If you want one number that captures how hot the AI hardware market is right now, look at SK hynix. On October 28, 2026, the South Korean memory maker and key Nvidia supplier reported record third-quarter results, with operating profit up about seven-fold from a year earlier.
The numbers
According to SK hynix’s results announcement and reporting from Yonhap, AFP and CNBC:
- Revenue: a record 104.62 trillion won for the third quarter.
- Operating profit: a record 83.33 trillion won, up 632% year over year.
- Net profit: 39.78 trillion won.
- Shareholder returns: about 100 trillion won (roughly $75 billion, per Bloomberg’s conversion) planned for 2026, including more than 30 trillion won in dividends and more than 60 trillion won in share buybacks and cancellations. That includes a third-quarter dividend of 20,000 won per share.
CNBC reported the results beat analyst estimates, and SK hynix shares rose on the news.
What is driving it
The short answer is AI. Training and running large AI models requires enormous amounts of fast memory, especially high-bandwidth memory (HBM) that sits right next to AI accelerators. SK hynix is one of the leading suppliers of that memory. Coverage from Yonhap described the quarter as a product of the “AI chip supercycle,” and AFP pointed to relentless AI hardware demand and higher chip prices.
The memory market has always been cyclical, swinging between shortage and oversupply. Right now it is firmly in shortage territory for AI-grade parts, which gives suppliers pricing power.
Why it matters beyond chipmakers
Memory sits underneath almost every AI project. When memory makers report record margins, it tells you two things:
- AI infrastructure spending is still accelerating. Cloud providers, AI labs and large enterprises are buying hardware at a pace that keeps suppliers sold out.
- Costs are flowing downstream. Higher memory prices eventually show up in server, cloud and even PC and phone pricing. Businesses planning AI and infrastructure budgets for 2027 should expect hardware costs to stay firm.
This lines up with other signals this week. Axios reported, citing US Census Bureau data, that US business investment in equipment linked to the AI boom has been growing at a pace not seen in decades.
What this means for IT companies
Record results at the bottom of the AI supply chain usually mean more work at every layer above it. For IT providers, the demand shows up in several ways.
- Data center and infrastructure services. Every new AI cluster needs design, installation, cooling, networking and ongoing management.
- Cloud cost optimization. With hardware prices high, companies want help getting more from what they already pay for. FinOps and right-sizing services become easier to sell.
- Hardware resellers and integrators. Clients planning refresh cycles will want advice on timing purchases and securing supply.
- AI application builders. Massive infrastructure investment only pays off if businesses build useful applications on top of it, which drives demand for development and integration work.
- Managed services. Mid-sized companies running AI workloads often lack in-house expertise and turn to MSPs for support.
The common thread is that buyers are actively planning and spending. When budgets are moving, the providers that reach decision makers early, with a relevant and specific offer, tend to win a bigger share of the work.
What to watch
Investors and buyers will be watching whether memory supply can catch up with AI demand, how long elevated pricing lasts, and whether big cloud providers keep raising capital spending. Upcoming earnings from major cloud and software companies will offer more clues about how much AI infrastructure spending is planned for 2027.
The takeaway
SK hynix’s record quarter is more than a chip story. It confirms that the AI infrastructure build-out is still in full swing, that hardware costs are likely to stay high, and that companies across the technology industry will need help planning, deploying and optimizing what they buy.
Sources: SK hynix Newsroom, CNBC, Yonhap, AFP
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