Summary

According to multiple news reports, Nvidia has notified its largest customers — Microsoft, Google, and Oracle — of price increases exceeding 15% on Grace Blackwell and Vera Rubin systems shipping in early 2027.

The driver is not Nvidia's own economics: it is a severe, structural shortage of high-bandwidth memory (HBM) and conventional DRAM. On Goldman Sachs' estimate, memory now accounts for 62% of the total material cost of a Vera Rubin NVL72 rack, up from roughly 53% on the prior GB300 generation — making memory the single largest cost line on the rack, ahead of the GPUs themselves on that basis.

Even Nvidia, sitting on roughly 75% gross margins, has chosen to pass this cost through to customers rather than absorb it, which is itself a signal of how severe the shortage has become. This is the direct demand-side mirror of the SK Hynix, Samsung, and Micron shareholder-return story already in motion: the same HBM scarcity fueling record memory-maker cash flow and buybacks is what is forcing Nvidia to raise prices on its own customers.

Results Review - SK Hynix, 2Q2026 a miss?

SK hynix reported record-breaking 2Q26 financial results on July 29, 2026, driven by intense AI memory demand and higher chip prices. Yet, stock price took a huge dip…

Predictionmarkets.orgCompany Reports

What happened

Nvidia reportedly warned its biggest server-building customers of price increases above 15% on AI server systems built around its Grace Blackwell and Vera Rubin platforms, with the higher pricing applying to systems shipping in early 2027. The increases vary by chip generation and memory configuration, but the underlying cause is consistent: memory input costs have risen far faster than Nvidia can absorb internally.

The scale of the memory bill on a Rubin rack is substantial. A full Vera Rubin NVL72 rack carries an estimated bill of materials of approximately $7.8 million. Estimates of memory's exact share of that total vary by methodology:

Who's affected?

●Direct: the hyperscalers named in reporting — Microsoft, Google, and Oracle — face materially higher capital costs to deploy the same amount of AI compute capacity, on top of existing project delays and labor shortages in the data-center build-out.

●Direct beneficiaries: memory suppliers — Micron, SK Hynix, and Samsung — control the great majority of global DRAM and HBM production and are capturing outsized pricing power as demand outstrips supply. This is the same dynamic underpinning SK Hynix's and Samsung's record cash flow and the large buyback-and-cancellation programs both companies have announced this year.

●Nvidia: protected on margin (it is passing the cost increase through rather than absorbing it) but exposed on demand — if 15%+ higher system prices cause any hyperscaler to slow or reallocate AI infrastructure spending, that is a second-order risk to Nvidia's own volumes.

●Indirect: any enterprise or cloud customer renting AI compute capacity from the affected hyperscalers, who may eventually see the cost passed one layer further down the chain.

Market expectations

What was priced in before: the broad expectation through much of 2026 was that memory would be a rising cost input for AI hardware, but not that it would eclipse GPU silicon as the largest single cost component of a flagship rack system.

Surprise magnitude: large. Contract DRAM prices rose 58-63% quarter-over-quarter in Q2 2026 alone, and Deloitte's full-year forecast calls for AI-server DRAM prices to roughly quadruple — a pace well above typical cyclical memory price swings.

Observed reaction: Nvidia's decision to raise prices rather than absorb the cost is itself the market signal — a company with substantial margin cushion (~75% gross margin) and historically strong negotiating leverage over its supply chain has opted not to shield customers from the increase, which suggests internal expectations are for the shortage to persist rather than resolve quickly.

Reaction vs. justified: passing the cost through protects Nvidia's own margins in the near term, but it also transfers real risk to hyperscaler capex plans; whether that reaction is 'justified' depends on whether AI infrastructure demand is elastic enough that a 15%+ system price increase changes hyperscaler build-out pace at the margin — a question the market has not yet had to answer at this scale.

Forward read: the market is effectively watching whether memory suppliers' pricing power (and by extension, capital-return capacity — see SK Hynix's 40 trillion won buyback-and-cancellation program and Samsung's, Micron's, SanDisk's, Kioxia's, Western Digital's, and Seagate's own return programs) continues to compound, or whether either new capacity or a hyperscaler demand pullback intervenes first.

Silicon Bakery - Breaking news, SK Hynix likely to boost value-up with huge share buyback, so what to expect?

SK Hynix said on Wednesday (Aug 19, 2026) it would buy back and cancel 40 trillion won ($28.61 billion) worth of treasury shares and allocate more than 50 per cent of free cash flow generated between 2025 and 2027 to boost shareholder returns, according to multiple news sources.

Predictionmarkets.orgYU GU

What to watch

●Whether hyperscalers push back on pricing, slow AI infrastructure orders, or accelerate their own proprietary silicon programs in response to a sustained 15%+ cost increase on Nvidia systems?

●Q3/Q4 2026 memory-maker earnings (Micron, SK Hynix, Samsung) for confirmation of whether DRAM/HBM pricing power is still accelerating or beginning to plateau?

●Any signal on new HBM capacity coming online meaningfully earlier than Deloitte's 2029-2030 estimate, which would be the clearest signal this shortage is closer to resolution than currently priced in?

●Whether Nvidia's own reported margins hold at current pass-through levels, or whether competitive or customer pressure eventually forces some cost absorption?

TL;DR

• Alibaba raised HK$80bn ($10.2bn) through the sale of 710mn new shares, with proceeds earmarked for chips, computing infrastructure and AI models.
• The shares were priced at HK$112.70, an 8.4% discount to Friday’s Hong Kong close, creating roughly 3.6% dilution for existing shareholders.
• June-quarter CapEx reached RMB67.7bn ($10.0bn), up 75% y/y, as Alibaba accelerated AI infrastructure spending.
• Alibaba still held RMB474.5bn ($69.9bn) of cash and liquid investments at the end of June, suggesting the equity raise is not necessarily a sign of near-term funding pressure.

Alibaba Raises $10.2 Billion for AI

Alibaba raised HK$80bn ($10.2bn) in one of Hong Kong’s largest follow-on share offerings, selling 710mn new shares at HK$112.70 each.

The placement price represented an 8.4% discount to Friday’s Hong Kong close. The new shares account for about 3.6% of Alibaba’s enlarged share capital, while its Hong Kong-listed shares fell sharply following the announcement.

Alibaba said the proceeds will be used entirely to expand its full-stack AI capabilities, including chips, computing infrastructure and AI models.

AI CapEx Is Approaching $10 Billion a Quarter

The financing comes as Alibaba sharply increases investment in AI infrastructure.

June-quarter capital expenditure reached RMB67.7bn ($10.0bn), up 75% from RMB38.7bn ($5.7bn) a year earlier.

Alibaba has already committed more than RMB380bn ($56.5bn) over three years to AI and cloud infrastructure, covering data centers, computing capacity, semiconductors and model development.

The spending is also weighing on cash generation. Free cash flow was a RMB44.7bn ($6.6bn) outflow in the June quarter, compared with a RMB18.8bn ($2.8bn) outflow a year earlier.

At the same time, Alibaba ended June with RMB474.5bn ($69.9bn) of cash and other liquid investments.

Cloud Growth Is Accelerating Alongside Spending

Alibaba’s AI infrastructure expansion is being accompanied by faster cloud growth.

AI cloud and compute services revenue rose 45% y/y to RMB48.4bn ($7.1bn) in the June quarter. AI-related product revenue reached RMB12.4bn ($1.8bn) and recorded its 12th consecutive quarter of triple-digit growth.

The combination of rising revenue and rapidly expanding CapEx makes the pace of AI monetization, infrastructure utilization and future cash generation increasingly important operating metrics.

Why Raise Equity With Nearly $70 Billion of Liquidity?

The share sale does not necessarily mean Alibaba is short of cash.

With RMB474.5bn ($69.9bn) of cash and liquid investments at the end of June, the company retains substantial liquidity. The decision may instead reflect the scale, duration and uncertainty of the next phase of AI investment.

Equity provides permanent capital: unlike debt, it does not require repayment or create additional fixed interest obligations. Raising capital upfront therefore gives Alibaba more balance-sheet flexibility to sustain a multi-year infrastructure buildout whose ultimate investment requirements and returns remain uncertain.

The trade-off is dilution. Existing shareholders now own a smaller proportion of the company, while the returns generated by the additional AI investment will take time to become visible.

Alibaba has also used other financing instruments for its AI buildout, including roughly $3.2bn of zero-coupon convertible notes issued in 2025. The latest placement adds a substantial equity component to that funding mix.

Source:

1. Bloomberg; https://www.bloomberg.com/news/articles/2026-08-23/alibaba-to-raise-10-billion-by-selling-shares-for-ai-expansion?srnd=homepage-asia
2. Reuters; https://www.reuters.com/business/retail-consumer/alibaba-set-open-down-8-hong-kong-after-102-billion-share-placement-plan-2026-08-24/?utm_source=chatgpt.com

SK Hynix shareholder return surprise:

multiple news sources report the the company will buy back and cancel 40 trillion won (USD28.61B) worth of treasury shares and allocate more than 50% of free cash flow (FCF) generated in 2025-2027 to boost shareholder returns (Aug 19, 2026).

What's the scale of the shareholder return?

The upper-bound of the buyback is approximately 3.3-3.4% of its outstanding shares.

In some sense, they company is buying back the dilution from its recent July 2026 U.S. IPO (raised US26.5B).

“More than 50% FCF” is another lucrative term, as the company's gross margin is estimated to stay in 80-90% level, driven by skyrocketed memory chip prices and limited capacities, despite capacity expansions that are not likely to materialize after 2027.

Recap on memory chip peers' shareholder return actions:

Western Digital: CF deployment toward buybacks & debt reduction. US4B additional share repurchase authorization (authorized Feb 2026).

Seagate: Pledged to return at least 75% of free cash flow to shareholders over time. In addition, A resumption of buybacks.

SanDisk: 1) An additional $14 billion share buyback program on August 5, 2026, bringing its total remaining share repurchase authorization to $15.5 billion. 2) Committed at its August 13, 2026 Investor Day to return 100% of excess cash to shareholders after business reinvestment.

Micron: Committed to returning 100% of excess free cash flow starting in December 2026.

Kioxia: Announced a 3-for-1 stock split effective October 1, 2026, alongside an aggressive up-to-800 billion yen share buyback program.

Samsung Electronics: Operates under a 2024–2026 Shareholder Return Program targeting a return of 50% of free cash flow (FCF), alongside an annual regular dividend totaling KRW 9.8 trillion.

TL;DR

• Nvidia is putting its balance sheet behind AI infrastructure: up to $105bn of guarantees for an OpenAI-linked Ohio data center, plus a $1.5bn investment in SB Energy.
• Anthropic is scaling its financing stack ahead of an IPO: a $10bn+ revolver, alongside roughly $15bn of financing being arranged for a Texas data center project.
• Dell’Oro has lifted its 2030 data center CapEx forecast to more than $3tn, up from $1.7tn just six months earlier.
• The bigger story is no longer just higher AI spending. The financing system around AI CapEx is expanding almost as quickly as the infrastructure itself.

Nvidia: Up to $105bn of Support for an OpenAI Data Center

Nvidia has agreed to provide a guarantee of as much as $105 billion to support OpenAI’s lease of a large Ohio data center being developed by SoftBank-owned SB Energy.

It will also invest another $1.5 billion in SB Energy, following a $1 billion investment earlier this year from OpenAI and SoftBank.

The scale matters.

Nvidia is no longer only selling the accelerators going into AI data centers. It is increasingly using its own balance sheet to help customers finance the infrastructure required to deploy them.

That effectively adds another source of capital to the AI buildout: the supplier itself.

The CapEx may ultimately sit elsewhere, but Nvidia is helping make that CapEx financeable.

Anthropic: $10bn+ Revolver, $15bn Data Center Financing, IPO Ahead

Anthropic’s revolving credit facility is set to rise above $10 billion, compared with the $2.5 billion five-year facility it secured last year.

The revolver is not direct data center CapEx. It is primarily a corporate liquidity facility and also reflects Anthropic’s preparations for a potential IPO.

But it sits alongside a much larger infrastructure financing effort.

Banks led by Morgan Stanley have been working on roughly $15 billion of debt financing for an Anthropic data center project in Texas, reportedly supported by Google.

That package has been described as including a $14 billion bridge loan and a revolving facility.

Anthropic has also filed confidentially for an IPO and could reach the public market as soon as this fall.

Its annualized revenue run rate reached more than $65 billion by the end of July, according to Bloomberg, while preliminary quarterly revenue exceeded $11.5 billion, versus $787 million a year earlier.

The financing logic is increasingly clear.

More revenue supports more debt capacity. More debt capacity supports more infrastructure. An IPO could add another major source of capital on top.

Anthropic is therefore building both a compute stack and a capital stack capable of funding it.

Dell’Oro: From $1tn to More Than $3tn

The industry-level numbers are moving even faster.

Dell’Oro Group has repeatedly raised its outlook for global data center CapEx as hyperscaler and AI infrastructure spending continues to exceed earlier expectations.

In February 2025, Dell’Oro expected annual worldwide data center CapEx to exceed $1 trillion by 2029.

Six months later, the estimate moved to $1.2 trillion.

By February 2026, Dell’Oro was forecasting $1.7 trillion by 2030.

Now that figure is above $3 trillion.

That means the 2030 outlook has increased by roughly 76% in about six months.

The latest revision reflects higher hyperscaler CapEx guidance, larger estimates for global data center power capacity and higher commodity costs.

High-end accelerators are expected to remain the single largest component of spending.

But the buildout is increasingly spreading across the rest of the stack: servers, networking, storage, power distribution, cooling and physical data center capacity.

Dell’Oro also expects the Top 4 US hyperscalers to account for about half of global data center CapEx.

AI-specialized cloud providers — including model developers and neoclouds — are expected to grow at a nearly 60% CAGR through 2030.

The implication is that AI infrastructure is becoming less of a GPU story and more of a full-stack capital cycle.

Source:

1. Reuters; https://www.reuters.com/business/media-telecom/nvidia-invest-15-billion-sb-energy-under-openai-data-center-deal-2026-08-17/?utm_source=chatgpt.com
2. Bloomberg; https://www.bloomberg.com/news/articles/2026-08-18/anthropic-pre-ipo-credit-facility-set-to-climb-past-10-billion
3. Dell'Oro; https://www.delloro.com/news/ai-buildout-maintains-momentum-as-data-center-capex-surpasses-3-trillion-by-2030/

Summary

Google won a bankruptcy auction for Spirit Airlines' internal business data at $10 million, edging out a $7.5 million bid from AI hiring platform Mercor. The dataset — over 100 million emails, hundreds of millions of Teams messages, 30M+ lines of code, and 7.5 billion passenger transaction records spanning two decades — is intended as AI/LLM training material. Against Alphabet's $4.201 trillion market cap the deal is financially invisible; its real significance is as a data point in AI labs' growing appetite for large, non-public operational datasets sourced from distressed companies, not as a market-moving transaction.

What happened?

Spirit Airlines ceased operations in 2026 after failing to emerge from its second Chapter 11 bankruptcy, reportedly driven in part by rising fuel costs tied to Iran-related geopolitical tensions. As part of the bankruptcy estate's asset disposition, Google's $10 million bid beat Mercor's $7.5 million offer for a large internal-data package. A federal bankruptcy judge still needs to approve the sale before it closes.

Significance of the event

Financial Scale: financially trivial — $10 million against Alphabet's $4.201 trillion market cap is roughly 0.00024% of market value, and a rounding error against Alphabet's ad/cloud revenue run-rate.

Novelty? it isn't novel that AI labs buy training data, but the source and composition are notable — a large, structured, non-public operational dataset (proprietary code, two decades of transaction records, competitor pricing intelligence) rather than scraped public web text. This reflects the broader, well-documented AI-industry scramble for data as public-web text supply is increasingly exhausted relative to frontier model appetite.

Narrative: reinforces, rather than complicates, the existing "AI data scarcity" narrative — labs increasingly sourcing proprietary, structured corpora (code, internal operations, transaction history) as a complement to public text.

Does it actually help Google's model training?

Directionally - YES but it matters to separate what kind of help:

Genuine incremental value: this dataset (emails, code, two decades of transaction records, competitor pricing intelligence) is structured, real operational business data — different in kind from scraped public web text. In principle it could help a model perform better on enterprise-workflow, code-understanding, and business-analysis tasks. That's a real, if narrow, training-data-diversity benefit.

But it's not the lever that actually determines model competitiveness right now: look at where Alphabet is actually putting its money — the $195-205B capex is almost entirely data centers, custom silicon (TPUs), and compute, not data acquisition. Frontier model capability today is overwhelmingly driven by compute scale. A $10M dataset purchase is a marginal, nice-to-have addition inside that system, not something that changes Google's competitive position on model quality.

What to watch out?

Gemini execution risk? Whether the next Gemini release lands on a credible timeline or slips again — the stock's ~9% drawdown from its April high has already been tied to "Gemini delays," not to any data deal, so further slippage (or another high-profile departure beyond the ones already reported) is the more direct read on execution risk than anything in this transaction.

Regulatory/antitrust track, not just this deal's own approval. Watch for rulings, discovery timelines, or settlement terms on either, since those carry far more financial/reputational weight than a $10M bankruptcy auction.

Actual capex spend vs. guidance, and how it's financed. Alphabet has already revised 2026 capex guidance upward twice (most recently to $195-205B) — watch whether the next quarterly print shows real spend tracking that number or running ahead of it. Separately, watch the financing side specifically: beyond the $22.93B bond and $18B equity offering already completed, check for additional debt/equity raises, and — more importantly — for off-balance-sheet structures. That's the harder-to-see leverage that matters more than the headline guidance number, and it's where hyperscalers in this cycle have shown a pattern of getting creative.

source:

1. Reuters; https://www.reuters.com/legal/litigation/google-buy-spirit-airlines-business-data-10-million-2026-08-17/

Lumentum delivered a broad Q4 FY2026 beat and issued Q1 guidance substantially above expectations, as AI-driven data-center demand lifted both optical components and systems revenue. Q4 revenue rose 109% yoy and 25% qoq to $1.01bn, ahead of consensus of ~$985mn, while non-GAAP EPS of $3.23 exceeded the $2.95 estimate.

The headline GAAP net loss of $7.2bn primarily reflected a ~$7.8bn non-cash loss related to the conversion of convertible notes, rather than a deterioration in operating performance. The more consequential incremental information was management’s Q1 outlook: revenue guidance of $1.225bn-$1.275bn was well above consensus of ~$1.16bn, while the non-GAAP operating-margin outlook reached 39.5%-40.5%.

TL; DR: Key Takeaways

Revenue growth broadened across both reporting segments. Components revenue increased 103% yoy to $649mn, above consensus of ~$637mn, while systems revenue rose 123% to $357mn, also exceeding expectations. The faster systems growth suggests demand is extending beyond individual optical components toward more integrated connectivity products.

Margins expanded faster than revenue. Non-GAAP gross margin reached 50.4%, versus 37.8% a year earlier and consensus of 48.8%. Non-GAAP operating margin rose to 36.6% from 15.0%, supporting non-GAAP net income of $326mn, up 416% yoy. The improvement may reflect a richer AI-related product mix, stronger utilization and operating leverage.

Q1 guidance reached the company’s target model more than one quarter ahead of schedule. Revenue guidance of $1.225bn-$1.275bn was substantially above consensus of ~$1.16bn. The $1.25bn midpoint implies ~24% qoq growth, while non-GAAP EPS guidance of $4.05-$4.35 exceeded expectations of ~$3.63. The 40% non-GAAP operating-margin midpoint also represents a further ~340 bps sequential expansion.

The $7.2bn GAAP loss was driven by a non-cash debt-conversion charge. The loss was mainly caused by the difference between the carrying value of convertible debt and the fair value of equity delivered in its conversion. It did not represent a comparable cash outflow or operating loss. However, the transaction may reduce future interest expense while increasing share count and EPS dilution.

AI optics moved closer to the compute system. Management cited initial revenue from optical circuit switching, progress in 1.6T cloud modules, rising demand for high-power CPO lasers and initial orders for external laser-source modules. These developments support the view that optical connectivity is moving closer to the compute system, although the timing and scale of commercial adoption remain uncertain.

A muted share reaction reflected elevated expectations. The stock was little changed after hours despite the beat and strong guidance, following a 43% gain over the previous six months. That reaction does not diminish the operating results, but indicates that investors may now require continued upside to already-rising estimates.

Key debates

• Is a ~40% non-GAAP operating margin sustainable?
• When will 1.6T, CPO and optical circuit switching become material revenue contributors?
• What is the continuing dilution impact of the debt conversion?
• How exposed is the outlook to hyperscaler concentration?

Source:

1. Company press release; https://investor.lumentum.com/quarterly-results/default.aspx

According to Korea's Digital Daily source: it is understood that Apple, which had recently been considering China's Changxin Memory Technology (CXMT) as a new supply chain to reduce costs, is facing difficulties in negotiations regarding further price reductions.

Paradoxically, as the workaround for low-cost Chinese components is blocked, Samsung Electronics and SK Hynix have relieved the burden of shipping general-purpose DRAM. This appears to be creating market dynamics where they are concentrating production lines on high-value AI memory, such as High Bandwidth Memory (HBM), thereby gaining complete control over global memory pricing power.

Check out our prior posts on this matter:

Apple interest thrusts China’s CXMT into memory chip spotlight

CXMT has been thrust into the global spotlight by the race for memory chips. Apple has begun testing the company’s DRam chips for devices sold in China, according to two people familiar with the matter

Predictionmarkets.orgJiayi ZHANG

Apple Raises Price While Micron Calls out Apple for Memory Shortage

Apple is raising prices of multiple key products as a pass-through of skyrocketed memorgy costs; While Microns seems to hold a different view. Apple vs Micron - Who Stands for the Truth? AppleResult33.33%MicronResult66.67%3 PollsEnded Apple raises prices of MacBooks, iPads as memory costs skyrocket SAN FRANCISCO,

Predictionmarkets.orgReuters

Market Rumor - Apple seeks to buy memory chips from blacklisted Chinese company

iPhone maker wants Trump administration to sign off on purchases to ease pressure from rising semiconductor prices.

Predictionmarkets.orgReuters

Source:

1. Digital Daily; https://www.ddaily.co.kr/page/view/2026080513445474844

SEOUL, Aug. 5 – A minority shareholder rights group said Wednesday its complaints against the chief executive officers (CEOs) of Samsung Electronics Co. and SK hynix Inc. over the companies' recently signed wage deals have been assigned to a regional police agency south of Seoul, according to Korean news sources quoted.

According to the Korean Shareholders’ Movement Headquarters on August 5, the Gyeonggi Nambu Provincial Police Agency assigned the case involving Samsung Electronics CEO Jun Young-hyun and Vice Chairman Roh Tae-moon, accused of breach of trust under the Specific Economic Crimes Aggravated Punishment Act, and the case involving SK Hynix CEO Kwak Noh-jung to its 1st and 2nd investigation divisions, respectively.

The Korean Shareholders’ Movement Headquarters maintains that performance bonuses should not be subject to labor-management collective bargaining but require shareholder meeting approval. They also claim that linking a fixed percentage of operating profits to performance bonuses has potential legal violations.

Separately, the group filed another complaint with the Corruption Investigation Office for High-ranking Officials, accusing Minister of Employment and Labor Kim Young-hoon of abuse of authority, obstruction of exercise of rights, and coercion. Their argument is that the government unduly intervened in the provisional agreement on performance bonuses between Samsung Electronics and its labor union in May.

Source:

1. The Chosun Daily; https://www.chosun.com/english/national-en/2026/08/05/BIDJRBSNDFFATAA6T6V4QSRW5Q/
2. Yonhap News Agency; https://en.yna.co.kr/view/AEN20260805003200315

Sandisk Corporation (Nasdaq: SNDK) and SK hynix Inc. today announced the release of the HBF™ (High Bandwidth Flash) technical specification through the Open Compute Project (OCP), advancing the workstream to drive HBF standardization for the AI inference era, just six months after the consortium began work in February 2027.

Key remarks by the companies are as below:

First HBF standard showcased within six months of consortium launch, expanding the ecosystem with participation from Google, Tenstorrent

Keynote by SK hynix Executive Vice President Kim Chun-sung and Vice President Kang Uk-song on opening day, offering solutions for next-generation AI infrastructure based on ‘Tiered Memory’

“Expanding the boundaries of memory and storage through HBF technology… contributing to new architectures that boost system efficiency”

Source:

1. Sandisk press release; https://www.businesswire.com/news/home/20260803297696/en/Sandisk-and-SK-hynix-Advance-Global-Standardization-of-High-Bandwidth-Flash-with-Release-of-First-OCP-Technical-Specification
2. SK Hynix press release; https://news.skhynix.com/en/hbf-at-fms-2026/
3. Yahoo Finance; https://finance.yahoo.com/technology/ai/articles/sandisk-sndk-sk-hynix-release-011325591.html

Alibaba Group Holding Ltd. released its biggest ever AI model, claiming performance on par with global leader Anthropic PBC in the latest Chinese breakthrough to challenge US rivals.

The new Qwen3.8-Max is built on 2.4 trillion parameters, a measure of a model’s sophistication, and ranks higher on several benchmarks than the headline-grabbing Kimi K3 from Moonshot that was recently unveiled. Alibaba shared results showing it delivering comparable or sometimes better scores than Anthropic’s Fable 5, a cutting-edge artificial intelligence model that was temporarily put under export controls by the US due to its advanced capabilities.

The debut comes days after Moonshot’s Kimi sent ripples through stock markets and Silicon Valley as it showed Chinese developers quickly catching up with the top models crafted by Anthropic and OpenAI despite relatively constrained computing resources. DeepSeek also just expanded access to its latest model, V4 Flash, while ByteDance Ltd. and MiniMax Group Inc. unveiled new video generators on Friday.

“Many investors continue to underestimate Chinese AI models because of US chip restrictions or general skepticism,” said Vey-Sern Ling, managing director at Union Bancaire Privée. “In reality, the gap is probably much closer, and narrowing fast. Alibaba’s Qwen 3.8 is another proof point, following Kimi K3.”

Alibaba’s shares surged by 7% in Hong Kong on Monday, the most in nearly a month.

The Hangzhou-headquartered internet pioneer will release the Qwen3.8-Max weights for public download next week, which will allow users to customize the technology, marking the next major move in the intensifying race among China’s AI contenders that include DeepSeek, Z.ai and ByteDance.

Alongside Kimi K3 at 2.8 trillion parameters — akin to brain synapses that help an AI system store, process and respond with the help of more information — Alibaba is delivering one of the biggest models to date. Also like Moonshot, however, Alibaba uses an approach that only activates a small proportion of the full parameter set per task, to maintain efficiency.

While DeepSeek’s latest is by far the most affordable among new marquee releases, Alibaba’s Qwen offering is also priced aggressively at $2 per one million input tokens and $6 per million outputs. Each AI system will use a different number of tokens to handle tasks, but that still makes Alibaba’s model look attractive compared to the best from the US leaders.

The new Qwen3.8-Max performs well in autonomous coding and long-horizon execution, and was able to independently perform a software engineering project over 16 days in internal testing, Alibaba said. Reducing performance degradation over long tasks or conversation is an ongoing challenge for AI developers as their models grow in complexity.

“Alibaba’s full stack of capabilities stands out,” Jefferies analysts Thomas Chong and Zoey Zong wrote after the release. With AI making an increasing contribution to revenue, “margin profile is expected to improve.”

Source: https://www.bloomberg.com/news/articles/2026-08-03/alibaba-drops-another-china-ai-model-with-breakthrough-performance

Apple is expected to raise prices on its next generation of premium iPhones this autumn as higher memory and processor costs squeeze margins across its hardware business.

The iPhone 18 Pro and Pro Max may cost $100 to $200 more than comparable current models, according to Bloomberg’s Mark Gurman. GF Securities analyst Jeff Pu has estimated a steeper increase of $250 to $300, citing higher prices for advanced processors, DRAM and NAND memory.

Apple’s first foldable iPhone is expected to start at at least $2,000. More expensive camera systems, a complex foldable display and tighter manufacturing yields are likely to push its production cost well above that of a conventional iPhone.

The expected increases follow mid-cycle price rises across parts of Apple’s Mac and iPad portfolios. Chief Executive Officer Tim Cook has described the surge in memory prices as a “100-year flood,” while warning that silicon costs will also remain elevated.

Apple is responding by reshaping its semiconductor supply chain.

The company agreed in July to spend more than $30 billion with Broadcom Inc. under a multiyear agreement covering custom silicon and wireless-connectivity components. The partnership is expected to produce more than 15 billion chips in the US, strengthening Apple’s domestic supply base even as component costs rise.

Apple Expands Broadcom Partnership with $30 Billion U.S. Supply Chain Commitment

Apple announced it will expand its partnership with Broadcom through a more than US$30 billion commitment focused on strengthening its U.S. supply chain. As part of the initiative, Apple will also invest US$1.5 billion in Broadcom’s manufacturing facility in Fort Collins, Colorado.

Predictionmarkets.orgBloomberg

At the same time, Apple has begun testing DRAM chips from China’s ChangXin Memory Technologies (CXMT), for devices sold in China. The move would give Apple another potential source of memory as supplies tighten and prices rise, though the company would face US political and regulatory scrutiny over its use of a Pentagon-blacklisted supplier.

Apple interest thrusts China’s CXMT into memory chip spotlight

CXMT has been thrust into the global spotlight by the race for memory chips. Apple has begun testing the company’s DRam chips for devices sold in China, according to two people familiar with the matter

Predictionmarkets.orgJiayi ZHANG

Apple’s interest has also drawn opposition from Micron Technology Inc. The US memory producer has argued against allowing Apple to purchase CXMT chips, while Apple has pushed for greater competition among suppliers to reduce its exposure to the shortage.

The dispute highlights the conflicting interests created by the memory crunch. Apple wants more supply and lower prices, while Micron and other established producers are benefiting from tight capacity and stronger pricing.

Apple Raises Price While Micron Calls out Apple for Memory Shortage

Apple is raising prices of multiple key products as a pass-through of skyrocketed memorgy costs; While Microns seems to hold a different view. Apple vs Micron - Who Stands for the Truth? AppleResult33.33%MicronResult66.67%3 PollsEnded Apple raises prices of MacBooks, iPads as memory costs skyrocket SAN FRANCISCO,

Predictionmarkets.orgReuters

Apple may ultimately pass more of those costs on to buyers. A $100-to-$200 increase would broadly match its recent Mac and iPad adjustments, while a larger rise would suggest that the company is no longer willing to absorb semiconductor inflation through lower margins.

The iPhone launch will therefore be more than a test of Apple’s pricing power. It will show how the global shift toward AI infrastructure is reshaping the economics of consumer electronics, as chipmakers prioritize higher-margin data-center demand and device makers compete for tighter supplies. Apple’s response — diversifying suppliers, deepening domestic partnerships and passing more costs to consumers — could become a model for how the broader hardware industry adapts to a structurally more expensive semiconductor market.

Source: https://www.bloomberg.com/news/newsletters/2026-08-02/apple-subscriptions-macbook-air-shortages-apple-to-make-glasses-health-device-ms

Two signals have been moving in opposite directions. On July 17, the Philadelphia Semiconductor Index closed 20.2% below its June 22 record, entering a technical bear market after its worst week in more than a year. The selloff deepened later in July, although chip stocks staged an 8.2% rebound on July 30 after strong Microsoft and Amazon results.

At the same time, Alphabet, Amazon, Meta and Microsoft are still on course to spend roughly $720 billion to $745 billion on capital investment this year. Three have raised or tightened their spending outlooks, while Microsoft says its underlying expansion plans remain intact despite an accounting change that reduced reported capex.

The apparent contradiction is therefore still real, but more precise: chip stocks are questioning the returns and valuations attached to the AI buildout, while the companies placing the orders continue to report strong demand.

So is AI spending in trouble, or is it fine and chip stocks just had a rough month? Which is it?

The bear case: The buildout is eating the cash that funds it

The number to watch is free cash flow, but not because it represents a dedicated budget for the next round of chips. Free cash flow measures what remains after operating cash has covered capital expenditure (capex). When it turns negative, the buildout is no longer being financed entirely from internally generated cash.

That does not force chip orders to fall immediately.

Cash reserves, debt, leases and outside financing can keep spending going. But it introduces a second constraint: the buildout must satisfy not only management’s conviction, but also lenders’ required returns and credit-market appetite.

Alphabet’s free cash flow went negative for the first time since it went public in 2004.

Source: Alphabet

Meta reported on July 29 that it made $31.9 billion in cash from operations and spent $31.1 billion on capex, leaving $784 million. That left Meta with free cash flow equal to roughly 2.5% of its operating cash flow. Its stock fell about 10%, even though revenue grew 28%.

Source: Yahoo Finance - GOOGL

Alphabet posted negative quarterly free cash flow, while Meta retained only $784 million. Microsoft remained strongly positive at $19.6 billion, although that was down 23% year over year. Amazon’s trailing-12-month free cash flow, which is not directly comparable with the quarterly figures, turned negative by $7.6 billion.

For two years, this buildout was paid for out of pocket, and money spent out of pocket needs no one's approval. From here, a growing share of it gets borrowed, and borrowed money comes with terms, whether it is a coupon, a maturity, and a lender's view of how long AI demand lasts.

Chipmakers sit at the end of that chain and don't get a vote in it. They find out when the order arrives. Or doesn't.

The bull case: Two years of being wrong about this exact call

The bear case has a weakness: betting against hyperscaler capex has lost money for two straight years.

Goldman Sachs found that at the start of both 2024 and 2025, most analysts expected spending to grow about 20%. It actually grew more than 50% both years. As of June, Goldman still thinks analysts are underestimating and expects roughly $1.1 trillion in 2027.

The demand-side numbers haven’t cracked, either. Google Cloud’s backlog sits near $514 billion, roughly 5x where it was a year ago. TSMC, the world’s biggest chipmaker, beat expectations and raised its own spending plans during the same week the chip index crashed, and its stock fell anyway.

When good news doesn’t help a stock, the problem is usually investor mood rather When strong results fail to lift a stock, the gap often lies in valuation and prior expectations rather than current operating performance. Investors may believe the good news was already priced in, or that future returns will not justify the capital required to deliver it.

Source: Yahoo Finance - TSM

My read: this is no longer about whether AI demand is strong enough (backlog numbers & capex increases already answered this). The actual question is, can spending approaching $1 trillion a year be sustained long enough to keep clearing a bar the market has priced for flawless execution?

Put simply, this looks more like the SOX’s 105% run from its March low to the June peak finally meeting a bar it couldn’t keep clearing. A 45%-plus year-to-date gain surviving a 20% drawdown is a market that priced in flawless execution and is now pricing in merely very good execution.

Source: Yahoo Finance - SOX

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What actually resolves this

Now, it’s a matter of whether capex nearing $1 trillion a year can keep being funded, and chip stocks, not hyperscaler stocks, are where this question gets tested first.

This is not a question a single earnings report answers. It gets resolved, one way or the other, over the next two quarters.

1. Free cash flow, not capex headlines.

Spending covered by operations answers to a CEO's conviction, but spending covered by bond issuance answers to credit markets, which can change their mind in a quarter for reasons that have nothing to do with AI. If FCF keeps compressing through Q3 and Q4, they’ll likely need to issue more debt, sooner, and possibly a larger amount at once.

Once a larger share of the buildout requires external financing, chip orders become more sensitive to interest rates, credit spreads, lease terms and lender appetite—in addition to underlying AI demand.

2. Whether Korea is a fundamentals signal or a leverage unwind.

SK Hynix is one of the clearest upstream indicators of AI accelerator demand because it is a leading supplier of high-bandwidth memory. But its share price is not a pure demand signal: it also reflects memory-cycle expectations, valuation, Chinese competition and unusually heavy leverage in the Korean market.

Reuters reported signs of forced unwinding and found that leveraged products had amplified market volatility. The cleaner comparison is therefore between HBM contract pricing, shipment guidance and the share price. If operating indicators remain firm while the stock continues falling, that would strengthen—but not prove—the case that positioning and leverage are driving much of the decline.

3. Whether Kimi K3 changes workload economics.

Moonshot AI says Kimi K3 approaches leading U.S. models at substantially lower cost, although its own technical report still places it behind the strongest proprietary systems overall. Benchmark performance alone does not establish that enterprises will move production workloads. Reliability, security, integration, latency and migration costs matter as much as token pricing.

The effect on chip demand is also ambiguous. A more efficient model may require less compute for each task, but lower costs can expand the number of tasks companies are willing to run. Watch for named enterprise migrations, declining contracts at incumbent AI providers and sustained changes in accelerator utilization—not benchmark scores alone.

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Sources

Business Insider: Goldman Sachs says the AI boom is bigger than investors think

CNBC: Philadelphia SE Semiconductor Index

Reuters: Meta cash flow craters as Zuckerberg doubles down on AI spending

Yahoo Finance: Meta, Microsoft, Amazon, and Alphabet are about to spend a shocking amount of money to dominate the AI era

Reuters: China's Moonshot unveils world's largest open AI model, closing in on US rivals