Market Flux Event
Apple Posts Record Q4 Revenue of $114.48B but Shares Fall as AI Costs Cloud Holiday Outlook
Read this in the Market Flux appApple reported fiscal fourth-quarter revenue of $114.48 billion, beating the $113.39 billion analyst estimate, with GAAP EPS of $2.10 topping the $1.99 consensus by $0.11. Net income rose 11.8% year over year to $30.7 billion, and the gross margin of 47.9% beat estimates of 47.5%, though both figures included roughly one percentage point and $0.05 per share, respectively, from tariff refunds. iPhone revenue of $57.58 billion and Mac revenue of $10.79 billion both beat estimates, with iPhone up 17.5% and Mac up 23.7% year over year. Services came in at $31.57 billion, essentially in line with the $31.58 billion estimate. iPad at $6.23 billion and Wearables at $8.30 billion both missed, as did Greater China at $17.54 billion against an $18.58 billion estimate, though China revenue was up 21% year over year in absolute terms.
Despite the beat, shares fell in after-hours trading as investors focused on margin pressure heading into the holiday quarter. Apple guided fiscal first-quarter 2027 revenue growth of 7% to 10% year over year, ahead of Wall Street's 6.1% consensus, but gross margin guidance of 45% to 46% disappointed analysts expecting 46.3%. CFO commentary attributed the compression to rising memory chip costs and ongoing investment in Siri AI infrastructure. CEO John Ternus said memory prices rose sequentially in the September quarter and that price increases on iPhone, Mac, and iPad helped offset those costs. He also confirmed the iPhone Duo remains supply-constrained, with no timeline for when supply and demand will reach balance. COO commentary pointed to advanced 2-nanometer node availability as the primary Q4 constraint, with memory expected to be the binding constraint for Mac and iPad in the December quarter.
Apple disclosed plans to monetize Siri AI usage, with server-based AI features including photo editing subject to usage limits and paid upgrades available through iCloud+. Ternus said Siri already handles more than 2.5 billion requests per day. On agentic AI, he emphasized Apple's intent to ensure user awareness, security, and privacy before broadly expanding capabilities. R&D spending surged 32.3% year over year to $11.7 billion, and operating expenses rose 20.6% to $19.2 billion, reflecting the scale of AI investment underway.
Apple's results landed against a turbulent backdrop for tech and AI stocks broadly. The Nasdaq 100 entered correction territory on November 2, closing down 1.39%, while the S&P 500 fell 0.51%. Among the day's biggest losers, Oracle dropped 8.8% after announcing a $20 billion combined debt and equity raise, AMD slid 7.5%, Datadog fell 6.3%, and Micron tumbled 5.8%. The selloff reflected deepening investor skepticism about the returns on massive AI infrastructure spending, with Oaktree co-chair Howard Marks questioning whether an estimated $7 trillion in AI investment can generate sufficient profit, calling it "in large part an arms race."
Palantir stood out as a rare bright spot, reporting Q3 revenue of $2.31 billion, beating estimates by $130 million, with non-GAAP EPS of $0.44 beating by $0.03. U.S. commercial revenue surpassed government revenue for the first time, with total U.S. revenue rising 119% year over year. CEO Alex Karp said AI demand is "much larger than we can service." Palantir also disclosed that a fine-tuned Nvidia Nemotron 4-billion-parameter model outperformed GPT-6 Astra and Claude Sonnet 5.5 on a real-world construction industry task at a fraction of the cost, achieving roughly 75% accuracy at $0.10 per million tokens versus 62% for GPT-6 Astra at $20 per million tokens. Separately, Anthropic disclosed nearly $900 billion in AI infrastructure commitments ahead of its IPO, even as its Q3 compute costs jumped 77% and outpaced revenue growth. Elon Musk acknowledged that running SpaceX's Grok Bot AI agent carried costs that were "unsustainably high relative to revenue," despite the service reaching 800,000 paid weekly users in October.
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- FaststocknewssAPPLE $AAPL Q4 REVENUE $114.48B BEATS, EPS $2.10 BEATS • Net sales: $114.48B vs $113.39B est, beat • EPS: $2.10 vs $1.99 est, beat, includes a favorable $0.05 from tariff refunds • iPhone: $57.58B vs $56.54B est, beat • Mac: $10.79B vs $9.61B est, beat • Services: $31.57B vs $31.58B est, in line • iPad: $6.23B vs $6.70B est, miss • Wearables, Home and Accessories: $8.30B vs $9.06B est, miss • Greater China: $17.54B vs $18.58B est, miss • Gross margin: 47.9% vs 47.5% est, beat, includes a favorable impact of about 1 percentage point from tariff refunds • Operating cash flow: $28B • Quarterly dividend: $0.27 per share CEO John Ternus says memory-chip prices rose in the September quarter from the June quarter, and that price increases on iPhone, Mac and iPad offset rising memory costs. Carrying inventory and lower costs for non-memory components also helped. He says the iPhone Duo is supply-constrained and Apple has no estimate of when it will reach supply-demand balance, per Reuters interview.
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- WallstenginePALANTIR SHOWS FINE-TUNED NVIDIA 4B AI MODEL BEATING GPT-6 ASTRA & CLAUDE SONNET 5.5 AT A FRACTION OF THE COST In its Q3 business update, $PLTR revealed that a fine-tuned $NVDA Nemotron 3 Nano model with just 4 billion parameters outperformed much larger frontier models on a real-world customer task. The results, based on classifying requests for information for construction firm McCarthy: • NVIDIA Nemotron 4B: ~75% accuracy at $0.10 per million tokens. • Claude Sonnet 5.5: ~66% accuracy at $4 per million tokens. • GPT-6 Astra: ~62% accuracy at $20 per million tokens. Palantir says the fine-tuned models are expected to save McCarthy approximately 30,000 drafting hours annually. Separately, NVIDIA itself is now training open AI models on its own supply chain data inside Palantir's Ontology, spanning millions of parts and thousands of suppliers. Jensen Huang says the partnership combines NVIDIA's Nemotron models with Palantir's Ontology to: “Reason, plan and orchestrate the journey from wafer to token.”
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