Is the AI chip pullback an opportunity or a risk? An analysis of the semiconductor investment divergence between JPMorgan and Morgan Stanley

In the first half of 2026’s AI chip bull market, investors had almost grown accustomed to the narrative that “every pullback is a buying opportunity.” But the market action in the first week of July put this familiar logic to a real stress test.

From July 1 to 2, the Philadelphia Semiconductor Index (SOX) fell by more than 11% over two trading days. Marvell Technology plunged 9.84% in a single day, with an intraday low of $237.20; over the two days, its cumulative decline exceeded 18%, and from its historical high of nearly $330 in June, the pullback was more than 25% within about three weeks. Micron Technology dropped by more than 11%, Intel fell 9%, and AMD gave back 7%. The VanEck Semiconductor ETF fell by more than 5%. This sell-off was not an isolated event; it was a systemic revaluation across the entire AI hardware chain.

Amid the same market trend, two top Wall Street investment banks delivered sharply different investment advice. JPMorgan strategist Mislav Matejka clearly stated that the recent weakness in semiconductor stocks should be treated as a buying opportunity. Meanwhile, Morgan Stanley’s chief U.S. equity strategist Michael Wilson sent clients a different signal: trim semiconductor exposure and rotate into hyperscale cloud providers.

The disagreement between the two institutions was not rooted in differing views on the long-term prospects for the AI industry—both confirm that the long-term AI trend has not changed. The real difference lies in their expectations for near-term valuation, market sentiment, and the pace of the next leg higher. This article dissects the two banks’ logic frameworks around this core divergence, analyzes potential future drivers for the AI chip sector, and extends the discussion to the structural impact of the AI boom on the crypto market.

Drivers of the Pullback: Multiple Factors Converging, Not a Logical Reversal

Before understanding the divergence between the two investment banks, it is first necessary to clarify the causes of this pullback.

First, the prior rally ran too far, and profit-taking pressure was concentrated and released. In the first half of 2026, the Philadelphia Semiconductor Index rose by more than 80%. In the storage segment, it gained 318.49% in the first half, ranking first among all subsectors in U.S. equities; computer hardware rose 165%, and semiconductor equipment and materials rose 129%. After the index delivers such a huge gain in a short period, any incremental negative catalyst can trigger large-scale profit-taking. During the sell-off in the first week of July, trading value surged sharply, reflecting the market’s anxiety concentrating into action.

Second, Meta’s announcement became the trigger for a sentiment turn. Last week, Meta announced it would begin selling its excess computing capacity to external customers. The market interpreted this signal as: even for hyperscale cloud providers with capital expenditure guidance as high as $145 billion in 2026, there may still be excess capacity in computing resources. Over the past two years, the semiconductor industry has been trading under the assumption of persistent shortages of GPUs and high-end memory. If Meta still has enough idle capacity available to rent out, that implies that future demand orders for GPUs, HBM, and NAND flash could be reduced. In its report, Wilson said bluntly that this move sent the market a message that “the growth rate of hyperscale cloud providers’ capital expenditures may be approaching a phase turning point.”

Third, warnings from Citi analysts amplified negative sentiment. Citi analysts questioned: if large cloud platforms cannot show investors that their massive investment in AI infrastructure will generate significant returns, can their high spending be sustained? This question directly targets the core narrative of the current AI investment wave: when the visibility of capital expenditure returns starts to blur, the foundation of the entire valuation framework becomes unstable. Goldman Sachs data shows that the total capital expenditure of four giants—Alphabet, Amazon, Microsoft, and Meta—in 2026 will reach $725 billion, up 77% from 2025; the share of hyperscale cloud providers’ capital expenditure in operating cash flow will rise to about 100%. Yet there is still no clear answer as to when AI businesses will deliver profits that match the investment.

Fourth, valuations are high and there is insufficient margin of safety. Even after nearly a 10% single-day plunge, Marvell’s trailing P/E ratio as of July 2 was still about 84x—significantly higher than the semiconductor industry average (about 75.5x). Nvidia’s current forward P/E is roughly 22x; after an adjustment that was nearly flat in the first half of the year, the valuation already appears “relatively cheap,” but after triple-digit surges, AMD and Intel are in the expensive valuation range. Goldman Sachs pointed out that Nvidia’s current P/E has fallen back to the mid-to-lower end of its three-year range—yet that precisely indicates how elevated valuations were before this pullback.

Overall, this pullback is more of a phased adjustment rather than a fundamental change in AI industry logic. Goldman Sachs expects global AI capital expenditure on computing, data centers, and power to reach approximately $7.6 trillion from 2026 to 2031. Global data center supply has increased from 30 GW in 2019 to 57 GW in 2024, and an additional ~65 GW is expected before 2030. AI demand growth is accelerating faster than infrastructure construction. These macro data suggest that AI infrastructure expansion is still in the first half.

The Essence of the Divergence: Two Different Rhythm Judgments in the Same Direction

The divergence between the two investment banks can be summarized in one sentence: both are bullish on AI, but they disagree on whether “now is the right time to buy.”

JPMorgan: Pullbacks Are Opportunities; the Upcycle Is Far from Over

In a report dated July 6, JPMorgan strategist Matejka laid out three core arguments.

First, the semiconductor upcycle has not topped out yet. Matejka wrote, “Meaningful incremental supply is unlikely to arrive before 2028.” High-bandwidth memory (HBM) supply from Micron, SK Hynix, and Samsung is sold out through 2026, and new wafer capacity is expected to come online meaningfully only after 2028. AI data centers are expected to consume about 70% of global memory chip output this year—analysts describe this as a structural supply shortage that gives producers sustained pricing power.

Second, the recent pullback in the Philadelphia Semiconductor Index and in the Korean stock market should be viewed as an opportunity to add positions. JPMorgan’s positioning priority for the tech sector is clear: “semiconductors outperform hyperscale cloud providers; hyperscale cloud providers outperform AI high-risk concept stocks.” In its mid-year outlook report, JPMorgan specifically designated Broadcom as a “strong buy” for the remainder of 2026, emphasizing that the AI-driven chip cycle is far from finished. JPMorgan analyst Harlan Sur noted that there are a large number of backlog orders for AI chips—order volume far exceeds current capacity, with revenue visibility extending into the more distant future.

Third, the macro environment is improving. Matejka believes that as concerns about stagflation gradually fade, the breadth of overall market participation in the second half of 2026 could expand further. JPMorgan expects global stock markets to hit new highs in the second half, supported by strong earnings prospects, easing inflation pressures, and relatively light investor positioning.

It is worth noting that JPMorgan is not indiscriminately bullish on all technology stocks. The bank is relatively cautious toward the “Magnificent Seven,” believing that although positive factors remain in earnings and valuation, the sector “may continue to face valuation de-rating pressure due to doubts over monetization prospects.” For sectors related to the AI “cannibalization effect,” such as software, business services, and media, JPMorgan maintains a “fundamentally bearish” stance.

Morgan Stanley: Momentum Is Weakening; Waiting for a Better Intervention Point

Morgan Stanley’s view is also built on confirmation of the long-term AI trend, but it differs on the short-term pace.

In its latest report, Wilson pointed out that recent momentum in the semiconductor sector has deteriorated, and the Philadelphia Semiconductor Index has fallen nearly 12% from its peak. High-beta momentum stocks (storage and chip stocks) recorded the largest two-day decline since the COVID-19 pandemic. Wilson judges that this pullback “may have further room to go.”

Morgan Stanley’s core logic is based on a “rotation trading” framework. As early as the annual outlook in November 2025, Wilson proposed a “market diffusion trading” framework: after the U.S. economy completed a rolling recession in April 2025 and entered a new expansion cycle, earnings growth would be better than expected, and the market’s leading force should rotate from AI capital expenditure beneficiaries to broader sectors. That judgment was interrupted by the Iran war in February 2026, but with oil prices retreating and inflation expectations stabilizing, Wilson believes conditions have matured again.

Morgan Stanley offered a specific analogy: semiconductor price action resembles silver closely—both experienced parabolic price surges and are highly tied to commodity markets. Morgan Stanley first raised this analogy in early June, and now believes it is coming true. Wilson further noted that this adjustment will be led by the storage sub-sector—because storage is the “most commodity-like” category within the semiconductor complex, with high price elasticity and a fast reversal.

In terms of positioning direction, Wilson explicitly recommends “sell chips, buy cloud”—reduce semiconductor exposure and rotate into hyperscale cloud computing providers. This is not a bearish view on AI; it is a rotation. During the AI investment cycle, there have been three similar adjustments, and Wilson believes this is the fourth. He is bullish on hyperscale cloud service providers such as Microsoft, Amazon, and Meta, arguing that their core businesses can buffer fluctuations related to AI. Wilson maintains his year-end target of 8,000 for the S&P 500.

Is the AI Bull Market Entering a Second Phase?

The divergence between the two investment banks fundamentally points to the same question: is the AI investment cycle shifting from a “concept-driven” stage to an “earnings verification” stage?

Looking back at the first stage of the AI bull market—roughly from 2023 to the first half of 2026—the market mainly traded AI concepts and future expectations. During this stage, as long as concept stocks tied to AI generally rose, the market showed a very high tolerance for valuations. The 318% gain in the storage segment in the first half and Marvell’s surge of over 220% at one point during the year are typical outcomes of this stage.

The market is now moving into the second stage. The characteristic of this stage is that the market pays more attention to earnings realization. AI chip, cloud computing, data center, and other infrastructure companies start to show clear differentiation, and companies’ actual revenue and order growth become the core factors determining stock performance. Global AI trading is “changing tracks,” with capital undergoing structural reallocation from “money-burning” cloud service providers to “profitable” hardware suppliers. Market pricing logic is gradually shifting toward verification of performance delivery and operating cash flow.

This judgment is well supported by data. Samsung Electronics’ preliminary Q2 results released on July 7 showed operating profit up 1,810.2% year over year to 89.4 trillion won (about $5.8 billion), marking the third consecutive quarter setting a record for operating profit. Longsys, a bull stock in storage chips, is expected to see net profit attributable to the parent company increase by 62,204% year over year to 74,394% in the first half of the year. These figures indicate that earnings realization in the AI hardware segment is already underway.

At the same time, Goldman Sachs predicts that hyperscale cloud providers’ capital expenditure as a share of operating cash flow will rise to about 100%. When capital expenditure growth far exceeds revenue growth, the market will naturally question the sustainability of this model. The next Q2 earnings reports will be an important window for observation. For stocks whose valuations have already been lifted further, the key indicators will be the continued growth of operating cash flow and the extent to which it aligns with expectations versus the broader market consensus.

Goldman Sachs noted that investors are underweight the “Magnificent Seven,” favoring hardware segments such as semiconductors that directly benefit from capital expenditure. Fund flow data also confirms this trend: according to Wind, during the week from June 29 to July 3, the top three sectors by net inflows into stock ETFs were semiconductor chips, communications, and securities. JPMorgan also mentioned that current AI trading is seeing internal differentiation: hardware stocks such as chips continue to attract funds, while heavily capital-invested technology companies are being sold off. This is highly similar to the situation before the dot-com bubble burst in 1999.

How the AI Boom Impacts the Crypto Market

Volatility in the AI chip market not only affects traditional capital markets, but also transmits into the crypto ecosystem through multiple channels.

First, AI infrastructure expansion drives the development of decentralized physical infrastructure networks (DePIN). As global data center supply rises from 30 GW in 2019 to 57 GW in 2024, and is expected to add about 65 GW more before 2030, decentralized physical infrastructure networks are emerging as an alternative way to supplement centralized computing power. As of March 2026, DePIN’s total market cap has reached approximately $9 billion to $10 billion. The massive demand for computing power for AI training and inference provides real application scenarios and revenue sources for DePIN projects. When centralized computing capacity is constrained by power bottlenecks or capital expenditure cycles, the relative value of decentralized computing networks may become even more prominent. Goldman Sachs pointed out that in some key markets, the grid interconnection queue period for data centers can be as long as 8 to 12 years—far longer than the GPU upgrade cycle—creating a differentiated competitive space for DePIN.

Second, the integration of AI Agents and the crypto sector is deepening. In the first quarter of 2026, “AI Agent tokens” underwent a sharp overall adjustment, with declines of 80% to 90%. But this drop was selective: tokens with “AI” in their names yet offering no practical use completely collapsed, while projects with real utility stabilized and rebounded. This differentiation aligns with the logic of the AI stock market’s transition from “concept-driven” to “earnings verification.” As costs for underlying infrastructure such as AI chips continue to decline, the deployment barrier for AI Agents is also lowering, which will provide more favorable development conditions for AI application-layer projects in the crypto ecosystem.

Third, there is a linkage effect between AI stocks and AI crypto assets in both capital flows and sentiment. On July 7 (Beijing time), AI-related stocks in the U.S. market such as AAOI, MRVL, AVGO, and ASML rose between 1.63% and 3.73%. Over the same period, the price of Bitcoin was trading around $64,000. The sell-off in semiconductor stocks earlier in July once dragged Bitcoin down to about $62,000, highlighting that the health of AI trading has become a relevant upstream indicator for the digital asset market.

From a long-term perspective, the relationship between AI stocks and AI crypto assets is not simply the same-direction or opposite-direction. When AI chip stocks pull back due to excessive valuations, some capital may seek AI crypto assets as an alternative exposure. Conversely, when AI infrastructure investment keeps expanding, the underlying value in sectors such as DePIN and decentralized computing is also reinforced. There is a complex relationship of capital rotation and value transmission between the two, making it worth ongoing attention.

Conclusion

The AI chip pullback in early July 2026 is essentially a concentrated correction of the excessive gains from the first half of the year. The divergence between JPMorgan and Morgan Stanley is not about a difference in the long-term AI trend, but rather about different judgments on short-term pacing and valuation levels. JPMorgan sees structural supply shortages and the continuation of the upcycle; Morgan Stanley sees momentum exhaustion and a window for rotation trades.

For investors, the core question in the current market is: is the AI investment cycle shifting from a “concept-driven” stage to an “earnings verification” stage? If so, the next market logic will shift from “who has an AI story” to “who has AI revenue.” The earnings reports of hardware giants such as Samsung and Micron Technology have already demonstrated the possibility of earnings realization, but hyperscale cloud providers’ capital expenditure return visibility remains the biggest uncertainty in the market.

AI’s impact on the crypto market is also moving from the conceptual level into the substantive level. The growth in DePIN market cap, the differentiation in the AI Agent sector, and the capital linkage between AI stocks and crypto assets all show that the integration of AI and crypto is becoming an unavoidable structural trend. No matter which direction AI chip stocks move in the short term, the long-term trend of AI infrastructure expansion has not changed—and the far-reaching impact of this trend on the crypto ecosystem is only just beginning to emerge.

FAQ

Q1: What is the fundamental disagreement between JPMorgan and Morgan Stanley on AI chip stocks?

Both investment banks are bullish on the long-term AI trend. Their disagreement is about the short-term trading rhythm. JPMorgan believes this pullback offers a buying opportunity, with the semiconductor upcycle lasting at least until 2028; Morgan Stanley believes the upside momentum in chip stocks is weakening and recommends taking profits and rotating into hyperscale cloud service providers. The core difference is their judgment on whether “current valuations are reasonable” and on which sector will lead the next phase.

Q2: What are the main reasons for this pullback in the AI chip sector?

There are four main factors: excessive gains in the first half (the Philadelphia Semiconductor Index rose by more than 80%), concentrated profit-taking pressure; Meta announced it would sell excess computing capacity, triggering market concerns about overbuilding AI infrastructure; institutions such as Citi questioned the visibility of returns on cloud providers’ capital expenditures; and valuations are at a high level with insufficient margin of safety. These factors are more corrections in sentiment and valuation than a fundamental change in AI industry logic.

Q3: Is the AI investment cycle entering a second stage?

Yes. The first stage (2023 to the first half of 2026) saw the market primarily trading AI concepts and future expectations, with AI concept stocks rising broadly. The market is now entering a second stage, characterized by: increased focus on earnings realization, clear differentiation among AI-related companies, and companies’ actual revenue and order growth becoming core factors driving stock performance. Earnings data such as Samsung’s Q2 operating profit up 1,810% also corroborate earnings realization in the hardware segment.

Q4: How will fluctuations in the AI chip market affect the crypto market?

The impact is mainly transmitted through three channels: AI infrastructure expansion provides real application scenarios for DePIN (decentralized computing networks), and DePIN’s market cap has reached about $9 billion to $10 billion; the AI Agent crypto track is experiencing a differentiation similar to AI stocks, with projects that have real usage showing more resilience; and there is a linkage effect between capital and sentiment between AI stocks and crypto assets—when semiconductor stocks were sold off, Bitcoin also fell.

Q5: Is it the right time to position in AI chip stocks right now?

It depends on the investor’s time horizon and risk preference. JPMorgan suggests that long-term investors use the pullback to build positions, believing the semiconductor upcycle is far from over. Morgan Stanley suggests waiting for a better entry point, believing the pullback may have further room. Investors should watch upcoming earnings reports from companies such as Nvidia and Micron Technology to assess whether AI demand can support the continuation of the current cycle.