HD Hyundai Electric is accelerating its US expansion, betting that demand for transformers and switchgear will surge as the AI “supercycle” drives a new wave of power consumption. https://t.co/PNxRLiTHsS ...

So data centers now are consuming around 45 gigawatts of power and you can switch between capacity and throughput. I'm going to stick with capacity. Okay.So 45 gigawatts of power and then there's lots and lots of third party estimates for where they're going to be in 2030. And they center around this, you know, 90-95 gigawatts. So you need to add 50 for 2035.There's a lot fewer estimates. You come around 160. So you look at the supply and these utilities are tracking all these data centers connecting to the ...

Overall, we've been on this AI first trajectory for uh uh over a decade now, and it's it's what we've been methodically thinking our way through. It's the reason why we've been working on TPUs for over a decade as an example. But I think specifically at this moment maybe the top question you know is definitely around uh compute capacity all the constraints be it power land uh supply chain uh constraints uh how do you ramp up to meet this extraordinary demand uh for this moment uh get our investments right f ...

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RT Cassandra Unchained (@michaeljburry)Why China will win AI in one chart.Power hungry Nvidia chips are not the way forward for the U.S.It is not just the total power advantage. It is the slope. https://t.co/qNXh1e4lZj ...

US-China AI Race - The US is structurally positioned to lose the AI arms race due to China's advantage in power generation development [2] - China's power generation development is accelerating, while US transmission grid development is decelerating due to permitting issues [2] - The US is investing capital into a race it is structurally positioned to lose [2] Technology and Innovation - Nvidia's development roadmap is essentially a power consumption roadmap, focusing on powering and cooling bigger silicon [1] - Efficiency gains cannot keep up with compute install, leading to climbing power needs [1] - The US needs to innovate with AI-tuned ASICs instead of relying on power-hungry chips [3] Market and Competition - Nvidia has a strong hold on development through investments and agreements with US AI companies and startups [3] - The DOJ Antitrust is investigating Nvidia, but intervention is uncertain [3] - The market needs to realize that LLMs may not be the path to AGI or ASI [3]

Summary of Key Points from the Conference Call Industry Overview - The conference call focuses on the **AI industry** in China and its global context, particularly in comparison to the US AI market. Key Insights 1. **Model Performance Rankings**: - Google's **Gemini 3** has surpassed OpenAI's **GPT-5** to become the top-performing model globally. [1] - China's **Kimi K2 Thinking** is now only **8% behind** Gemini 3, improving from a **10% gap** last month. [3][37] - The performance of Kimi K2 has improved significantly, indicating that Chinese models are catching up to US counterparts despite chip restrictions and lower capital expenditures. [3] 2. **AI Power Demand**: - China's AI power demand is projected to grow to **4%** of national power consumption by **2030E**, while the US is expected to face serious constraints due to grid limitations. [2][19] - The US and China will account for an estimated **46%** and **35%** of global IDC power consumption, respectively. [19] 3. **Power Generation and Infrastructure**: - China has over **400 GW** of annual power generation growth and a **50,000 km** ultra-high-voltage (UHV) transmission network, which supports its AI infrastructure. [32] - In contrast, the US lacks a coordinated grid system, leading to significant power constraints for AI development. [66] 4. **Cost of Power**: - The average power tariff in the US is estimated to be **55% higher** than in China, leading to projected AI power costs of **US$60 billion** for the US by **2030E** compared to **US$29 billion** for China. [34][73] - Total IDC power costs are expected to grow at a **22% CAGR** in the US and **17% CAGR** in China from **2025E** to **2030E**. [77] 5. **Smartphone AI Integration**: - ZTE launched a smartphone powered by ByteDance's **Doubao Mobile Assistant**, showcasing AI capabilities. However, its commercial success is uncertain due to access restrictions from major apps like WeChat and Alipay. [4][88] - The competition for "access control" among major internet platforms in China poses challenges for the viability of such AI services. [4] 6. **AI Chip Market**: - The US approved the sale of **NV H200** AI chips to China, but China is likely to pursue AI chip independence, preferring to develop local capabilities. [5] Additional Insights - **Data Center Power Consumption**: - Global data center power consumption is expected to grow from **486 TWh** in **2024** to **1,301 TWh** by **2030E**, accounting for **4.2%** of global power consumption. [56][114] - The US data centers are projected to consume **604 TWh** by **2030E**, which will be **33% higher** than China's consumption. [117] - **Market Dynamics**: - The competition in the AI model market is intensifying, with significant improvements in performance from both Google and Moonshot's models. [81] - The gap in performance between Chinese and US models has narrowed from **16% to 8%** over the past three months, indicating rapid advancements in Chinese AI capabilities. [87] - **Challenges in the US**: - The US faces significant challenges in expanding its electrical grid to meet the rising demands of AI-driven data centers, with an estimated **12 GW** power shortfall by **2028E**. [125] This summary encapsulates the critical points discussed in the conference call, highlighting the competitive landscape of AI in China and the US, along with the implications for power consumption and infrastructure development.

Data Center Energy Consumption - By 2030, global data centers are estimated to consume power equivalent to Japan's entire economy [1] - This represents a doubling of current data center power consumption [1] Technology & Efficiency - The industry questions whether technology-driven efficiencies can offset the growing demand for power in data centers [1] Event Announcement - The Bloomberg New Economy Forum will be held in Singapore from November 19-21 [1]

Market Fundamentals & Trends - Data center vacancy rates across North America are below 3% [3] - Tier one markets experience even lower vacancy rates, below 1% [3] - The imbalance between data center supply and demand could reach 10 gigawatts by 2030 [5] - Data center infrastructure demand is expanding to newer geographies [5][10][11] Power & Infrastructure - Grid lead times for power are currently 5 to 7 years [6] - Data center consumption is expected to quadruple from 2-3% to 10-12% of total power consumption in the next 5+ years [7] - Utilities are implementing deposit requirements for turbines and other infrastructure [8] - Collaboration among tech companies, utilities, and data center developers is crucial for securing power [8] - Alternative power solutions like nuclear and renewables are becoming increasingly important [9] Investment & Valuation - Investors are questioning data center valuations amid concerns about an AI bubble [1] - Meta is investing $29 billion in a data center project in Louisiana [10]

Here to discuss the Dennis said further. Nancy Nancy Angler Lafferty, Angler investment CEO, who says, quote, It's about the power, stupid. Okay.Hit me with that thesis. Oh, and you always find the right nugget to pull out. Yeah, well, I mean, look, there's there's expected to be I mean, open air has said they need 20 gigawatts of of power at 16 billion per two gigawatts.That's that's a fair amount of change. And then you have all of these just using Texas as an example. You have all of these companies open ...