My two focuses in October were to prepare for OCP and actively learn in the supercomputing club. I always believed that Luck = Opportunity + Preparation, and I try to learn as much as I can online before I join a conference. Most of the time, it leads to interesting conversations and useful opportunities.
I was also really lucky at UCSD - for the first time, an American company loaned our team a $250,000 server equipped with eight AMD GPUs for us to dismantle. That is unheard of in other parts of the world. It was also my first time seeing hardware components like NICs and PCIe up close.
There are a couple of things I learned from the Open Compute Project. But the most important thing I learned was that there’s money in the table for a reason. Through talking with business leaders, I learned about business/legal practices that made it difficult to simplify the supply chain.
Shortage
The surge in AI infrastructure demand has triggered aggressive purchasing by hyperscalers and “neoclouds,” leading to widespread component shortages. Based on my conversations in OCP, Hard drives are sold out for two years, driving SSD prices up for everyone else. Generators are reportedly on three-year backorders. Delivering a fully equipped, PUE-efficient data centre on schedule now requires coordination across dozens of suppliers and vendors — each facing its own bottlenecks. Walking the OCP floor, one thing became clear: hyperscalers dominate this market, making up about 80% of vendors’ clientele.
A hyperscaler I spoke with was cautious about the risks, but noted that when they cancelled a data center contract, a competitor quickly filled the gap. Demand surged afterward, and in hindsight, they realised they should have accepted the contract.
Margins
OEM vs ODM. Original Equipment Manufacturers assemble components from different manufacturers in China. They sell completed systems with warranties and software via value-added suppliers. ODMs like Quanta manufacture to customer specifications. With the AI Infrastructure boom, Oracle and Meta have bypassed OEMs to work directly with ODMs. While OEMs historically apply a markup of 30%, ODMs operate on thinner margins of 1%. However, ODMs require large contracts, making direct access challenging for smaller businesses.
Catching up in technology is incredibly difficult, but changing a business model is even harder. It seems every OEM has taken a page from Clayton Christensen’s Innovator’s Dilemma by taking the hit in margins to capture market share. Nvidia has also open-sourced the reference architecture for its Blackwell servers, further levelling the playing field. As a result, OEMs are beginning to look more like ODMs. I also learned that both ODMs and OEMs rely on many of the same Chinese component manufacturers for smaller parts, which narrows the differentiation even more.
Some component manufacturers
12V Bus Bar - Amphenol, Bizlink, Interplex, JPC, Lotes
Slide Rail - Fositek, Repon, Yuans Tech, Cheng Fwa, Kingslide
UQD/MQD - Auras, Danfoss, Envicool, Fositek, Foxconn, Lead Wealth, Lotes, Netonx, Nidec, Parker, Readore, Staubil
Lessons
A hyperscaler I spoke with was cautious about the risks, but noted that when they cancelled a data center contract, a competitor quickly filled the gap. Demand surged afterwards, and in hindsight, they realised they should have accepted the contract.
Reflection
Recently, Sequoia talked about two things - Rolef mentioned that there are too many talented people chasing not-so-interesting problems (like it's 1999), and Doug mentioned that most money made will be in the application layers, directly to the consumer. It’s making me rethink my approach, but I’m dedicated to winning (outcome) and not dying (path dependent). In a world that's changing, the “taste” of your problem matters than your current skillset. And taste depends on context; context is dependent on those who hold information.
