Why Google's TPU Could Beat NVIDIA's GPU in the Long Run

The artificial intelligence (AI) boom is one of the biggest technology shifts we've seen, and the profits are enormous. The company that sells the best AI chip—the engine powering the AI—is the one that holds the most power. For years, NVIDIA's Graphics Processing Units (GPUs) have been the undisputed champion, but Google is betting its own custom chips, the Tensor Processing Units (TPUs), will win the future.

Lessons from the Bitcoin Mining Gold Rush

To understand this fight, you can look back at the history of Bitcoin. Bitcoin mining started with people using their regular home CPUs. That quickly proved too slow, so people switched to using GPUs. GPUs were much faster because they were great at handling lots of simple calculations at once (parallel processing).

However, the real change happened around 2013 with the arrival of ASICs (Application-Specific Integrated Circuits). An ASIC is a chip designed for one job only—in this case, solving Bitcoin’s specific cryptographic puzzle (SHA-256) . These specialized circuits delivered such an enormous leap in speed and, critically, power efficiency, that GPUs were instantly made irrelevant and unprofitable for mining Bitcoin. An ASIC can be orders of magnitude more efficient than even a high-end GPU for Bitcoin mining. The GPU, the general-purpose chip, lost to the custom-built ASIC.

This is the parallel many see for the AI industry: Google’s TPU is a form of ASIC—a specialized circuit designed for the narrow task of AI matrix math, threatening to do to the GPU what Bitcoin ASICs did to GPU mining.

(A quick caveat: While ASICs dominate Bitcoin, GPUs are still widely used in the mining industry because not all cryptocurrencies use the same algorithm. Many 'altcoins' are designed to be "ASIC-resistant," meaning they are best mined using GPUs or CPUs, allowing flexibility for miners .)

The Problem with the Swiss Army Knife

NVIDIA’s GPUs, like the powerful Blackwell and Rubin systems, are fantastic because they're flexible. They were originally for gaming but were adapted for AI. Think of a GPU as a Swiss Army knife: it can do a lot of things pretty well.

Google's TPU, however, is a hyper-specialized calculator designed by Google engineers only for the specific type of math that AI needs. It strips away all the parts a GPU needs for graphics, resulting in less "architectural baggage" and less "wasted silicon". This extreme specialization means that for large-scale AI work, the TPU is far more efficient.

Vertical Integration Cuts the Middleman

Vertical integration means Google controls every step, from designing the chip to training the model to running the final service. Imagine a chef who owns the farm, the butcher shop, and the restaurant. They control the whole process.

Google is the only major player that designs the chips (TPUs), writes the AI software (XLA), and runs the cloud service (Google Cloud) that offers both to customers. They are making their own "shovels" for their own "mines," which gives them a huge cost advantage.

In contrast, AI competitors like OpenAI have to buy NVIDIA's chips at a huge profit margin, and then pay a separate company (like Microsoft Azure) to plug those chips in and host them. By cutting out the middleman and the profit markup, Google can offer an entire spectrum of AI services far cheaper, at scale (and still profitable) via its cloud. The ultimate result is that Google can squeeze significantly more performance per dollar than its competitors.

The True Cost of Power and the GW Metric

The most powerful financial incentive to switch to TPUs comes from operating costs, mainly power and cooling. To run a world-class AI system, companies talk about Gigawatts (GW) of power—a single GW is one billion watts, which represents the total power required to run the AI factory.

Running just 1 GW of data center capacity can cost an estimated \$1.3 billion annually just in electricity. The superior efficiency of the TPU means that for the same 1 GW of electricity consumption, a TPU-powered data center can achieve an estimated $100\%$ to $200\%$ more computing output than a comparable GPU-powered center.

This difference is critical: paying an extra billion dollars a year on electricity just to keep the lights on is an unsustainable financial disadvantage that ultimately affects every competitor using less efficient hardware.

The Pivot to Inference: Why Efficiency Wins

The AI market’s true profit will come not from training the model (the expensive university phase) but from inference (the cheap, quick phase of using the model to answer a billion user queries).

The massive market for training chips may be a bubble, but the market for inference is much larger. When that shift is complete, companies must compete fiercely on cost and efficiency. Since TPUs are designed for low-cost, high-volume operations, they are better set to dominate this future use-case of neural networks, especially if they are running against GPUs with twice the operational costs.

This is why, for people making models, the choice is clear: to maintain long-term profitability and competitive pricing, they will have to choose the processor that delivers the highest output for the lowest cost.

Real-World Validation: Anthropic and Meta's Interest

The market is already signaling a shift:

• Anthropic's Massive Commitment: AI lab Anthropic, creator of the Claude models, has announced a landmark expansion of its use of Google Cloud’s TPU chips, planning to access up to one million TPUs, a deal worth tens of billions of dollars . Anthropic specifically chose TPUs due to the "strong price-performance and efficiency" they have seen over several years. This move, expected to bring over a gigawatt of capacity online, is the largest external commitment to TPUs to date. (Read the details here: Anthropic Expanding our use of Google Cloud TPUs and Services ).
• Meta Platforms (Facebook) Interest: Meta Platforms is reportedly in talks with Google to spend billions of dollars on TPUs for its own data centers starting in 2027, with the possibility of renting capacity as early as next year. This signals that even NVIDIA's largest customers are exploring alternatives to gain a check on NVIDIA's pricing influence and improve long-term performance.

The Future Beyond Matrix Math: Ternary Weights

The AI world is already looking beyond the matrix multiplication that dominates today. Cutting-edge research is exploring ternary weights, where model parameters are restricted to just three values: ${-1, 0, +1}$.

• The Goal: This change allows the most expensive step— multiplication—to be replaced with much faster and simpler addition, subtraction, or zeroing. This is a huge push toward ultimate efficiency, especially for low-power inference on edge devices.

This trend highlights the preparedness of the two chip architectures:

What This Means for the Future

NVIDIA is not standing still; its Q3 Fiscal 2026 earnings were a record \$57.0 billion in revenue, with Data Center revenue hitting \$51.2 billion. But the growing adoption of TPUs introduces a long-term risk to NVIDIA's core business model.

NVIDIA is forced to mutate its general-purpose GPU into a more specialized, ASIC-like chip by adding more Tensor Cores to catch up to the TPU's efficiency. The challenge is that NVIDIA’s true moat has always been its CUDA software ecosystem. Pivoting too quickly to a different chip architecture could risk abandoning years of software development and customer loyalty .

Meanwhile, Alphabet is thriving, with strong Q3 2025 earnings where revenue reached \$102.3 billion and Google Cloud revenue grew 34%. The ultimate conclusion for many is that the inevitable bubble pop will cause fewer repercussions for Google than for the companies built entirely on NVIDIA's margins, leaving the integrated, cost-conscious TPU as the sustainable winner.