t have lock-in. You’ll be able to use this. You’ll be able to go out there and...

Uh, go out there and integrate it. So it is a, again, a little bit of path dependency there. It’s a little bit catching up in, from that perspective as a whole, ultimately in the longterm, I do think like some of the open weight stuff probably does make sense for the same reason. Open source made sense within a lot of the software ecosystem. It does actually spur a lot of enterprise uptake. can pay for support and other things around it. And the bigger thing will ultimately be, can you sell it faster? One of the things about SaaS, so software as a service, has always been, there’s no real barrier to entry for you switching to someone else, except for the fact that I made, especially for enterprise SaaS, an enterprise decision, and I don’t want to switch away from your product now there’s nothing really at the end of the day that makes Salesforce versus some other CRM versus some other productivity tool that different from one another. And at the end of the day, for a lot of the LLMs, especially as we start hitting plateaus in noticeable performance improvement for people. They might become quite interchangeable in which case it becomes similar to a SaaS decision. Do I want to choose the closed source one where

I will have to pay for it forever and also potentially have it go down and only have a single source vendor. Or am I going to take the open weight, open source version where maybe I’ll still pay for it to be hosted, but I can always be comforted that I can always like take it, roll it, and put it in my own infrastructure as well.

Grace Shao (29:15)

Yeah, I think right now where we’re at as the models, their own performance are getting closer and closer and like the gap is not as wide anymore. I see your point. It becomes like an infrastructure. And then I guess for enterprises, biggest issue or the hurdle is really the switching cost of like the bureaucratic switching costs. It’s like going through the legal work internally, making sure all your departments are upgraded the same way that that becomes a switching cost. So then

I guess incumbents still have the advantage once they’re like chosen as the default provider, they get to kind of own that space, right? ⁓ I want to talk about agents. You kind of mentioned earlier, like enterprises like Google are essentially plugging in Gemini into every single productivity tool. And right now we’re hearing a lot about agentic AI, how that’s going to even increase the capabilities of these productivity tools even further. How do we understand

James Wang (29:53)

Yeah.

Grace Shao (30:13)

what even is a gendered AI right now. I think a lot of people still think of AI as just chat GPT chatbots.

James Wang (30:20)

Yeah, I mean, agentic AI became a big buzzword. personally, my personal take, I’ll define it a second, but my personal take is eventually agentic as a term will go away. And we’re just going to say AI because all the big model companies are also going that direction in being agentic. Now, agentic, what does it mean? Well, definitions vary, but at the end of the day, it’s, it doesn’t just chat with you.

It goes and does something. So instead of I plug into chat, GPT say, Hey, where would be a good place to go on vacation? I would tell the agent, okay, I want to go on vacation. helps me research, but then it also helps me book the tickets, the hotel and like give me like the roadmap and plan and stuff like that and do the things for me. So it’s that layer of being able to interact with the world, whether it’s like true real world or whether it’s like digital world in terms of booking stuff, it can actually do things for you. Now, why, why I say it’s eventually just all going to be agentic, in which case we’re stopped going to stop saying agentic. It’s because the direction of where the model companies have gone is this direction. Uh, as some of the performance differences have disappeared, they’ve implemented more and more agentic tools. would say the most advanced agentic area, even though we don’t usually term it that way, is a lot of the developer tools. Ultimately, at the end of the day, for all the developer tools, they will take your input and they will make changes on your machine, on your code, and commit it. So at the end of the day, it’s doing things.

James Wang (32:19)

So at the end of the day, in terms of these agentic tools, where developer tools have been the most advanced because they actually go out there and make changes on your machine, your code, commit it, ⁓ all the different model companies have been rolling out more and more tools that specifically are helping you do things in the real world. As you stop having as much difference in how well they chat with you, there’s going to need to be other differentiation. And the place where they’re going, where there’s lower hanging fruit, is being able to actually implement and do things for you. that’s why I think agentic is interesting. Agentic is actually going to be big, but it’s just not that interesting of a term because ultimately most all the AI companies will likely go into it and implement it with their models.

Grace Shao (33:07)

You’re right, because I think even just this week Deep announced a new like v3 too and they’re saying oh our capabilities are really focused on a genetic AI and every single model is kind of coming out say the same thing and then this is a bit random but it reminded me of this like funny thing was growing up my friend who’s German descent one day asked me she said grace in your household do you guys say eating Chinese food Chinese food do you call Chinese food Chinese food I said no we just say we’re gonna eat food tonight you know and because he normalized and that’s default thing then you wouldn’t really like actually add these prefaced ⁓ adjectives, right? So I get your point about that. ⁓ What kind of agentic products are actually good right now, actually on this point? While models are all becoming more agentic, what actually is it being used in right now? And what tools are actually proving themselves to be really capable and productivity and enforcing?

James Wang (34:03)

Yeah, I mean, I’ve seen various attempts to do things like ⁓ shopping aids, ⁓ different things with, yes, travel booking, concierge services, ⁓ email responses, bot, chat bot, like customer service ticket, chat bot kind of things. I would still say like among these different things, the most advanced is actually still the developer tools ⁓ ultimately.

Grace Shao (34:07)

Mm.

James Wang (34:30)

⁓ it’s close to the companies. It’s close to the people creating it. It’s right now the most advanced area that I see. ⁓ but there’s been a lot of experiments in many of these different areas and they are starting to get better and better and work better and better. So I do actually expect for a lot of these different things where, especially where I guess the framework that I would put on it is if the agentic application is low enough stakes from the perspective of there is a human in the loop that will check it at some point, like, hey, I’m going to book your vacation. Here’s all the pieces of booking your vacation. And you look at it and go, yes, you are correct. I am going to Athens, Greece, not Athens, Georgia, and something like that. If there’s a human in the loop and something where there’s a check, ⁓ these are actually applications that the AI can do quite well and is actually something that’s very implementable currently. So that’s kind of the layer that I put on it. But yeah, like some of these areas are getting quite advanced.

Grace Shao (35:32)

Yeah, like you said, you need to have that human verification. It reminded me of that new show. It was like a silly rom-com. It’s like they bring these women to Paris, but it’s actually like Paris, Texas, and everyone get off the flight and they’re like, my God, this is not the Paris I imagined. Okay, I wanna kinda go into China a little bit. Like I said, you were one of the first paid subscribers to my newsletter and I very much focus on the China space, although I do cover a bit of APAC and different companies as well. What piqued your interest in China AI? Because it’s not like you actually directly invest in China AI, right? So tell us a bit about that.

James Wang (36:11)

Yeah, we’re not able to for various ⁓ investment restrictions, some of our investors and whatnot. But at the end of the day, ⁓ it’s a global market. China is a huge market and China also has a lot of talent within it. It’s kind of funny because it’s like, why was I interested, for example, with your newsletter? ⁓ One, you write well, so there’s that. But also it’s just like having the perspective within the market is super important.

So I actually still have a lot of conversations with Chinese companies. They know I can’t invest, but they’re always kind of interested in also learning about what’s happening in Silicon Valley, et cetera. So I keep a pulse on the Chinese market that way. And that helps inform my decisions, but also my understanding of like, what does the landscape look like in the U S it’s both compare and contrast, but it’s also thinking longer term. What’s going to happen as all of these companies go more and more global.

whether or not they’re competing directly in China or the US, you’re going to encounter each other in the rest of the world, right? So there’s a question in terms of that. As for why, there’s also not very many good sources on China. I think you covered this in some of your articles about some of your own personal history, Grace, but it’s just like, even recently when I was trying to prepare a presentation for talking to some folks about some of the things happening in China where AI is being pushed.

especially by the state into a lot of areas like insurance and whatnot. I was trying to Google like what’s going on in China with that, like with just like very simple terms. And really what came up for me was like New York Times articles about Chinese surveillance state is China like doing these things to the Uyghurs is like what it’s like all of these different things that were obviously had its own like bias, let’s say. ⁓ And at the end of the day, it’s like China.

Grace Shao (37:36)

Hmm.

James Wang (38:02)

especially for the West has been a little bit of a cipher. It’s either can’t innovate at all and only copies things, or it’s like the crazy, huge country that suddenly like will be able to overtake everyone and like whatever it is. It’s the country that who’s the state dictates everything and thus has like total control and everything. At the same time, it’s like, it’s like a super like, like, you know, lot of the private sector stuff does things, but also like the states can’t seem to like do anything right. And then there’s corruption and ⁓ rockets, rocket fuel being used in hot pot or whatever. And the reality is it’s like, it’s all of these things together. Right. But a lot of the way that the media portrays it is fairly biased in terms of that. So it’s always very useful. And I’d say critical for investors in any part of the world to have a pulse on definitely the two biggest economies in the world. Right.

Grace Shao (38:58)

Yeah, I think I can go on forever about the media coverage. I wouldn’t even say it’s biased. I would just say the media business model itself actually awards, you know, clicks and attention and in this time and age and what gets attention, the joke amongst a lot of like I expect journalists in the APAC region, it would say it’s big China, bad China, weird China. So I was like, my God, it’s so many people. It’s so big, weird kind of, my god, they eat dogs, which like honestly, majority of people don’t, but sure, I’m sure some certain small segments of people do have weird diets, right? And then it’s like, like bad, bad, bad, like it’s so bad. So I think that gets the clicks, but I do get your point, you know, not to kind of bring it back to myself too much, but it really is why I started writing about it. was like, there should be a nuance understanding what’s happening, especially in the business space when it’s sometimes not really relevant to what we just talked about, the big, bad and weird. It’s just innovation and business. So I want to bring it back to that. lot of people are talking about China being very, very strong and in industrial planning, right? I think this is something that’s all of a sudden for some reason, making headlines all over the U S and last like three months, whereas like industrial planning didn’t come out three months ago. They they’ve been around for the last 30 years, really.

From your investor lens, where do you see China moving the fastest? it like only sectors within industrial policy support, like the EVs and the hardware and the robotics? Or do you think China actually has its own mayor and certain sectors are being overlooked? And in kind of comparing that to where you’re seeing the US in terms of the companies you’re investing in, what are things you can actually learn from? I think we can talk about that a bit.

James Wang (40:44)

Yeah, I mean, in terms of China, there’s definitely a strong advantage in some of those physical industrial areas, ⁓ including say industrial AI, because it doesn’t exist in the US. The US doesn’t really build that many things anymore. It’s really hard to actually get any sort of manufacturing up. I can tell stories in terms of lioness. I can tell stories in terms of some of the med tech companies have helped try to like figure out where to do manufacturing. Essentially, you can’t do it in the US.

So all of those sectors and areas ultimately do end up being a very strong advantage for China because it exists there and it doesn’t exist here. In terms of like other things that are overlooked, mean, China and actually Asia in general has had an interesting brilliance within consumer, like the consumer sort of trends there, the apps, the like different ways that, yeah, sure. Like WeChat and other things like.

Overall, like China has a much more interesting grasp of like some of the consumer landscape than the US has. I think the, the, I wouldn’t say it’s the, it is, it’s like the stereotype is essentially US companies are the ones that do enterprise SaaS. And the other side of it, which isn’t really spoken at least around here is actually China and actually a lot of Asia is really good at consumer.

A larger consumer market, maybe you can argue it’s like some aspects of that, but maybe you can argue it’s some aspect of taste as well. That may be changing ⁓ over time, especially as like the two markets are more and more divorced from each other. ⁓ Ultimately, the U.S. will probably have its own like consumer ecosystem because it’s divorced away from some of the Asian companies in China in particular. China will get separated from like the enterprise sass in the U.S., in which case there has to be its own stack.

So maybe that will change over time, but there definitely is like sort of a strong, I wouldn’t say internal cult. I wouldn’t say like cultural from a cultural perspective, but more like cultural from there have been entrepreneurs, successful tech companies and sort of playbooks on like, how do you do this? That are much more mature, say in like the Chinese ecosystem than in the U S ecosystem. Well, you know, the last big consumer app was Snapchat and before that, you know, Instagram and Facebook rewinding to the dinosaurs.

Grace Shao (43:06)

Yeah, I think that’s interesting. And I think I’ve been hearing more from founders in AIPAC, not just China, but they’re saying with AI, they actually think there’ll be more opportunities in the enterprise space. And the reasoning is because a lot of the reasons why China or South Korea, a lot of these countries at that time, I would say in the 90s, did not want to adopt a lot of SaaS from the US was actually because they frankly didn’t even have the infrastructure in place as in you know internet was not that like you know, you took What’s the word for it ubiquitous and then it was like they don’t have the ability to actually even Serve the you know the need and then on top of that It’s not just China that has a lot of SOEs actually a lot of Asian countries have a lot of state majority companies and I think people again in the West might not realize that

And because of that, there’s actually a lot of concern on data privacy issues. And they’d rather have maybe even a shittier quality product than actually jeopardize their data being ⁓ taken by someone else, a third party. So that also goes back to why a lot of companies now in Asia actually want to adopt open source AI versus sending their data across the world. Not quite literally, but giving that re-access to like say a chat jpg or a google gemini so i think there’s a lot of reasons why people might find more use cases of sas ai in asia now ⁓ and they might actually you might see more entrepreneurs in this space popping up ⁓ i think on that i want to ask a question on vertical ai i think we kind of touched on healthcare right now we’re talking about there’s a potential growth in enterprise softwares are ai empowered

How do we understand vertical AI? Will they still basically have to rely on the incumbents ⁓ infrastructure to build their tools? should we actually kind of see them grow out? should they be compared to the Googles and the Microsofts of the world? Or should they have their own kind of racetrack themselves?

James Wang (45:19)

I think it’s more their own racetrack because it’s kind of a very different ecosystem based on how AI is evolving. So I think the first layer to talk about is just do any of the LLM model companies have a strong, durable advantage outside of, know, OpenAI and chat GBT has a big brand. Google has a lot of distribution. Alibaba has a lot of distribution. It’s like, is their durable advantage? All of their compute tends to be the same.

You know, they’re using the same GPUs. They’re mostly still on CUDA. Maybe it’s moving a little bit, but they’re mostly still on Nvidia GPUs. The models are basically the same. They’re all transformer based models, essentially. And their data at the end of the day, scraped from the internet, is largely the same. Yeah, maybe it varies a little bit, but it’s largely the same. They’re ultimately going to be very, very similar. That just doesn’t give you a lot of differentiation across that. that goes towards like, well, ultimately it might be capped in terms of how different these things are.

That goes into the difference between that and the internet, right? The internet was very much an aggregated landscape where it’s like everything’s on the internet. You can go find, like go through to Google, whatever, find whatever you’re looking for. You can go to centralized marketplaces, you know, how about like Amazon, whatever you can find your things. And ultimately like it very much like put people into the same place.

What AI is doing, interestingly, is if you think about where there’s actual durable advantage, it’s probably still the computers largely going to be the same. The models in terms of deep learning models, whether they’re transformers or not, are probably also going to be fairly similar. The difference is going to be in the proprietary data. And once you actually get down to the proprietary data level, we’re not just talking about, within your individual corporation, you have your OK.

Internal documents or whatever fine. That’s like one thing Maybe you can still use like chat GBT or like one or whatever like some sort of open source LLM But what if you have raw acoustic data from ultrasounds to be able to detect liver disease? You’re not gonna be able to put that into chat GBT at the same time like that data Right now with the current models and compute you don’t actually need that sophisticated of a model

or even that much compute to make it actually do something really interesting with where AI has advanced to at this point. The same has happened with a lot of the drug discovery area, material science area, industrial AI, which again, China has an advantage in terms of this with their actual like running operations and data gathering exercises there. But that’s where the vertical AI comes into play. If you essentially have the ability to have this proprietary data that’s very expensive and difficult to generate,

Grace Shao (47:40)

Yeah.

James Wang (48:09)

that you have a great proprietary source for, you can build a durable advantage with your specific models there. So that’s where we’re seeing a lot of split in this area. And the way that this will work is less like aggregation, like the horizontal aggregation of search engines or marketplaces. Instead, what you have is a lot of vertical productivity, where you can build.

Like say for drug discovery, can very easily imagine where if you can make a huge amount of productivity increase in drug discovery, that is a massive market, even if you stay just there forever.

Grace Shao (48:46)

Yeah, and it’s not like these big tech incumbents are going to move into that space. Frankly, it’s not it’s not just like your point. It’s not even just having the money to buy these data. It’s also having the know-how and the like decades of, you know, data gathering that you had to collect. And you can’t really get that immediately right now, right? From the market, from just like the public market or anything. It’s very different from the data you’re scraping from the Internet. Yeah.

James Wang (49:09)

They also do have their own flywheels that prevent followership. So the flywheel is specifically this. You get the data when it’s cheap because no one thinks it’s valuable. We have one company that literally did this in terms of raw acoustic data. No one usually keeps raw ultrasound data. You usually just have the images, if that, that is kept. No one keeps the raw ultrasound. They bought a lot of it.

Afterwards, in terms of other AI companies that come, they go, ⁓ actually, this stuff is super useful. We didn’t realize that they also approach hospitals and say, let’s buy this. The hospitals wise up, right? They’re like, ⁓ this is actually useful. We’re not going to sell you it for next to nothing. We’re going to sell it to you for a lot of money. In the meantime, the other AI company has been using this data to generate revenue, to do these things, to get to a certain level of quality, such that now it’s like, the gap is getting bigger and bigger. The data is getting more and more expensive because it’s becoming like clear that it’s valuable. At the same time, the level of productivity, the level of quality of your AI is likely going to lag the incumbent that already gathered a bunch of data and is generating data at the same time and continuing to improve the model. So they have their own flywheel effects with these as well.

Grace Shao (50:24)

I see, see. And I think, okay, I don’t know if this is too small of a use case, but I can already imagine, like I just had a baby, right? And then, you know, we went through the private clinic kind of system in Hong Kong where you basically get, they know you have corporate insurance, so they just like make you pay it a ton. And you go in every month, which is absolutely ridiculous. But at same time, when you are a mother, you want to check in and see if there’s anything that needs to be, you know, there’s any flaring issues. Whereas I know in the public sector, if you go to the public system,

You don’t get like ultrasounds until you’re what, like three months into your pregnancy, 12 weeks in. You don’t get checked very regularly. It’s usually every trimester maybe once or twice max. ⁓ So yeah, like for something that’s not simple, but as predictable as like a healthy pregnancy, I can imagine if you have an AI telling the mom and you just pay like 20 bucks a month, like everything looks normal, everything looks fine.

I can totally imagine that being quite useful for the general mass. if they flare up anything like glaring, that’s something that needs more attention, you can then go into a medical staff’s office for proper checkup.

James Wang (51:27)

Totally.

Right. Well, for one, congratulations. But for two, there’s all there’s so think about it. So one of our companies actually has a product like this, not for the consumer. But ⁓ I’m sure after the baby was born, the doctor did a child hip dysplasia check. So basically checking whether or not the child has this specific condition where if you catch it early, all you need to do is it’s

Grace Shao (51:41)

Thank you.

Mm-hmm.

James Wang (52:07)

not all you need to do. It’s kind of annoying. You do have to put braces on the child and everything, but it fixes through a few months the problem for the child’s entire life. So it’s very worth it versus having a condition the child’s entire life. One of our companies, ultrasound company has an AI. Basically their end to end product is to do a quick sweep with the ultrasound where it tells you hip dysplasia. Yes, no. So in terms of that,

Grace Shao (52:29)

Mm-hmm.

Yeah, actually, my baby, we had to go through for that and you have to go to like you have to wait like two weeks for the specialist to check you and the specialist you have to wait hours in a clinic like it’s a long tedious process and it’s expensive. So I can imagine this just being lot more affordable and you can actually deploy it to the mass like across like mass market a lot faster.

James Wang (52:46)

Yes. Well, even with that, it’s just like, again, taking it from like a, I don’t know, evil commercial VC hat, but not really. It’s just like, think about it from a hospital’s perspective. That check is really easy to miss. Like you have checklists, you have all of these things, but you can forget quite easily. That is a huge impact if you forget and it isn’t caught, right? At the same time, it’s like, it’s a doctor that needs to go do the thing. It’s a very valuable resource that needs to stop get sectioned out to go do the thing. If you are a hospital and you are able to basically just have a nurse do a quick sweep and scan and tells you yes, no, and you’ve checked it off the box, you’re probably willing to actually pay a lot for that at the end of the day. And it actually is more beneficial for the consumer too because it is actually doing the thing, super important. It gets done. It’s very accurate.

A lot of the vertical AI areas are this way. like, it’s not just productivity increases from the, it’s not just like cost decreases maybe is how I see it. It’s not just the cost goes down. It’s just that the quality of it, the productivity of it, the like accessibility that goes up. And if anything, a lot of cases, the hospitals that whatever the vertical AI case is perfectly happy to actually pay up to their cost, previous cost of the thing.

because it’s just so much more reliable, easier, and takes away other workflow concerns. So that’s why a lot of this vertical AI stuff is interesting. Its individual price tends to be actually higher, which is not what you typically think with AI, but it’s just more accurate, easier, smoother, and better for the workflow.

Grace Shao (54:25)

Yeah. I can see that. Yeah, that’s really interesting. I want to talk a little bit about the big picture policies between China and the US right now. I know you invest across the stack, including some of the infra stuff. When we talked about earlier, you said, look at data centers as well, So help us understand this. Data centers aren’t new.

Like, you know, AI needs a lot of energy. AI needs a lot of data centers. How do we understand the relationship between these moving pieces?

James Wang (55:11)

Yeah, mean, the big thing is AI data centers tend to take a lot more power ⁓ in general for lot of the internet services and other things. ⁓ Because if you’re using GPUs inference, these tend to be much more power hungry components. For internet services, part of the reason why SAS could basically make money with queries that are fractions, fractions of ascent.

is because essentially it’s almost free. Like you can use a lot of the way that the internet worked is much more around uptime. So if you actually have, and this may get a little bit technical, if you say have like AWS cloud provider share resources, you’re able to surge up and down your capacity and share it across in terms of virtual instances. actual cost of service, a lot of websites, even massive ones is actually not super high. It’s only high from the perspective of like it may be millions of dollars.

But then again, you’re making billions of dollars off of your service that you’re servicing it from. It’s actually not very high. For AI in general, ⁓ its inference costs have been dropping a lot. But even so, with larger models, with needing a ton of memory, with needing a ton of these different things, with GPUs that themselves are both power hungry, but also heat, generate a lot of heat, you basically need to spend the currency of AI is essentially power.

Grace Shao (56:21)

Mm-hmm.

James Wang (56:38)

You need to spend power to literally power the GPUs or whatever XPUs, like TPUs, whatever thing you’re doing to run the AI. And you also need to cool it, which also is generally active, which means it’s also power. So all of it boils down to, okay, we need to spend power to be able to do this thing. It’s the closest thing to it is actually like cryptocurrencies in terms of you actually think of the one-to-one translation between power and actually the thing, ⁓ like what the thing does. So.

Because of that, the sheer density of power requirements means that usually some of these data centers that are trying to serve AI might exceed the power able to be provided from a local grid that was otherwise serving, just like city, resident, and like normal kind of activity. And you are seeing a lot of these data centers for that reason basically doing their own power purchase agreements.

having their own power plants. So they’re not actually on the grid, but they’re basically connected to their own power plants or connected to some of these power systems that are not within like say residential grids or something like that. So that’s been a big part of like why AI has needed that.

Grace Shao (57:38)

Mm-hmm.

As like a average user of AI, should that mean that we should just be more mindful and not use so much AI? Or does that mean that the future of energy consumption will drop as technology advances? Like, how do we understand that? Because like, you know, when we use the internet, it’s not like we think about, my God, how much power consuming, right?

James Wang (58:11)

Yeah. And the thing that I said before was if you take various stats, it’s somewhere between like 70 to 90 % decrease in inference cost each, like each year. So why haven’t like, you know, inference costs falling through the floor while we’re getting more advanced models, we get reasoning models, which actually use way more tokens or words in order to spit out like the same number of tokens that you see.

Grace Shao (58:36)

Yeah.

James Wang (58:38)

So we’re using more and more and more. And that’s why, even though the cost has been dropping so rapidly, we’ve basically kept pace or exceeded it in terms of power. That being said, there’s a question. Where will some of that power requirement ultimately go? How much will be needed? And yeah, will it be the case that we end up just needing exponentially more power? So there’s actually a piece on my sub stack that a

a hedge fund buddy of mine, hedge fund friend of mine from Bridgewater wrote, he does a commodity hedge fund now. His point is actually, even if you take very aggressive estimates as for how much power needs will grow for AI, it’s around like a 3.5 % incremental. That 3.5 % is basically the growth rate that we had during the 1950s in terms of the US power grid growing.

That can pretty easily be hit by renewables, which have intermittency problems. So you basically need battery storage, which is why we also invest in stationary batteries in that area. Or it can be hit by natural gas, or it can even be hit by just retiring coal plants slower. So actually, a lot of the power needs are not as insurmountable as you might think. And I personally suspect it will ultimately be the case that as we plateau in terms of, hey, this thing like

We don’t need it to like give us like, has much reasoning anymore. We just needed to book us vacation tickets or something like that. That’ll ultimately level off while the requirements in terms of compute costs, in terms of power costs will keep falling too.

Grace Shao (1:00:14)

I see, I see. And I think it’s also interesting, so just spoke to David Fishman recently. He’s an energy expert on the China space. And he was saying that, like he kind of mentioned in passing the US side, which is like essentially the US energy kind of, I guess conundrum is more exacerbated because

the center of living has not increased drastically. So people’s consumption of energy have not actually increased drastically. Whereas in China, over the last two decades, energy consumption has been increasing anyway because of urbanization, because of modernization of maybe your home, the economy as a whole. So there’s been more energy planning in China to actually support that kind of energy increasing demand.

And when that AI is now part of the picture, it doesn’t feel like a sudden gap that needs to be filled because you have the renewables, you have small nuclear plants being built out, et cetera. So it’s interesting to hear your perspective that actually the increase in demand, the increasing energy demand is actually not that significant. I think, again, headlines of news articles often really highlight that and really showcase a different picture where sometimes it’s more about like, OK.

People are experiencing higher utility bills. The grid cannot actually support local economies or local people’s livelihood anymore. It seems like it’s causing a big issue for the average citizen. ⁓ But yeah, thank you for putting that into perspective.

James Wang (1:01:42)

It’s totally, well, it’s a self-inflicted issue on the US side. Again, like the US has expanded faster than that at periods in its history. There’s a lot of different energy sources that you can actually use to go after that. It’s just the problem is political in part, like the US has a lot of bureaucracy red tape that’s hard to cut through, in which case it’s hard to build anything economically in the US, which is part of the problem. There’s no nuclear being built.

Grace Shao (1:01:47)

Mm-hmm.

James Wang (1:02:09)

So like you’re saying, China is actually building nuclear at a pretty rapid clip. The US is at best unretiring or maybe retiring slowly its existing nuclear capacity. It’s actively retiring its coal capacity, whereas China is what building a new coal. I think it’s one or two new coal plants every week or something like that in terms of the pace. like it’s just a very different kind of environment.

But it’s also not because yeah, the U S has no technological ability to go after that. It’s yes. Like you’re saying it’s like we have plateaued and a lot of our energy use. There’s also been a big push towards green renewable energy sources, which especially with the U S grid, low power storage, ⁓ it has its own challenges and can’t actually do the base load for AI. So if we wanted to, the U S could actually pretty quickly solve its problem. The question is, is there the political will and is there the willingness to stomach some of the trade offs for sake?

James Wang (1:03:09)

higher carbon cost.

Grace Shao (1:03:11)

Yeah, and I think that’s something David talked about as well in that episode ⁓ where it’s like the trade-off in China is more like, okay, we need more energy so we build more coal, but it doesn’t mean that we stop our renewable. But just because we have renewable doesn’t mean that we stop our coal. The trade-off obviously can be criticized, know, environmental issues, pollution, et cetera. But again, it’s just state level, I guess, mandate or state level priorities a bit different. ⁓

So we’re not a political show. We’re going to move on from that. I want to ask you about the Inflation Reduction Act. So this relates to what you just talked about, a lot of the push on renewable energy. then Trump kind of taking it 180 degree on this. So the IRA was introduced in 2022. It tried to make solar and wind more affordable on the grid. How did that actually work out?

What does it mean now with the Trump’s one big beautiful bill? Give us a high level explanation what’s happening there.

James Wang (1:04:09)

⁓ let’s see, data center developers keep getting whiplash in terms of renewables being good and then bad and then maybe not so bad, but not good either. Something like that. I think that’s sort of the quick high level. I mean, so, ⁓ a lot of the incentives, ⁓ were definitely something, things that a lot of data centers, lot of other folks, like hyperscalers tried to take advantage of, ⁓ when the inflation reduction act was more the law of the land before, you know,

Some of that got thrown out, big, beautiful bill, et cetera. ⁓ But I mean, the big challenge for the US, though, even just stepping back from that, is regardless of how much legislation you throw at it, it’s just like the CHIPS Act, right? You can throw as much legislation at the CHIPS Act to say, we’re suddenly going to build all our chips in the US now, or something like that. And it’s like, well, ⁓ you’re not spending enough money to do that.

And also legislation doesn’t like magically change things unless it specifically hit some of the core problems, which is yeah, the US doesn’t have for chips is like the US doesn’t have enough like labor for this like expertise moved over for sure. It’s ever for the power side. The problem actually goes back to the same thing we just talked about. Transmission interconnects lines old, hard to do, lots of red tape, lots of bureaucracy. It’s hard to build much anywhere.

unless you’re building in places that might not actually be super optimal for say like data centers. So, you know, some of the South in terms of Texas or Southwest has been more amenable to some of the data center and like power build out. It’s also hot there. It would really be nice to put it in a colder place. So you have less power needs to cool the thing too. ⁓ The bigger story, I think with all of this, there’s been a lot of legislation that the US keeps throwing out.

Grace Shao (1:05:54)

Yeah.

James Wang (1:06:00)

Maybe the bigger story I’d say is just the legislation has done some things around the margins. It has not made like a huge 80 20 change, at least from what I’ve seen. It’s like the same problems, the same ultimate macro problems that plague the U.S. and building stuff. And also it’s aging power grids and interconnect problems between different grids are still the same ones, like regardless of the legislative regime that we’re in.

Grace Shao (1:06:24)

Yeah.

An agent issue with the grid is actually like also just a reflection of like, frankly, the US developed and modernized so much earlier than China. And the grid just by nature is older and therefore the capacity and capability is like weaker because technology advance. Right. I think sometimes people forget about that. Just the reality that China didn’t become China that we know of today until like this decade. And the US has been basically the US that we know of today. The last four decades. Right. ⁓

Grace Shao (1:07:33)

Then I have one last question for you. I have one last question for you and it’s a question I ask every single guest that comes on the show, which is what is one differentiated view you have? Our show is called Differentiated Understanding. It’s about how you piece together the information you have and how you form a differentiated view, right? So what is something that you think is a bit non-consensus or against what the majority might think?

James Wang (1:07:35)

Sounds good. Yeah, I mean, I probably would have said it was my view about the vertical AI thing before, because I was talking about that a lot earlier than a lot of other folks, when there was still the talk about foundational models, which still is somewhat talked about. People are really pushing that a little bit less, that foundational models will cover every single use case in existence. And I think there’s been a lot more consensus moved towards that. So maybe

That was a very non-consensus view I had. The consensus has moved more towards. Let’s see, is there any other big non-consensus view right now? ⁓ I think I have one, actually. So another one. So my personal take, because of the way that LLMs have developed and everything, and a lot of the different AI areas have developed, I actually think a lot of the value

of AI from a GDP economy, et cetera, perspective will ultimately be socialized. I don’t mean that as in the government will. Yeah, I don’t mean the government will take it and redistribute it. I don’t mean like something will happen from that perspective or socialism will suddenly take over the US or something like that. What I mean is in terms of economic theory and whatnot, you can either have excess profits be captured by specific corporations and companies.

Grace Shao (1:09:03)

What does that mean?

James Wang (1:09:25)

which is frankly as a VC what I’m trying to invest in and basically have essentially monopolistic power, whatever, and base essentially have a lot of rents from society gathered towards the corporation or the company, or you can have a go to labor or you can actually have that value be socialized. Meaning because of competition, because of diffusion of the technology, because it can’t be controlled as much, it just improves society’s lives.

and isn’t actually excess captured by any single company. Even though like we have these huge model companies, they’re absorbing a lot of money, all these different things are happening. My personal take is like, they don’t actually have such strong barriers. Do I think OpenAI will go to zero? No, I think they have a pretty strong consumer brand. Do I think Google will go to zero? No, they have a lot of things to like distribute out. There’s a lot of uses for it. The companies will still survive, but they won’t become like essentially like world like consuming companies in the way that some people have talked about AI or talked about AI as in it’s a sector where a couple of large companies will suddenly take over everything. I actually think AI will diffuse within the economy quite a bit where we’ll use it in our everyday lives, but we won’t necessarily need to pay a company a huge amount to do it. For example, in the future, you might have edge models that just run on a very like a fairly powerful inference chip on your smartphone.

And you don’t need to pay ChatGPT or anyone else for that. It’s just something that makes your life easier, better. And it’s just there. So that’s one of my takes. I actually think the majority of the value hard to measure as that is will probably be socialized.

Grace Shao (1:11:07)

That’s really interesting. think that reminds me of something I wrote about recently and I think we engaged online about this as well, which is ⁓

the diffusion of AI will in some way look like the diffusion of internet, where it’s not like we just think of four companies as internet companies anymore, but even the tangible real world. Like, you you think about food delivery, you would have never imagined a food delivery company is an internet company. However, it is an internet company these days, whether it’s Food Panda or, you know, like Maytwan or, you know, Seamless in the US, that’s actually like...

not a physical world business only, right? And like when you think of a ride hailing, when you think about even like, I don’t know, apartment hunting, whatnot, it’s not limited to just the physical world. Internet companies actually encompasses all these things that we do. It’s just become the infrastructure. So you’re saying AI essentially will just be part of everything we do and it’ll be empowering everything we do. And it won’t just be limited to like the five companies that we think about nowadays. Yeah. Cool.

Thank you so much, James. Really, really, really helpful, really insightful conversation. And I really enjoyed talking to you.

James Wang (1:12:16)

Enjoy talking with you too, this was great, thanks so much, Grace.

Grace Shao (1:12:19)

Thank you.

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