The market has finally realized that AI disruption is no longer a distant threat.
On February 14, according to news from the Chasing Wind Trading Desk, Morgan Stanley stated in its latest research report that as AI models advance non-linearly and at an accelerated pace, the market's pricing of disruption risks has begun to show a domino effect:
<blockquote>
Just a month ago, the market estimated that about 4% of the MSCI Europe index weight faced AI disruption risks; a week ago, this proportion rose to 7%; and on February 13, this number had jumped to 24% (including the banking sector).
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The report pointed out that Morgan Stanley believes that as cutting-edge AI models reach a critical breakthrough—GPT-5.2 has achieved or surpassed human expert levels on 71% of professional tasks—investors must reassess their asset allocation logic.
Morgan Stanley has shifted its stance from neutral to cautiously viewing cyclical stocks relative to defensive stocks, noting that the European credit market offers cheap downside hedging opportunities, focusing on utilities, semiconductors, defense, and tobacco, which are seen as the most resilient safe havens.
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The bank emphasized that it is necessary to reassess which assets cannot be &#34;replicated&#34; by AI—these will become the value anchors of the new era. In an age where intelligence and labor can be infinitely replicated, true value will return to those things that cannot be replicated—physical assets, regulatory barriers, network effects, human experiences, proprietary data.
<h2>The Astonishing Leap in AI Capabilities: 71% of Professional Tasks Have Been Conquered</h2>
Humans are not good at understanding nonlinear changes, and the progress of AI models is a typical example of nonlinear acceleration.
Morgan Stanley stated that the data shows an astonishing speed of progress: Grok 4, launched in July 2025, scored 24% on the GDPVal test, meaning that this model can reach human expert levels on 24% of real professional tasks; just five months later, the GPT-5.2 released on December 12, 2025, scored a soaring 71%.
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What is GDPVal? It is an indicator that measures the performance of AI models in real-world knowledge work, covering actual tasks of experienced professionals across various industries. Research by OpenAI found that cutting-edge models complete these tasks about 100 times faster than industry experts and at about 1/100th of the cost.
The report emphasizes that even more shocking breakthroughs are on the horizon. If the scale law of training large language models (LLM) continues to hold in 2026—which Morgan Stanley believes is very likely—multiple cutting-edge LLMs from the U.S. are expected to be launched in the first half of 2026, with capabilities far exceeding current models. The reason is simple: the five major LLM developers in the U.S. are currently using about 10 times the computational power to train the next generation of models compared to current models.
**
<h2>The Domino Effect of Disruption Risk: From Software to Banking</h2>
The speed of change in market perception is equally astonishing.
Morgan Stanley's tracking shows that the market initially began to question the potential sharp slowdown in revenue growth for the software industry over the next few years, but soon this concern spread like a domino effect to broader economic disruption risks—changes in competitive landscape, employment impacts, deflationary pressures, and more.
This is reminiscent of the evolution of market psychology during the early COVID-19 pandemic: In January, it was merely about demand and supply chain risks; by February, it expanded to industries such as travel and leisure, industrials, and banking; by March, it evolved into a full market sell-off, ultimately triggering significant policy actions.
Currently, Morgan Stanley estimates that about 10% of the MSCI Europe Index weight (excluding banks) is perceived by the market to face substantial AI disruption concerns, which rises to 24% when including banks. Concerns in the banking sector are relatively new, primarily focused on broader economic deflation and employment issues, as well as (to a lesser extent) deposit competition concerns related to AI.
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Notably, these &#34;disruptive stocks in market debates&#34; have fallen from a peak price-to-earnings ratio of 24 times in early 2025 to today's 16.4 times. However, Morgan Stanley warns that, referencing the valuation trends of those &#34;uncontested disruptive stocks&#34; (which fell from 24.7 times to 11.1 times), there may still be further downside potential in valuations.
<h2>Who Can Survive in the AI Era?</h2>
In the face of this disruption storm, Morgan Stanley provides a framework for assessment, combining five dimensions to judge the resilience of sectors and individual stocks:
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AI Exposure Level: Are they the disrupted party, &#34;disruption targets in market debates,&#34; enablers, or protected parties?
Nature of Business: Service provider, physical assets, commodities, or computing power?
Cyclicality: Cyclical stocks, defensive stocks, or others?
Investor Holdings: Current position levels?
Individual Stock Momentum: Fundamental overlay factors.
</blockquote>
Based on this framework, Morgan Stanley identifies the most resilient sectors in order as: utilities, semiconductors, defense, tobacco, and personal and household care products.
Morgan Stanley states that European utility companies almost dominate the top 20 list of the most disruption-resistant stocks. The common characteristic of these companies is that they provide physical infrastructure that cannot be replicated by AI, belong to defensive industries, and are relatively underweight in the current environment.
In contrast, service-intensive sectors such as software, business services, media and entertainment, travel and leisure, as well as transportation, diversified finance, and banking, are considered to face the highest pressures from the spread of disruption risks.
<h2>Eight Asset Classes That Cannot Be Replicated by AI</h2>
Meanwhile, Morgan Stanley emphasizes that once AI reaches a transformative level, the value of those asset classes that cannot be &#34;replicated&#34; by AI will rise. This is a key framework for understanding future asset allocation:
<blockquote>
A. Physical Scarcity: Real estate, energy and power assets, transportation infrastructure, data centers, mineral metals, water resources, casino licenses in limited jurisdictions, theme park land, cruise ports and dock rights, spectrum licenses, fiber optic cable networks, etc.
B. AI Adopters with Pricing Power: The threshold for demonstrating pricing power is rising.
C. Unique Luxury Goods, Real Estate, and Services.
D. Network Effects: Large tech platforms, online marketplaces, healthcare businesses with patient relationships.
E. Genuine Unique Human Experiences: Media businesses with strong brands, sports assets/teams, music, and other performances that value the human element.
F. Regulatory Scarcity: Businesses with various licenses, approvals, and protected franchises.
G. Proprietary Data and Brands: AI adopters with proprietary datasets and IP libraries.
H. A Range of Semiconductor Assets: Such as leading processes, ASML's EUV lithography, TSMC's manufacturing expertise, and rare earth processing for chips.
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<h2>Credit Market: Cheap Downside Protection</h2>
Although concerns about AI disruption have begun to affect some credit markets, particularly in the leveraged loan sector, European investment-grade spreads remain near post-global financial crisis lows. Even as implied volatility in the stock market has been rising, credit volatility remains unusually subdued.
However, if concerns about AI disruption spread to more sectors (along with the anticipated acceleration in issuance), it may begin to challenge the resilience of the credit market.
Morgan Stanley believes that the credit options market offers investors a good entry point to prepare for spread widening. Given Europe's relatively low tech exposure, overall yields still at high levels, policy support, and economic growth resilience, the cost-effectiveness of these hedging tools is particularly notable.
<h2>Computing Power Demand Gap: An Invisible Supply Crisis</h2>
On the other side of AI disruption is the frantic demand for computing power infrastructure. Multiple data points indicate that the growth rate of computing power demand far exceeds current supply forecasts:
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<li>
Google executives recently stated that the company may need to double its computing power every 6 months, &#34;reaching 1000 times in 4-5 years.&#34; In comparison, Morgan Stanley predicts a compound annual growth rate of about 210% for NVIDIA's computing power sales from 2025 to 2028; extrapolating over 5 years, the cumulative computing power is about 300 times—far below the 1000 times required by Google.
</li>
<li>
OpenRouter data shows that from the end of November 2024 to the end of November 2025, the average weekly token demand is expected to grow by over 2200%. Token usage is a direct proxy indicator of computing power demand.
</li>
</ul>
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<li>More critically, the computational intensity of individual LLM queries is rapidly increasing. Research institution METR points out that the average duration of &#34;work&#34; performed by AI for each customer query doubles every 7 months.</li>
</ul>
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According to the research report, even if the number of customers remains unchanged, this growth means that the demand for computing power will significantly outpace NVIDIA's projected annual compound growth rate of about 120%.
Morgan Stanley states that this supply-demand imbalance has already manifested in the market:
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CoreWeave is able to renew leases for older generation NVIDIA GPUs (Hopper) at 95% of the original price, far exceeding the price implied by the economic depreciation of the chips over time;
Google's &#34;power shell&#34; leasing deal guaranteed for Anthropic and FluidStack brings about an 18.5% unleveraged capital return rate to Bitcoin miner Hut8, equivalent to a power access premium of about 300%.
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The market's awareness of the risks of AI disruption is accelerating. Morgan Stanley's latest research report points out that the weight of companies facing AI disruption risks in the MSCI Europe Index has risen from 4% to 24%. With breakthroughs in AI model capabilities, investors need to reassess their asset allocation and focus on defensive sectors such as utilities and semiconductors. The report emphasizes that future value will return to assets that cannot be replicated by AI, such as physical assets and human experiences

- Morgan Stanley's latest research indicates that the market is rapidly recognizing the imminent threat of AI disruption, with concerns growing from 4% to 24% regarding the MSCI Europe Index's exposure to AI risks over the past month.  
- As AI models achieve significant milestones, including surpassing human performance in 71% of professional tasks, investors are advised to reevaluate asset allocations, focusing on resilient sectors like utilities and semiconductors.  
- The firm's assessment framework suggests that assets resistant to AI replication, such as physical goods and regulatory barriers, will gain value in this evolving landscape.

This week, the wolf of "AI disrupts everything" has finally arrived