Bank of America delivered a striking forecast this week: global semiconductor sales will surge 30% year-over-year in 2026, pushing the industry past a historic $1 trillion annual sales milestone for the first time. But buried within that bullish outlook lies a more nuanced story—one that could reshape the AI chip landscape and challenge Nvidia's seemingly unassailable dominance.

The Custom Chip Revolution

The shift is being driven by application-specific integrated circuits (ASICs) developed by hyperscalers such as Google, Meta, and Amazon. After years of feeding Nvidia's growth with insatiable demand for GPUs, these tech giants are turning to custom silicon designed specifically for their workloads.

Google's Tensor Processing Units (TPUs) have gained significant traction, with major clients including OpenAI, Meta Platforms, Apple, and Anthropic expressing interest. The custom chips offer advantages in power efficiency and cost-per-inference that general-purpose GPUs cannot match for certain workloads.

Broadcom has emerged as the primary beneficiary of this trend, partnering with hyperscalers to design and manufacture custom AI accelerators. Bank of America's semiconductor analyst forecasts Broadcom as one of six stocks positioned to lead the $1 trillion surge.

Why Now?

Several factors are accelerating the custom chip transition:

Scale economics: At the volumes Google, Meta, and Amazon operate, even marginal improvements in cost or efficiency translate to billions in savings. Custom chips optimized for specific inference workloads can deliver 2-3x better performance per dollar than general-purpose GPUs.

Supply constraints: Nvidia's chips remain in high demand, and hyperscalers experienced painful allocation shortfalls during the 2023-2024 buildout. Diversifying supply through custom silicon reduces single-vendor risk.

"As these giants work to reduce their reliance on Nvidia, they are turning to Broadcom for custom AI accelerators designed specifically for their inference workloads."

— Bank of America Research

Inference economics: The AI market is transitioning from training-dominated spending to inference-dominated spending. Training requires massive parallel compute that Nvidia excels at; inference workloads benefit more from specialized optimization that custom chips can deliver.

What It Means for Nvidia

Nvidia's position remains formidable—the company's GPUs are still the standard for AI training, and its CUDA software ecosystem creates significant switching costs. RBC Capital Markets recently initiated coverage with an outperform rating and a $240 price target, citing a $500 billion backlog and continued training demand.

However, the emergence of credible alternatives introduces competition to a market Nvidia has effectively monopolized. As inference becomes a larger share of AI compute spending, the addressable market for Nvidia's highest-margin data center products could face pressure.

The company is responding by accelerating its own custom chip partnerships and enhancing inference-specific products. But the days of hyperscalers having no choice but Nvidia appear to be ending.

Broadcom's Opportunity

Wells Fargo recently upgraded Broadcom to overweight, viewing the recent pullback as a buying opportunity. The firm sees "meaningful incremental catalysts" in 2026 as custom ASIC revenue ramps.

Broadcom's approach differs fundamentally from Nvidia's. Rather than selling standardized chips, Broadcom partners deeply with hyperscalers to co-design silicon optimized for their specific requirements. This model creates sticky relationships and recurring design revenue even before manufacturing begins.

The company's AI-related revenue is expected to grow substantially as new custom designs reach production in 2026 and 2027.

The $1 Trillion Semiconductor Market

Bank of America's broader semiconductor forecast highlights an industry at an inflection point. Key themes for 2026 include:

  • AI infrastructure buildout: Continued data center construction drives demand across memory, processors, and networking chips
  • Memory supercycle: High-bandwidth memory for AI applications is seeing unprecedented demand
  • Advanced packaging: Chiplet architectures require sophisticated packaging that commands premium pricing
  • Custom silicon growth: ASICs take share from general-purpose processors in both AI and other applications

Investment Implications

For investors, the custom chip trend suggests a more nuanced approach to semiconductor exposure. Rather than simply owning Nvidia, a diversified portfolio might include:

  • Broadcom: The leading custom ASIC partner for hyperscalers
  • TSMC: Manufactures chips regardless of who designs them
  • AMD: Wells Fargo's top semiconductor pick, benefiting from data center diversification
  • ASML: Essential equipment provider for advanced manufacturing

The $1 trillion semiconductor industry will have room for multiple winners. But the competitive dynamics are shifting, and the hyperscalers that built Nvidia's dominance are increasingly charting their own silicon destiny.