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Nvidia reported $46.7 billion in revenue for fiscal Q2 2026 in their earnings announcement and call yesterday, with data center revenue hitting $41.1 billion, up 56% year over year. The company also released guidance for Q3, predicting a $54 billion quarter.
Behind these confirmed earnings call numbers lies a more complex story of how custom application-specific integrated circuits (ASICs) are gaining ground in key Nvidia segments and will challenge their growth in the quarters to come.
Bank of America’s Vivek Arya asked Nvidia’s president and CEO, Jensen Huang, if he saw any scenario where ASICs could take market share from Nvidia GPUs. ASICs continue to gain ground on performance and cost advantages over Nvidia, Broadcom projects 55% to 60% AI revenue growth next year.
Huang pushed back hard on the earnings call. He emphasized that building AI infrastructure is “really hard” and most ASIC projects fail to reach production. That’s a fair point, but they have a competitor in Broadcom, which is seeing its AI revenue steadily ramp up, approaching a $20 billion annual run rate. Further underscoring the growing competitive fragmentation of the market is how Google, Meta and Microsoft all deploy custom silicon at scale. The market has spoken.
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ASICs are redefining the competitive landscape in real-time
Nvidia is more than capable of competing with new ASIC providers. Where they’re running into headwinds is how effectively ASIC competitors are positioning the combination of their use cases, performance claims and cost positions. They’re also looking to differentiate themselves in terms of the level of ecosystem lock-in they require, with Broadcom leading in this competitive dimension.
The following table compares Nvidia Blackwell with its primary competitors. Real-world results vary significantly depending on specific workloads and deployment configurations:
*Performance-per-watt improvements and cost savings depend on specific workload characteristics, model types, deployment configurations and vendor testing assumptions. Actual results vary significantly by use case.
Hyperscalers continue building their own paths
Every major cloud provider has adopted custom silicon to gain the performance, cost, ecosystem scale and extensive DevOps advantages of defining an ASIC from the ground up. Google operates TPU v6 in production through its partnership with Broadcom. Meta built MTIA chips specifically for ranking and recommendations. Microsoft develops Project Maia for sustainable AI workloads.
Amazon Web Services encourages customers to use Trainium for training and Inferentia for inference.
Add to that the fact that ByteDance runs TikTok recommendations on custom silicon despite geopolitical tensions. That’s billions of inference requests running on ASICs daily, not GPUs.
CFO Colette Kress acknowledged the competitive reality during the call. She referenced China revenue, saying it had dropped to a low single-digit percentage of data center revenue. Current Q3 guidance excludes H20 shipments to China completely. While Huang’s statements about China’s extensive opportunities tried to steer the earnings call in a positive direction, it was clear that equity analysts weren’t buying all of it.
The general tone and perspective is that export controls create ongoing uncertainty for Nvidia in a market that arguably represents its second most significant growth opportunity. Huang said that 50% of all AI researchers are in China and he is fully committed to serving that market.
Nvidia’s platform advantage is one of their greatest strengths
Huang made a valid case for Nvidia’s integrated approach during the earnings call. Building modern AI requires six different chip types working together, he argued, and that complexity creates barriers competitors struggle to match. Nvidia doesn’t just ship GPUs anymore, he emphasized multiple times on the earnings call. The company delivers a complete AI infrastructure that scales globally, he emphatically stated, returning to AI infrastructure as a core message of the earnings call, citing it six times.
The platform’s ubiquity makes it a default configuration supported by nearly every DevOps cycle of cloud hyperscalers. Nvidia runs across AWS, Azure and Google Cloud. PyTorch and TensorFlow also optimize for CUDA by default. When Meta drops a new Llama model or Google updates Gemini, they target Nvidia hardware first because that’s where millions of developers already work. The ecosystem creates its own gravity.
The networking business validates the AI infrastructure strategy. Revenue hit $7.3 billion in Q2, up 98% year over year. NVLink connects GPUs at speeds traditional networking can’t touch. Huang revealed the real economics during the call: Nvidia captures about 35% of a typical gigawatt AI factory’s budget.
“Out of a gigawatt AI factory, which can go anywhere from 50 to, you know, plus or minus 10%, let’s say, to $60 billion, we represent about 35% plus or minus of that. … And of course, what you get for that is not a GPU. … we’ve really transitioned to become an AI infrastructure company,” Huang said.
That’s not just selling chips. that’s owning the architecture and capturing a significant portion of the entire AI build-out, powered by leading-edge networking and compute platforms like NVLink rack-scale systems and Spectrum X Ethernet.
Market dynamics are shifting quickly as Nvidia continues reporting strong results
Nvidia’s revenue growth decelerated from triple digits to 56% year over year. While that’s still impressive, it’s clear the trajectory of the company’s growth is changing. Competition is starting to have an effect on their growth, with this quarter seeing the most noticeable impact.
In particular, China’s strategic role in the global AI race drew pointed attention from analysts. As Joe Moore of Morgan Stanley probed late in the call, Huang estimated the 2025 China AI infrastructure opportunity at $50 billion. He communicated both optimism about the scale (“the second largest computing market in the world,” with “about 50% of the world’s AI researchers”) and realism about regulatory friction.
A third pivotal force shaping Nvidia’s trajectory is the expanding complexity and cost of AI infrastructure itself. As hyperscalers and long-standing Nvidia clients invest billions in next-generation build-outs, the networking demands, compute and energy efficiency have intensified.
Huang’s comments highlighted how “orders of magnitude speed up” from new platforms like Blackwell and innovations in NVLink, InfiniBand, and Spectrum XGS networking redefine the economic returns for customers’ data center capital. Meanwhile, supply chain pressures and the need for constant technological reinvention mean Nvidia must maintain a relentless pace and adaptability to remain entrenched as the preferred architecture provider.
Nvidia’s path forward is clear
Nvidia issuing guidance for Q3 of $54 billion sends the signal that the core part of their DNA is as strong as ever. Continually improving Blackwell while developing Rubin architecture is evidence that their ability to innovate is as strong as ever.
The question is whether a new type of innovative challenge they’re facing is one they can take on and win with the same level of development intensity they’ve shown in the past. VentureBeat expects Broadcom to continue aggressively pursuing new hyperscaler partnerships and strengthen its roadmap for specific optimizations aimed at inference workloads. Every ASIC competitor will take the competitive intensity they have to a new level, looking to get design wins that create a higher switching costs as well.
Huang closed the earnings call, acknowledging the stakes: “A new industrial revolution has started. The AI race is on.” That race includes serious competitors Nvidia dismissed just two years ago. Broadcom, Google, Amazon and others invest billions in custom silicon. They’re not experimenting anymore. They’re shipping at scale.
Nvidia faces its strongest competition since CUDA’s dominance began. The company’s $46.7 billion quarter proves its strength. However, custom silicon’s momentum suggests that the game has changed. The next chapter will test whether Nvidia’s platform advantages outweigh ASIC economics. VentureBeat expects technology buyers to follow the path of fund managers, betting on both Nvidia to sustain its lucrative customer base and ASIC competitors to secure design wins as intensifying competition drives greater market fragmentation.
