$480 BILLION “BURNED” ON THE STOCK MARKET: THE AI BUBBLE HAS BURST IN CHINA. IS THE US NEXT?

Artem Honcharenko, PhD

Head of the Institute of Reintegration, Rehabilitation and Professional Development of Veterans “Architecture of Resilience” at KNUBA

October 27, 2026 • author’s analysis

The Chinese crash: what really happened to the market

Not long ago, makers of chips, optical components for data centers and robotics were called the main winners of the new technological revolution. Investors bought their shares, counting on future growth in demand for artificial intelligence. At the end of October, this confidence gave way to a massive sell-off.

According to Reuters dated October 26, 2026, the STAR 50 index, which tracks the performance of leading technology and innovation companies on the Shanghai exchange, lost about 40% in less than four months. The total reduction in market value is estimated at roughly $480 billion. On October 27 the index closed at 1,252.28 points, a one-day decline of 4,81%. [1–2]

Two points of precision matter here. The STAR 50 is not a list of China’s 50 largest purely AI companies, but an index of the technology and innovation segment, in which many companies are linked to semiconductors and AI. And the $480 billion did not “vanish” from companies’ bank accounts: it refers to a fall in the market valuation of share holdings.

Yet this does not make the scale of the correction any smaller. Reuters cites examples of shares of individual makers of robots, microchips and optical equipment falling by tens of percent. The market suddenly remembered a simple economic requirement: good technology does not yet guarantee profit sufficient to justify any share price. [1]

Why the market cooled: inflated expectations versus real profits

At the peak of the rally, the price/earnings (P/E) multiple of the STAR 50 index, according to Reuters’ estimate, came close to 250. Even after the sharp decline it stood at about 100, while the benchmark for the Nasdaq 100 in the same article is roughly 30. The two indices are not identical in structure, but the gap shows how much future growth investors had already priced into Chinese equities. [1]

On top of the expensive valuations came doubts about the pace of demand for AI hardware, possible export restrictions, tax factors and risks to the sale of Chinese components to foreign data centers. None of these reasons alone explains the whole crash, but together they made the market vulnerable.

My conclusion is simple: the price of a share must be separated from the usefulness of a technology. A company may create a needed product yet be unjustifiably expensive on the stock exchange. Conversely, a fall in its share price does not cancel the technological effect already achieved.

Two things must be clearly separated: the economic effectiveness of artificial intelligence and the speculative value of the companies that earn money from it. The stock market can be wrong in its expectations. But if the technology really shortens research time, raises productivity and allows well-founded decisions to be made faster, this value does not disappear along with falling share prices.

Does AI really speed up human work: what is proven and what is only promised

As a researcher, I believe artificial intelligence should be judged not by the loudness of presentations but by independent experiments with control groups. Such studies already exist.

In an MIT study published in Science in 2023, 453 professionals performed professional writing tasks. In the group with access to ChatGPT, the average completion time fell by 40%, while the quality of the result, according to expert ratings, rose by 18%. This is a real experiment, but it does not prove that any complex research will automatically speed up by the same 40%. [3]

In an experiment by Harvard Business School and Boston Consulting Group involving 758 consultants GPT-4 users performing tasks within the model’s capabilities worked more than 25% faster and completed more than 12% more tasks. At the same time, on a task outside the tool’s reliable capabilities, using AI led to a worse result. [4]

Scientific analytics is especially telling. A review of 25 studies, published in Frontiers in Pharmacology in 2025, recorded a time reduction of more than 50% in 17 papers. In some cases, automated abstract screening shrank 5–6-fold, and manual workload, at 95% recall of relevant materials, shrank 6–10-fold. This is above all about individual selection stages, not the whole path from idea to verified conclusion. [5]

There is also a caveat. In a randomized METR experiment in 2025, 16 experienced developers, who performed 246 real tasks, worked with the AI tools of the time on average 19% longer, although they themselves believed they had sped up. Hence a tool that helps a beginner may slow down a specialist on well-familiar work if the cost of verifying results is too high. [6]

Conclusion: we must measure not the volume of generated text but the number of correct and verified results per unit of time.

One example that changes the scale: 100,000 documents

Imagine an organization that needs to do an initial review of 100,000 documents — contracts, scientific publications, reports, citizens’ appeals or reconstruction materials. If each document gets only three minutes, we have 5,000 person-hours, that is, 625 eight-hour working days. A team of ten analysts would work for about 12.5 weeks at full capacity.

Now suppose a verified system automatically performs deduplication, initial classification and contradiction search, and generates a list of exceptions. If its quality allows reducing specifically manual screening by 90%, the equivalent of 500 person-hours will remain at this stage.

This is an illustrative model, not the result of a specific implementation. It does not separately account for the cost of computing, configuration, quality control and missed critical cases. Therefore one cannot mechanically claim a tenfold speed-up of the entire research.

But it explains where real value arises: an expert is no longer obliged to review every unit of information manually if automated selection has proven the required accuracy. The expert moves from mass reading to in-depth work with complex cases.

The market is beginning to mature. The mere promise of a technological revolution is no longer enough. The investor wants to see profit, and the organization wants a concrete result: how much time has been saved, how many errors prevented, how many appeals processed and how much it costs to maintain the system. A real competitive advantage will belong to those who can prove the economic effectiveness of AI.

War changes the productivity formula: why this matters for Ukraine

In our country, an employee may be highly qualified and motivated yet unable to cancel an air raid alert or a missile attack. Human safety must always remain the priority.

In a survey by the European Business Association conducted in February 2026 among 131 top managers of the Association’s member companies, 45% of respondents named work stoppages during air raid alerts among the common constraints, and 82% cited attacks on the energy system among the factors with the greatest negative impact on business. This is not a statistic of average hours lost for all of Ukraine, but it is weighty evidence of operational obstacles. [7]

Take a hypothetical project that requires 100 productive analytical hours. If the actual availability of working time is 70%, completing it will require about 143 hours of planned working resource. In a modeled hybrid process, where 25 hours remain for the human and machine processing and accompanying operations add 10 hours, the overall estimate is roughly 46 hours. The difference is about 3.1 times.

These numbers should not be taken as a norm for Ukrainian companies: the 70% availability and the structure of operations are only scenario assumptions here. The real effect will be determined by the nature of the tasks, the readiness of the data and the architecture of the system.

The principle, however, is important. If computing, data and task queues are properly backed up and geographically distributed, some operations can continue even while employees are in a shelter. But if the system is tied to a single office server without backup connectivity and power, there will be no such advantage.

In Ukraine, using artificial intelligence is not simply a wish to be technologically modern. It is a question of resilience. A person has the right to safety, rest and protection during war. At the same time, properly built digital infrastructure can ensure the continuity of some critical processes. Our task is not to replace people but to strengthen their capabilities with technology.

The main revolution is not a text in a minute but detecting a problem before the report

I believe the next change will be deeper than the automatic preparation of analytical memos. It is about round-the-clock monitoring, where data arrive continuously, the system detects deviations, checks them against rules, classifies the risk and passes the information to a responsible person or performs a safe action permitted in advance.

For this purpose, cybersecurity has long developed the platforms SIEM and SOAR. International guidance from 2025 describes them as tools for collecting events, detecting suspicious activity, alerting specialists and automating defined parts of the response. At the same time, such systems require constant configuration, testing and qualified oversight. [8]

In Ukraine, this principle can be adapted to monitoring reconstruction, financial control, environmental indicators, cyber threats and appeals from veterans and their families. For example, a new appeal could automatically reach the appropriate service, while an anomalous change in tender documentation could generate a signal for an auditor. But decisions on medical care, restrictions of rights or financial sanctions cannot be entrusted to a generative model without proper independent control mechanisms.

The future belongs to systems that do not merely accumulate information but continuously analyze events. Artificial intelligence can help detect anomalies, verify them and trigger pre-agreed response scenarios. But critical decisions must remain under proper human control. It is precisely the combination of automation and accountability that will determine the quality of new digital ecosystems.

The price of error: where automation must not be confused with trust

The larger the scale of automation, the more costly a systemic error can be. A model can convincingly invent a source, miss an important document or grant access to confidential data to an unsuitable user. That is why medical, personal and financial materials require data minimization, control of access rights, logging of operations, accuracy checks and separate approval procedures for critical actions.

The scientific community must distinguish the speed of machine processing from the reliability of a conclusion. This is especially relevant when AI helps to produce scientific reviews or analytics for management decisions.

We must distinguish the speed of information processing from the reliability of the knowledge obtained. Artificial intelligence can significantly accelerate the search, comparison and systematization of data, but this does not remove the need to verify sources, check methodology and ensure the reproducibility of results.

Will the Chinese crash be repeated in the US?

This is the question that sounds loudest today. However, the 40% decline of China’s STAR 50 does not mean an automatic 40% fall in American stocks. Reuters compares the Chinese correction with a roughly 6% pullback of the Nasdaq from its recent high. These are different indices, periods and business structures; a direct forecast on such a basis would be unfounded. [1]

Risks for global technology stocks do exist: high expectations, large capital expenditure on computing infrastructure, uncertain demand and difficult monetization. But the degree of vulnerability of each company depends on its profits, cash flows, debt, customers and the cost of the capital it has raised.

In my view, the key question for the markets has already changed: not “who announced AI most loudly,” but “who managed to turn the technology into a verified economic result”. In this logic, a reduction in speculative valuations may even push business toward more disciplined investment.

What I propose to Ukrainian organizations

There is no need to halt digital transformation because of a stock market crash in another country. But neither should one thoughtlessly buy expensive AI solutions simply because it is fashionable. A rational approach can be reduced to five practical steps.

1. Measure the current process: the duration of operations, cost, errors, waiting time for a response and the workload on people.
2. Choose a narrow pilot: for example, document classification, duplicate search or prioritization of appeals, rather than automating the whole of management at once.
3. Define the limits of autonomy: what the algorithm may do on its own, what it may only suggest, and what requires a mandatory human decision.
4. Check quality and security: missed critical cases, false alarms, access to personal data, vendor dependence and resilience to failures.
5. Count the full economics: licences, computing, integrations, support, team training — and the value of the time that was saved.

When these indicators are not measured, talk of savings of five or ten times turns into marketing. When they are measured, the organization can make decisions based on evidence.

Conclusion: a stock-market bubble may burst, while the technological revolution continues

The Chinese crash is a serious warning for investors. It shows that even promising industries are not protected from inflated valuations, unjustified expectations and sharp shifts in market sentiment.

But for me, as a researcher and head of an institution working on issues of human capital and the recovery of Ukraine, the conclusion is different: technology should be assessed by its impact on real tasks, not by stock-market hype.

If automation shortens the time taken to process appeals, helps detect risks in time, supports continuity of work during war and frees specialists for responsible decisions, it already has practical value. But this value must be measured, and safety must be proven.

The AI bubble may burst. The need for verified knowledge, resilient organizations and timely decisions will not.

Sources and methodological notes

[1] Reuters. “In China, the AI bubble has already burst”, October 26, 2026 Source

[2] Investing.com. SSE STAR 50: historical data, closing of October 27, 2026 Source

[3] Noy S., Zhang W. «Experimental evidence on the productivity effects of generative artificial intelligence». Science, 2023 Source

[4] Harvard Business School / Boston Consulting Group. «Navigating the Jagged Technological Frontier», 2023 Source

[5] Abogunrin S. et al. «How much can we save by applying artificial intelligence in evidence synthesis?». Frontiers in Pharmacology, 2025 Source

[6] METR. «Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity», 2025 Source

[7] European Business Association. “Four Years of the Large-Scale War: How Business Operates in Ukraine”, February 24, 2026 Source

[8] Australian Signals Directorate and international partners. “Implementing SIEM and SOAR platforms: Executive guidance”, May 27, 2025 Source

Note on the calculations: the example with 100,000 documents and the model of staff availability in wartime conditions are illustrative assumptions. They should be distinguished from the results of specific controlled studies and checked against one’s own data before an investment decision.