Ares Management CEO Michael Arougheti said the company is avoiding financing the high-performance processors that run high-end AI models, and is instead investing in the data centers that house those chips. He said due diligence on data center projects is simpler, the relative returns are attractive, and it is impossible to conduct due diligence to assess the value decay and depreciation curves of GPU technology. The company estimates AI capital expenditure demand at about $5 trillion, of which about $2 trillion is for physical AI computer rooms and supporting infrastructure, and $3 trillion is for purchasing GPU chips.
Hangzhou-based compute-in-memory AI chip company Micro-Nano Core has completed a Series C2 financing round of over 1 billion yuan, led by Jiantou Investment, with the funds to be used for product development and iteration as well as supply chain mass-production stocking. Combined with the Series C1 round completed in September, the company has raised over 2 billion yuan within two months and a cumulative 3 billion yuan over half a year. Its 3D-CIM architecture targets large-model inference; all three of its chips for edge and cloud sides have been taped out, with customer sampling and the start of volume supply expected in early 2027.
Hangzhou-based compute-in-memory AI chip company Micro-Nano Core has completed a Series C2 financing round of over 1 billion yuan, led by Jiantou Investment, with the funds to be used for product development and iteration as well as supply chain mass-production stocking. Combined with the Series C1 round completed in September, the company has raised over 2 billion yuan within two months and a cumulative 3 billion yuan over half a year. Its 3D-CIM architecture targets large-model inference; all
FuriosaAI, a South Korean AI chip company, announced on October 29 that it had completed a KRW 820 billion Series D funding round at a post-money valuation of approximately KRW 3.8 trillion. The Elec reported that, due to strong demand from domestic and overseas investors, the fundraising target was raised from the originally planned KRW 450 billion to KRW 750 billion up to KRW 820 billion; the South Korean government's National Growth Fund contributed KRW 370 billion as the largest investor, and overseas investors included Infinitum and Singapore's QUDA Capital Partners. The funds will be used mainly to expand supply capacity for the second-generation NPU Renegade (RNGD), and to jointly develop a third-generation AI accelerator for the hyperscale data center market with US-based Broadcom. SiliconANGLE reported the same day that the round amounted to USD 620 million at a valuation of USD 2.9 billion, and said the funds would be used to expand production capacity for next-generation TCP chips.
FuriosaAI, a South Korean AI chip company, announced on October 29 that it had completed a KRW 820 billion Series D funding round at a post-money valuation of approximately KRW 3.8 trillion. The Elec reported that, due to strong demand from domestic and overseas investors, the fundraising target was raised from the originally planned KRW 450 billion to KRW 750 billion up to KRW 820 billion; the South Korean government's National Growth Fund contributed KRW 370 billion as the largest investor, an
On October 20, 2026, Gartner released a forecast stating that AI vendors will invest nearly $1 trillion in infrastructure in 2026 and another $6 trillion from 2027 to 2030; of that, more than $3.5 trillion may need to be written down by the end of 2030 because revenue will not cover costs. Gartner also noted that most AI-native vendors use asset-light models, with less than half of AI revenue flowing to companies that own substantial infrastructure; GPU rental neoclouds carry the most concentrated risk. AI-optimized IaaS spending is expected to rise from $66 billion in 2027 to $174 billion in 2030, and Gartner believes this growth rate is insufficient for most vendors to recoup their investments before existing AI chips are replaced. Subsequent reports offered differing figures for the size of the shortfall: one report said total investment from 2026 to 2030 would be $7 trillion, with revenue of at most $3.6 trillion in 2030, and stated that $8.5 trillion in revenue would be needed to cover the investment and a 12% return requirement, leaving a gap of about $5 trillion, with more than $4 trillion in assets facing impairment risk.
On October 20, 2026, Gartner released a forecast stating that AI vendors will invest nearly $1 trillion in infrastructure in 2026 and another $6 trillion from 2027 to 2030; of that, more than $3.5 trillion may need to be written down by the end of 2030 because revenue will not cover costs. Gartner also noted that most AI-native vendors use asset-light models, with less than half of AI revenue flowing to companies that own substantial infrastructure; GPU rental neoclouds carry the most concentrat
Vinci executive Dinesh Panneerselvam introduced Vinci's approach to bringing simulation into chip design using physical AI. According to SiliconANGLE, Vinci4D showcased its physical AI solution at SEMICON West, combining physics foundation models with GPU-native physics kernels, called Continuous Physics Reasoning, enabling engineers to obtain both accuracy and fast physical simulation at design speed. The model is trained on Vinci's own data, deployed within customers' firewalls, and can directly read existing design files. Its $250 million Series B funding will support expansion into electromagnetics, fluid dynamics, and the automotive and aerospace sectors. There are currently no subsequent development facts.
Vinci executive Dinesh Panneerselvam introduced Vinci's approach to bringing simulation into chip design using physical AI. According to SiliconANGLE, Vinci4D showcased its physical AI solution at SEMICON West, combining physics foundation models with GPU-native physics kernels, called Continuous Physics Reasoning, enabling engineers to obtain both accuracy and fast physical simulation at design speed. The model is trained on Vinci's own data, deployed within customers' firewalls, and can direct
Huawei has obtained a seven-year, $2.25 billion offshore loan provided by six Chinese banks. Related reports say the funds will be used for AI chip R&D, research, and inventory stockpiling. The reports also note that even though buyers such as DeepSeek plan to deploy its accelerators on a large scale, Huawei's production output remains strictly limited; Huawei's R&D investment in the first half of the year reached $17 billion, with negative cash flow of $5.6 billion. Currently, this event has only the above single-source report, with no subsequent factual developments.
Huawei has obtained a seven-year, $2.25 billion offshore loan provided by six Chinese banks. Related reports say the funds will be used for AI chip R&D, research, and inventory stockpiling. The reports also note that even though buyers such as DeepSeek plan to deploy its accelerators on a large scale, Huawei's production output remains strictly limited; Huawei's R&D investment in the first half of the year reached $17 billion, with negative cash flow of $5.6 billion. Currently, this event has on
Vinci executive Dinesh Panneerselvam introduced Vinci's approach to bringing simulation into chip design using physical AI. According to SiliconANGLE, Vinci4D showcased its physical AI solution at SEMICON West, combining physics foundation models with GPU-native physics kernels, called Continuous Physics Reasoning, enabling engineers to obtain both accuracy and fast physical simulation at design speed. The model is trained on Vinci's own data, deployed within customers' firewalls, and can directly read existing design files. Its $250 million Series B funding will support expansion into electromagnetics, fluid dynamics, and the automotive and aerospace sectors. There are currently no subsequent development facts.
T-Head releases the new-generation Zhenwu V900 training-and-inference integrated AI chip. According to Jiwei.com, the chip was released on September 22 at the Hangzhou Yunqi Conference; single-chip performance reaches three times that of Zhenwu M890, can meet the training and inference needs of trillion-parameter large models, and is planned for mass production and sale in the first quarter of 2027. The report says Zhenwu V900 carries 216GB of memory, chip-to-chip interconnect bandwidth of 1200GB/s, natively supports FP8 and FP4 low-precision computing, and can form a thousand-card full-bandwidth high-speed interconnect super node with the self-developed ICN Switch interconnect chip. T-Head also for the first time announced its Yitian server CPU plan, with Yitian 720 and Yitian 730 to be launched in 2027. There are currently no subsequent development facts.
T-Head releases the new-generation Zhenwu V900 training-and-inference integrated AI chip. According to Jiwei.com, the chip was released on September 22 at the Hangzhou Yunqi Conference; single-chip performance reaches three times that of Zhenwu M890, can meet the training and inference needs of trillion-parameter large models, and is planned for mass production and sale in the first quarter of 2027. The report says Zhenwu V900 carries 216GB of memory, chip-to-chip interconnect bandwidth of 1200G
T-Head releases the new-generation Zhenwu V900 training-and-inference integrated AI chip. According to Jiwei.com, the chip was released on September 22 at the Hangzhou Yunqi Conference; single-chip performance reaches three times that of Zhenwu M890, can meet the training and inference needs of trillion-parameter large models, and is planned for mass production and sale in the first quarter of 2027. The report says Zhenwu V900 carries 216GB of memory, chip-to-chip interconnect bandwidth of 1200GB/s, natively supports FP8 and FP4 low-precision computing, and can form a thousand-card full-bandwidth high-speed interconnect super node with the self-developed ICN Switch interconnect chip. T-Head also for the first time announced its Yitian server CPU plan, with Yitian 720 and Yitian 730 to be launched in 2027. There are currently no subsequent development facts.
Bank of America, Blackstone, Citi and Morgan Stanley jointly announced an AI financing arrangement totaling $63 billion. According to Cailianshe, the four institutions will set up an SPV to purchase custom AI chips and networking equipment from Broadcom, which are expected to be delivered in 2027 and then leased to Anthropic on a long-term basis; the financing includes approximately $43.5 billion in senior secured loans, accounting for 69% of the total financing, while the subordinated secured loans are mainly committed by Blackstone at about $18 billion, and Broadcom provides a residual value guarantee of up to $29 billion on the hardware. The Wall Street Journal reported that the financing was finalized after Broadcom CEO Hock Tan negotiated with bank executives over the weekend and agreed to raise the loan interest rate; Anthropic uses the chips under a lease arrangement, with a shell company borrowing to purchase them and repaying the loans with rental payments.
Bank of America, Blackstone, Citi and Morgan Stanley jointly announced an AI financing arrangement totaling $63 billion. According to Cailianshe, the four institutions will set up an SPV to purchase custom AI chips and networking equipment from Broadcom, which are expected to be delivered in 2027 and then leased to Anthropic on a long-term basis; the financing includes approximately $43.5 billion in senior secured loans, accounting for 69% of the total financing, while the subordinated secured l