Business headlines from the Apex backend, organized by sectors and major public companies for faster market scanning.
A pullback earlier in the week left a dent in market momentum, but futures indicated efforts to stabilize as traders weighed upcoming earnings from major tech names and scheduled remarks from Federal Reserve policymaker Kevin Warsh.
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A new report, carried by Yahoo Finance, says Microsoft is ramping its push into designing its own AI chips, an effort meant to reduce reliance on Nvidia even as competitors such as Google and Amazon have already moved further toward custom silicon.
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Meta Platforms rolled out a new AI coding agent called Muse Code on Aug. 5, aiming to win over software developers by undercutting the price of competing offerings from Anthropic and OpenAI.
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Bristol-Myers Squibb said Aug. 10 it expects to spend about $2.3 billion on a new manufacturing plant in Houston. The same market report highlighted Eli Lilly’s progress in the United Kingdom, setting up a contrast between new U.S. capacity buildout and overseas momentum.
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A recent market comparison highlights AMD’s eight-quarter streak of revenue growth against Arm Holdings’ more uneven pattern, underscoring how business models in the AI supply chain can translate into very different top-line momentum.
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A market report tied to Morgan Stanley points to gold’s latest breakout as a potential setup for additional gains, though details of the firm’s underlying thesis were not provided in the post.
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The company announced the next coupon amount tied to the Alerian MLP Index ETN listed on NYSE Arca, a periodic cash distribution designed around performance of the index it tracks.
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Nvidia NVDA is scheduled to release its second-quarter earnings after the market close on Wednesday, Aug. 26, a timing that puts the focus on how demand, product mix, and margins are tracking across the company’s AI-driven data center business. A preview interview from Yahoo Finance points to three “mission-critical” areas investors will likely scrutinize.
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The GPU giant is putting more weight behind the buildout of the physical infrastructure that supports AI data centers, citing an emerging bottleneck: enough power, in the right place, at scale.
