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Luke Kawa

Applied Digital soars after posting quarterly revenue beat in Q1, touting strong pipeline of data center demand from hyperscalers

Shares of Applied Digital are soaring more than 25% after the company reported better-than-expected results for its fiscal Q1 and said hyperscalers are lining up to secure capacity as it plans a multiyear expansion.

During the conference call with analysts, Chairman and CEO Wes Cummins said that Applied Digital is “in advanced discussions with an investment-grade hyperscaler” to lease capacity at its Polaris Forge 2 campus, which is expected to begin to come online in 2026 and could scale to up to 1 gigawatt. He added that management has “also entered negotiations with two additional hyperscalers for two new locations.”

The data center company, which counts Nvidia and CoreWeave among its major share and warrant holders, booked $64.2 million in revenues (Bloomberg-compiled consensus estimate: $46.1 million) with an adjusted diluted loss per share of $0.03 (estimate: loss of $0.13) for the three-month period ended August 31.

Its big revenue beat was driven by “tenant fit-out” revenue from CoreWeave as Applied Digital began to ready a data center for use by installing power, cooling, networking, and other infrastructure. While CFO Saidal Mohmand said these revenues are a “one-time, low-margin business,” he still expects them to “ramp significantly over the next quarter” and finds it “strategically important” that APLD’s customers can rely on them “for end-to-end services required to deploy state-of-the-art data centers.”

During the conference call, Cummins reiterated his expectation that Applied Digital will reach a run rate of $1 billion of net operating income within five years.

The options-implied move for the stock on earnings was a whopping 17.6%, per Bloomberg data.

Applied Digital is also one of the components in the Roundhill Meme Stock ETF, which relaunched this week.

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Michael Burry flags “troubling” jump in Nvidia’s supply commitments

“The Big Short” investor Michael Burry — famous for betting against the 2008 housing bubble — just warned of a major risk in Nvidia’s latest annual report, pointing to a sixfold surge in purchase obligations over the past year.

In a Substack post Thursday, Burry called the increase from $16.1 billion to $95.2 billion in just 12 months “troubling,” noting that Nvidia has been forced to place noncancelable purchase orders well before knowing the final demand for its AI chips. The surge is partly tied to supplier TSMC requiring longer-term contracts, he added.

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Vistra beats Q4 earnings expectations for adjusted EBITDA, but dips on income decline

Power provider Vistra, a key player in the AI energy trade, reported better-than-expected adjusted earnings results early Thursday, but shares dipped in early trading as Q4 net income dropped.

The Texas-based company, which supplies nuclear- and natural gas-fueled power to wholesale and retail markets, reported:

  • Net income of $233 million, a decline of 52% from Q4 2024.

  • Adjusted EBITDA from ongoing operations of $1.74 billion vs. the $1.71 billion expected by Wall Street analysts.

  • Vistra maintained previously issued guidance for full-year EBITDA from ongoing operations and adjusted free cash flow from ongoing operations.

Vistra shares soared 258% in 2024 amid a flurry of excitement over the AI energy boom. Last year was more muted, with the stock rising 17%. So far in 2026, shares were up roughly 9% before the report.

Vistra beats fourth-quarter core profit estimates on AI-driven power demand

Vistra beats fourth-quarter core profit estimates on AI-driven power demand

  • Net income of $233 million, a decline of 52% from Q4 2024.

  • Adjusted EBITDA from ongoing operations of $1.74 billion vs. the $1.71 billion expected by Wall Street analysts.

  • Vistra maintained previously issued guidance for full-year EBITDA from ongoing operations and adjusted free cash flow from ongoing operations.

Vistra shares soared 258% in 2024 amid a flurry of excitement over the AI energy boom. Last year was more muted, with the stock rising 17%. So far in 2026, shares were up roughly 9% before the report.

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Claire Yubin Oh

Sandisk rises on partnership with SK Hynix to standardize memory chip architecture tailored for AI data centers

Sandisk is up 3% in premarket trading on Thursday after it began its global standardization strategy of high-bandwidth flash (HBF) memory solutions with SK Hynix.

SK Hynix commented in a press release on Thursday that “by making HBF an industry standard, together with Sandisk, we will lay the foundation for the entire AI ecosystem to grow together,” adding that the companies will set up a dedicated workstream to work on the standardization under the Open Compute Project, the world’s largest organization dealing with data center technologies.

First debuted last February, Sandisk’s HBF technology lies in between ultrafast high-bandwidth memory (HBM) and high-capacity SSDs. That is, these have more storage capacity than HBMs, but are still fast enough to be utilized in AI inferencing (albeit not as quick as HBM).

Sandisk has previously argued that this hybrid architecture is central to AI services that need user applications but require a significant amount of fast interconnect between GPUs. The latest announcement also notes that HBF technology is expected to be more cost-efficient compared to alternatives of similar scale.

The launch, which was shared in an kickoff event on Thursday evening, starts SK Hynix and Sandisk’s workflow, which was announced when the two companies signed a memorandum of understanding “to standardize the specification, define technology requirements and explore the creation of a technology ecosystem” last August, per Sandisk’s press release at the time. Ultimately, by collaborating with SK Hynix, one of the three key HBM suppliers, to standardize and commercialize the technology, Sandisk is manufacturing somewhat of a first-mover advantage to offer the system-level “AI-optimized memory architecture” required for AI inference markets, rather than focusing on the performance of a single chip element.

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