SK Hynix $SKYH Earnings Shed Light on Memory Capacity and Demand

I fully expect more volatility in the AI hardware names, but lot of interesting takeaways from $SKHY earnings today. Think of this pullback as time for you to do the research and understand where this industry is moving. So here are few notes and commentary that stood out to me from their earnings. One of the concerns with memory and a lot of hardware is optimization. I think the market is rightfully worried about sustainability of margins, but I think TAM growth is not something that’s being priced in. At the very least, I think there’s uncertainty whether securing additional capacity would lead to peak cycle or will growth in inference market would give enough TAM expansion to even out the potential depreciation in margins. I think this is the big issue with a lot of hardware at this juncture, and why we’re seeing such a huge selloff in $SMH. Management addressed concerns about memory oversupply and demand depreciation: “Our planned capacity expansion is based on market-demand visibility secured through customer partnerships.” “Investment and production ramps will be implemented in phases and aligned flexibly with confirmed customer demand. We do not believe our medium-to-long-term investment plans will lead to immediate oversupply.” “We do not expect these efficiency gains to reduce overall infrastructure demand. Instead, they should lower the cost and adoption barriers for AI services, expanding the user base and range of applications.” Again, as I’ve been talking about for a while, all this optimization imo will lead to competitive pricing in AI models, but not in demand destruction for hardware (at least not memory). SK Hynix $SKHY on data-center leasing and efficient AI models: “We view these developments not as signs of an AI investment slowdown, but as a transition toward higher utilization of infrastructure already built at scale and accelerated monetization.” “As AI models improve and software optimization advances, the compute volume and cost per task continue to decline.” “As models and systems become more efficient, the same infrastructure can support more users and services, broadening AI adoption rather than reducing infrastructure demand.” Commentary around HBM: "As HBM4 shipments ramp up in earnest and 1cnm conventional DRAM shipments increase, we expect H2 bit growth to be higher than in H1.” “Growing HBM4 sales and a higher mix of value-added products should lift blended ASP, driving higher shipment volumes and improved earnings in H2.” “HBM4 competitiveness is not only about delivering the required performance. It also requires the ability to supply at scale with stable yields and consistent quality.” “We began mass production for key customers in Q2, and HBM4 yield and quality are already approaching the levels of mature HBM3. Our focus is now on steadily ramping production capacity.” “Discussions on '27 HBM supply volumes & pricing are underway with key customers & are progressing smoothly.” This tells me that there’s clear customer demand for HBM4 and onwards and production capacity is still needs to be ramped, which means sustainable higher margins on at least HBM4 and onward. Only time will tell how quickly memory TAM can grow from here and how long production capacity will take to ramp up for HBM4 and onwards, but from this I continue to think memory still remains a huge bottleneck in the AI trade. Not Financial Advice! Just my thoughts that can change/evolve as more news comes out.