U.S. stocks fell for a third straight session Tuesday as higher long-term bond yields put fresh pressure on richly valued technology names. The Nasdaq Composite dropped 1.33%, underperforming the Dow Jones Industrial Average and the S&P 500, while the 30-year U.S. Treasury yield briefly touched 5.338%, its highest level since 2007. The move was part of a wider global bond sell-off that also pushed long-dated yields higher in France, Germany, Japan and the U.K. Markets are increasingly focused on the growing debt burden tied to artificial intelligence expansion. According to figures cited in the report, AI-related bond issuance has reached $489 billion so far this year, well above an earlier full-year 2025 estimate of roughly $322 billion, while The Wall Street Journal reported that nine major technology companies have about $3 trillion in off-balance-sheet AI commitments. That backdrop hit semiconductors, memory, optical communications and AI cloud-service providers especially hard. Investors are also weighing fiscal deficits, oil-driven inflation risks tied to the Iran situation, and a heavy event calendar that includes U.S. tariffs on some Canadian products, a 20-year Treasury auction, Federal Reserve minutes and China’s one-year LPR decision.

Goldman Sachs said investor concerns over MediaTek and AMD taking ASIC share from Broadcom may have gone too far, arguing that the current setup has created a buying opportunity rather than a reason to step back. In a second-quarter preview dated Aug. 18, the bank kept its Buy rating on Broadcom and reiterated a $525 price target, versus a current share price of $392. Goldman said the market has become too conservative on competition, leaving Broadcom’s AI revenue outlook and networking business underappreciated. The firm projects Broadcom’s AI revenue at roughly $57 billion in FY2026, broadly in line with consensus, and $133 billion in FY2027, about 12% above consensus. Goldman said that gap points to a systematic underestimation of Broadcom’s competitive position next year. The report said the stock’s reaction after earnings will likely depend on three issues: quantified guidance for FY2027 AI revenue, updates on the ASIC competitive landscape, and data center readiness for FY2027 deployments. Goldman also argued that Broadcom’s edge lies not only in chip design but in high-volume manufacturing, delivery history, and customer relationships, while data center capacity remains the key bottleneck for the broader AI supply chain.

U.S. stocks finished lower, according to data from MSX.COM, with the Dow Jones Industrial Average slipping 0.22%, the S&P 500 falling 0.69%, and the Nasdaq declining 1.33%. The VIX volatility index rose 4.28% by the close. AI-linked stocks broadly weakened in the session. Baidu posted the steepest decline among the names listed, down 12.73%, followed by CoreWeave at 12.1%, Teradyne at 8.77%, Marvell at 7.82%, and Micron at 7.02%. MSX was described as a major RWA trading platform that has listed hundreds of RWA tokens, covering tokenized exposures tied to popular U.S. stocks and ETFs including Nvidia, GOOGL, MSFT, AMZN, META, TSM, and AMD.

Nvidia is extending its AI playbook beyond semiconductors and into financing, using its cash flow and funding capacity to shape how large-scale AI infrastructure gets built. Over the past week, the company reached agreements with Goldman Sachs, Blackstone, BlackRock and Apollo to support a GPU financing effort that could reach as much as $500 billion. It then said it would provide up to $105 billion in support for OpenAI’s Ohio data center project. Chief Executive Officer Jensen Huang said many frontier AI labs are growing faster than their balance sheets and long-term credit capacity can support, creating the need for outside capital to fund infrastructure. Nvidia also plans to back related loans with support covering as much as 25% and has continued making equity investments across the AI supply chain. As Google’s TPU and AMD step up competition, Nvidia’s technology edge in chips is facing pressure, and the company appears to be using a combined chips-and-capital model to deepen control over the broader AI ecosystem.

Over the past year, leading listed Bitcoin miners have been recasting themselves as energy infrastructure platforms, AI cloud providers and digital infrastructure companies. The pivot has already reshaped revenue at Core Scientific and TeraWulf, where hosting and high-performance computing now account for most sales. But the article argues that the real cost of an all-in shift is flexibility: once miners replace ASIC fleets with GPU-based AI capacity and lock themselves into 15- to 20-year contracts, they may not be able to switch back when Bitcoin mining becomes profitable again. The piece points to Marathon Digital and Hut 8 as more cautious examples. Both kept mining operations and Bitcoin holdings in place while funding AI expansion, preserving an option that fully converted miners are giving up.

OpenAI, Nvidia and SB Energy have confirmed a large data center project in the US, with OpenAI signing a 20-year lease and Nvidia providing up to $105 billion in backing. The project is located in Ohio’s PORTS-Pike campus and is set to build at least 10GW of new power capacity, with the first 800MW expected in 2028. Nvidia will also directly invest in the project, while SB Energy will develop and operate the site. The deal gives OpenAI long-term access to capacity and ties Nvidia more closely to data center infrastructure. On the same day, Jensen Huang said AI factories now need land, power and shells to be secured in advance, not just chips, packaging, memory and networking. The report also points to a broader trend: major tech firms now have nearly $3 trillion in off-balance-sheet AI-related commitments, according to The Wall Street Journal’s analysis of nine companies’ filings.

Liquid cooling is moving from an optional feature to core infrastructure for high-end AI servers as chip power consumption climbs, according to TrendForce. The research firm said liquid-cooling penetration for AI chips is projected to rise from about 33% in 2025 to 53% in 2026, then approach 60% in 2027. It linked that shift to rapidly evolving processors from NVIDIA, AMD and Google, with single-chip thermal design power already exceeding 1 kW and full rack-scale systems reaching several hundred kilowatts. TrendForce said NVIDIA remains the clearest force behind broader adoption. Its Vera Rubin platform has adopted a fanless, full-liquid-cooling design that extends beyond GPUs and CPUs to include CX9 NICs, busbars, power boards and optical transceiver modules. Even with a possible delay to the Kyber NVL144 platform, the firm expects overall GPU rack shipments to grow more than 30% year over year, while NVIDIA GB/VR rack shipments could double in 2026. AMD is also expanding from standalone GPU products to full AI platform systems, with its Helios rack-scale solution set to become a focus from the second half of 2026 and larger-volume shipments expected in 2027. TrendForce added that more than 80% of Google’s AI servers already use liquid cooling. On the supply side, Taiwanese companies including AVC, Auras and Jentech have secured positions in cold-plate and heat-spreader segments.

Off-balance-sheet commitments tied to AI infrastructure at nine major U.S. technology companies have reached roughly $3 trillion, according to a Wall Street Journal report published on Aug. 18 and cited by Odaily. The companies named were Alphabet, Meta, Microsoft, Amazon, Oracle, Nvidia, Broadcom, SpaceX and AMD. The total was about five times their combined capital expenditures of $600 billion over the past year, about three times their outstanding lease and long-term borrowing balances, and roughly 50% higher than the prior estimate of about $1.8 trillion just two months earlier. The commitments were mainly made up of about $1.2 trillion in uncommenced leases and about $1.9 trillion in purchase obligations, both of which can stay off the balance sheet before rent starts or goods and services are delivered under current accounting rules. The report also pointed to a separate layer of risk in residual value guarantees, or RVGs. Citing a Bloomberg report from Aug. 15, the article said those structures had grown to about $70 billion. Rating firms including Moody’s and S&P Global Ratings, along with bond investors, have raised concerns over how quickly these contingent liabilities are building across the AI financing chain.