Jalapeño - Why is OpenAI's first AI chip more important than you think?

There is a detail in OpenAI's announcement on June 24 that I find more interesting than the chip itself: they use their own AI model to speed up the chip design process. In other words, the AI ​​designs its own chips to make it run faster: a loop that until a few years ago was a fantasy idea. But the results also say something: from the first design to completing the sample production process (tape-out) took only 9 months. This is said to be one of the fastest application specific integrated circuit (ASIC) development cycles in the history of the high-performance semiconductor industry. The chip is called Jalapeño. And although the name sounds fun, this is a step that the AI ​​industry has been waiting for a long time.

What does Jalapeño do and why is it different?

Jalapeño is not a general-purpose processor. It is designed for one thing only: running inference, which is the part where the AI ​​"answers" users in real life, not the part that trains the model from scratch. OpenAI describes this as an "Intelligence Processor" built from a white paper dedicated to modern large language models, not a reused old design.

Here's what's important: most AI chips today, including NVIDIA's GPUs, are inherently optimized for the training problem: massive parallel processing, raw computing power, and entire software ecosystems accumulated over the years. But inference is a different problem: it requires low latency, high throughput, and especially cost efficiency when running continuously 24/7. Jalapeño is designed to address exactly those bottlenecks: costly data movement, balance between compute and memory, internal networking efficiency. The physical size of the chip is also worth noting: this is a reticle-sized ASIC, which is the largest that can be produced in one pass on silicon. In initial testing, the chip is said to outperform current systems in performance per watt, and an independent source estimates inference costs could be about 50% lower than NVIDIA's GPUs.

Why inference is where NVIDIA is most vulnerable to challenge

I want to stop at this point for a moment because it is often overlooked in articles about AI chips. When it comes to "AI chips", people often think of model training, and there, NVIDIA is an almost absolute hegemon. The reason isn't just the hardware: it's CUDA, the software ecosystem that NVIDIA built over nearly twenty years, which makes switching to another platform extremely costly in terms of time and effort. Almost all training tools, math libraries, and machine learning frameworks are optimized for CUDA. This is a moat that opponents have tried to destroy for many years without success.

Jalapeño focuses on inference tasks But inference is different. Once the model has been trained, running it is not dependent on CUDA in the same way. This is why inference has become the most important bridgehead for AI labs and big technology companies to try building their own chips, and also why this market is moving faster than anyone thought. By some estimates, inference currently accounts for about two-thirds of all real-world AI computing, and that proportion will only increase as AI products become more widely deployed.

The race that OpenAI has just officially joined

What I find interesting is that OpenAI is not the first to go in this direction. They are just the last of the largest group. Google has had tensor processing unit (TPU) processors since 2016 with the latest version being TPU v7 Ironwood. Amazon has Trainium, which specializes in training, and Inferentia, which specializes in inference, with more than 500,000 Trainium2 chips running in the wild. Microsoft has the Maia 200. Meta has the MTIA, an internal inference chip for the models in their social network systems. According to some industry analysis, these self-designed specialized chip lines are growing at 44.6% per year. And this is a race where not only NVIDIA, but other giants such as Microsoft, Amazon, and Meta are participating

Where is NVIDIA in this picture?

To be honest, the answer is not as simple as "NVIDIA lost." In the short term, Jalapeño cannot replace NVIDIA GPUs for all tasks: especially training and flexible tasks. NVIDIA is not standing still either: the new generation Rubin platform is being mass produced and promises to reduce inference costs by 10 times compared to Blackwell. And most importantly, the CUDA ecosystem is still an advantage that cannot be easily equalized overnight. But in the long term, the trend is clear. As more and more large organizations make their own chips for inference tasks, NVIDIA's market share in this segment, which is still above 90%, will gradually fragment. Some analysts predict NVIDIA's inferred market share could drop to 20-30% by 2028. NVIDIA is still winning in terms of revenue because absolute demand is still growing, but the market share pie is being divided in ways that no one thought of a few years ago.

The long-term question: who controls the AI ​​stack?

I think the most important thing from the Jalapeño announcement is not the specific chip: it's the strategic signal. OpenAI doesn't just want to make the best AI model; they want to control the entire chain from hardware to software to product. The plan to deploy Jalapeño to a gigawatt data center scale with Microsoft is not just to save costs, it is the foundation for OpenAI to no longer depend on third parties at the most important infrastructure layer.