AI’s $1 Trillion Bet: What Could Trigger A Massive Market Unwind?

The global artificial intelligence boom is showing signs of growing vulnerability as concentrated adoption, debt-funded investment and rapidly improving low-cost Chinese AI models raise questions about whether the current spending cycle can be sustained, according to 360 ONE Asset's August 2026 Panorama report.

The report argues that the risks extend beyond technology companies and could spill into credit markets and economies if AI investment loses momentum.

However, it sees India as relatively insulated from a potential global AI boom-bust cycle because its equity market is less concentrated than those of the US, Taiwan and South Korea.

AI adoption remains concentrated

Despite the rapid expansion of AI, adoption has yet to become broad-based. Data cited in the report shows that about 22% of US firms were using AI by July 2026, with adoption significantly higher among larger companies.

AI use stood at 47% among firms with 250 or more employees, compared with 22% among businesses employing one to four people. By sector, adoption was highest in information services at 45%, followed by professional services at 39% and finance and insurance and education at 37% each.

The labour-market impact is also appearing more clearly through hiring rather than layoffs. AI-exposed sectors such as information and professional and business services have experienced sharper declines in job openings than the broader market, while layoffs have not shown a comparable divergence. This suggests companies may be becoming more cautious about hiring as they look to automate tasks.

Debt fuels AI infrastructure spending

One of the report's key concerns is the growing dependence on debt to finance the AI build-out.

Capital expenditure by US hyperscalers and other AI companies has continued to rise sharply and is increasingly being funded through borrowing. The report warns that this could transmit vulnerabilities in the AI investment cycle into credit markets.

The risk is amplified by what it describes as circular financing, in which AI labs, hyperscalers and other companies enter deals that effectively link spending and future revenue. Such arrangements account for a sizeable portion of AI labs' spending and hyperscalers' forward revenue, according to the report.

At the same time, companies are looking for ways to make AI spending cheaper. The blended price paid per million tokens has fallen roughly 45% since May 2026, partly because usage is shifting away from expensive frontier models towards cheaper open-weight alternatives.

Chinese AI models add pressure

The report also highlights the rapid progress of Chinese AI models. Chinese models are now estimated to trail US models by only around six months on performance measures, while offering substantially better cost efficiency.

Several Chinese models can perform comparable tasks at a fraction of the cost of leading Western frontier models. This creates another potential pressure point for the AI investment cycle: if businesses can achieve similar capabilities with cheaper models, demand for expensive frontier computing infrastructure could weaken.

Semiconductor rally raises bubble concerns

The report draws a parallel between the current semiconductor rally and the late stages of the technology boom of the 1990s.

In the late 1990s, software and hardware stocks diverged, with gains becoming concentrated in hardware. A similar pattern has emerged recently, with semiconductor stocks surging while hyperscaler stocks have remained relatively range-bound.

Valuations add to the concern. While price-to-earnings multiples have not risen dramatically because of strong earnings growth, price-to-book valuations have reached levels comparable to or above the dot-com era. The report says global technology hardware's price-to-book valuation is 156% above its long-term average, leaving limited room for earnings disappointments.

Taiwan, South Korea more exposed

The potential fallout is particularly significant for Taiwan and South Korea, whose markets have benefited heavily from the AI-led investment cycle.

Rising US imports of information and communication technology goods have boosted economic activity and equity markets in both countries. Their stock markets have consequently outperformed broader emerging markets.

But the report warns that an AI investment reversal could trigger financial contagion. Taiwan's top company accounts for 39% of its market capitalisation, while the top two companies in South Korea account for 47%. By comparison, the top 10 companies make up 33% of US market capitalisation.

India relatively insulated

India's market is considerably more diversified. The country's top 10 companies account for around 19% of total market capitalisation, compared with 33% in the US, 39% in Taiwan and 47% in South Korea.

This lower concentration could make Indian equities less vulnerable to a sharp unwinding of the AI trade.

However, India is not completely insulated. The country has seen a sharp rise in data-centre investment proposals, with capacity expected to expand substantially over the next decade. A global pullback in AI capital expenditure could slow this build-out and result in weaker growth than currently anticipated.

What could trigger an AI unwind?

The report identifies risks on both the demand and supply sides of the AI ecosystem. On the demand side, these include infrastructure overbuilding, weaker-than-expected productivity gains, a macroeconomic slowdown and a shift towards cheaper open-weight models.

On the supply side, risks include technological advances that reduce computing requirements, the breakdown of circular financing arrangements, losses in private credit and tighter regulation following AI-related incidents.

The central warning is that these risks could reinforce one another. A slowdown in AI demand could weaken infrastructure investment, while falling investment could hurt companies and lenders exposed to the sector. Conversely, weaker financing could itself reduce the spending needed to sustain the AI build-out.

The report therefore does not argue that AI's technological progress is ending. Instead, it highlights the gap between technological potential and the financial assumptions underpinning the current investment boom.

If productivity gains fail to justify the scale of spending, or cheaper models reduce demand for costly infrastructure, the AI cycle could face a sharp repricing. India, while relatively better positioned because of its diversified market, could still feel the impact through its growing data-centre ecosystem.