After analyzing over ten thousand financial reports, Morgan Stanley found that the "service + cycle" segment, which was sold off, actually has the highest AI adoption rate and the strongest bargaining power.

Recently, Wall Street has been filled with a sense of “AI anxiety.”

Market concerns stem from the rise of generative AI (GenAI) and AI agents, which threaten to completely disrupt many traditional “service + cycle” companies—especially in software, information services, and financial intermediaries. This panic has led to indiscriminate selling across related sectors.

But this may be a huge mispricing.

On February 25, Morgan Stanley’s US Equity Strategy and Themes team released a report stating that recent reactions to the “AI disruption theory” in the US stock market have been excessive.

First, after the sharp decline, this group, considered “disruptive targets,” now only accounts for 13% of the S&P 500’s total market value. This explains why the overall market index has seen limited pullback recently, while internal sectors have experienced intense volatility.

Second, the valuations and crowdedness of this group are already at very low levels. According to Morgan Stanley data, the relative valuation of the “service + cycle” sectors is at the 9th percentile since 2010—almost the cheapest in history. Meanwhile, institutional net exposure has fallen to the 20th percentile, indicating extreme underweighting.

Morgan Stanley bluntly states: “The bearish view on GenAI seems to underestimate the ability of established software providers to participate in this new innovation cycle.”

In fact, these sold-off groups are not being disrupted—instead, they are among the highest AI adoption rates and have the strongest pricing power (ranking in the top third) in Morgan Stanley’s AI mapping analysis.

Market “victims” are actually the biggest “beneficiaries.” These heavily fallen sectors are precisely those with high AI user concentration.

Quantitative gains are already evident—it’s not just empty talk but real money.

Investors generally doubt whether AI can truly save or make money for companies at this stage. Data provides a positive answer.

Morgan Stanley’s team analyzed over 10,000 financial reports and conference calls using AI models. The results show that companies are gaining tangible AI benefits, and the momentum is continuing to grow.

In the recently completed Q4 2025, 30% of companies identified as “AI adopters” mentioned at least one “quantifiable financial impact” from AI during their earnings calls.

This proportion was 24% in Q3 2025 and only 16% in Q4 2024. For the broader S&P 500 index, the figure has risen to 21%.

Morgan Stanley states: “Currently, the most frequently mentioned quantifiable benefits mainly focus on ‘financial impacts’ (including revenue growth and cost savings), and mentions of this have doubled compared to the previous quarter.”

Fundamentally, AI adopters with strong pricing power are not only maintaining but accelerating their forward net profit margin expectations.

Morgan Stanley forecasts that AI adoption will contribute 40 basis points of profit margin growth to the overall S&P 500 in 2026.

Data confirms the profit expansion of “AI adopters.”

From 2024 to 2025, AI adopters’ EBIT profit margins expanded by 310 basis points—twice the pace of the MSCI Global Index during the same period. Morgan Stanley analysts expect about 80% of the redemptive benefits from AI to come from improved cost efficiency.

For example, Citibank states: “So far this year, AI-driven automated code reviews have exceeded 1 million instances, greatly boosting developer productivity, saving approximately 100,000 hours weekly.”

European companies are the most aggressive. Surveys show that up to 35% of European firms plan to use AI to reduce their workforce—significantly higher than the roughly 10% in other regions. This points to stronger future profit margin performance.

Historical Reflection: Lessons from the Smartphone Era of 2007

To better understand current market logic, Morgan Stanley looks back to 2007.

At that time, the iPhone had just been released, and the market was also gripped by “disruption panic.” Industries like gaming, PCs, printers, GPS, and desktop software were believed to be facing extinction.

Data shows that in the years following the iPhone launch, the performance of these “disrupted concept stocks” was highly divergent.

Similarly impacted, Google, which successfully capitalized on mobile advertising opportunities, rose 28%; Nokia, however, plummeted 73%.

After testing multiple fundamental variables, Morgan Stanley found that the most critical indicator of stock performance during epoch-defining technological shocks is “forward earnings changes.”

In other words, those who can leverage AI to achieve profit growth will be the last laughing in the capital markets. Since late 2023, AI adopters’ earnings upgrades have been roughly twice those of AI disruptors, and this gap is widening as investment returns accumulate.

For example, after the iPhone’s release, the Spearman rank correlation coefficient between forward earnings and stock price performance was as high as 0.9 (strong correlation).

Morgan Stanley concludes: “What we are experiencing now is a typical feature of a major investment cycle. Capital will flow not only to structural leaders but also to cyclical leaders. Bottom-up stock selection strategies are especially important at this time.”

Moats are deeper than imagined: compliance, trust, and proprietary data

Regarding specific industries facing AI disruption, Morgan Stanley’s analysts provide detailed logical analyses, revealing which are truly disrupted and which are merely panicking:

Software Industry: Panic peaks; AI is not a “new category” but a “new capability.”

The software sector has recently experienced a sharp valuation correction. The current average valuation multiple (EV/Sales around 4.4x) has fallen back to the lows seen during the cloud computing panic of 2014-2016.

There are three main concerns: AI startups stealing market share, the collapse of seat-based business models, and GPU costs driving down margins.

But Morgan Stanley argues these worries are misplaced: “Generative AI fundamentally expands enterprise software capabilities. The question isn’t whether software can monetize in this innovation cycle, but who will participate in building these additional capabilities.”

Morgan Stanley believes AI essentially enhances enterprise software by solving the pain point of unstructured data. Existing giants with distribution channels, proprietary data, and workflow control are actually the biggest beneficiaries.

Consumer Finance and Payments: Trust and compliance cannot be replaced by AI.

Recent market fears suggest “Agentic AI” could autonomously shop and bypass traditional credit card networks.

Morgan Stanley dismisses this: “We are skeptical that agentic AI can significantly disrupt credit card exchange networks. Trust systems, fraud protection, credit extension, and customer rewards are critical.”

In these highly data-intensive and regulated industries, licenses and balance sheets are natural barriers. AI will only accelerate backend improvements in underwriting, fraud detection, and customer service.

Morgan Stanley expects banks and consumer finance firms to leverage AI to significantly boost operational leverage, with further profit margin improvements in 2026 and 2027.

Internet and E-commerce: The next-generation “Agentic Commerce” will grow big

Morgan Stanley predicts that “agentic commerce” capable of autonomously comparing prices and placing orders will be the next major unlock for generative AI.

This will make consumer funnels more conversational, personalized, and interactive. By 2030, Morgan Stanley estimates agentic commerce will add an extra $50 billion to $115 billion in spending to the US e-commerce market.

Platforms with extensive logistics infrastructure, unique inventory, and strong fulfillment capabilities will not be replaced but will leverage AI to expand their online wallet share.

Transportation: Heavy assets benefit from AI, light assets face real risks

Transportation is one of the industries most susceptible to AI impact. However, Morgan Stanley highlights significant internal differentiation.

Heavy-asset operators—those with fleets, railways, and warehouses—will be the primary beneficiaries. Physical AI (autonomous trucks, humanoid robots) will structurally reduce labor costs and improve asset utilization.

Conversely, “light asset” freight brokers (3PLs), which profit from information asymmetry, face genuine disruption risks. Generative AI is commoditizing freight matching, continuously squeezing broker margins.

Real estate and commercial insurance: Highly complex non-standard businesses are hard to replace

For commercial real estate services and large commercial insurance brokers, the market underestimates their complexity.

Large commercial policies require intricate contract analysis, risk modeling, and compliance checks. Morgan Stanley notes: “AI cannot replace the specialized knowledge needed for market access and regulatory oversight.”

In commercial real estate, AI’s role is more about “augmentation” than “replacement.” These labor-intensive firms will cut backend costs through AI. Morgan Stanley estimates that AI automation in public REITs and CRE services could generate up to $34 billion in financial impact—about 16% of their operating cash flow.

The job market: Will AI trigger massive unemployment?

The ultimate concern behind all “AI disruption” theories is that AI will cause large-scale unemployment among white-collar workers, leading to economic recession and reduced consumption.

Morgan Stanley, reviewing 150 years of technological change (electricity, tractors, computers, the internet), states that history proves each major innovation profoundly changes the labor structure but “does not replace labor.”

Instead, technology creates new jobs. Morgan Stanley expects that as AI deepens, companies will need roles like “Chief AI Officer,” as well as new professions such as “product manager-engineer hybrids,” “AI supply chain forecasters,” and “computational geneticists.”

In summary, the sweeping wave of new technology indeed causes pain to the old order. But when markets overreact and wrongly kill valuable assets, returning to core business fundamentals—focusing on proprietary data, physical assets, and long-term profitability—is the best way to navigate the tech cycle.

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