Google has been crushing it lately in many departments, but one of those areas is in its TPUs, an alternative to NVIDIA's GPUs for training AI models. And they've actually used it in their latest Gemini three model with all of this happening shares an NVIDIA have just fallen 3% because a report came out that said meta one of NVIDIA's huge customers is possibly going to strike a deal with Google to use its TPUs, its tensor processing units for data centers. Due to this NVIDIA stock fell 3% and NVIDIA came back with their own response. Today on the podcast, I want to talk about the state of the AI chips market.
NVIDIA has a massive lead, but can they maintain that lead? And is Google the sleeping giant that's going to not just come out of nowhere and ring up tons of users in the AI space for their chatbot Gemini, but maybe also challenge NVIDIA in a major way on the chips. We're getting into all of that before we do. I wanted to say, if you want to try any of the AI models that I mentioned on the show today, I would love for you to try out my startup, which is AI box.ai.
You can access to over 40 of the top models, everything from Gemini to cloud to open AI to 11 labs for audio, tons of cool image models, and it's all on one platform for $20 a month. You can chat with all of the models in the same chat thread and compare the responses side by side. If you want to check it out, it is AI box.ai. I'll leave a link in the description.
All right, let's get into what NVIDIA said. So with all of the news, NVIDIA stock falling after this kind of rumored report of meta and Google striking up a big TPU processing deal, NVIDIA has come back and said that it's GPU's are a generation quote unquote, ahead of Google's AI chips. In via put out the statement on Thursday, and this is as NVIDIA has more than 90% of the market for AI chips with its graphic processing units. This is essentially what analysts are looking at and saying, but Google's in house chips have gotten increased intention because of how viable and alternative they're powering the second biggest model, which is Gemini three in the entire world right now.
So due to all of this, NVIDIA is trying to maintain that it has a superior chip. And I don't think this is really something that everyone will argue with Google says that their TPUs have a better architecture. And from a lot of analysts that I've seen that are specifically in the chip space, they do admit that the TPU is a better architecture or some of them will will call it that famously, Chamond Paliapatia, who is an early investor into Grock, another alternative to NVIDIA chip processing company says that the TPUs and Grock are a better form of chips than what NVIDIA has, but NVIDIA has such a massive market. And I think what a lot of people are not really talking about is that NVIDIA isn't just a chip.
They have a really great way of pulling multiple chips together. They have a really great infrastructure and they have a really great software platform that a lot of these, a lot of these chip providers rely on for training models in response to this rumor deal with Google. NVIDIA said, quote, we are delighted by Google's success. We've made great advances in AI and we continue to supply Google.
NVIDIA is a generation ahead of the industry. It's the only platform that runs every AI model and does it everywhere. Computing is done. This post is coming as their shares, like I said, are falling 3% on Thursday after the report of meta, possibly striking out this deal with Google.
In the same post, NVIDIA said that its chips are more flexible and powerful compared to the ASIC chips like Google's TPU, which are essentially designed for a single company or function. NVIDIA is the latest generation of chips. They're known as the Black Wells. And they are more general purpose and useful for a lot of different things, but they're not specifically for AI because if you remember, NVIDIA's history, they really came out creating GPUs for gamers originally.
That's how they got a lot of notoriety and making regular consumers' computers great. They then really scaled during the crypto boom when people were using them for mining crypto and as the crypto market was crashing, the AI market started really taking off and they kind of switched into that industry. Because of this approach, where they've hit three different industries, their chips are quite good for many things, but they're not optimized exclusively for training AI models. And this is what the ASIC chips like Google's TPU are specifically designed for.
With 90% of the market, it feels like NVIDIA only has room to kind of decrease in market share. Googles in house chips have gotten a lot of attention as an alternative, which are really expensive, but also really powerful. Like Black Wells chips are kind of the best in class, but they're good for a lot of different things like I mentioned. And Google's chips are designed for one thing in particular, which is exclusively training AI models.
They do it very well in the very optimized, so you can make them for a lot cheaper. Unlike NVIDIA, Google doesn't sell its TPU chips to companies yet, but it uses them for a lot of internal tasks and it allows companies to rent them on their Google Cloud, whereas NVIDIA, right? They'll just sell their GPUs to anyone. People buy tons of GPUs, build data centers, and then we'll rent them out.
So it's kind of an interesting business model where Google's keeping all their TPUs in house. Google, when they released Gemini 3 earlier this month, it was, you know, obviously CVR, it was the top on a lot of different benchmarks, and it was trained exclusively on TPUs, not NVIDIA's GPUs, which got a lot of headlines. What they said about this, Google spokesperson said, we are experiencing accelerated demand for both our custom TPUs and NVIDIA GPUs we are committed to supporting both as we have four years. Go doesn't want to bite the hand of the feeds.
I'm right and come up and be like, hey, we have this big competitor. We're going to take on NVIDIA because they are a huge customer to NVIDIA. Currently, as they're scaling up their TPU chips, they need to buy a ton of NVIDIA GPUs that they put in their data centers, especially for Google Cloud, where people are renting them to train AM models and other things. NVIDIA CEO Jensen Hwang has kind of addressed the whole rising TPU competition in a recent earnings call earlier this month, and he said that Google was a customer for his company's GPU chips and that Gemini can run on NVIDIA's technology, which is a good point in Gemini can run on the TPUs.
It was trained on the TPUs, but also could run on NVIDIA's technology. He also mentioned that he was in touch with Demise Hasabi, the CEO of Google DeepMind, and he said that he apparently hasabi stacks in him to say that the tech industry theory that using more chips and data will create more powerful AM models, often called scaling laws by AI developers, is intact, meaning if you buy more chips, your model gets better. And this is something that we saw with early versions of chat GPT 4.0, where Sam Antle came out with a bunch of benchmarks and said, like, basically, if you give the model $10,000 of compute answer question, the question gets, like, insanely better. Of course, it's not, it's, you know, not very cost efficient.
And so we don't do that today. But if you wanted your model to get better, you basically would just give it access to more compute. And this was with older models. So imagine GPT 5 and a lot of Gemini 3, a lot of these newer models, they will get better and better with more compute.
And the reason why Jensen brings that up is because there is a perfect theory for if, for some reason, there was some sort of plateau where the researchers could figure out ways to make the models better. All they would need to do is buy more compute and that would just feed straight into Nvidia. Now, of course, they have to figure out how to make them more efficient and they have to figure out how to get them for cheaper and make the chips cost less money. But it really is the future where they would just need to buy more and more chips.
So Nvidia says that scaling laws are going to lead to even more demand for their chips and their system. And if this is true, Nvidia could continue to grow. Thank you so much for joining the podcast today. If you enjoyed the episode, it would mean the world to me.
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