Tech Giants’ Custom Chip Strategy Adds New Chapter
On October 14, 2025, OpenAI announced a strategic partnership with semiconductor giant Broadcom to co-develop and deploy OpenAI’s first custom AI processor. This decision marks OpenAI joining the ranks of tech giants like Google, Meta, and Amazon in reducing dependence on NVIDIA GPUs through proprietary chip design, while deeply optimizing for their own AI workloads and accelerating the development of Artificial General Intelligence (AGI).
Partnership Background and Motivation
NVIDIA Dependence Dilemma
OpenAI currently heavily relies on NVIDIA’s H100 and upcoming B200 GPUs for model training and inference. As GPT-5 and subsequent models continue to scale, computational demands grow exponentially. NVIDIA GPU supply constraints, high prices, and production capacity limited by TSMC’s production schedules create cost and timeline pressures for OpenAI.
Financial Burden: Estimates suggest OpenAI’s annual GPU procurement spending from NVIDIA exceeds $1 billion. Training a GPT-5-level large language model requires tens of thousands of H100 GPUs running continuously for months, with single training costs potentially reaching hundreds of millions of dollars.
Supply Chain Risk: Global AI companies competing for NVIDIA GPUs results in delivery cycles extending to 6-12 months. Supply chain concentration risk makes OpenAI recognize that controlling proprietary chip design capabilities is key to ensuring technological leadership.
Customization Needs: General-purpose GPUs cannot fully optimize for GPT series models’ specific computational patterns. Custom chips can hardware-accelerate Transformer architecture, attention mechanisms, large-scale matrix operations, improving performance and energy efficiency ratios.
Broadcom’s Strategic Value
OpenAI chose Broadcom as partner rather than Intel or AMD based on the following considerations:
Custom ASIC Expertise: Broadcom has extensive custom chip (ASIC) design experience, having assisted Google in developing TPUs (Tensor Processing Units). This project model allows clients to maintain design leadership while Broadcom provides comprehensive support including chip design, manufacturing coordination, and packaging testing.
Non-Competitive Relationship: Unlike NVIDIA producing AI accelerators competing for market share, and unlike Intel and AMD with their own AI chip product lines, Broadcom maintains a pure partnership relationship, eliminating OpenAI’s concerns about technology leakage or commercial competition.
Industry Connection Network: Broadcom maintains close relationships with foundries like TSMC and Samsung, helping OpenAI secure advanced process capacity. Simultaneously possessing rich resources in packaging, testing, and supply chain management.
Custom Chip Technical Planning
Design Goals and Architecture
OpenAI’s custom chip (temporary codename undisclosed) core design goals include:
Transformer Architecture Optimization: Modern large language models are based on Transformer architecture, involving extensive matrix multiplication, attention computation, layer normalization operations. Chips will design dedicated hardware units for these computational patterns, reducing redundant logic and improving throughput.
Large-Scale Distributed Training Support: GPT-5-level model training requires thousands to tens of thousands of chips working together. Chips integrate high-speed interconnect interfaces (similar to NVIDIA NVLink), reducing communication latency and bandwidth bottlenecks, improving distributed training efficiency.
Inference Optimization Mode: Beyond training, inference is another critical AI chip application. ChatGPT processes billions of requests daily, with inference costs accounting for a significant portion of overall operational expenses. Chips will provide low-latency, high-throughput inference modes, reducing service costs.
Memory Bandwidth Breakthrough: AI computation is often limited by memory bandwidth rather than computational power. Chips adopt HBM3 (High Bandwidth Memory) or more advanced HBM4, providing TB/s-level memory bandwidth, alleviating data transfer bottlenecks.
Process and Mass Production Planning
Advanced Process Selection: Expected to adopt TSMC’s 3nm or 2nm process. Advanced processes provide higher transistor density, lower power consumption, higher clock frequencies, forming the foundation for achieving performance goals.
Mass Production Timeline: Based on industry development cycles, chips typically require 18-24 months from design initiation to mass production. OpenAI and Broadcom’s partnership began mid-2025, with first chips expected in mass production by late 2026 or early 2027.
Capacity Planning: Initially producing thousands to tens of thousands of chips quarterly, gradually expanding to over 100,000 chips per quarter. Considering GPT model scale, OpenAI may need hundreds of thousands to millions of chips.
Parallel with Existing Solutions
Importantly, custom chips won’t completely replace NVIDIA GPUs but will be used in hybrid configurations:
Training Phase: Large model initial training may still use NVIDIA H100/B200, leveraging mature ecosystem and toolchains. Custom chips for specific optimization phases or incremental training.
Inference Phase: ChatGPT, DALL-E, and other product inference services gradually migrate to custom chips, reducing costs and latency.
Research Experiments: Small experimental model training prioritizes custom chips for validating new architectures and algorithms.
Technical Implementation Challenges
Design Complexity
Architecture Innovation Risk: Designing AI accelerators involves extensive architecture innovation. How to balance theoretical design with actual performance? Incorrect architecture decisions may result in chip performance falling short of expectations.
Verification and Debugging: Modern chips contain billions of transistors, making functional correctness verification extremely difficult. Discovering major bugs after tape-out results in costly corrections, potentially delaying months.
Software Ecosystem Establishment: Chip hardware is only part of the equation; supporting compilers, drivers, framework integration are equally important. OpenAI needs to develop complete software stacks enabling seamless migration of existing PyTorch/TensorFlow code.
Manufacturing and Supply Chain
TSMC Capacity Competition: Major customers like Apple, NVIDIA, AMD, Qualcomm occupy most of TSMC’s advanced process capacity. As a new customer, how OpenAI secures sufficient capacity presents challenges.
Yield and Cost: Advanced process initial yields may only reach 50-70%, affecting costs and delivery timelines. As processes mature, yields improve to 80-90%, but require time accumulation.
Geopolitical Risk: TSMC’s primary capacity is in Taiwan, facing geopolitical uncertainties. OpenAI may need to evaluate backup options like Samsung or Intel foundries.
Business Model Adjustment
Investment Return Period: Chip R&D investment may reach hundreds of millions to billions of dollars. When can cost savings recoup investment? This depends on production volume, performance improvement magnitude, and NVIDIA GPU price trends.
Technical Talent Competition: Chip design requires substantial specialized talent; OpenAI must compete with NVIDIA, Apple, Google for recruitment. Salary costs and talent acquisition difficulty are considerations.
OpenAI User and Business Growth
Platform Scale Explosive Growth
Data released by OpenAI at DevDay 2025 shows remarkable growth:
800 Million Weekly Active Users: Up 100 million from 700 million one month prior, a monthly growth rate exceeding 14%. ChatGPT has become the world’s most popular AI application, with users across over 200 countries and regions.
4 Million Developers: Developer platform registrations reach 4 million, tripling year-over-year. These developers utilize OpenAI APIs to build chatbots, code assistants, content generation tools, and other applications.
8 Billion API Requests Per Minute: API call volume reaches 8 billion per minute, equivalent to 133 million per second. This scale requires massive infrastructure support, explaining OpenAI’s urgent need for custom chips to reduce costs.
Apps SDK Launch
App Store Ecosystem: OpenAI launches Apps SDK, allowing developers to build paid applications directly inside ChatGPT. This resembles the App Store model, where developers can sell professional features, customized GPTs, and enterprise solutions.
Revenue Sharing Model: OpenAI takes a percentage (estimated 20-30%) of developer revenue, creating new income streams. This also incentivizes developers to invest in ChatGPT ecosystem, expanding platform functionality.
Enterprise Application Acceleration: Enterprise developers can build dedicated ChatGPT applications integrating internal data and workflows, enhancing employee productivity. This drives ChatGPT’s transformation from consumer tool to enterprise-grade platform.
Industry Chain Reactions
AI Chip Market Restructuring
NVIDIA Monopoly Position Shaken: Tech giants like OpenAI, Google (TPU), Meta (MTIA), Amazon (Trainium/Inferentia), and Microsoft (Maia) developing custom chips challenge NVIDIA’s monopoly in the AI accelerator market.
Broadcom Benefits: As custom chip design partner, Broadcom receives orders from multiple tech giants. Google TPU and Meta chips involve Broadcom participation; OpenAI’s addition further solidifies its position.
TSMC Order Diversification: AI chip demand expands from NVIDIA alone to multiple customers, diversifying TSMC’s revenue sources and reducing dependence on single customers.
Startup Pressure
Intensified Cost Competition: Large tech companies reducing AI service costs through custom chips can offer lower prices or higher performance. Startups relying on cloud APIs or GPU rentals face obvious cost disadvantages.
Widening Technology Gap: Custom chips require hundreds of millions in investment and top talent, affordable only to well-funded giants. Startups struggle to follow, potentially widening technology gaps.
Vertical Integration Trend: From AI models, chip hardware, cloud infrastructure to application services, tech giants achieve vertical integration. Startups need differentiated positioning to avoid head-on competition.
Comparison with Other Tech Giants
Google TPU
Earliest Start: Google began developing TPUs in 2015, currently on fifth generation (TPU v5). Years of accumulated experience make TPUs perform excellently in training and inference.
Specialized Architecture: TPUs optimize for TensorFlow framework, tightly integrated with Google AI R&D. However, relatively closed, primarily for Google internal use with limited external rental options.
Meta MTIA
Inference Specialized: Meta’s MTIA (Meta Training and Inference Accelerator) primarily optimizes for inference, supporting AI functions for Facebook and Instagram like content recommendation and ad targeting.
Cost Driven: Meta processes billions of user requests daily with massive inference costs. MTIA helps reduce inference costs by over 50%, improving profit margins.
Amazon Trainium/Inferentia
Cloud Rental Model: Amazon’s custom chips primarily provide to customers through AWS cloud services rather than solely internal use. This model expands markets but faces NVIDIA competition.
Price Advantage: AWS claims Trainium training costs 40% less than NVIDIA GPUs, Inferentia inference costs 70% less. Attracts enterprise customers to migrate workloads.
OpenAI’s Differentiation
Model-Specific Optimization: OpenAI chips focus on GPT series models, extremely optimizing for Transformer architecture, potentially surpassing general solutions in specific workloads.
Inference Cost Core: ChatGPT’s business model relies on low-cost inference. If chips can reduce inference costs by 50-70%, this will dramatically improve financial health, supporting free tiers and pricing strategies.
AGI Goal Driven: OpenAI pursues AGI (Artificial General Intelligence), requiring unprecedented computational scale. Custom chips are necessary infrastructure for achieving this goal.
Impact on Taiwan Semiconductor Industry
TSMC Profit Opportunities
Increased Orders: OpenAI custom chips adopting TSMC advanced processes may bring hundreds of millions to billions in annual revenue. Combined with Google, Apple, and NVIDIA orders, this solidifies TSMC’s leading position.
Advanced Packaging Demand: AI chips often adopt advanced packaging technologies like CoWoS (Chip-on-Wafer-on-Substrate), combining HBM memory with logic chips. TSMC needs to expand advanced packaging capacity.
Supply Chain Drivers
Design Services: Taiwan IC design service companies (like Global Unichip, Faraday Technology) may participate in OpenAI chip design verification.
Testing and Packaging: ASE Technology, SPIL and other OSAT firms may receive orders.
Substrate Materials: Unimicron, Nan Ya PCB and other PCB manufacturers supply high-end substrates.
Memory: Nanya Technology, Winbond provide HBM or related memory products.
Talent Mobility
Overseas Recruitment: OpenAI and Broadcom may recruit Taiwan semiconductor talent, particularly senior engineers from TSMC, MediaTek. Salary and stock incentives may be highly attractive.
Technology Return: Some Taiwanese engineers working in US on OpenAI projects may bring experience back to Taiwan, promoting local AI chip industry development.
Long-Term Strategic Significance
Technical Autonomy
Avoiding Chokepoints: Controlling core chip design capabilities, OpenAI avoids NVIDIA supply chain constraints. Even if NVIDIA restricts shipments or significantly raises prices in future, OpenAI has backup options.
Technical Leadership Advantage: Chips optimized for proprietary models may provide higher performance than general-purpose GPUs. This technical advantage translates to model iteration speed, service quality, cost control competitiveness.
AGI Resource Preparation
Computational Scale Requirements: Industry estimates achieving AGI may require 100-1000 times current largest AI model’s computational resources. Custom chips enable OpenAI to plan such ultra-large-scale deployments.
Energy Efficiency Considerations: Training and running AGI-level models may consume power equivalent to several nuclear power plants. High-efficiency chips are necessary not only for cost reduction but also environmental sustainability.
Business Model Transformation
Lower API Pricing: After chip costs decrease, OpenAI can lower API pricing, attracting more developers and enterprise customers, expanding market share.
New Product Possibilities: Low-cost inference enables OpenAI to launch more free or low-price products, like free ChatGPT Pro features, real-time voice translation, personal AI assistants.
Hardware Product Exploration: Future OpenAI may launch hardware products like AI servers with custom chips, edge computing devices, entering hardware markets.
Risks and Uncertainties
Technical Execution Risk
First Chip Design: OpenAI lacks chip design experience; even with Broadcom assistance, whether first product achieves expected performance remains uncertain. History includes failed tech giant chip projects.
Timeline Delays: Chip development often encounters delays. Design issues, verification bugs, low process yields may postpone mass production timelines, affecting OpenAI’s strategic planning.
Market Environment Changes
NVIDIA Counterattack: Facing customer custom chip threats, NVIDIA may lower prices, improve performance, strengthen software ecosystem counterattacks. CUDA ecosystem’s powerful inertia shouldn’t be underestimated.
AI Demand Slowdown: If AI market growth falls short of expectations, OpenAI’s large-scale chip investment may become financial burden. When demand shrinks, custom chip fixed cost disadvantages emerge.
Competitive Landscape
Google, Meta Lead: Google TPU has developed for ten years, Meta MTIA already in mass production. OpenAI enters market late, needing time to catch up in technical maturity.
Startup Challenges: AI chip startups like Cerebras, Graphcore, SambaNova offer innovative architectures. Can OpenAI chips surpass these specialized players in performance and cost?
Conclusion
OpenAI’s partnership with Broadcom represents the latest case of vertical integration trends in the AI industry. Through custom chips, OpenAI seeks technical autonomy, cost optimization, and performance improvement, laying hardware foundations for realizing AGI vision. This partnership will reshape AI chip market landscape, challenging NVIDIA’s monopoly position while bringing new business opportunities to Broadcom, TSMC, and Taiwan’s supply chain. However, chip development is full of risks and uncertainties; whether OpenAI can successfully execute this ambitious plan requires years to verify. Regardless of outcome, tech giants’ custom chip trend is irreversible, and AI hardware competition enters a white-hot phase.
