Can Nvidia hit $6 trillion? Wedbush's Matt Bryson on memory, open-weight AI and Intel

Nvidia is approaching a record high, and Wedbush analyst Matt Bryson discusses what could be needed for the chipmaker to reach a $6 trillion market capitalization. He says tight memory supply may benefit Nvidia but expects memory to remain insufficient next year; he also comments on potential AI-demand implications of Nvidia-backed Reflection and reported partnerships or customer pullbacks involving other firms.

Analysis

The key transmission is not Nvidia’s potential valuation milestone; it is whether memory availability limits the number of complete AI systems customers can deploy. Scarcity can reinforce Nvidia’s allocation advantage and support system-level pricing, but it can also defer recognized revenue and leave accelerator demand unfulfilled. Treat “tight supply helps Nvidia” as a relative-positioning claim, not proof of higher consolidated margins: the relevant checks are memory availability, system shipments, and whether customers accept delivery delays or redesign around constraints. Memory suppliers may gain pricing leverage, while cloud buyers face slower capacity additions and potentially higher deployment costs.

Over 1–3 months, watch Nvidia guidance and shipment cadence against hyperscaler capex commentary; the thesis weakens if supply constraints translate into lower deliveries or customer digestion rather than a short timing delay. Over 6–18 months, open-weight models could lower inference costs and broaden workloads, a potential volume tailwind for Nvidia, but efficiency gains could also reduce compute per task. Model availability alone does not establish incremental paid usage.

Reports of a possible Musk–TSMC partnership are not enough to underwrite a material change in Intel’s competitive position. Any effect depends on actual foundry commitments, process, volume, and timing; until then, this is headline risk rather than a thesis. Likewise, Microsoft or Meta reducing Claude exposure would chiefly bear on Anthropic’s distribution and funding prospects, not automatically on Nvidia’s hardware demand: workloads can migrate among models while still requiring compute. The contrarian risk is that investors capitalize long-run AI demand before memory-constrained delivery and utilization are demonstrated.

Key Decisions for Investors

• Prefer a staged, catalyst-driven long bias in NVDA over chasing record highs: add only if shipment/guidance evidence shows memory constraints are delaying rather than canceling deployments. Reassess on any guidance cut, weaker data-center shipment commentary, or hyperscaler capex retrenchment.
• Track memory lead times, pricing, and supplier commentary as the key confirmation signals. A memory-supplier relative-value position versus AI-system buyers is only a watch item until the article’s broad scarcity claim is corroborated by company-specific data.
• Do not trade INTC against TSM on the reported potential partnership alone. Require verified foundry scope, committed volumes, process qualification, and timing; absent those, the news does not establish a change in Intel’s economics.
• Treat Reflection’s open-weight model and reported Claude pullbacks as second-order demand indicators, not stand-alone NVDA catalysts. Monitor model usage, inference volumes, and cloud capex; broadening workloads supports the bullish case, while falling compute intensity or delayed deployments would falsify it.