Executive Summary

In recent weeks, market attention has focused on Google’s Gemini 3 and the decision to make TPU available to external users, widely interpreted as a counterattack against NVIDIA.

However, the breakthroughs of Gemini 3, the evolution of TPU technology, and Google’s sales strategy are three separate narratives rather than a single causal chain. The real story lies in how Google and NVIDIA, through competition, are propelling each other forward and collaboratively reshaping the rhythm of AI infrastructure.

Gemini 3 has not diminished NVIDIA’s position, and the external sale of TPU is not a challenge but a form of balance. Both companies are managing trust and timing in different ways, moving from zero-sum competition toward an era of collaborative governance and shared growth.

Introduction

In recent weeks, the market has once again entered a familiar cycle of excitement.

Google’s Gemini 3 has been described as reclaiming leadership in AI models. The efficiency and design of the TPU have drawn renewed attention, and Google’s announcement to make TPU available externally has fueled expectations of a new “TPU versus GPU” battle.

Yet the market may be focusing on the wrong thing. The breakthrough of Gemini 3, the technical strength of TPU, and Google’s sales strategy are three separate storylines. They are not a single chain of cause and effect but a more intricate form of interaction.

The real story is not about who is catching up with whom. It is about how Google and NVIDIA are accelerating each other through competition and collaboratively driving the transformation of AI infrastructure.

A Misread Lead: TPU Has Always Been Ahead, but Success Lies Beyond Hardware

TPU has maintained a leading position in AI chip design for years. From architecture and system integration to cluster topology, Google has built a complete design and computation chain.

Yet such hardware advantage does not guarantee the success of a model. A few years ago, Google released models of uneven quality even during periods when TPU was far ahead. These experiences remind us that no matter how powerful the hardware, it cannot replace the maturity of science, engineering, and rhythm.

The success of Gemini 3 does not come from a sudden leap in TPU performance but from Google’s broader progress in AI science, data processing, and model governance. If the same research team were to train on an NVIDIA platform with equivalent compute, the results might not differ significantly.

Hardware defines the upper limit, but breakthroughs arise from the rhythm of people and systems. This is why Google’s governance capability has grown visibly in recent years. It has realigned the timing of research, engineering, supply chain, and capital, creating a more stable and predictable cycle for innovation and deployment.

As governance returns to the center, the logic of competition in the AI industry begins to shift.

From Performance to Governance: The New Rules of AI Competition

While much of the discussion still centers on which chip is faster, the real shift has already occurred beyond architecture. As software stacks and toolchains mature, performance gaps can be offset by scale and time. If two systems differ in efficiency, the gap can be closed with more chips or longer training cycles. In the end, what determines success is not the speed of a single chip but the overall cost curve and governance capability.

This is also why the idea of “cost-performance” often blurs reality. The buyer’s cost is the seller’s price, and without clear pricing and supply conditions, cost-performance becomes little more than a narrative.

If Google treats TPU as a core intellectual property, it will naturally maintain a high margin structure. Clients like Meta that choose TPU may care less about the lowest possible price and more about reducing risk and increasing autonomy.

The true focus of competition has already shifted. Technical differences are only surface features. The real divide in AI now lies in governance structure and decision rhythm. From this perspective, AI competition is no longer just a technological race but a collaborative experiment in governance.

TPU for Sale and Market Trust: How Google Is Rewriting the Rules

At first glance, Google’s decision to offer TPU externally seems like a direct challenge to NVIDIA. In reality, it primarily affects the unstable wave of in-house ASIC projects across the cloud and AI industry.

Over the past two years, nearly every major company in this space has tried to reduce its dependence on NVIDIA by developing custom AI chips. Yet chip design is only the beginning. The real challenges lie in the software ecosystem, system integration, and production stability.

As these internal projects face delays or yield issues, Google’s TPU emerges as an ideal secondary source. Rather than replacing NVIDIA, the availability of TPU adds stability to the market and serves as a buffer against risk.

Viewed this way, TPU’s external sales are not just a technical event but a collaborative act of trust. Google is repositioning itself within the supply chain, moving from a technology participant to a governance facilitator.

The technical ceiling remains the same, but the boundaries of trust are being redrawn. This shift places Google in an increasingly central role as an institutional designer within the AI ecosystem.

Who Is Catching Up? NVIDIA’s Countermove and the Formation of a Collaborative Structure

Looking back over the past three years, NVIDIA’s rapid iteration seems less like a race for chip performance and more like an effort to catch up with Google’s system-level design philosophy. From GH200 to GB200 and GB300, from NVL8 to NVL72, the intent behind these architectures has been to narrow the gap with Google’s TPU systems.

Today, each company has its own strengths. TPU continues to lead in cross-cluster stability and system integration, while NVIDIA keeps advancing in interconnect speed and memory architecture. The gap between the two is closing, and the industry narrative is gradually shifting as a result.

NVIDIA’s new architectures are no longer just hardware upgrades but expressions of governance rhythm. Similarly, Google’s decision to offer TPU externally is not a statement of performance but a structural adjustment. Both companies are managing the market’s perception of “leadership” in different ways.

Their relationship is not a zero-sum dynamic but a form of collaborative growth. Google’s research progress drives NVIDIA’s product design, while NVIDIA’s hardware evolution, in turn, extends Google’s research rhythm.

This kind of collaborative competition is becoming the new normal for the AI industry.

Conclusion: When Competition Becomes Collaboration, the Meaning of Leadership Changes

The competition for computing power today is no longer about whose chips are faster.

The external sale of TPU does not alter the physical limits of computation but expands the options for governance. The breakthrough of Gemini 3 comes from progress in people and systems, not from a hardware miracle.

The contest between NVIDIA and Google is gradually shifting from performance to trust, rhythm, and capital allocation. The market may have misread the direction of this race.

NVIDIA and Google are not adversaries but twin engines driving each other’s success. Google’s advances in model performance validate the scalability of NVIDIA’s architecture, while NVIDIA’s hardware ecosystem supports Google’s research rhythm and governance efficiency.

One side is pushing the boundary of compute, while the other is redefining the boundaries of application and governance. Their interaction reflects the true state of the modern AI industry: competition and collaboration coexist, shaping and expanding each other.

Gemini 3 does not weaken NVIDIA’s position, and the external sale of TPU does not signal its decline. The resonance between the two marks a new stage of maturity for the AI industry, moving from the age of a single leader toward a more plural and balanced order.

When a rival’s success also advances your own, leadership is no longer a zero-sum game but a collaborative journey. The real competition between NVIDIA and Google now lies in how they can continue to build trust, rhythm, and efficiency within this shared ecosystem.

Note: AI tools were used both to refine clarity and flow in writing, and as part of the research methodology (semantic analysis). All interpretations and perspectives expressed are entirely my own.