THE APEX TIMES
Nvidia’s Jensen Huang tells investors AI selloff is a “buying opportunity,” points to deeper SK hynix memory partnership
The Nvidia CEO said the recent pullback in chip stocks is not a sign that AI demand is weakening, even as the company and SK hynix announced a multiyear effort to co-develop next-generation memory for Nvidia’s AI “factory” roadmap.
Nvidia CEO Jensen Huang pushed back on the recent rout in semiconductor stocks on June 8, calling the selloff a buying opportunity and arguing that the AI buildout is still in its early stages. Huang framed last week’s market turbulence as a near-term positioning event rather than a fundamental change in how quickly companies will buy AI infrastructure, saying, “We are at the outset of the AI revolution.”
The selloff centered on June 5, when the semiconductor sector suffered its worst single-session drop since March 2020, according to investing coverage. Nvidia shares fell on the day as well, with the company described as shedding 6.2% in that session. Reports tied the selling to macro pressure and shifting expectations, including a May jobs report that raised concerns about interest-rate increases and uncertainty that AI spending plans could be met.
Huang’s message landed as investors tried to determine whether AI infrastructure demand was merely taking a breather or whether the valuation of chipmakers had gotten ahead of itself. In the same coverage, other market voices characterized the move as overbought positioning unwinding rather than the start of a durable downdraft, even as some analysts compared the magnitude of tech and AI market swings to late-1990s-style euphoria.
Huang’s comments also came just days after Nvidia and SK hynix announced a multiyear technology partnership aimed at memory chips for AI “factories.” Those AI factories are the data-center-scale systems that train frontier AI models and run large numbers of AI workloads. The June 7 Nvidia press release said the partnership is designed to advance next-generation memory aligned with Nvidia’s AI infrastructure roadmap and to expand supply to match longer development cycles.
Nvidia said the work will extend beyond a single product line, with SK hynix set to codevelop memory for Nvidia’s Vera Rubin AI supercomputers, Vera CPUs, RTX Spark-powered PCs, and Jetson Thor robotic computing platforms. It also outlined software and design-tool collaboration: SK hynix will use Nvidia CUDA-X libraries (Nvidia’s suite of GPU-accelerated tools for AI and computing workloads) and PhysicsNeMo (Nvidia’s AI framework for physics and simulation) to speed up semiconductor simulation and technology computer-aided design workflows.
The companies further said SK hynix will develop “factory digital twins,” meaning 3D, simulation-based representations of semiconductor manufacturing sites that can help optimize operations. Nvidia’s release described the effort using Nvidia Omniverse and OpenUSD for building and optimizing 3D scenes, plus Nvidia cuOpt (an optimization engine) and Nvidia Metropolis for simulation and operations planning. In practical terms, the partnership is positioned as a way to reduce friction between chip design, manufacturing execution, and the scaling of AI systems that depend on advanced memory.
Even so, the disclosures leave key questions unanswered. The partnership announcement did not publish specific memory chip technical parameters (such as capacity targets), the size and timing of supply commitments, or any financial terms, nor did the company connect the agreement directly to near-term earnings impacts from the selloff described in market reporting. What to watch next, analysts and investors will likely focus on Nvidia’s upcoming guidance on AI infrastructure demand, and whether additional details around memory roadmaps and supply availability emerge as systems built on Vera Rubin and related platforms move from planning into production.
Why It Matters
- The CEO’s comments reinforce Nvidia’s view that AI demand remains intact despite short-term volatility in high-growth tech and semiconductors.
- By partnering deeply on memory, Nvidia is indicating that scaling AI systems will depend not just on GPUs, but also on advanced memory supply and faster design-to-manufacturing cycles.
- The focus on digital twins suggests companies may be trying to reduce manufacturing bottlenecks that can constrain AI hardware ramp timelines.
- If the market continues to price semiconductors based on macro rates, pairing that with concrete supply-chain partnerships could affect how investors assess risk and timing.
Sources
Key Facts
- Nvidia CEO Jensen Huang described the recent semiconductor selloff as a buying opportunity and said AI infrastructure demand is still in its early stages.
- Market reporting tied the June 5 semiconductor drop to rate fears linked to a May jobs report and uncertainty after Broadcom’s AI guidance for the prior quarter.
- On June 7, Nvidia and SK hynix announced a multiyear technology partnership focused on next-generation memory for AI factories.
- Nvidia said the partnership will support memory supply tied to longer development cycles for advanced memory technologies.
- Nvidia said SK hynix will codevelop memory for Nvidia platforms including Vera Rubin AI supercomputers, Vera CPUs, RTX Spark-powered PCs, and Jetson Thor robotic computing platforms.
- The companies said they will use Nvidia CUDA-X and PhysicsNeMo to accelerate semiconductor simulations, and Nvidia Omniverse, OpenUSD, cuOpt, and Metropolis to support semiconductor “digital twins.”
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