Jonathan Ross

Founder of Groq

Mentions

Appeared in

Discussed in

Key positions and views

NVIDIA's market power stems from its monopsony on High Bandwidth Memory (HBM), not its CUDA software, and its future involves high revenue share but low unit volume share.

The primary bottleneck for scaling AI globally is the availability of massive energy infrastructure, particularly nuclear power, which nations like China are aggressively pursuing.

Hyperscalers develop custom AI chips not primarily for mass deployment, but as a strategic lever to negotiate better pricing and supply allocation from NVIDIA.

The demand for AI inference is so profoundly unmet that compute capacity is the main variable limiting revenue growth for major AI model providers.

The US holds a 2-3 year advantage over China in supplying AI compute to allied nations, a dynamic reflected in how each country's AI models are economically optimized (US for inference, China for training).

Podcast consensus on Ross

Points of consensus

▶Both sources attribute the prediction that NVIDIA will reach a $10 trillion valuation within five years to Jonathan Ross.Feb–Mar 2026

▶Ross consistently identifies supply chain components, specifically High Bandwidth Memory (HBM) and interposers, as the primary bottleneck for NVIDIA's GPU production, rather than its ability to manufacture the silicon dies themselves.Feb 2026

▶He repeatedly connects national AI ambitions to massive investments in energy infrastructure, citing China's plan for 150 new nuclear reactors and Japan's efforts to bring reactors back online as crucial for powering AI.Feb 2026

▶Ross's analysis suggests that the demand for AI inference is so high that it is currently compute-limited, stating that doubling the inference capacity for companies like OpenAI and Anthropic would nearly double their revenues in a month.Feb 2026

Points of debate

▶Ross asserts that NVIDIA's CUDA software moat is 'incorrect' for the inference market, a position that directly challenges the widely held industry view that CUDA creates significant, long-term customer lock-in across all AI workloads.

▶He claims hyperscalers build their own AI chips primarily as a negotiation tactic to gain leverage over NVIDIA on pricing and supply, which contrasts with the common narrative that these custom chips are intended to fully replace NVIDIA hardware at scale.Feb 2026

▶Ross posits that HBM suppliers are hesitant to increase supply because the current scarcity allows them to maintain very high profit margins, suggesting a deliberate market strategy rather than a pure technological or manufacturing limitation.Feb 2026

▶His statement that Groq can deliver chips in six months versus a more than two-year lead time for NVIDIA GPUs presents a dramatic contrast in supply chain agility that implicitly questions the operational efficiency of the market leader.Feb 2026

Key themes

▶NVIDIA's Constrained DominanceFeb–Mar 2026

Jonathan Ross portrays NVIDIA not as an unassailable monopoly but as a dominant player whose growth is capped by critical supply chain bottlenecks, particularly High Bandwidth Memory (HBM), where it acts as a monopsony (single dominant buyer). He predicts NVIDIA will maintain over 50% of AI chip revenue but its share of the total number of chips sold will plummet to as low as 10% within five years.

Investors should view the HBM supply chain and the emergence of specialized, high-volume inference chips as leading indicators of potential shifts in NVIDIA's market power, even if its revenue dominance persists.

▶The Geopolitics of Compute and EnergyFeb 2026

Ross frames the global AI race as a battle for compute infrastructure and the massive energy sources required to power it. He highlights China's plan for 150 nuclear reactors and Japan's $65 billion AI fund as evidence of this linkage, while warning that Europe's economy is at risk if it fails to build sufficient energy and compute capacity.

National energy policy, especially regarding nuclear and large-scale renewables, is now a direct and critical component of a country's long-term AI strategy and future economic competitiveness.

▶The Strategic Game of Custom Silicon

According to Ross, the primary motivation for hyperscalers to develop their own AI chips is not to replace NVIDIA entirely but to gain strategic leverage in supply allocation and pricing negotiations. The existence of a viable in-house alternative, even if not mass-produced, fundamentally changes the power dynamic with the dominant supplier.

The success of in-house chip projects should be measured less by their deployment volume and more by their impact on a company's purchasing power and supply chain resilience against dominant vendors.

▶Inference as the Untapped Revenue FrontierFeb 2026

Ross argues that the market for AI inference is severely underserved, to the point where leading AI labs could almost double their revenue in a month if their inference compute capacity were doubled. This positions inference as a primary economic driver where speed and efficiency are paramount, challenging the industry's heavy focus on training performance.

The market may be significantly underestimating the latent commercial demand for AI inference, suggesting that companies providing faster or more efficient inference-specific hardware are poised for explosive growth.

Source episodes

Sentiment over time

Not enough data for timeline

Changes over time

Market Foundation Analysis

Ross establishes his view of the market's scale, claiming that ChatGPT has reached approximately 10% of the world's population as weekly active users.

Supply Chain Diagnosis

He details the core bottlenecks in AI hardware, identifying NVIDIA's monopsony on HBM and the limited supply of interposers as the key constraints limiting its annual GPU output to around 5.5 million units.

Geopolitical Landscape Assessment

Ross outlines the global AI competition, contrasting US model optimization (inference cost) with China's (training cost) and highlighting massive national energy investments in China (150 nuclear reactors) and Japan ($65B for AI).

Competitive Positioning

He positions his company, Groq, as a nimble competitor, citing a six-month chip delivery time compared to NVIDIA's two-year lead and noting a developer base of 2.2 million.

Future Projections

Ross makes several bold forward-looking predictions, including NVIDIA's unwavering path to a $10 trillion valuation and a future where it captures most AI chip revenue but only a small fraction of the unit volume.

Suggested prompts

How does Ross's theory of hyperscalers using custom chips for 'leverage' explain recent project cancellations like Tesla's Dojo chip? &nearr;What specific market signals would validate or invalidate Ross's prediction that NVIDIA's chip volume share will drop to 10% while its revenue share remains over 50%? &nearr;If Ross is correct that energy is a primary bottleneck for AI, which countries are best positioned to lead in AI development over the next decade based on their stated energy policies? &nearr;How does Ross's claim that the CUDA moat is irrelevant for inference impact the competitive landscape for companies like Groq that focus on high-speed inference solutions? &nearr;

Key concepts

NVIDIA Valuation 2 ep AI Chip Supply Chain 2 ep High Bandwidth Memory (HBM) 1 ep Geopolitical AI Competition 1 ep Energy Infrastructure 1 ep Inference vs. Training 1 ep Hyperscaler Strategy 1 ep Groq LPU 1 ep CUDA Moat 1 ep Market Monopsony 1 ep

Notable quotes

“Jonathan Ross predicts that NVIDIA's market capitalization will likely exceed $10 trillion within five years.”

Jonathan Ross · Groq Founder, Jonathan Ross： OpenAI & Anthropic Will Build Their Own Chips & Will NVIDIA Hit $10TRN

“Hyperscalers build their own AI chips primarily to gain leverage over NVIDIA in supply allocation and pricing, not necessarily to deploy them in mass production.”

Jonathan Ross · Groq Founder, Jonathan Ross： OpenAI & Anthropic Will Build Their Own Chips & Will NVIDIA Hit $10TRN

“If OpenAI and Anthropic were given double their current inference compute capacity, their revenues would almost double within one month.”

Jonathan Ross · Groq Founder, Jonathan Ross： OpenAI & Anthropic Will Build Their Own Chips & Will NVIDIA Hit $10TRN

“China plans to build 150 nuclear reactors to secure the energy required for its AI ambitions.”

Jonathan Ross · Groq Founder, Jonathan Ross： OpenAI & Anthropic Will Build Their Own Chips & Will NVIDIA Hit $10TRN

Report last updated: Apr 21, 2026

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