Executive Summary

Jensen Huang’s “even free chips” remark is not simply about price. It is a deliberate attempt to reset the rules of competition. By shifting the focus from chip cost to power-constrained economics, total cost of ownership, and revenue per watt, NVIDIA positions itself as the designer of AI factories rather than a commodity supplier. In this framing, ASICs are relegated to the role of secondary components, their best prospects limited to niches or direct-customer projects. The logic is strategically compelling under the assumption of limited power, since every gigawatt must translate into maximum revenue. Yet this is not the whole truth. In practice, hyperscalers continue to develop and deploy their own ASICs to diversify supply, reduce dependence, and maintain bargaining leverage. What Huang’s interview reveals is less a technical argument than a move to redefine the competitive terms themselves. While strategically powerful, it remains only partly aligned with the complexities of the real market.

Introduction

In a recent BG2 interview, Jensen Huang made a statement that quickly became a headline: “Even if a competitor’s chips were free, customers should still choose NVIDIA.”

At first glance, the remark sounds bold and confident, almost like another signature performance from Huang.

Yet if we pause to reflect, the real aim of this statement is to reshape the market’s frame of discussion. It elevates NVIDIA from being seen as a “supplier” to being regarded as the “designer of AI factories,” while pushing ASICs into the lower tier of “component providers.” This is not just a technical comparison. It is a struggle over narrative power.

From Price to Power: Shifting the Battlefield

Most people view the advantage of ASICs as lower cost and cheaper unit prices.

Jensen Huang’s move is to avoid the price debate altogether and instead shift the battlefield to “total cost of ownership” and “revenue per watt.”

This reframes the discussion from chip pricing to system-level revenue efficiency. Within this framework, cheap chips no longer matter.

For a data center, the scarcest resource is not chips but electricity.

Under the assumption of unlimited power, choosing ASICs could yield higher ROI and margins for cloud service providers. But in the reality of limited power, only NVIDIA can maximize both revenue and total gross profit.

That is why Huang argued that if NVIDIA can generate more tokens and greater revenue with the same one gigawatt of power, then even free competitor chips are not worth it.

The underlying message to cloud service providers (CSPs) is clear: if you acknowledge that power is limited, you have to choose NVIDIA.

This is not just marketing spin. It is an attempt to redefine the evaluation standard, moving it from chip cost to performance per watt and ultimately to revenue per watt. Once the market accepts this framework, the pricing advantage of ASIC vendors loses its voice.

ASICs Missed the Right Moment

Jensen Huang pointed to the “timing problem” of ASICs, using Google’s TPU as an example: its success came from capturing the market when it was still small.

Although he did not state it explicitly, the implication is an industry S-curve. Early entrants can capture the whole market, but now the market has entered a stage dominated by industry giants.

For new ASIC startups, this means there are no more low-barrier entry points. Any competition must confront NVIDIA’s full ecosystem, which makes the return on investment extremely low.

He went further, highlighting the structural challenge of the ASIC business model: margins may be high in the early stage, but once the business scales, customers often shift to custom chips through the COT (Customer-Owned Tooling) model, where customers internalize design to control cost. The larger an ASIC company grows, the more likely it is to be bypassed.

This tension is visible in the market today. Graphcore, despite strong technical achievements with its IPU architecture, struggled to scale cloud adoption, finding only limited traction through Microsoft Azure. Broadcom, by contrast, has taken a different path, positioning itself as a partner that helps hyperscalers design custom silicon. These examples suggest two diverging trajectories: independent startups face steep barriers to survival, while established players with the right alliances can still capture value in the ASIC space.

In this light, ASICs appear best suited for smaller but stable niche markets, or for scenarios where large customers directly support them, rather than trillion-dollar core compute markets.

The CSP Dilemma: Rationality vs. Strategy

By this logic, NVIDIA seems to be the only answer. Yet in practice, CSPs are not that straightforward.

They face another set of considerations:

• They do not want to be locked in with a single supplier.
• Even with a slightly lower ROI, they are willing to invest in ASICs to gain bargaining power.
• To maintain supply chain autonomy, they sometimes accept choices that appear economically less rational.

This is why, when Jensen Huang put forward the “free chip” argument, what he was really doing was not a technical comparison but a battle for narrative control:

• He reframed the debate by changing the metrics of evaluation, steering it away from chip prices toward broader measures like system efficiency and revenue generation.
• He used the thought experiment of “free chips” to dismantle the logic behind ASICs.
• He redefined ASIC’s advantage as something that only fits the past or small markets.
• He anchored the debate in the reality of limited power, making NVIDIA’s value appear as the only rational choice.

Through this interview, his goal was to ensure that CSPs remember not the performance gap between GPUs and ASICs, but that the essence of the competition is no longer about which chip is faster or cheaper.

Discussion: Strategically Right, But Not the Whole Truth

Under the assumption of limited power, Jensen Huang’s argument carries strong strategic validity. Yet it is not an absolute truth in either technical or commercial terms.

Technical Dimension

• ASICs are not always at a disadvantage. For specialized workloads, their performance per watt can exceed that of GPUs.
• The versatility of GPUs comes with a cost. While NVIDIA’s ecosystem is powerful, it can also mean overdesigned capacity for certain tasks, where TCO does not always come out ahead.
• Huang assumes that models evolve too quickly for ASICs to catch up. But if workloads stabilize, ASICs could regain cost and efficiency advantages.

Business Dimension

• ASICs are not confined to small markets. AWS, Google, and Microsoft all continue to expand their own ASIC investments, showing that hyperscalers can sustain them at scale.
• CSPs are not driven by performance alone. They also seek supply chain diversification. If NVIDIA’s margins become too aggressive, hyperscalers may accelerate ASIC investment even at lower ROI, to preserve autonomy.

Taken together, Huang’s claim is “strategically correct” but not “a universal truth.” It reflects a framework that elevates NVIDIA while downplaying the rationale for CSPs to pursue ASICs.

In the end, the real significance lies less in the technical debate and more in the power of narrative.

By reframing the battlefield around power and revenue per watt, Huang is not only defending NVIDIA’s market position, he is reshaping the very terms of competition.

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.