Çin’in güç avantajı, ABD yapay zeka hisselerinde olası yüzde 89 düşüşü tetikleyebilir

Kısaca

Çinli oyuncular yaklaşık 90% performansı yaklaşık 10% maliyetle elde ediyor (kaynakta belirtilen karşılaştırma). Enerji maliyetleri ABD şirketlerinin toplam maliyetinin önemli bir kısmını oluşturuyor olabilir. Çinli rakiplerin maliyet rekabeti sürerse ABD hisselerinde yükseklikler azalabilir ve 89% çöküş olasılığı gündeme gelebilir.

Ana mesele

Çinli yapay zeka oyuncuları maliyet farkıyla ABD rakiplerinden daha hızlı değer yaratabilir

Ne değişti?

ABD firmalarının piyasa değerleri üzerindeki baskı olasıdır; enerji maliyetleri bu farkı derinleştirebilir

Beni nasıl etkiler?

ABD ve küresel yapay zeka hisselerinde olası gerileme riskinin önceden farkındalığı

Ne oldu?

Çinli yapay zeka oyuncuları maliyet avantajını sürdürerek ABD’den daha düşük maliyetle benzer performans elde edebilir iddiası öne çıktı.

Neden şimdi?

Enerji maliyetleri gibi yapısal farklar, küresel yapay zeka rekabetini hızla değiştirebilir.

Neden önemli?

ABD firmalarının piyasa değeri ve yatırımcı güveni üzerinde baskı oluşabilir; küresel rekabet dengesi değişebilir.

Kimler etkileniyor?

• ABD yapay zeka firmaları
• ABD yatırımcılar
• küresel yapay zeka tedarik zinciri

Sektör ve piyasa etkisi

Yapay zeka ve teknoloji hisselerinde oynaklık artabilir; yatırım akışları değişebilir.

Riskler

• Piyasa değerlerinde hızlı düşüş
• maliyet baskıları nedeniyle kârlılık azalması
• regülasyon ve enerji tedarik zinciri riskleri

Takip edilmesi gerekenler

• ABD-Çin yapay zeka rekabetindeki maliyet farkı
• enerji maliyetlerinin değişimi
• rakamları etkileyebilecek politika değişiklikleri

Haberin tamamı

The current focus of the world's governments, business and media when it comes to artificial intelligence AI is on the dangers it poses to jobs, democracy and even the future of humanity itself. For many of a certain generation, these fears crystallise in the cold but relentlessly courteous voice of HAL, the onboard AI in Stanley Kubrick's 2001 A Space Odyssey.

After a crew member questions HAL's diagnosis of a faulty antenna, the system secretly lipreads the astronauts, discovers their plan to disconnect it, and kills them one by one. Nobody who sees the film is likely to forget the chilling reply from HAL to last surviving crewman Dave Bowman's order to "open the pod bay doors" so he can re-enter the spacecraft after being locked outside by HAL during a spacewalk "I'm sorry, Dave.

I'm afraid I can't do that." As it stands, though, potentially far more chilling for U.S. AI firms is the huge structural disadvantage that they have compared to their Chinese counterparts and the massive drop in their companies' stock market valuations that this may soon catalyse. "The key problem here is that the major Chinese AI players currently achieve around 90% of the performance of their U.S.

competitors but at only about 10% of the cost," Mehrdad Emadi, head of risk analysis and energy derivatives markets consultancy Betamatrix, in London, exclusively told OilPrice.com last week. "Up to half of U.S. AI firms' costs is the electricity needed to drive the data centres, and this is only likely to increase from here," he said. "But the much lower costs of their Chinese AI rivals are likely to stay where they are, and may even edge lower," he underlined.

"Meanwhile, the performance of China's AI is likely to keep increasing to a point where it nears 100% of its U.S. rivals," he added, "and that looks like a deadly convergence for American AI firms." Underpinning this huge discrepancy in costs between U.S.

and Chinese AI firms is the fundamental difference in the way the electricity power grids were designed and built in the first place, highlights Steve Keen, honorary professor at University College London and the economist who most cogently warned that the economic crisis that began in 2007 was imminent.

"China benefits from its centralised power grid that was designed from the outset by engineers rather than the decentralised one in America designed by accountants and similar," he exclusively told OilPrice.com. "The American grid may have been cheaper to install initially, but it can't transmit energy as widely across the country as the Chinese system, and it loses much more power than the Chinese grid when it does so," he underlined.

Related Saudi Arabia Restarts East-West Oil Pipeline More specifically, China's grid operates under a unified national strategy controlled by the State Grid Corporation, allowing power to move seamlessly across thousands of kilometres. By comparison, the U.S. grid is split into three isolated interconnections -- Eastern, Western, and Texas/ERCOT -- which makes moving power far more difficult and costly.

To increase the amount of energy transmitted at any given time, operators must either increase current or voltage. In this context, China's power grid is built on an extensive backbone of 800-1,100 kilovolt kV Ultra-High Voltage lines utilising Direct Current. This combination enables it to move up to 12 gigawatts GW of power -- the output of 10 nuclear power plants -- down a single transmission corridor over 3,000 kilometres with almost zero power loss. By contrast, the U.S.

grid can get nowhere near this level of total power generation, or near-zero power loss over distance, as it relies on standard 345-500 kV trunk lines in just a High-Voltage Alternating Current set up. In sum, the U.S.'s fragmented, aging grid acts as a physical ceiling on how large and how fast American tech companies can scale their AI clusters, whereas none of these constraints apply to China's system -- or, by extension, to its AI firms.

It has been widely conjectured that these surging power demands in the U.S. could be met by increased oil and gas flows, or supply from other sources. However, on the renewable energy side, solar and wind power still fail to provide the 'always-on' supply that AI data centres demand, given the intermittency of both sources. Nuclear power -- including small modular reactors SMRs -- has become a recent focus, with U.S.

hyperscalers including Microsoft, Amazon, and Google eyeing tens of billions of dollars in investment in such projects. Critically, though, there is a massive time lag and potentially huge additional costs attached to both mainstream and SMR nuclear projects. Traditional nuclear projects in the U.S. have routinely suffered from multi-billion-dollar cost overruns and 15-year development timelines. SMRs, despite lower upfront costs, still target construction windows of up to seven years.

Moreover, the need for regulatory approvals from agencies such as the U.S. Nuclear Regulatory Commission, combined with physical construction constraints, means the commercial SMR sector is unlikely to hit scale until the late 2030s. On the fossil fuel side, oil remains too valuable to burn for baseload grid power, leaving natural gas as the obvious main feedstock for the AI power grid going forward.

"But the problem here," said Emadi, "is that everyone's after the same equipment at the same time to convert it into electricity for the grid, and there are very few suppliers who make what's needed." A prime example of this, he highlighted, is the gas turbine manufacturing market, which is dominated by the 'Big Three' oligopoly of the U.S.'s GE Vernova, Germany's Siemens, and Japan's Mitsubishi Heavy Industries.

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Kaynaklar

• OilPrice.com