UBS: Open-Source Models MomentumAI Demand & En

UBS: Open-Source Models Momentum

AI Demand & Enterprise Adoption

> Broadening Demand: AI demand remains exceptionally strong and is expanding rather than slowing down, despite heightened scrutiny around return on investment (ROI).

> Transition to Production: Enterprises are shifting from early testing and proof-of-concepts into full production and large-scale deployment.

> Incremental Categories of Spend: Companies like Perplexity, AlphaSense, ElevenLabs, Sierra, and Cursor reported exponential growth (e.g., Perplexity experiencing a 3x increase in ARR year-to-date) driven by new, non-cannibalistic categories of AI spending such as voice, digital coworkers, agents, and coding.

Pricing Shifts & Cost Optimization

> Consumption-Based Pricing: Vendors like Microsoft (with Copilot) are transitioning certain clients from traditional seat-based licensing to consumption-based pricing.

> Focus on Token Costs: This shift has intensified enterprise focus on maximizing ROI per token, making model-harness optimization, smart routing, and workload-specific model selection crucial components.

> Reallocation, Not Reduction: Demand is not shrinking; rather, it is being strategically reallocated toward the most cost-effective models and workloads.

Open-Source Momentum & Implications for NVIDIA

> Closing Performance Gap: Open-source models have significantly narrowed the performance gap against frontier solutions over the past year.

> Explosive Token Utilization: Open-source adoption has surged from sub-1% last year to 1% at the start of 2026, multiplied several times over by mid-2026, and is projected to potentially represent the majority of tokens used over time.

> Favorable for NVIDIA: This trend is viewed as highly positive for NVIDIA due to its software leadership (such as the Nemotron family) and the fact that most open-source models are trained and fine-tuned on NVIDIA hardware, resulting in superior inference performance.

> Expanded Infrastructure Demand: While open-source adoption pressures frontier-only economics, it ultimately expands total inference demand by turning lower-cost models into viable solutions for a broader range of mature workflows.

Fast-Inference Infrastructure

> Optimal Use Cases: Cerebras (CBRS) enterprise customer feedback indicates that wafer-scale engine (WSE) technology is ideally suited for latency-sensitive search and one-shot retrieval workloads.

> Long-Running Workloads: Long-running agentic workflows continue to prioritize model quality, tooling, and orchestration over raw inference speed.

> Market Share: NVIDIA's view that fast inference accounts for roughly 10-20% of the overall inference market remains intact, though use cases are expected to expand as token costs decline and architectural advancements (like disaggregated solutions and stacked-memory SRAM architectures) improve.

$NVIDIA(NVDA.US) $Cerebras(CBRS.US)