The Battle for AI Silicon： a brief overview of the chip market and who is winning
This is a version that normally appears in the newsletter every Saturday. I thought， because it's so important， that I should publish it here today as well.
Two days ago， NVIDIA reported $81.6 billion in quarterly revenue， with data center sales alone hitting $75.2 billion， up 92% year over year. Those numbers are so large they almost lose their meaning. To put them in perspective： NVIDIA's data center business now generates more revenue in a single quarter than most Fortune 500 companies produce in an entire year. The AI chip market has become one of the most consequential economic arenas on the planet， determining who can train the next frontier model， who can serve billions of inference requests， and ultimately， who controls the infrastructure layer of artificial intelligence itself.
But beneath the headline dominance， something more interesting is happening. The market is fragmenting. Google has split its latest TPU generation into two separate chips for the first time， one for training， one for inference. AMD is shipping competitive hardware and building rack-scale systems that directly challenge NVIDIA's architecture. Cerebras and Groq have demonstrated that specialized silicon can outperform general-purpose GPUs for specific workloads by an order of magnitude. And in China， Huawei is assembling a parallel compute ecosystem that operates entirely outside the Western supply chain， with DeepSeek's V4 model now running natively on Chinese chips.
The question worth examining is whether NVIDIA's position as the undisputed platform of AI compute will hold as the market matures， or whether the shift from training to inference， the rise of vertical integration， and the geopolitical fracturing of the semiconductor supply chain will produce a fundamentally different competitive landscape.
NVIDIA： The Platform， Not Just the Chip
Understanding NVIDIA's dominance requires looking beyond raw compute performance. The company's real advantage is systemic. CUDA， the programming framework introduced in 2006， has accumulated roughly four million developers worldwide. Every major AI lab， from OpenAI to Anthropic to Meta AI， builds on CUDA. The libraries， the debugging tools， the kernel optimizations， the deployment pipelines： they all assume NVIDIA hardware. Switching costs are not just financial but organizational. Migrating away from CUDA means rewriting code， retraining teams， and accepting months of reduced productivity.
On top of this software moat， NVIDIA has built what analysts increasingly call a "copper moat，" the proprietary NVLink interconnect system that connects GPUs within rack-scale systems at bandwidths far exceeding any external networking solution. The latest Blackwell 300 and upcoming Vera Rubin platforms sell not as individual chips but as integrated AI factories： dozens of GPUs， custom CPUs， liquid cooling， high-bandwidth memory pools， and networking fabric bundled into a single purchasable unit. For customers building large training clusters， this integration eliminates enormous amounts of engineering work.
The Battle for AI Silicon： a brief overview of the chip market and who is winning
This is a version that normally appears in the newsletter every Saturday. I thought， because it's so important， that I should publish it here today as well.
Two days ago， NVIDIA reported $81.6 billion in quarterly revenue， with data center sales alone hitting $75.2 billion， up 92% year over year. Those numbers are so large they almost lose their meaning. To put them in perspective： NVIDIA's data center business now generates more revenue in a single quarter than most Fortune 500 companies produce in an entire year. The AI chip market has become one of the most consequential economic arenas on the planet， determining who can train the next frontier model， who can serve billions of inference requests， and ultimately， who controls the infrastructure layer of artificial intelligence itself.
The financial results reflect this. NVIDIA's fiscal 2026 revenue reached $215.9 billion， with $193.7 billion from the data center segment alone， a 68% increase year over year （NVIDIA， 02/25/2026）. The company's Q2 FY2027 guidance of $91 billion suggests the trajectory has not slowed. Gross margins remain near 75%， indicating that despite increasing competition， NVIDIA retains substantial pricing power （SEC Filing， 05/20/2026）.
The roadmap underscores the strategy. Blackwell Ultra ships this year， Vera Rubin follows in the second half of 2026 with HBM4 memory and a new CPU architecture， and Rubin Ultra arrives in 2027 with four GPU dies per package and up to one terabyte of HBM4e. NVIDIA has deliberately shifted to a one-year product cadence， which creates a structural problem for competitors： by the time a rival ships a chip designed to match Blackwell， NVIDIA has already moved on to Rubin.
Google TPU 8t/8i： Vertical Integration as Weapon
Google represents the most serious long-term threat to NVIDIA's position， but the nature of that threat is often misunderstood. Google does not need to replace NVIDIA on the open market. Google needs to reduce its own dependency on NVIDIA within Google Cloud and for its internal AI workloads， primarily Gemini and DeepMind's research.
The eighth-generation TPU， announced at Google Cloud Next in April 2026， marks an architectural first： Google split the design into two distinct chips. The TPU 8t is built for large-scale training， scaling up to 9，600 chips per superpod with a new optical 3D torus interconnect called Virgo that can link over one million TPU 8t chips in a single cluster with near-linear scaling efficiency. The TPU 8i targets inference and reasoning workloads， featuring 288 GB of HBM alongside 384 MB of on-chip SRAM， three times more than the previous Ironwood generation， specifically designed to hold the large key-value caches that modern language models require during inference （Google Blog， 04/22/2026）.
The split is important because training and inference have fundamentally different hardware requirements. Training demands raw compute throughput and massive parallelism. Inference， especially for reasoning models and agentic systems that chain multiple inference calls together， demands low latency， large memory for context windows， and energy efficiency. By building dedicated silicon for each workload， Google can optimize in ways that a general-purpose GPU never can.
The deeper advantage is vertical integration. Google controls the models （Gemini）， the cloud platform， the data centers， the chip design， and the internal demand. DeepMind acts as a permanent large-scale customer whose needs feed directly back into hardware design. This kind of hardware-model co-design is extraordinarily difficult for a merchant chip vendor like NVIDIA to replicate.
The critical limitation remains ecosystem breadth. TPUs are powerful within Google's software stack， particularly JAX and Pathways. Outside that world， they are far less portable than CUDA-based GPUs. Notably， Google itself continues to offer NVIDIA's Vera Rubin platform on Google Cloud， a tacit acknowledgment that many customers still need or prefer the NVIDIA ecosystem （TechCrunch， 04/22/2026）. Google's TPU strategy is best understood not as a frontal attack on NVIDIA， but as a dual-sourcing and bargaining play.
AMD， Cerebras， Groq： Challengers From Every Angle
AMD occupies the most strategically important position after NVIDIA and Google. For any enterprise or hyperscaler seeking to reduce NVIDIA dependency without committing to Google's vertically integrated stack， AMD is the natural alternative. The company reported $34.6 billion in total revenue for 2025， with its data center segment growing 39% year over year in Q4 （AMD， 01/2026）. The current MI350 series ships with 288 GB of HBM3e and up to 8 TB/s of memory bandwidth.
The upcoming MI400， expected in the second half of 2026， targets direct competition with NVIDIA's Vera Rubin. AMD's most powerful weapon may ultimately be price： reports suggest MI450 could be priced approximately 40% below comparable NVIDIA chips （SemiAnalysis， 2026）. The persistent challenge remains ROCm， AMD's CUDA alternative， which has improved substantially but still lacks the depth of NVIDIA's developer ecosystem.
Cerebras and Groq have abandoned the GPU paradigm entirely for inference workloads. Their argument is simple： during autoregressive token generation， the workload is memory-bandwidth-bound， not compute-bound. GPUs are structurally mismatched for this task. Cerebras addresses this with the Wafer Scale Engine， a single chip occupying an entire silicon wafer， holding 4 trillion transistors and 44 GB of on-chip SRAM. Independent benchmarks confirm that the CS-3 delivers 21 times faster throughput than NVIDIA's B200 at 32% lower cost for inference workloads （SemiAnalysis， 2025）. In May 2026， Cerebras partnered with AWS to offer its inference capabilities through Amazon Bedrock.
Groq takes a different path. Its Language Processing Unit uses static compiler scheduling， where the entire execution graph is planned down to individual clock cycles before inference begins. The result is deterministic latency： every token takes exactly the same amount of time to generate. Groq achieves up to 1，200 tokens per second for large models with sub-100 millisecond time-to-first-token. In December 2025， NVIDIA acquired a non-exclusive license to Groq's inference technology， a strong signal that even the market leader sees SRAM-centric architectures as the future of inference （Groq， 12/2025）.
DeepSeek V4 on Huawei Chips： China's AI independence becomes real
The geopolitical dimension of the chip market has moved beyond theory into operational reality. Since 2020， US export controls have blocked Huawei and SMIC from accessing EUV lithography machines. Without EUV， cutting-edge chip production below 7nm was widely considered impossible. Yet SMIC has found a workaround using DUV lithography with quadruple patterning， enabling structures in the 5nm class， albeit with severe tradeoffs： early yield rates around 20%， meaning four out of five chips came off the line defective （Asia Financial， 2025）.
Huawei's CloudMatrix 384 system integrates 384 Ascend 910C chips and delivers approximately 300 petaflops of BF16 compute， nearly double NVIDIA's GB200 NVL72. The cost： 3.9 times the power consumption and roughly triple the price （Igor's Lab， 2025）. China's strategy is brute force， compensating for chip-level inefficiency with sheer scale. This works because energy is cheaper and more abundant in China： the country plans to add 3.4 terawatts of new generation capacity over the next five years， nearly six times the US figure （Oxford Energy， 02/2026）.
The decisive turning point came in April 2026， when DeepSeek released V4， the first Chinese frontier model explicitly trained and optimized for Huawei Ascend chips. V4 uses a mixture-of-experts architecture with up to one trillion total parameters， 37 billion activated per inference. Following the release， China's largest tech companies， Alibaba， ByteDance， and Tencent， rushed to secure hundreds of thousands of Huawei chips （Reuters， 04/29/2026）. What DeepSeek demonstrated is that the bottleneck was never hardware alone， it was the software layer： compilers， distributed training frameworks， communication libraries. With V4， that software stack has reached sufficient maturity. A fully China-controlled AI ecosystem that does not require CUDA now exists.
Conclusion
The AI chip market in 2026 is splitting along three axes. Workload： training remains NVIDIA-dominated， while inference opens doors for specialists. Openness： NVIDIA locks in via CUDA/NVLink， Google offers vertical optimization， AMD provides the open alternative. Geopolitics： two parallel ecosystems are now operational， Western （NVIDIA/CUDA） and Chinese （Ascend/CANN）. The market is not witnessing a dethroning， but a fragmentation that rewards different architectures for different purposes.
NVIDIA is not about to be dethroned. No competitor matches its combination of compute performance， software ecosystem， production volume， system integration， and supply chain depth. The company's $81.6 billion quarterly revenue and 75% gross margins speak to a business with extraordinary structural advantages. But the nature of those advantages is shifting. The CUDA moat is being complemented and partially superseded by the "copper moat，" the proprietary networking stack that binds customers at the system level.
The more important development is that the market itself is diversifying. Inference， which is growing faster than training and will likely constitute the majority of AI compute demand within the next two to three years， favors different hardware characteristics： low latency， large on-chip memory， energy efficiency， and deterministic performance. This is the opening that Cerebras， Groq， and Google's TPU 8i are exploiting.
China's AI compute ecosystem is becoming functionally independent， not through chip-level parity but through a combination of brute-force scaling， cheap energy， model-architecture optimization， and a maturing domestic software stack. The AI chip market of 2030 will not be a single global arena. It will be two parallel systems with limited interoperability， each with its own hardware standards， software ecosystems， and competitive dynamics.
The battle for AI silicon is no longer just a corporate rivalry. It is a contest over the means of production for the most consequential technology of our time.
Groq and NVIDIA licensing agreement （12/2025） https://groq.com/newsroom/groq-and-nvidia-enter-non-exclusive-inference-technology-licensing-agreement-to-accelerate-ai-inference-at-global-scale
Reuters： DeepSeek V4 adapted to Huawei chips （04/24/2026） https://www.reuters.com/world/china/deepseek-v4-chinese-ai-model-adapted-huawei-chips-2026-04-24/
Reuters： Chinese firms scramble for Huawei chips after DeepSeek V4 （04/29/2026） https://www.reuters.com/world/china/big-chinese-tech-firms-scramble-secure-huawei-ai-chips-after-deepseek-v4-launch-2026-04-29/
Oxford Energy： China data centre advantage （02/2026） https://www.oxfordenergy.org/wpcms/wp-content/uploads/2026/02/Comment-The-China-data-centre-advantage.pdf
Epoch AI： AI Chip Production （01/2026） https://epoch.ai/data-insights/ai-chip-production
Epoch AI： Hyperscalers and compute ownership （04/2026） https://epoch.ai/data-insights/hyperscalers-control-most-compute
Epoch AI： AI chip supply chain constraints （03/2026） https://epoch.ai/data-insights/ai-chip-supply-chain-constraints
But beneath the headline dominance， something more interesting is happening. The market is fragmenting. Google has split its latest TPU generation into two separate chips for the first time， one for training， one for inference. AMD is shipping competitive hardware and building rack-scale systems that directly challenge NVIDIA's architecture. Cerebras and Groq have demonstrated that specialized silicon can outperform general-purpose GPUs for specific workloads by an order of magnitude. And in China， Huawei is assembling a parallel compute ecosystem that operates entirely outside the Western supply chain， with DeepSeek's V4 model now running natively on Chinese chips.
The question worth examining is whether NVIDIA's position as the undisputed platform of AI compute will hold as the market matures， or whether the shift from training to inference， the rise of vertical integration， and the geopolitical fracturing of the semiconductor supply chain will produce a fundamentally different competitive landscape.
NVIDIA： The Platform， Not Just the Chip
Understanding NVIDIA's dominance requires looking beyond raw compute performance. The company's real advantage is systemic. CUDA， the programming framework introduced in 2006， has accumulated roughly four million developers worldwide. Every major AI lab， from OpenAI to Anthropic to Meta AI， builds on CUDA. The libraries， the debugging tools， the kernel optimizations， the deployment pipelines： they all assume NVIDIA hardware. Switching costs are not just financial but organizational. Migrating away from CUDA means rewriting code， retraining teams， and accepting months of reduced productivity.
On top of this software moat， NVIDIA has built what analysts increasingly call a "copper moat，" the proprietary NVLink interconnect system that connects GPUs within rack-scale systems at bandwidths far exceeding any external networking solution. The latest Blackwell 300 and upcoming Vera Rubin platforms sell not as individual chips but as integrated AI factories： dozens of GPUs， custom CPUs， liquid cooling， high-bandwidth memory pools， and networking fabric bundled into a single purchasable unit. For customers building large training clusters， this integration eliminates enormous amounts of engineering work.
The financial results reflect this. NVIDIA's fiscal 2026 revenue reached $215.9 billion， with $193.7 billion from the data center segment alone， a 68% increase year over year （NVIDIA， 02/25/2026）. The company's Q2 FY2027 guidance of $91 billion suggests the trajectory has not slowed. Gross margins remain near 75%， indicating that despite increasing competition， NVIDIA retains substantial pricing power （SEC Filing， 05/20/2026）.
The roadmap underscores the strategy. Blackwell Ultra ships this year， Vera Rubin follows in the second half of 2026 with HBM4 memory and a new CPU architecture， and Rubin Ultra arrives in 2027 with four GPU dies per package and up to one terabyte of HBM4e. NVIDIA has deliberately shifted to a one-year product cadence， which creates a structural problem for competitors： by the time a rival ships a chip designed to match Blackwell， NVIDIA has already moved on to Rubin.
Google TPU 8t/8i： Vertical Integration as Weapon
Google represents the most serious long-term threat to NVIDIA's position， but the nature of that threat is often misunderstood. Google does not need to replace NVIDIA on the open market. Google needs to reduce its own dependency on NVIDIA within Google Cloud and for its internal AI workloads， primarily Gemini and DeepMind's research.
The eighth-generation TPU， announced at Google Cloud Next in April 2026， marks an architectural first： Google split the design into two distinct chips. The TPU 8t is built for large-scale training， scaling up to 9，600 chips per superpod with a new optical 3D torus interconnect called Virgo that can link over one million TPU 8t chips in a single cluster with near-linear scaling efficiency. The TPU 8i targets inference and reasoning workloads， featuring 288 GB of HBM alongside 384 MB of on-chip SRAM， three times more than the previous Ironwood generation， specifically designed to hold the large key-value caches that modern language models require during inference （Google Blog， 04/22/2026）.
The split is important because training and inference have fundamentally different hardware requirements. Training demands raw compute throughput and massive parallelism. Inference， especially for reasoning models and agentic systems that chain multiple inference calls together， demands low latency， large memory for context windows， and energy efficiency. By building dedicated silicon for each workload， Google can optimize in ways that a general-purpose GPU never can.
The deeper advantage is vertical integration. Google controls the models （Gemini）， the cloud platform， the data centers， the chip design， and the internal demand. DeepMind acts as a permanent large-scale customer whose needs feed directly back into hardware design. This kind of hardware-model co-design is extraordinarily difficult for a merchant chip vendor like NVIDIA to replicate.
The critical limitation remains ecosystem breadth. TPUs are powerful within Google's software stack， particularly JAX and Pathways. Outside that world， they are far less portable than CUDA-based GPUs. Notably， Google itself continues to offer NVIDIA's Vera Rubin platform on Google Cloud， a tacit acknowledgment that many customers still need or prefer the NVIDIA ecosystem （TechCrunch， 04/22/2026）. Google's TPU strategy is best understood not as a frontal attack on NVIDIA， but as a dual-sourcing and bargaining play.
AMD， Cerebras， Groq： Challengers From Every Angle
AMD occupies the most strategically important position after NVIDIA and Google. For any enterprise or hyperscaler seeking to reduce NVIDIA dependency without committing to Google's vertically integrated stack， AMD is the natural alternative. The company reported $34.6 billion in total revenue for 2025， with its data center segment growing 39% year over year in Q4 （AMD， 01/2026）. The current MI350 series ships with 288 GB of HBM3e and up to 8 TB/s of memory bandwidth.
The upcoming MI400， expected in the second half of 2026， targets direct competition with NVIDIA's Vera Rubin. AMD's most powerful weapon may ultimately be price： reports suggest MI450 could be priced approximately 40% below comparable NVIDIA chips （SemiAnalysis， 2026）. The persistent challenge remains ROCm， AMD's CUDA alternative， which has improved substantially but still lacks the depth of NVIDIA's developer ecosystem.
Cerebras and Groq have abandoned the GPU paradigm entirely for inference workloads. Their argument is simple： during autoregressive token generation， the workload is memory-bandwidth-bound， not compute-bound. GPUs are structurally mismatched for this task. Cerebras addresses this with the Wafer Scale Engine， a single chip occupying an entire silicon wafer， holding 4 trillion transistors and 44 GB of on-chip SRAM. Independent benchmarks confirm that the CS-3 delivers 21 times faster throughput than NVIDIA's B200 at 32% lower cost for inference workloads （SemiAnalysis， 2025）. In May 2026， Cerebras partnered with AWS to offer its inference capabilities through Amazon Bedrock.
Groq takes a different path. Its Language Processing Unit uses static compiler scheduling， where the entire execution graph is planned down to individual clock cycles before inference begins. The result is deterministic latency： every token takes exactly the same amount of time to generate. Groq achieves up to 1，200 tokens per second for large models with sub-100 millisecond time-to-first-token. In December 2025， NVIDIA acquired a non-exclusive license to Groq's inference technology， a strong signal that even the market leader sees SRAM-centric architectures as the future of inference （Groq， 12/2025）.
DeepSeek V4 on Huawei Chips： China's AI independence becomes real
The geopolitical dimension of the chip market has moved beyond theory into operational reality. Since 2020， US export controls have blocked Huawei and SMIC from accessing EUV lithography machines. Without EUV， cutting-edge chip production below 7nm was widely considered impossible. Yet SMIC has found a workaround using DUV lithography with quadruple patterning， enabling structures in the 5nm class， albeit with severe tradeoffs： early yield rates around 20%， meaning four out of five chips came off the line defective （Asia Financial， 2025）.
Huawei's CloudMatrix 384 system integrates 384 Ascend 910C chips and delivers approximately 300 petaflops of BF16 compute， nearly double NVIDIA's GB200 NVL72. The cost： 3.9 times the power consumption and roughly triple the price （Igor's Lab， 2025）. China's strategy is brute force， compensating for chip-level inefficiency with sheer scale. This works because energy is cheaper and more abundant in China： the country plans to add 3.4 terawatts of new generation capacity over the next five years， nearly six times the US figure （Oxford Energy， 02/2026）.
The decisive turning point came in April 2026， when DeepSeek released V4， the first Chinese frontier model explicitly trained and optimized for Huawei Ascend chips. V4 uses a mixture-of-experts architecture with up to one trillion total parameters， 37 billion activated per inference. Following the release， China's largest tech companies， Alibaba， ByteDance， and Tencent， rushed to secure hundreds of thousands of Huawei chips （Reuters， 04/29/2026）. What DeepSeek demonstrated is that the bottleneck was never hardware alone， it was the software layer： compilers， distributed training frameworks， communication libraries. With V4， that software stack has reached sufficient maturity. A fully China-controlled AI ecosystem that does not require CUDA now exists.
Conclusion
The AI chip market in 2026 is splitting along three axes. Workload： training remains NVIDIA-dominated， while inference opens doors for specialists. Openness： NVIDIA locks in via CUDA/NVLink， Google offers vertical optimization， AMD provides the open alternative. Geopolitics： two parallel ecosystems are now operational， Western （NVIDIA/CUDA） and Chinese （Ascend/CANN）. The market is not witnessing a dethroning， but a fragmentation that rewards different architectures for different purposes.
NVIDIA is not about to be dethroned. No competitor matches its combination of compute performance， software ecosystem， production volume， system integration， and supply chain depth. The company's $81.6 billion quarterly revenue and 75% gross margins speak to a business with extraordinary structural advantages. But the nature of those advantages is shifting. The CUDA moat is being complemented and partially superseded by the "copper moat，" the proprietary networking stack that binds customers at the system level.
The more important development is that the market itself is diversifying. Inference， which is growing faster than training and will likely constitute the majority of AI compute demand within the next two to three years， favors different hardware characteristics： low latency， large on-chip memory， energy efficiency， and deterministic performance. This is the opening that Cerebras， Groq， and Google's TPU 8i are exploiting.
China's AI compute ecosystem is becoming functionally independent， not through chip-level parity but through a combination of brute-force scaling， cheap energy， model-architecture optimization， and a maturing domestic software stack. The AI chip market of 2030 will not be a single global arena. It will be two parallel systems with limited interoperability， each with its own hardware standards， software ecosystems， and competitive dynamics.
The battle for AI silicon is no longer just a corporate rivalry. It is a contest over the means of production for the most consequential technology of our time.
Groq and NVIDIA licensing agreement （12/2025） https://groq.com/newsroom/groq-and-nvidia-enter-non-exclusive-inference-technology-licensing-agreement-to-accelerate-ai-inference-at-global-scale
Reuters： DeepSeek V4 adapted to Huawei chips （04/24/2026） https://www.reuters.com/world/china/deepseek-v4-chinese-ai-model-adapted-huawei-chips-2026-04-24/
Reuters： Chinese firms scramble for Huawei chips after DeepSeek V4 （04/29/2026） https://www.reuters.com/world/china/big-chinese-tech-firms-scramble-secure-huawei-ai-chips-after-deepseek-v4-launch-2026-04-29/
Oxford Energy： China data centre advantage （02/2026） https://www.oxfordenergy.org/wpcms/wp-content/uploads/2026/02/Comment-The-China-data-centre-advantage.pdf
Epoch AI： AI Chip Production （01/2026） https://epoch.ai/data-insights/ai-chip-production
Epoch AI： Hyperscalers and compute ownership （04/2026） https://epoch.ai/data-insights/hyperscalers-control-most-compute
Epoch AI： AI chip supply chain constraints （03/2026） https://epoch.ai/data-insights/ai-chip-supply-chain-constraints
Chubby♨️@kimmonismus · X