How China Wins The AI War

Summary

Prof G Markets — “How China Wins The AI War” (May 7, 2026)
Podcast Hosts: Scott Galloway & Ed Elson
Guest: Alice Hahn, Director at Greenmantle, Co-host of China Decode Podcast
Episode Theme:
A deep dive into the escalating AI competition between China and the U.S.—focusing on Chinese AI startup Deepseek, domestic chip manufacturing, geopolitical implications, and the broader technology “arms race.”

Episode Overview

This episode investigates China's advances in AI, centering on the rapid ascent of Deepseek and the country's push to build a full-stack AI ecosystem independent of U.S. technology. Alice Hahn joins Ed Elson to dissect China's competitive posture, the evolving semiconductor landscape, and the political and security ramifications of the so-called “AI war” between the U.S. and China. The conversation frames AI as both a technological and diplomatic battleground, comparing it to the nuclear arms race and exploring what a future equilibrium might look like.

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Key Discussion Points & Insights

1. Market Movements & Deepseek’s $50 Billion Valuation

• [01:45] Ed Elson sets the stage with market briefings, highlights Deepseek’s fundraising, and questions whether $50B is a low valuation compared to U.S. peers (Anthropic, OpenAI at $1T+ valuations):
  • “Deepseek… targeting a $50 billion valuation in its first ever fundraising round…that number, $50 billion strikes me as small. Would you agree?”
• [04:14] Alice Hahn clarifies that Chinese tech valuations are structurally lower, with Deepseek’s funding being state-led rather than market-driven:
  • “This is a feature, not a bug, of Chinese valuations. … Chinese capital markets [are] just not as developed as they are in the U.S.”

Notable:

• Deepseek’s V4 model boasts a 1 million-token context window, competitive with top western LLMs.

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2. The Chip War: Domestic Ecosystems vs. U.S. Hardware

• [05:29] Ed plays a tense podcast moment with Nvidia CEO Jensen Huang raising alarms about Chinese LLMs running on domestic (Huawei) hardware instead of Nvidia’s.
  • [06:07] Jensen Huang (clip): “The day that Deepseek comes out on Huawei first, that is a horrible outcome for our nation.”
• [07:18] Alice Hahn analyzes this fear:
  • Huawei’s Ascend 950PR chips, now being adopted by Bytedance, Alibaba, Tencent, are “2.87 times better on inference than Nvidia’s H20s” (the limited chips allowed for sale to China).
  • “If you continue to push China with … export controls, you incentivize … China to say, ‘We… want to prioritize what is coming out of Huawei and SMIC.’”
• China’s calculus:
  • Short-term bottleneck is chip supply; domestic chipmakers can't yet match the scale or training power of Nvidia/TSMC, but they are creating “good enough” inference alternatives.
  • “China is prioritizing inference. And inference is still an open game…”

Notable Quotes:

• [07:18] Alice Hahn: “It creates good enough alternatives. China is prioritizing inference.”
• [09:53] Alice Hahn: “Right now the political priority trumps what is efficient. … Beijing is trying to kickstart a domestic ecosystem.”

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3. Clustering Hardware & Smart Workarounds

• [12:13] Alice Hahn explains that while Huawei can’t match Nvidia’s most advanced chips yet, China is rapidly closing the gap using techniques like clustering multiple less powerful chips to match U.S. capacity—akin to “a student cheating on an exam and … copying a smarter student.”
  • “If a Huawei chip is 10 times less compute efficient … maybe you cluster 10 of them to equal … Nvidia.”
• She also points out supply chain vulnerabilities:
  • Chip equipment (from companies like AMD, ASML) and inputs like helium remain pressure points the U.S. could leverage.

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4. Multi-Layered AI Race: Who’s Winning Where?

• [16:43] Alice Hahn introduces her “five-layer cake” analogy for comparing U.S./China AI capabilities:
  1. Rare Earths & Upstream Materials: China dominates.
  2. Energy/Electricity: China has “twice the amount of electricity output” as the U.S.—a bottleneck for American data centers.
  3. Data Center Infrastructure: U.S. leads, already surpassing China’s 2030 target.
  4. Model Capabilities: U.S. leads, but China is closing the gap.
  5. Applications: “A mixed bag,” with China showcasing standout “agentic” applications; Meta’s failed bid for China-spawned Manus is evidence.
  • “Right now it’s really mixed across the board. But China tends to dominate on some of these upstream as opposed to downstream [layers].”

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5. AI as a Geopolitical Weapon: The “Nuclear” Analogy

• [18:44] Ed presses on the analogy of AI as a nuclear weapon.
  • [20:01] Alice Hahn: Cites Henry Kissinger’s late-life advocacy for a U.S.-China AI arms control dialogue:
    • “Kissinger… with Eric Schmidt … said that AI and autonomous weapons was going to be what nuclear weapons were during the Cold War.”
  • Beijing is alarmed by the deployment of advanced U.S. AI (Claude, Mythos) in defense and the risk of catastrophic cyberattacks, pushing China to frame AI and quantum tech as national security issues.
• Quantum leapfrog:
  • If AI can hack everything, quantum advancements could be China’s next strategic fortress.

Notable Quotes:

• [21:12] Alice Hahn: “This is part of the reason why China has tried to make AI not just an economic issue, but also a national security issue.”

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6. The Real Battle is Political, Not Just Technological

• [23:18] Ed: “It seems…this question of the AI race between America and China, increasingly, it’s not actually about the technology, it’s about diplomacy.”
• [23:56] Alice Hahn draws on history and Kissinger’s work:
  • “China can in this AI age create its own AI capabilities without American…support.”
  • She stresses the necessity of “continued strategic dialogue and diplomacy,” both at state and expert-track levels.
  • Without it, “as Kissinger rightly predicted…will create massively tragic outcomes…”

Notable Quotes:

• [25:21] Alice Hahn: “If we do ultimately come to a point where there is mutually assured destruction in AI, then we may be able to achieve equilibrium. But that rests on both sides having extremely strong and telegraphed AI capabilities.”

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Memorable Quotes & Moments

• [06:07, Jensen Huang]: “The day that Deepseek comes out on Huawei first, that is a horrible outcome for our nation.”
• [07:18, Alice Hahn]: “China is prioritizing inference. And inference is still an open game in terms of the chip making capabilities.”
• [20:01, Alice Hahn]: “AI and autonomous weapons [are] what nuclear weapons were during the Cold War…”
• [23:56, Alice Hahn]: “China can in this AI age create its own AI capabilities without American largely support or input. … That question and its answer resides on…whether there is going to be continued strategic dialogue and diplomacy.”
• [25:21, Alice Hahn]: “If we do ultimately come to a point where there is mutually assured destruction in AI, then we may be able to achieve equilibrium. But that rests on both sides…”

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Important Timestamps & Topics

• [01:45] — Market news, Deepseek valuation and capabilities.
• [04:14] — Why Deepseek’s lower valuation is expected.
• [05:29] — Tense Jensen Huang clip and reactions on U.S.-China chip rivalry.
• [07:18] — Huawei’s chip leap, U.S. export controls, inference vs. training.
• [12:13] — Clustering chips, supply chain bottlenecks, and smart catch-ups.
• [16:43] — Five-layer cake: Upstream materials to downstream applications.
• [18:44] — The AI “nuke” analogy and national security framing.
• [23:56] — AI risk, strategic dialogue, and needed expert-level diplomacy.
• [25:21] — Mutually assured destruction as possible equilibrium.

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Closing Analysis: Is AI Making Us Dumber?

• [26:29] Ed highlights fresh research showing Gen Z’s cognitive skills are falling—a trend exacerbated by AI use in education:
  • “Students who use AI tools for homework assignments experience a 55% reduction in overall brain activity… your brain is more impaired … than if you were twice over the legal alcohol limit.”

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Summary Table:

| Segment | Main Point | Key Quote / Time | |--------------|----------------------------------------------------------|----------------------------------| | Deepseek | China’s LLM leader, lower valuation, state funded | Alice: “Feature, not a bug…”[04:14]| | Chips/Hardware| Huawei’s ascendance; Nvidia’s fear; inference v. training| Jensen: “Horrible outcome…”[06:07]| | Supply Chain | Bottlenecks; cluster strategies | Alice: “Cluster 10 of them…”[12:13]| | Five-Layer AI| China leads upstream; U.S. leads models & infra | Alice: “Five layer cake…”[16:43] | | AI as Nuke | Diplomatic, not just tech; Kissinger’s warning | Alice: “What nuclear weapons were…”[20:01]| | Diplomacy | Equilibrium via mutually assured destruction in AI | Alice: “Tragic outcomes…”[25:21] |

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Bottom Line

• China isn’t as far ahead on cutting-edge AI chips—but is developing “good enough” inference hardware, aiming for independence.
• The U.S. leads on model performance and data center power—but faces resource and energy bottlenecks.
• Both nations view AI as a critical security and diplomatic issue, not just an economic one.
• The greatest risk is the absence of sustained, meaningful strategic dialogue between the U.S. and China—echoing the era of nuclear brinkmanship, with AI as the new arms race.

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Next Episode Tease:
Ed Elson previews an upcoming conversation with Daniel Yergin, continuing the exploration of geopolitics and global markets.

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For more analysis on tech, AI, and geopolitics, follow Prof G Markets and check out Alice Hahn’s work on the “China Decode” podcast.

Transcript

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[Ed Elson]

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[Clip Speaker / Jensen Huang]

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[Ed Elson]

Today's 15 that's the percentage of Americans who say they'd be willing to take a job where their direct supervisor is an AI program. We're not sure if that tells us how good American AI has gotten or how bad American bosses are. Money market matter if money is evil, then that building is hell. Show goes up the Pressure Network welcome to Property Markets. I'm Ed elson. It is May 7th. Let's check in on yesterday's market vitals. The major indices extended their rally on reports that the US And Iran were reviewing a deal to end the war and gradually reopen the Strait of Hormuz. That news also sent Brent crude tumbling. Treasury yields also dropped. Meanwhile, AMD stock soared nearly 20% after the chip maker beat expectations and raised its guidance. Okay, what else is happening? Chinese AI startup Deepseek is targeting a $50 billion valuation in its first ever fundraising round. Leading the financing is China's biggest state backed semiconductor investment vehicle known as the Big Fund. The country's goal, build a full stack AI ecosystem that can rival the United States. Deepseek first grabbed Wall Street's attention last year with R1, a powerful model that was built at a fraction of the cost of leading Silicon Valley LLMs. And last month it released V4, a model that is now competitive with top US players on a number of benchmarks. So here to discuss Deepseek and the state of the US versus China AI race, we are speaking with our friend Alice Hahn, director at Greenmantle Co host of the China Decode podcast. Alice, thank you for joining us again. I kind of want to start with the valuation here, $50 billion, because at first glance it seems quite low when you compare it to Anthropic, which is trading at a trillion. ChatGPT, OpenAI, sorry, also trading at a trillion on the secondary markets. These companies are ready to go public. And then I also know that this is an extremely powerful model, it's extremely popular, it's the leading model in China. So I guess that number, $50 billion strikes me as small, I guess. Would you agree? And then also, what else strikes you about this news?

[Alice Hahn]

I completely agree, Ed, but this is a feature, not a bug, of Chinese valuations. Even if you look at Chinese tech companies historically, the valuations at the Ford PS much lower than what you see in the Mag 7, what you see in the US. This is a feature of Chinese capital markets just not being as developed as they are in in the us. And more importantly, in terms of the fundraising for Deep Seq itself, yes there will be some private fundraising, but it's largely, I think, going to be state led as opposed to what you're seeing in the US with the anthropics and the OpenAI's of the world. So I think it's completely reasonable and within the bounds of what I expect, that this valuation is orders of magnitude lower than what we're seeing in the States. More importantly, I think when you look at the capabilities, what has been interesting is that it is now announced deep seq v4, that it can do a 1 million token context window. This is comparable to Gemini and what we're seeing out of Anthropic and OpenAI. But there's big, big questions moving forward about the hardware side of things which we can get into. Will DeepSeek be able to access via Huawei and elsewhere, the kind of chips it needs to continue to power its models?

[Ed Elson]

Well, that is exactly where I would like to go and actually I want to Play a clip for you. This was an exchange recently between Jensen Huang and Dwarkesh Patel is a podcaster who had Jensen Huang on his program. And they were talking about China and the fact that Nvidia has been selling chips to China. And it got quite heated and it really pressed on this exact issue of should we be selling chips to China if they're going to use those chips and use them to develop extremely powerful LLMs and AI models? Let's just play the clip and get your reaction.

[Clip Speaker / Jensen Huang]

The day that Deepseek comes out on Huawei first, that is a horrible outcome for our nation.

[Alice Hahn]

Why is that? Because, I mean, currently you can have

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a model like deepseek that can run on any accelerator.

[Alice Hahn]

Why would that stop being the case in the future?

[Clip Speaker / Jensen Huang]

Well, suppose it doesn't. Suppose it optimized for Huawei. Suppose it's optimized for their architecture. It would put ours at a disadvantage. You described the situation. A company developed software, developed an AI model, and it runs best on the American tech stack. I saw that as good news. You set it up as a premise that it was bad news. I'm going to give you the bad news that AI models around the world are developed and they run best on not American hardware. That is bad news for us.

[Ed Elson]

So I guess the striking thing here is one is how uncomfortable Jensen Huang appears to be when he gets this question about China. I mean, this is the first time I've seen him as defensive as he was in this exchange. But it does get to the heart of that question, like, should we be selling chips to China? And also, where are we in that stage? Because we know that the policy's gone off on, off on. We keep on changing our minds. If you could just dive into all of that for me.

[Alice Hahn]

Yeah, well, firstly, I'm a big fan of both Jensen and Dorkesh, and I listened to that very attentively, very recently, I would say, just to break it down a little bit, what is important is that Huawei's Ascend 950PR, which just came out, and it's been powering the deep seq v4. The big companies like Bytedance, Baba Tencent are now rushing to order these chips. These are inference chips. And so just looking at the stats alone, they can perform better on inference by 2.87 times more than the existing Nvidia chips that can be sold to China. These are the H20s, not the H2 hundreds, although those have been approved. China hasn't allowed those imports as of yet. And secondly, they improved the Multimodal generation efficiency by 60%. So just looking at what is available from Nvidia compared to Huawei, apples and oranges, Huawei is doing better on the inference side of things. But if you compare that to the H2 hundreds or if you compare that to the Rubens, Huawei is still a great deal behind. But it seems like Beijing is trying to prioritize a domestic ecosystem, a domestic marketplace of inference chips, such that it will no longer have to rely on Nvidia. So basically, exactly what Jensen Huang expressed as his big fear to d', orcash, which is that if you continue to push China with, for instance, this chip export controls, the chip equipment export controls that just recently have been announced against Hua Hong, which is a major Chinese semiconductor company, then you incentivize across the supply chain, China to say, hey, we are not going to import your next generation, although they will be outdated of chips. We want to prioritize what is coming out of Huawei and smic. Now, what does that mean for Chinese AI companies? It means that yes, to some extent the performance will be behind what you see in America. But the way that I think about it is that it creates good enough alternatives. China is prioritizing inference. And inference is still an open game in terms of the chip making capabilities. Yes, Nvidia is really the king when it comes to the training side of the chip infrastructure. But it's still unclear whether or not it can be the winner when it comes to inference chips. And here I think the Chinese companies like Huawei and SMIC could really give Nvidia a run for its money in the long term. But right now Nvidia is the clear leader. A lot of US companies like Broadcom, AMD are also clear winners. The last thing that I will end on is that currently there isn't enough chips being produced domestically by Chinese chip makers to meet the demand on compute and inference. And that is going to be, in the short term, the biggest bottleneck China ordered. These Chinese Companies rather ordered 2 million H2 hundreds earlier this year because there was so much demand for its AI models. None of that has been approved, obviously by Beijing. Right now Huawei is saying that it can do 750,000 units of its Ascend chips. If you just look at the numbers alone and if you even look at sort of the longer term statistics on how many chips China can produce and the compute output, China is by some estimates only going to be able to produce 2% of what Nvidia and TSMC can produce in 2027 by looking just at compute outputs that is, I think, huge. But right now the political priority trumps what is efficient. And the political priority from Beijing is to kickstart a domestic ecosystem that will be able to rival Nvidia long term.

[Ed Elson]

So just going back to some of those chips that you mentioned there. So you got the Nvidia H20, which was the chip that was essentially designed to be a dumber, slower chip than the highest end that is created by Nvidia that was designed for China. And you're telling us Huawei's now got a chip that is three times more powerful on the inference perspective, almost three times more powerful. So they're not interested in those H20 chips so much anymore, it seems, but still significantly behind the H200 and the Rubin. And those are Nvidia's really fantastic chips. And they're still behind on that front. I guess the question then becomes like, how far behind are they? And also how quickly have they improved recently? Because Huawei, I mean, it was a name in the AI world, but not that big of a name. And suddenly it seems to be gaining a lot of momentum and a lot of steam. As an observer, I'm reading about it a lot more. I mean, are they getting close to a point where they could actually rival Nvidia's most advanced chips, which we do not allow to go to China?

[Alice Hahn]

They're not yet close, but there are smart workarounds. So firstly, on the training front, obviously there's a lot of about Chinese AI companies distilling models coming out of anthropic and OpenAI that helps them. It's almost as if it's a student cheating on an exam and it's just copying a smarter student. Right? This is what is allowing them to really catch up on the inference front. And the inference is really when a student, having learned the information, takes the exam. So even the answers to the queries that you give to say an LLM or to an agent here, Chinese models are actually doing extremely well. And I think the long term bet is that if a Huawei can produce a lot more in terms of chips, it can do these clusters. And you may have heard of this idea of this cluster architecture. So for instance, if a Huawei chip is 10 times less compute efficient than an Nvidia chip, maybe you cluster 10 of them to equal the equivalent of an Nvidia. That I think will be the long term bet. A lot of it rides on how much capacity Huawei and SMIC can do right now. And this connects to Iran. There is a real risk that helium, for instance, which is critical for chip making, is actually going to be a bottleneck and a headwind for Chinese chip makers. Another thing that could be a headwind is if the US decides it's actually going to do more restrictions on chip equipment. China's even more reliant actually on the chip equipment providers like amd, for instance, and asml. If there are more restrictions there, that could again set back China's domestic chip production.

[Ed Elson]

We'll be right back after the break. And by the way, we are heading out on tour at the end of the month, so if you want to get tickets to a show near you, head to profgmarketstor.com it's going to be a lot of fun.

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[Alice Hahn]

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[Maria Sharapova]

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[Alice Hahn]

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[Maria Sharapova]

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[Ed Elson]

We're back with Prof. G Markets. When you think about all of the different, I guess, pieces of leverage or I guess all of the different races would be the right way to describe it. I mean you've got compute capability, America's winning on that front, it seems. You've got energy, you've got supply chain, you've got, I think access to the Strait of Hormuz seems to be increasingly an important piece of this. I mean, when you think about all of the different fronts on which the AI war is being waged, on which of them is China winning against America and on which of them is America winning.

[Alice Hahn]

So I think about it as a five layer cake. And in this regard I was happy that Jensen Huang also had a similar framework from upstream to downstream. So we start from the upstream, which is going to be basically the rare earths that are used, for instance, that power semiconductors and data centers. This is where China has huge dominance. We've seen that as of last year as well with the export restrictions. The second layer that we're going to look into is the energy layer where China again has, it seems, an electricity front, a great deal of leverage. It has twice the amount of electricity output that the Americans have. That is going to be a bottleneck for American data center rollout as well moving forward. Number three is the data center infrastructure layer where the US has done exceptionally well. China will only get to 60 gigawatts targeting by 2030. The US is basically already there as of now. And then the fourth layer is going to be the models themselves where again, based on the status quo right now the US is still leading, although that gap is at times closing because Chinese models are getting more efficient. They are distilling and improving at a rapid pace. And then it's the application layer where I actually think it's a bit of a mixed bag. China is making really interesting applications. This is why Meta wanted to buy and failed to buy Manus. It's a Gentec AI that came out of mainland China and moved to Singapore. I think there will be really interesting applications coming out of China, especially on the agentic front. They could rival what you see in America. It's not just an LLM game and that China has proven that. So I see it as a five layer cake. Right now it's really mixed across the board. But China tends to dominate on some of these upstream as opposed to downstream.

[Ed Elson]

You mentioned Manus there, which is this company that Meta was trying to buy as A Chinese company or I guess a Singapore based company, but has Chinese roots. And then Beijing banned it, which seems to sort of lend itself to the argument that this is becoming quite a hostile race. It's fully a competition. If America tries to get access to stuff that's happening in China as it relates to AI, Beijing will ban it, they will stop it. Same thing is true over in America. And an analogy that I am increasingly hearing when it comes to the AI race is that it's sort of like getting your hands on a nuclear weapon. And this is more and more relevant the more we hear in the headlines, I mean, anthropic coming out with Mythos. And we learn that there's this AI technology that could hack every single cybersecurity system in the world. It could literally bring the infrastructure, the digital infrastructure of a nation to its knees. Because it's seeming more and more that maybe it's a fair comparison, but I don't know, maybe it's a bridge too far. Do you think that that is a reasonable analogy when it comes to the AI race?

[Alice Hahn]

Just to clarify, which analogy in particular are you preferring to, Ed?

[Ed Elson]

The nuclear weapon. The nuclear weapons.

[Alice Hahn]

The nuclear weapons. You know, Kissinger, before he passed away a couple of years ago, wrote a book, as you probably know, with Eric Schmidt, in which he basically said that, that AI and autonomous weapons was going to be what nuclear weapons were during the Cold War when he was Secretary of State and national Security advisor. And he was deeply worried. So much so that he in the last few years of his life went to Beijing to speak with Xi Jinping and high level officials to set up the framework for strategic dialogue which Biden did take up towards the end of his administration. A strategic dialogue between Beijing and Washington on AI arms controls. Now there's an open ended question as to whether or not when Trump meets meets with Xi, likely next week in Beijing, if there is going to be a kickstart to that discussion, a new strategic dialogue will emerge. But I was just in Beijing last week, Ed, and what was very startling to me was the level of fear as to how effective US AI technology has been in its application. In the Iran conflict. There was a lot of concern as well about the use of Claude by the Department of War, by defense community in America. And when Mythos came out, I've seen consternation in China about what that means for Chinese national security. One thing that I will end on is that this is part of the reason why China has tried to make AI not just an economic issue, but also A national security issue. It also made Quantum a national security issue too. Quantum is also going to be key for decryption and encryption. To your point, if there is going to be a mythos that can hack into every system, Quantum may be able to leapfrog that and create a new fortress. So these are all these ongoing concerns, but I think the view from Beijing right now is that there needs to be high level strategic discussion. I won't be surprised if after this meeting between Trump and Xi, there is a revival from the Beijing side about ongoing strategic dialogue concerning AI weapons. Because right now things do look very scary from China's vantage point.

[Ed Elson]

Just going back to the Dwarkesh Jensen Huang interview, I mean, it seemed like there was sort of this miscommunication in the dialogue, where on the one hand, the argument is being made to Jensen Huang that if AI is a nuke, if it is comparable to a nuke, you shouldn't be selling equipment that they are going to use to go and build that nuke and therefore nuke America. And then he's saying, no, they're already building it. They have the ability. So do we want them to be using U.S. made equipment or do we want them to be using China made equipment? Which I feel like didn't quite get to the heart of the disagreement. And something. I was thinking about this and I'd like to get your thoughts. I mean, running with the nuke analogy, China has nukes.

[Alice Hahn]

Yeah.

[Ed Elson]

They haven't dropped them on the U.S. and the reason they haven't dropped them on the U.S. i think is because

[Alice Hahn]

they don't want mutually assured destruction.

[Ed Elson]

Mutually assured destruction. And so to me, I just. It seems as though this question of the AI race between America and China, increasingly, it's not actually about the technology, it's about diplomacy and it's about the relationship between the two. And it's about getting us to a place where China doesn't feel that there is any reason to drop the AI equivalent of a nuclear bomb on America. And it seems to be that that's the trajectory where this is headed. This is a political discussion, not a technological one. I'd just be interested to get your thoughts on that and how this topic is kind of evolving.

[Alice Hahn]

I love that question. As I was listening to that podcast, it was clear that neither of them were historians or politics majors as they approached the question. Because as you know, Ed Chun, China built the with no American help. It was largely Soviet aid. And then they had engineers, physicists who came and built it. By 64, they surprised the world by launching a successful nuclear test in China. That shows you historically that China can in this AI age create its own AI capabilities without American largely support or input. I think what they confuse is this question can in the meantime Wall street monetize China's AI development. Clearly Jensen has an incentive to make that case, which benefits parts of America. But regardless of that argument, China will find a way to create a rival AI system if that AI system is antagonistic or not. That question and its answer resides on your point, Ed, which I completely agree. Is there going to be continued strategic dialogue and diplomacy? Kissinger understood this and he harping on about it even towards the end of his life. And I think it'll be very, very sad and ultimately tragic for humanity if we don't have Washington and Beijing continued discussion about their capabilities and intentions as it pertains to AI. And more importantly, if there isn't track two diplomacy where these Chinese tech companies are also involved because you also need experts in the room to help both sides assess the capabilities. My concern right now is those two sides, both on the expert as well as on the diplomatic side, are very far apart. And that, as Kissinger rightly predicted in his last book on AI, is going to create massively tragic outcomes that we have yet to see, far greater than we have yet to witness in our lifetime. But if we do ultimately come to a point where there is mutually assured destruction in AI, then we may be able to achieve equilibrium. But that rests on both sides having extremely strong and telegraphed AI capabilities.

[Ed Elson]

Alice Hahn, director at Greenmantle, co host of the China Decode podcast Alice, Trump and Xi are likely to meet next week. Maybe we'll get some answers when that happens. Thank you so much for joining us.

[Alice Hahn]

Thanks so much. Ed,

[Ed Elson]

Here's a question for you. Is AI making us dumber? That is the question our research lead Mia Silverio asked in one of her recent substacks and her findings were pretty conclusive. The answer is yes. According to Dr. Jared Horvath, a neuroscientist who recently spoke in front of Congress, every generation in history has been smarter than their parents generation except for Gen Z. Young people underperform on quote, every cognitive measure from IQ to literacy to basic memory skills. Skills. One look at the data will confirm that this is indeed true. Roughly half of American 12th graders are now scoring below a basic level of math and roughly a third of them don't have basic reading skills. In fact, that is the worst rating that we've seen among that group in three decades. But it isn't just America. Over the past two decades, science, math and reading scores among teenagers in high income countries have all fallen nearly 5%. Meanwhile, literacy rates for adults have fallen roughly 3%. In sum, yes, we are literally getting dumber now. Is this just because of AI? No. As Mia points out, there are other factors, such as social media addiction and also the pandemic which stunted learning for millions of children around the world. But we should also acknowledge the extent to which AI is still playing a role here and the fact that it actually is accelerating the trend. One study found that Students who use AI tools for homework assignments experience a 55% reduction in overall brain activity, which means that when you use ChatGPT, your brain is actually more impaired, more suppressed, than if you were to be twice over the legal alcohol limit. However. However, unlike alcohol, where your brain can actually recover over the long term, the impairment effects of using AI appear to compound even after the AI tool is removed from usage. In other words, every student in America is effectively operating as if they were a drunk driver. So will AI make us more productive as a society? Probably. But will it also make us dumber as a society? Certainly. In fact, it already has. If you want to read more about this, you can go check out mia's [e-mail] Mia Silverio she discusses this trend in detail and also many other interesting trends in business and in the economy. That post is called Is AI Making Us Dumb? The answer is yes. Okay, that's it for today. This episode was produced by Claire Miller and Alison Weiss, edited by Joel Patterson and engineered by Benjamin Spencer. Our video editor is Brad Williams. Our research team is Dan Shalon, Isabella Kinsel, Kristen o' Donoghue and Mia Silverio. And our social producer is Jake McPherson. Thank you for listening to Profg Markets from Prof. G Media. If you liked what you heard, give us a follow up.

[Alice Hahn]

Follow.

[Ed Elson]

I'm Ed Elson and tune in tomorrow for our conversation with Daniel Jurgen.