In the pharma industry, when a generic drug wins approval, it creates a structural market shift. Being therapeutically equivalent to the branded drug, but 80–85% cheaper, generics quickly capture volume, market share and revenue, hurting the branded drug and the shares of the company selling the branded drug.
Market speculation is treating the reported Google–Meta TPU talks as if it’s the same dynamic — that Google is creating a “generic GPU” that will gut Nvidia’s dominance overnight.
This analogy couldn’t be more divorced from reality.
Before jumping to doomsday predictions for Nvidia’s AI dominance , we need to understand four things clearly:
- Do Google’s TPUs Threaten Nvidia’s Dominance?
- What the reported Google–Meta talks may entail
- Whether Google is supplanting Nvidia — or merely supplementing it
- Which is a bigger threat to Nvidia
Do Google’s TPUs Threaten Nvidia’s Dominance?
ASICs or Application-Specific Integrated Circuits are essentially single-purpose tools. These are custom-designed for a single, critical task, like Google’s Tensor Processing Units (TPUs) for deep learning. This extreme specialization yields performance and power efficiency far superior to general-purpose GPUs for that specific job. The trade-off is rigidity. Once etched into silicon, ASICs can’t be repurposed, and that means sacrificing flexibility for performance and efficiency.
Nvidia’s GPUs sit at the other end of the spectrum. These are highly versatile and can handle a wide variety of AI workloads, which is why these general-purpose accelerators have become the default hardware for the entire AI industry—even though a single chip can cost up to $40,000 and supply is often constrained.
For startups, the choice is straightforward. Startups typically prioritize speed to market and minimizing massive initial capital expenditures. So, they stick with general-purpose Nvidia GPUs that are readily available, flexible across computing tasks and have lower upfront costs. Although individual Nvidia GPUs are expensive, designing custom ASICs requires a minimum of tens of millions of dollars in upfront investment.
For hyperscalers, the economics differ and custom silicon becomes cost-effective over time. These operate global data centers at a massive scale, where even fractional improvements in power efficiency or performance per dollar translate into billions in savings over several years. Custom silicon also gives hyperscalers greater control over how their AI workloads are built and optimized.
But these savings don’t eliminate their need for Nvidia. AI compute demand is growing at a staggering rate that no hyperscaler can rely solely on internal chips.
Against this backdrop, Google’s TPUs pose a real, but limited, challenge. Reports suggest Google’s TPU strategy could capture as much as 10% of Nvidia’s annual revenue. This implies that Nvidia would still get to keep the remaining 90%. Google also remains one of Nvidia’s largest customers, and renting Nvidia GPUs to customers remains a major revenue driver for Google Cloud.
What The Reported Google–Meta Talks May Entail
One speculation is Meta might lease TPUs from Google Cloud, starting as early as next year, with the intent of deploying them in its own data centers by 2027. But, if Meta was looking to replace Nvidia for core model training, it would need the chips already.
Next, there is the PyTorch incompatibility. PyTorch originated in Meta’s AI research labs, and it seamlessly integrates with NVIDIA’s CUDA platform, providing a high-level interface that allows developers to easily harness the parallel processing power of Nvidia GPUs.
By contrast, Google’s TPUs cannot run PyTorch natively. These need a translation layer, PyTorch/XLA, which adds complexity and potential overhead, requiring developers to navigate XLA-specific features, and adjustments to existing GPU workflows. If Meta were truly considering TPUs for large-scale AI operations, it would first need to address this added complexity for the XLA layer.
PyTorch has become a dominant framework in AI research, giving Meta a strategic edge to contribute to and influence the standards and best practices within the broader AI community. While PyTorch is now governed under the Linux Foundation, Meta remains a primary technical contributor and retains substantial influence. Switching to hardware like Google’s TPUs that requires PyTorch workarounds would potentially undermine Meta’s control and influence over the foundational AI software it helped establish. For these reasons, an approach heavily favoring GPUs currently seems strategically sound for Meta for maintaining influence within the AI research and development landscape.
Meta’s discussions with Google are likely focused on optimizing Llama inference models. By making Llama publicly available, Meta aims to build a broad ecosystem around its AI. To succeed in the enterprise market and compete with OpenAI and Anthropic, Llama must run efficiently on major cloud infrastructures, particularly Google Cloud. Ensuring seamless compatibility and high performance on Google’s TPUs is therefore critical, even though Meta doesn’t need to own the hardware. Any speculative TPU acquisitions may serve as a development testbed for inference optimization.
TPUs are not designed for graphics rendering, and Meta’s long-term vision for the Metaverse, VR/AR, and advanced digital avatars requires cutting-edge graphical processing power — which only GPUs can deliver.
This goes to show that Meta has diverse and demanding computational needs that no single hardware type can satisfy. TPUs may be used for specific, power-efficient Llama inference tasks, especially as a cloud cost-saving measure or for development testbeds. But for graphics-heavy workloads and large-scale model training, Meta may continue to rely on Nvidia’s GPUs.
Is Google Supplanting Nvidia Or Merely Supplementing It
Nvidia has publicly played down any threat from Google. On X, the company wrote: “We’re delighted by Google’s success — they’ve made great advances in AI and we continue to supply to 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. NVIDIA offers greater performance, versatility, and fungibility than ASICs, which are designed for specific AI frameworks or functions.”
Google struck a similarly diplomatic tone, emphasizing that demand for both processor families is rising: “We are experiencing accelerating demand for both our custom TPUs and Nvidia GPUs; we are committed to supporting both, as we have for years.”
Google does have meaningful advantages—especially its “full-stack” integration from AI research to cloud delivery. But challenging Nvidia’s dominance requires far more than scale. It requires overcoming nearly two decades of ecosystem lock-in. More than 4 million developers worldwide build on Nvidia’s CUDA platform, creating a moat that is extraordinarily difficult to dislodge, even by a company as large as Google.
That’s why TPUs are unlikely to replace GPUs outright. Instead, they serve as a strong alternative for specific high-volume, predictable workloads, while GPUs remain the industry default, because of their flexibility, broad developer support, and compatibility with rapidly evolving AI research workflows. In short, TPUs broaden the market rather than displacing the incumbent. Nvidia remains the foundational platform for AI computation.
This context should also explain why Meta’s discussions with Google don’t meaningfully threaten Nvidia. For a company operating at Meta’s scale, diversification is an operational hedge. Meta can deploy TPUs for targeted inference workloads while continuing to consume massive volumes of Nvidia GPUs for large-scale training and other dynamic workloads.
Nvidia is not standing still either. It has recently expanded into the ASIC market with a new business unit, signaling an acknowledgment that hyperscalers—including Google, Amazon, and Microsoft—are increasing their investments in custom chips to reduce dependency on general-purpose GPUs. Nvidia’s strategic move aims to ensure it remains central even as the architecture mix evolves.
So, there’s no question of Google supplanting Nvidia, it is only supplementing it.
Which Is a Bigger Threat to Nvidia?
Nvidia’s multi-trillion-dollar bull case rests on a single principle: the “bigger-is-better” scaling laws, which describe how AI system performance improves as training data, model parameters, or computational resources increase. These laws have driven surging GPU demand and underpinned much of Nvidia’s market valuation.
However, some experts—including former OpenAI co-founder Ilya Sutskever—argue that the era of brute-force scaling may be giving way to an “age of wonder and discovery,” where algorithmic innovations, and architectural advances could deliver comparable or superior results with far less compute. If these efficiency gains begin to decouple performance from raw hardware, Nvidia’s growth thesis could face a meaningful headwind.
Recently, Nvidia CEO Jensen Huang highlighted a text from Demis Hassabis, CEO of Google DeepMind, confirming that scaling laws remain “intact”—a reassuring sign for Nvidia’s compute-driven growth model.
Nvidia’s growth trajectory will remain intact, as long as compute demand remains insatiable, and until efficiency-driven alternatives alter the narrative.
In summary, Nvidia is a buy and so is Google.
Please note that I am not a registered investment advisor and readers should do their own due diligence before investing in this or any other stock. I am not responsible for the investment decisions made by individuals after reading this article. Readers are asked not to rely on the opinions and analysis expressed in the article and encouraged to do their own research before investing.