The Custom Silicon Wave: Why AI Hardware Just Changed Forever
The era of generalist GPUs is giving way to hyper-specialized custom silicon. Here is what the sudden hardware wave means for tech moats, venture capital, and the cost of compute.
The AI hardware world just went through an incredibly busy two weeks. In late June, custom chips went from ideas on a page to working physical hardware. OpenAI, Etched, Amazon, and SambaNova all made massive moves at the exact same time.
To understand why, think of general graphics processors (GPUs) as world-class generalist chefs. They can cook any dish, but they spend a lot of time cleaning up, reading recipes, and finding ingredients. Custom chips (called ASICs) are like specialized sushi chefs who only slice tuna. They do just one job, but they do it incredibly fast with zero wasted effort.
In late June, the industry decided it was time to bring in the sushi chefs.
Inside the June Silicon Wave
The velocity of this hardware wave is unprecedented. Four major players moved almost simultaneously:
OpenAI’s “Jalapeño”: Partnering with Broadcom and Celestica, OpenAI built its first custom chip in just nine months. Early test samples are already running internal machine learning workloads in the lab. They are planning to deploy these custom chips at a massive 10-gigawatt scale by 2029.
Etched’s “Sohu”: This startup came out of stealth with a TSMC 4nm chip designed purely for transformer models. They skipped all the usual general-purpose parts to focus only on AI math. They reached first-pass silicon success, raised $800 million, and booked over $1 billion in forward contracts. Etched claims their 8-chip server can run Meta’s Llama 70B model at 500,000 tokens/s! (while this is a self-reported claim that has not been independently verified yet) This shows how fast custom chips can be.
Amazon’s Trainium: For over 10 years, Amazon kept its custom chips strictly inside its own cloud. Now, they are in early talks to sell their directly to other data centers. This is a massive shift that challenges Nvidia’s crown.
SambaNova’s SN50: This startup launched its fifth-generation AI chip aimed at making enterprise AI cheaper and easier to run. Their system can run in standard, air-cooled data centers so companies do not have to rebuild their facilities. SambaNova is looking to raise up to $1 billion at a $10 billion valuation.
Rethinking the “Moat”
This wave of custom hardware has venture capital rethinking what makes an AI company defensible. For years, investors poured money into software startups that optimize models to run on generic GPUs. But as giant tech companies build custom chips that hardwire these optimizations directly into the silicon, those software-only moats are getting squeezed.
But this does not mean the infrastructure layer is dead. As Tunguz said moats do not always have to be there on day one. While hardware startups usually need a massive technical lead to start, enduring moats can actually be earned over time through relentless execution, branding, and distribution. He points for example to Salesforce, which won the cloud CRM market over technically superior rivals simply because they executed and scaled better over ten years.
The New Bottom Line: Hardware Dictates the Game
The AI race is no longer just about who can write the best algorithms. Software is quickly becoming a cheap commodity. The real, defining advantages of the next decade are physical: designing custom silicon, securing power grids, and building efficient hardware. The players who win the physical layer will control the future of AI.