South Korea’s SK Hynix on Wednesday posted record quarterly revenue and profit, boosted by a strong demand for its high bandwidth memory used in generative AI chipsets.

Here are SK Hynix’s third-quarter results versus LSEG SmartEstimates, which are weighted toward forecasts from analysts who are more consistently accurate:

• Revenue: 24.45 trillion won ($17.13 billion) vs. 24.73 trillion won
• Operating profit: 11.38 trillion won vs. 11.39 trillion won

Revenue rose about 39% in the September quarter compared with the same period a year earlier, while operating profit surged 62%, year on year.

On a quarter-on-quarter basis, revenue was up 10%, while operating profit grew 24%.

SK Hynix makes memory chips that are used to store data and can be found in everything from servers to consumer devices such as smartphones and laptops.

The company has benefited from a boom in artificial intelligence as a key supplier of high-bandwidth memory or HBM chips used to power AI data center servers. 

“As demand across the memory segment has soared due to customers’ expanding investments in AI infrastructure, SK Hynix once again surpassed the record-high performance of the previous quarter due to increased sales of high value-added products,” SK Hynix said in its earnings release. 

HBM falls into the broader category of dynamic random access memory, or DRAM — a type of semiconductor memory used to store data and program code that can be found in PCs, workstations and servers.

SK Hynix has set itself apart in the DRAM market by getting an early lead in HBM and establishing itself as the main supplier to the world’s leading AI chip designer, Nvidia. 

However, its main competitors, U.S.-based Micron and South Korean-based tech giant Samsung, have been working to catch up in the space.

“With the innovation of AI technology, the memory market has shifted to a new paradigm and demand has begun to spread to all product areas,” SK Hynix Chief Financial Officer Kim Woohyun said in the earnings release.

“We will continue to strengthen our AI memory leadership by responding to customer demand through market-leading products and differentiated technological capabilities,” he added.

The HBM market is expected to continue to boom over the next few years to around $43 billion by 2027, giving strong earnings leverage to memory manufacturers such as SK Hynix, MS Hwang, research director at Counterpoint Research, told CNBC.

“[F]or SK Hynix to continue generating profits, it’ll be important for the company to maintain and enhance its competitive edge,” he added.

A report from Counterpoint Research earlier this month showed that SK Hynix held a leading 38% share of the DRAM market by revenue in the second quarter of the year, increasing its shares after having overtaken Samsung in the first quarter. 

The report added that the global HBM  market grew 178% year over year in the second quarter, and SK Hynix dominated the space with a 64% share.

Celestica CEO Rob Mionis explained how his company designs and manufactures infrastructure that enables artificial intelligence in a Tuesday interview with CNBC’s Jim Cramer.

“If AI is a speeding freight train, we’re laying the tracks ahead of the freight train,” Mionis said.

He pushed back against the notion that the AI boom is a bubble, saying that the technology has gone from a “nice to have” to a “must have.”

Celestica reported earnings Monday after close, managing to beat estimates and raise its full-year outlook. The stock hit a 52-week high during Tuesday’s session and closed up more than 8%. Celestica has had a huge run over the past several months, and shares are currently up 253.68% year-to-date.

Mionis described some of Celestica’s business strategies, including how the Canadian outfit chose to move away from commodity markets and into design and manufacturing. He told Cramer that choice “has paid off in spades” for his company.

Celestica’s focus on design and manufacturing enables the company to “consistently execute at scale,” he added.

He detailed Celestica’s data center work, saying the company makes high-speed networking and storage system for hyperscalers, digital native companies and other enterprise names.

Mionis praised the company’s partnership with semiconductor maker Broadcom, saying Celestica uses Broadcom’s silicon in a lot of its designs.

“What it means for us is when they launch a new piece of silicon — so the Tomahawk 6 is their 1.6 terabyte silicon — when they launch that into the marketplace, they’ll work with us to develop products, and those products end up in the major hyperscalers.”

Sign up now for the CNBC Investing Club to follow Jim Cramer’s every move in the market.

Disclaimer The CNBC Investing Club Charitable Trust owns shares of Broadcom.

Elon Musk‘s Wikipedia rival Grokipedia got off to a “rocky start” in its public debut, but Wikipedia founder Jimmy Wales didn’t even have to take a look at the AI’s output to know what he expected.

“I’m not optimistic he will create anything very useful right now,” Wales said at the CNBC Technology Executive Council Summit in New York City on Tuesday.

Wales had plenty of choice words for Musk, notably in response to allegations that there is “woke bias” on Wikipedia. “He is mistaken about that,” Wales said. “His complaints about Wiki are that we focus on mainstream sources and I am completely unapologetic about that. We don’t treat random crackpots the same as The New England Journal of Medicine and that doesn’t make us woke,” he said at the CNBC event. “It’s a paradox. We are so radical we quote The New York Times.”

“I haven’t had the time to really look at Grokipedia, and it will be interesting to see, but apparently it has a lot of praise about the genius of Elon Musk in it. So I’m sure that’s completely neutral,” he added.

Wales’ digs at Grokipedia — which has its own wiki page — were less about any ongoing spat with Musk and more about his significant concerns about the efforts by all large language models to create a trusted online source of information.

“The LLMs he is using to write it are going to make massive errors,” Wales said. “We know ChatGPT and all the other LLMs are not good enough to write wiki entries.”

Musk seems equally certain of the opposite outcome: “Grokipedia will exceed Wikipedia by several orders of magnitude in breadth, depth and accuracy,” he wrote in a post on Tuesday night.

Wales gave several real-world examples of why he doesn’t have faith in LLMs to recreate what Wikipedia’s global community has built over decades at a fraction of the cost — he estimated the organization’s hard technology costs as $175 million annually versus the tens of billions of dollars big tech companies are constantly pouring into AI efforts, and by one Wall Street estimate, a total of $550 billion in AI spending expected by the so-called hyperscalers next year.

One example Wales cited of LLM’s inaccuracy relates to his wife. Wales said he often asks new chatbot models to research obscure topics as a test of their abilities, and asking who his wife is, a “not famous but known” person, he said, who worked in British politics, always results in a “plausible but wrong” answer. Any time you ask an LLM to dig deep, Wales added, “it’s a mess.”

He also gave the example of a German Wiki community member who wrote a program to verify the ISBN numbers of books cited, and was able to trace notable mistakes to one person. That person ultimately confessed they had used ChatGPT to find citations for text references and the LLM “just very happily makes up books for you,” Wales said. 

Wales did say the battles into which he has been drawn, by Musk and by AI, do reinforce a serious message for Wikipedia. “It’s really important for us and the Wiki community to respond to criticism like that by doubling down on being neutral and being really careful about sources,” he said. “We shouldn’t be ‘wokepedia.’ That’s not who we should be or what people want from us. It would undermine trust.”

Wales thinks the public and the media often give Wikipedia too much credit. In its early days, he says, the site was never as bad as the jokes made about it. But now, he says, “We are not as good as they think we are. Of course, we are a lot better than we used to be, but there is still so much work to do.”

And he expects the challenges from technology, and from misinformation, to get worse, with the ability to use LLMs to create fake websites with plausible text getting better and likely able to fool the public. But he says they will have a hard time fooling the Wiki community, which has spent 25 years studying and debating trusted information sources. “But it will fool a lot of people and that is a problem,” he said.

In some cases, this same new technology, which “makes stuff up that is completely useless,” may be useful to Wikipedia, he said. Wales has been doing some work on finding limited domains where AI can uncover additional information in existing sources that should be added to a wiki, a use of gen AI he described as currently being “kind of okay.”

“Maybe it helps us do our work faster,” he said. That feedback loop could be very useful for the site if it developed its own LLM that it could train, but the costs associated with that have led the site to hold off any formal effort while it continues to test the technology, he added.

“We are really happy Wiki is now part of the infrastructure of the world, which is a pretty heavy burden on us. So when people say we’ve gotten biased, we need to take that seriously and work on anything related to it,” Wales said.

But he couldn’t resist putting that another way, too: “We talk about errors that ChatGPT makes. Just imagine an AI solely trained on Twitter. That would be a mad, angry AI trained on nonsense,” Wales said.