Elon Musk recently invoked the Kardashev Scale, sparking a conversation about an undeniable reality: AI Data Centers are devouring energy at an exponential rate.

Historically, we viewed this problem through the lens of conservation. We worried about efficiency. But the math is becoming unforgiving. Analysis from the SRC’s Decadal Plan for Semiconductors suggests a grim timeline: by approximately 2040, global computing demand will outstrip the entire world’s energy supply.

We are sprinting toward an invisible but solid “Energy Wall.” Without radical breakthroughs, the exponential growth of AI could hit a hard ceiling as early as 2030.

Source:The Wall Street Journal, WSJ

But what if the solution isn’t to use less, but to access more? What if the solution is to move the infrastructure off-planet entirely?

It sounds like science fiction, but it is actually a conclusion rooted in hard physics that dates back to 1960.

1. Not Sci-Fi, But Science (Princeton, 1960)

To understand why we are now talking about launching servers into orbit, we must travel back to June 1960.

While the world was obsessed with the Space Race, Freeman Dyson, a legendary physicist at the Institute for Advanced Study in Princeton, published a short but profound paper in the prestigious journal Science.

The paper was titled “Search for Artificial Stellar Sources of Infrared Radiation.”

At the time, astronomers were struggling with how to find extraterrestrial civilizations. Most were focused on “listening” for radio waves (like in the movie Contact). Dyson proposed a completely different perspective: Don’t listen for what they say; look for what they consume.

Dyson’s logic boiled down to three core concepts:

• A. The “Exponential Growth” is Irreversible: Dyson noted that any industrial civilization (humans included) experiences “Malthusian growth.” Simply put, a civilization’s appetite for energy grows exponentially. Eventually, planetary resources (fossil fuels, nuclear, or surface solar) hit a hard limit.

• B. The Only Exit is the Star: The Earth intercepts only about one two-billionth of the Sun’s total energy. The rest flows uselessly into deep space. Dyson deduced that an advanced civilization must eventually build a structure to intercept and collect that lost energy.

• C. The Infrared Signature (Waste Heat): This was his brilliant insight into thermodynamics. Energy used is not destroyed; it is converted into heat. If a civilization wraps a star to generate electricity, that heat must be radiated away to prevent the structure from melting. Therefore, an advanced civilization wouldn’t look like a bright star—it would look like a dark object glowing intensely in Infrared.

2. Debunking the Myth: It’s Not a “Sphere”

Pop culture (like Star Trek) often depicts a Dyson Sphere as a solid, hollow metal shell completely enclosing a star.

This is a misunderstanding. Dyson himself later clarified that a solid shell is mechanically impossible; gravity would tear it apart.

The scientifically accurate concept is a “Dyson Swarm.” Imagine a swarm of mosquitoes surrounding a streetlamp. It involves billions of independent satellites, solar mirrors, and space stations orbiting the sun in a dense formation. They form a loose but massive network to harvest energy.

3. The Convergence: Why We Are Building the “First Lego Brick” Today

Back to 2025. Why are we dusting off a 60-year-old physics paper? Because AI has driven us right into the “Energy Wall” Dyson predicted.

The concept of Space-Based Data Centers proposed by today’s tech industry is essentially the micro-implementation of a Dyson Swarm. Here is why the physics and economics finally make sense:

• Unlimited Solar Power (Concept B): Instead of waiting for that tiny fraction of sunlight to filter through Earth’s atmosphere, we can place data centers in Sun-Synchronous Orbits (SSO) or on the Moon’s “Peaks of Eternal Light.” There, solar panels receive pure, unfiltered energy 24/7.

• Radiative Cooling (Concept C): The “waste heat” problem Dyson identified has a natural solution in space. The background temperature of the universe is 2.7 Kelvin (-270°C). Projects like the EU’s ASCEND and startups like Lumen Orbit are validating how to dump heat directly into this deep freeze, eliminating the need for the massive water-cooling systems used on Earth.

• In-Situ Consumption: There is no need to beam electricity back to Earth (which is inefficient). We use the power right where it is gathered to run the compute, and then beam only the processed data back.

• Plummeting Launch Costs: Thanks to SpaceX (Falcon 9 and Starship) and Blue Origin, the cost to put heavy hardware into orbit is crashing down, making this economically viable.

• Satellites as Servers: Future constellations like Starlink or Kuiper won’t just be communication relays; they will be orbital compute nodes.

Further Reading & Deep Dives

To prove that sending data centers into orbit is not just a pipe dream, here are three key references from government, academia, and the private sector that form the theoretical bedrock of “Space-Based Computing.”

1. The Definitive Official Study: The EU ASCEND Project

Funded by the European Commission, this massive study aimed to verify whether orbital data centers could be a solution for achieving net-zero emissions.

• Project Title: ASCEND (Advanced Space Cloud for European Net zero emission and Data sovereignty)

• Lead Agency: Thales Alenia Space (on behalf of the European Commission)

• Key Conclusions:

  • Technical Feasibility: The report confirms that utilizing space’s “unlimited solar energy” and “extreme cold environment” effectively solves the two biggest pain points of terrestrial data centers: power consumption and heat dissipation.

  • Environmental Threshold: The report honestly notes that to be environmentally viable, we need to develop new launch systems that are 10x greener than current rockets (aligning with the goals of Starship regarding full reusability and fuel efficiency).

2. Academic Architecture Analysis: Cost Models & Cooling

This paper provides a detailed examination of the economic models for Space-Based Data Centers (SBDC), specifically quantifying the trade-offs between latency and cost.

• Paper Title: Space-Based Data Centres: A Paradigm for Data Processing and Scientific Investigations

• Publication: A. A. Periola & M. O. Kolawole (2019, IEEE Access)

• Core Insights:

  • The Cost Trade-off: While launch costs remain high, SBDCs offer massive savings on expensive terrestrial land and increasingly scarce cooling water resources.

  • Best Use Case: The architecture is described as perfect for “Non-Real-Time AI Training”, where high bandwidth is needed but millisecond latency is not critical.

3. Industry Whitepaper: The Business Case for AI Training

This is the most relevant document regarding the current startup ecosystem and Elon Musk’s perspective, offering a concrete path to commercialization.

• Whitepaper: Why we should train AI in space

• Publisher: Lumen Orbit (now Starcloud) — A Y Combinator-backed startup.

• Core Arguments:

  • Energy Arbitrage: Solar panels in space are 5-8x more efficient than on Earth due to the lack of atmospheric interference and the absence of night (in specific orbits).

  • Ultimate Passive Cooling: Leveraging the Stefan-Boltzmann Law, heat can be radiated into the 2.7K (-270°C) background of deep space with near-zero energy consumption.

  • Strategic Positioning: The paper proposes a specific business model: focusing exclusively on AI Model Training rather than Inference, cleverly sidestepping the physical limitations of communication latency.

Conclusion

By moving AI compute to space to harvest solar energy directly, we are effectively placing the first “Lego brick” of a Dyson Sphere.

AI, once thought to be limited by Earth’s energy constraints, might be the very catalyst that pushes humanity out of the cradle. We are watching science fiction become engineering reality, one orbital server rack at a time.

Note: The Kardashev Scale

The Kardashev Scale was introduced in 1964 by Soviet astronomer Nikolai Kardashev. He posited that as a technological civilization advances, its energy consumption grows exponentially. He classified civilizations into three levels:

• Type I (Planetary): A civilization that can utilize all the energy available on its home planet (Earth).

• Type II (Stellar): A civilization capable of harnessing the total energy output of its star (the Sun)—i.e., by building a Dyson Swarm.

• Type III (Galactic): A civilization that can control the energy of its entire galaxy (the Milky Way).

At our current technological level, humanity has not yet reached Type I status. We are currently estimated to be at approximately Type 0.7.

Source：

https://all-fiction-battles.fandom.com/wiki/The_Kardashev_Scale

https://fermatslibrary.com/s/search-for-artificial-stellar-sources-of-infrared-radiation

I’ve tried to break down next year’s ASIC trends and supply chain outlook in this post. Just wanted to share my findings with you all! If I got anything wrong, please let me know. I’d love to correct it so we can all grow and learn together

The trajectory for high-end AI ASIC chips suggests they will become the next critical growth engine for the AI server supply chain, surpassing high-end GPU growth in terms of percentage increase from 2025 through 2026.

Key Market Momentum and Forecasts

High-end ASIC chip shipments are projected to experience significant year-over-year (YoY) growth, exceeding 40% in both 2025 and 2026.

• ASIC Growth Superiority: The growth momentum of high-end ASIC chips is notably superior to high-end GPUs. High-end ASIC YoY growth is projected at 40.6% in 2025 and 41-43% in 2026, compared to high-end GPU YoY growth of 30% and 23% respectively.

• 2026 Shipment Volume: Total high-end ASIC shipments are forecasted to reach 7.234 million units in 2026.

• Primary Driver: Google’s TPU chips are identified as the strongest growth engine for ASIC shipments, continuing to lead the volume share among ASIC chips. Google’s TPU shipments are projected to reach 3.325 million units in 2026, showing an increase of nearly 1 million units compared to 2025.

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Google TPU (The Strongest Driver)

Google’s TPU supply chain is a key area of focus for 2026.

• TPU v7p (Ironwood): This mainstream version is scheduled for mass production in 3Q25. It will use the 3nm process and 192GB HBM3E memory.

◦ Architecture: It maintains the classic 64-accelerator design and utilizes the HLC architecture, with four accelerators per single motherboard.

◦ Scale-up: The scale-up range will be expanded to 9,216 accelerators across 144 cabinets. This expansion relies on the OCS (Optical Interconnect) architecture, which provides a unidirectional bandwidth of 600GB/s.

• Future Planning: The TPU v7e (A5921) is scheduled for mass production in 2H26. MediaTek (聯發科) is expected to mass-produce the TPU v7e by the end of 2026.

• Supply Chain: Amkor is anticipated to enter the TPU advanced packaging supply chain in 2H26.

AWS Trainium

AWS Trainium shipments are expected to decline in 2026 compared to 2025, as the platform faces a product transition period.

• 2026 Shipment: Shipments are estimated at 1.476 million units in 2026.

• Trainium3: This version is expected to enter mass production in 2Q26, utilizing the 3nm process and 144GB HBM3E memory.

• Trainium3 MAX: This server system will debut a liquid cooling architecture (water-to-air cooling solution). The architecture is similar to NVL72, featuring four ASICs per computing board.

◦ Interconnect: AWS is collaborating with Astera Labs on the Scale-up solution to achieve Any-to-Any interconnectivity.

◦ Future Outlook: Given the close partnership between AWS and Astera Labs (a major promoter of UALink), the next-generation Trainium4 server is speculated to adopt the UALink architecture, which is projected to launch its first solution in 2027.

• Supply Chain: Alchip is the main chip design partner for Trainium3. Wiwynn and QCT are responsible for the front and back-end server assembly.

Meta ASIC (Athena, Iris)

Meta’s high-end AI ASIC products are expected to achieve volume shipments in 2026.

• 2026 Shipment: Shipments are projected to reach 600,000 units in 2026, a significant increase from the 50,000 units shipped in 2025.

• Athena: Scheduled for mass production in 4Q25 (5nm, 216GB HBM3E). The chip will utilize a blade server cabinet design.

• Iris: Scheduled for mass production in 2H26 (3nm, 288GB HBM3E).

• Roadmap: Meta has a rapid product roadmap, including Arke (2027f, 2nm, HBM4) and Olympus (2028f, 2nm, HBM4E(e)).

• Supply Chain: Broadcom (博通) is the sole design partner for Meta ASIC, supplying the complete accelerator interconnect and networking solutions (including Tomahawk 5/6 and Tomahawk Ultra). Quanta (廣達) is actively vying for the Iris server back-end assembly orders.

Microsoft Maia

Microsoft’s next-generation Maia 200 is set for mass production in the second half of 2026.

• Maia 100: This was an experimental product, with combined shipments of 23,000 units across 2024 and 2025.

• Maia 200: Expected to enter mass production in 2H26, with an estimated full-year shipment of 330,000 units. It uses the 3nm process and 128GB HBM3 memory.

• Future Plans: The subsequent Maia 300 may potentially adopt Intel 18A process and advanced packaging technology.

Open AI

Open AI has announced an ambitious long-term ASIC deployment plan.

• 10GW Plan: Open AI declared on October 13, 2025, a plan to deploy 10GW of ASIC by the end of 2029, estimated to total at least 5 million accelerators.

• Titan1: The first-generation AI ASIC (Titan1) is projected for mass production in 4Q26 (3nm, 144GB HBM4).

• Titan2: Projected for mass production in 4Q27 (2nm, 288GB HBM4)

Table 1: High-End AI Accelerator Shipment Volume and Forecast (2024–2026) (Units: Thousands)

Table 2: Key ASIC Accelerator Specifications and Planning (2024–2028)

Source：DIGITIMES

This thanksgiving trip took us from Austin to Denver. Usually, we plan our stops around Costco, but on this route through the Texas panhandle, there was almost nothing—except for Love’s Travel Stops.

Seeing so many of them sparked my curiosity. As investors and founders, we often look at high-tech moats, but Love’s offers a masterclass in business model evolution. It’s not just a “gas station”; it is a comprehensive service ecosystem for the logistics industry.

Here is the breakdown of an empire built on diesel and smart pivots.

1. The Origin Story: Spotting Opportunity in Failure

Love’s is fully owned by the Love family (founded by Tom Love). It started in Watonga, Oklahoma, when Tom leased an abandoned service station to create “Musket Station.”

• The First Pivot (1970s): Tom boldly introduced Self-Service Gasoline. This reduced labor costs and allowed him to sell fuel at competitive prices, quickly capturing market share in price-sensitive rural areas.

• The C-Store Model: He opened a site in Guymon, Oklahoma, that combined self-service gas with a “Mini Stop.” This was the prototype for the modern Convenience Store model: use low-margin fuel as a Loss Leader (traffic driver) to convert customers into high-margin retail buyers (cigarettes, drinks, food).

2. Riding the Macro Tailwind

Two major events in 1980 supercharged their growth:

1. The completion of the Interstate Highway System.

2. The Motor Carrier Act of 1980, which deregulated the trucking industry.

This created an explosion in long-haul trucking. In 1981, Love’s opened its first true “Travel Stop” in Amarillo, Texas. They shifted their target persona from “general drivers” to “professional truckers.”

• Service Upgrade: 24/7 operations, showers, and massive truck parking lots.

• The Result: The company evolved from a “street-side convenience store” to a critical “transportation infrastructure provider.”

Today, they operate 640+ locations across 42 states and are a fixture on the Forbes list of America’s Largest Private Companies (2023 Revenue est. $45B - $60B, depending on oil prices).

3. The Moat: Vertical Integration & Closed-Loop Ecosystem

Love’s isn’t just selling gas; they are capturing value at every link in the chain. This is a classic “Closed-Loop Ecosystem”:

• Musket Corporation (Trading): Their commodities trading and logistics arm. They handle fuel procurement, hedging, and wholesale, maximizing the Fuel Margin.

• Gemini Motor Transport (Logistics): Their own fleet. They transport fuel from terminals to stations, ensuring supply chain stability and lowering logistics costs.

• Speedco (Maintenance): On-site truck repair and tire services.

• Factoring (Fintech): Financial services for trucking companies.

Furthermore, they have a Real Estate Moat. Acquiring large plots of land right off major highway exits (necessary for truck turning radiuses) involves high regulatory hurdles. It is incredibly difficult for new entrants to replicate their footprint.

The industry has three main players: Pilot Company (majority-owned by Berkshire Hathaway), Love’s, and TravelCenters of America (TA).

What fascinates me most is that Tom Love repeatedly rejected Wall Street’s IPO offers. He understood a fundamental truth that plagues many public tech companies today: Quarterly earnings pressure kills long-term vision.

By staying private, Love’s avoided the pressure to cut CapEx (Capital Expenditures) to please analysts. This allowed them to invest counter-cyclically—buying land and expanding when oil prices crashed or the economy dipped.

Takeaway

Tom Love’s journey—from an abandoned station to a multi-billion dollar empire—is a lesson in cash flow and customer retention. Every step he took addressed a core pain point:

“How do I get the people passing by to stop longer and spend more on my property?”

Whether you are building a SaaS platform or a hardware startup, the question remains the same.

How to Apply Tom Love’s Strategy, Spirit, and Opportunity Discovery in the Modern Era

Tom Love’s success was not built on “inventing new technologies,” but on “restructuring existing needs” and “extreme operational discipline.” In an era dominated by AI and digitization, studying his strategy is arguably even more valuable. He demonstrated how to mine immense profits from “unsexy” traditional industries by solving fundamental inefficiencies.

1. Opportunity Discovery: Identifying “Fragmentation & Friction”

Tom Love’s core insight was identifying a disconnect: fueling and shopping were separate activities (inefficiency), and the basic needs of truck drivers were being ignored.

How to apply this today:

• Seek out “Forgotten Infrastructure”:

  • Tom Love’s Era: Gas stations were dirty, and no one cared about the driver’s experience.

  • Modern Era: Observe industries that are “essential but offer a terrible experience.” Examples include elderly care, blue-collar recruiting, waste management, and traditional logistics warehousing. These sectors are often low on digitization and high on fragmentation—ripe for consolidation and optimization.

• Practice the “Bundling Strategy”:

  • The Love’s Model: Bundling “Fuel + Snacks + Showers + Parking.”

  • Modern Application: Consider what else your target audience needs immediately before or after completing a task.

    • Example: Modern gyms are no longer just about equipment; they bundle physical therapy, nutrition subscriptions, and social co-working spaces.

• Focus on “Humanity in B2B”:

  • The Insight: Most saw truckers merely as “capacity”; Tom Love treated them as “people” (who need hot showers and hot food).

  • Modern Application: In SaaS or Enterprise Services, look beyond features to “emotional value.” Making a user’s workflow less stressful and more dignified is a powerful competitive moat.

2. Strategic Execution: From MVP to Ecosystem

Love’s was not built in a day. It started with leasing a single abandoned station.

How to apply this today:

• Low-Cost Trial ( The MVP Mindset):

  • The Precedent: With only $5,000, Tom Love didn’t buy land; he leased an abandoned site.

  • Modern Application: Do not start with heavy CapEx or massive app development. Use existing tools (No-code, existing platforms) to test the business model. Only deploy significant capital once you have validated that “someone is willing to pay.”

• The Courage for Vertical Integration:

  • The Precedent: Once scaled, Love’s integrated upstream (Musket for trading, Gemini for transport).

  • Modern Application: Many startups fail by relying too heavily on third-party platforms (e.g., Facebook Ads or Amazon Logistics). Learn from Love’s: once your core business is stable, gradually control your supply chain or own your data. That is where the margin lies.

• Asset Duality:

  • The Precedent: Love’s appears to be retail, but fundamentally, it is a real estate business (controlling highway exits).

  • Modern Application: Consider if your business can accumulate “Digital Assets” or “Network Effects.” Build a community (digital real estate), not just a transactional product.

3. Entrepreneurial Spirit: Servant Leadership & “On-Site” Culture

Tom Love famously said: “We sell what everyone else sells (fuel, cola). Why should they choose us? Because of Clean Places and Friendly Faces.”

How to apply this today:

• The “Clean Toilet” Philosophy:

  • The Habit: Even as a billionaire, Tom Love would inspect the restrooms during site visits. This represents an obsession with detail.

  • Modern Application: Whether in software or hardware, your “toilet” is your customer support response time, your UI/UX usability, and your refund process. These unglamorous details determine retention.

• Contrarian Investing:

  • The Habit: The Love family historically purchased land during economic recessions when it was cheapest.

  • Modern Application: When the market is chasing a bubble (like a specific AI hype cycle), remain calm and preserve cash. When the market panics and crashes, that is your moment to acquire premium assets (talent, technology, user base).

Hey everyone! I recently moved to Austin and am still getting used to everything—the Texas heat and the Austin traffic, but we’re adjusting pretty well so far. We also went to our very first F1 race last week, and it’s totally different from watching it on TV. The sound of the engines in person really gets your heart racing. Congrats to Max from the Red Bull team for taking the win that day!

This is my first long-form article diving deep into the industry. I’d love for more friends in this field to reach out via email and chat about your thoughts on data centers, energy, AI applications, or anything related—I’m really interested in all of it. In the future, I’ll be sharing more articles like this, along with some updates on my personal life.

Today we will explore the development trends and key points of 800V HVDC. Most of the information is based on public data and supply chain research. At the OCP conference held in October 2025, Nvidia released a paper titled “< 800 VDC Architecture for Next-Generation AI Infrastructure >,” which details the 800 VDC power architecture Nvidia is promoting to address the power consumption challenges of AI data centers.

Why is the 800V HVDC architecture needed, and why now?

In one sentence: The power consumption of AI servers is increasing, requiring a new power supply system to meet efficiency needs.

The current mainstream power solution involves 33kW and 72kW power shelves placed in IT racks (using 48-54V [approx. 50V] DC as the intermediate bus voltage). As GPU power consumption soars (e.g., the Blackwell GPU requires 130 DrMOS chips), this architecture suffers from excessive current when transmitting 48V, leading to severe power loss and requiring a large amount of copper cabling, making it unsustainable.

A simple calculation: powering a 1MW rack requires 200 kg of copper busbars. A 1GW data center (about 1000 racks) would cumulatively need 500,000 tons of copper, which is completely unfeasible in terms of cost and physical space.

Therefore, the NVIDIA technology blog published an article on May 20th this year titled: NVIDIA 800 VDC Architecture Will Power the Next Generation of AI Factories. This was the first time they introduced their plan to transition data center power infrastructure to 800 VDC to the public.

The old approach was for each rack to do its own power conversion. AC power would come in from the outside, be converted to 48V DC by PSUs (Power Supply Units) at the bottom, and then converted again to the IV (Intermediate Voltage) used by the chips.

This method faced several problems: each GPU board had hundreds of power chips (DrMOS), which consumed a lot of power, got very hot, and used too much copper wire—amounting to about 200 kg of copper for an entire cabinet.

The new architecture is called HVDC (High Voltage Direct Current). It centralizes the AC to 800V DC conversion, sends it to the server rows, and only steps down the voltage near the chips. This largely borrows from electric vehicle (EV) charging technology, making it cheaper and faster to deploy.

The advantages of this are:

1. Improved Efficiency: At the same power level, increasing the voltage to 800V significantly reduces the current ($P=VI$), thereby drastically cutting power loss during transmission.

2. Cost Savings: Lower current means less copper is needed, saving on material costs.

3. Mature Technology: This 800V architecture heavily borrows from electric vehicle (EV) charging technology, making its deployment cheaper and faster.

At the same time, the biggest risk is the centralized Single Point of Failure (SPOF): If this centralized high-voltage PSU fails, it could cause a large number, or even an entire row, of cabinets to go down. Therefore, redundant design is crucial, which also increases the demand for BBUs (Battery Backup Units).

According to Nvidia’s 800V HVDC white paper, four levels of power transmission and evolutionary stages are outlined, including two solutions:

1. Power Rack (stepping up voltage from 480V to 800V) + Power Supply Unit (PSU) (stepping down 800V to...)

2. Solid State Transformer (SST), used to perform 10k-33kVAC to 800VDC conversion.

Nvidia’s current hope is that solution (2) will be the final one implemented. This would completely eliminate many components of the traditional LV AC level (like LV switchgear), achieving the most streamlined, efficient, and reliable “Grid to Rack” power chain to support AI factories with power densities of 1 MW or higher.

Calculating the Number of CSP Power Racks from Data Centers

From the industry’s TAM perspective, OpenAI (OAI) announced that Stargate will invest $400B between 2025-2028 to build 7GW of computing power. OAI has also declared plans with NVDA/AMD/AVGO to build 10GW/6GW/10GW of computing power, totaling 26GW.

One GW requires approximately 1,000 800 VDC power racks (1 GW ÷ 1 MW/rack = 1,000). Based on OAI’s plan, this would require about 26,000 800 VDC power racks. Of course, this is the most optimistic scenario, giving us a ceiling for estimation.

HVDC is basically an architecture designed specifically for the NVL576. The volume will depend on the demand for the NVL576.

However, this isn’t necessarily the case for ASICs. It depends on whether individual CSPs want to adopt it. The author believes that in the long run, everyone will adopt it; it’s just a matter of pace. Because the NVIDIA ecosystem has now defined 800 VDC as the common standard for AI Factories, if AWS adopts the latest NVIDIA reference architecture (or an equivalent self-developed solution), it is highly probable they will ultimately use 800 VDC (or a hybrid form).

According to research, it is currently known that Meta will need 4,000 racks in the next 2 years, and Google will need 6,000 racks in the next 2 years.

Deep dive into each element of the HVDC power rack

Currently, there are two types of HVDC power racks: the OCP (Open Compute Project) standard and the NVIDIA standard 800V high-voltage DC power rack.

• OCP (±400V): A transitional or compatibility solution. Led by Cloud Service Providers (CSPs), it uses a positive and negative 400V output, which can adapt to existing 400V equipment while also supporting 800V.

• NVIDIA (Monopolar 800V): A direct-to-800V solution. The goal is to convert to 800V DC right from the substation and directly power future MW-level AI racks, pursuing higher end-to-end efficiency.

From this point, we begin to quantify the impact of this specification change on the supply chain.

Calculating the Number of CSP Power Racks from Data Centers

• From the industry’s TAM perspective, OpenAI (OAI) announced that Stargate will invest $400B between 2025-2028 to build 7GW of computing power. OAI has also declared plans with NVDA/AMD/AVGO to build 10GW/6GW/10GW of computing power, totaling 26GW.

• One GW requires approximately 1,000 800 VDC power racks (1 GW ÷ 1 MW/rack = 1,000). Based on OAI’s plan, this would require about 26,000 800 VDC power racks. Of course, this is the most optimistic scenario, giving us a ceiling for estimation.

• HVDC is basically an architecture designed specifically for the NVL576. The volume will depend on the demand for the NVL576.

• However, this isn’t necessarily the case for ASICs. It depends on whether individual CSPs want to adopt it. The author believes that in the long run, everyone will adopt it; it’s just a matter of pace. Because the NVIDIA ecosystem has now defined 800 VDC as the common standard for AI Factories, if AWS adopts the latest NVIDIA reference architecture (or an equivalent self-developed solution), it is highly probable they will ultimately use 800 VDC (or a hybrid form).

• According to research, it is currently known that Meta will need 4,000 racks in the next 2 years, and Google will need 6,000 racks in the next 2 years.

Deep dive into each element of the HVDC power rack

Currently, there are two types of HVDC power racks: the OCP (Open Compute Project) standard and the NVIDIA standard 800V high-voltage DC power rack.

• OCP (±400V): A transitional or compatibility solution. Led by Cloud Service Providers (CSPs), it uses a positive and negative 400V output, which can adapt to existing 400V equipment while also supporting 800V.

• NVIDIA (Monopolar 800V): A direct-to-800V solution. The goal is to convert to 800V DC right from the substation and directly power future MW-level AI racks, pursuing higher end-to-end efficiency.

Supply Chain Players and Estimates

Nvidia’s currently announced 800V HVDC partners include:

• Silicon providers: Analog Devices, Infineon, Innoscience, MPS, Navitas, OnSemi, Renesas, ROHM, STMicroelectronics, Texas Instruments

• Power system components: Delta, Flex Power, Lead Wealth, LiteOn, Megmeet

• Data center power systems: Eaton, Schneider Electric, Vertiv

• GB300 BBU becomes standard, suppliers include Shunda, Sinpro, AESKY.

Semiconductors: GaN, SiC. NVTS, Innoscience

• According to the latest NVTS conference call, the company expects its AI data center business to see significant growth starting from the end of 2026.

• Innoscience predicts the global GaN power semiconductor industry will have a compound annual growth rate (CAGR) as high as 98.5% from 2024 to 2028, mainly due to collaboration with the NVIDIA ecosystem to bring AI infrastructure into the Megawatt era.

• However, because Innoscience is a Chinese company, I personally believe NVTS will benefit more than Innoscience

Power Whip usage increases significantly, Value increases by ~3.1x:
One NVL576 unit will use about 100 Power Whips (NVL 72 only has 8). The reason is Kyber’s shift to 800V HVDC side-mounted power and a single-rack design power of about 600 kW. Industry data from OCP/GTC shows that a large number of flexible, high-current whips will be used for cabling; multiple industry presentations directly give a usage of ≈100 whips/rack.

Power Supplies: Delta, Vertiv

• According to foreign investment estimates, the Average Selling Price (ASP) of a Power Rack is about $30,000 USD (excluding PSUs, assuming one power rack supports one compute rack). Based on Delta’s average gross margin for assembly products over the past decade, it is expected that Delta’s gross margin (GM) on this product will be at least 30%.

• Competition

  • From an HDVC market share perspective, Delta currently holds 60-65% and Vertiv 20%. The difference is because Delta provides the most complete solution: an integrated Solid State Transformer (SST) + PSU + PDU + BBU product, with an efficiency of 98.5% (ranked #1 in reviews), making it the only “Grid to Chip” architecture provider.

  • The most critical part is the Solid State Transformer (SST). From NV’s roadmap, we can see they hope to completely eliminate many components of the traditional LV AC level (like LV switchgear), achieving the most streamlined “Grid to Rack” path.

  • In Delta’s SST layout, the key is its cooperation with the US Department of Energy (DOE) since the rise of EVs in 2022, directly participating in the drafting of the UL + IEEE C57.16 SST standard, making it the sole Asian technical review representative.

    • What is UL + IEEE C57.16 SST Standard Draft ?

      • C57.16 was originally used to regulate the safety structure and testing methods of traditional transformers, such as voltage tolerance, thermal design, and magnetic interference. Now, it adds the new application of “SST (Solid State Transformer),” which means replacing traditional coil and iron core designs with electronic chips like SiC and GaN to convert 13.8 kV to 800 V DC for high-voltage direct current transmission.

      • Basically, whoever can get the UL + IEEE C57.16 certification can enter the international power grid and AI data center supply chain. Early participants include Delta, Eaton, and Siemens.

      • < 800 VDC Architecture for Next-Generation AI Infrastructure >

Conclusion

• Most optimistic about 3665 BizLink, 2308 Delta Electronics

  • Referencing Goldman Sachs estimates, Delta’s 2026/2027/2028 EPS are 40/70/117, respectively. The most critical factor for the increase is the gross margin, reaching 42% in ‘27.

  • Estimating BizLink-KY 2026/2027 EPS at 60/75, respectively.

• HVDC architecture significantly reduces TCO: It improves energy efficiency, reduces equipment investment, saves space, and lowers maintenance costs. A full transition to HVDC power supply could reduce a data center’s TCO by approximately 20% to 30%. Therefore, NV will actively introduce HVDC in the subsequent Rubin Ultra.

• Delta’s upside potential lies in the combined gross margin of PSU, PDU, and BBU increasing to 40%-45% or more. The market share for HVDC is expected to rise to over 70%. (Currently, Delta has 60-65%, Vertiv 15-20%).

• BizLink’s Power Whip usage increases significantly, Value increases by ~3.1x: One NVL576 unit will use about 100 Power Whips (NVL 72 only has 8). The reason is Kyber’s shift to 800V HVDC side-mounted power and a single-rack design power of about 600 kW.

  • Stock momentum support: BizLink’s overseas convertible bond, issue size: 300 million USD, conversion price: 1423.5 NTD (30% premium).