A Three Trillion Dollar Gamble and Global Diffusion: The Dual Narratives of AI in 2026

marsbitPubblicato 2025-12-23Pubblicato ultima volta 2025-12-23

Introduzione

Analysis of 2026 AI trends from top institutions (a16z, Goldman Sachs, J.P. Morgan, Morgan Stanley, BlackRock) reveals two competing narratives: 1) AI infrastructure capital expenditure is projected to reach $3 trillion, with less than 20% currently deployed. Major cloud providers are heavily investing in data centers and GPUs, but J.P. Morgan cautions that near-term profit boosts will be limited to select companies. True productivity gains remain years away, indicating 2026 will still be an intensive investment phase rather than a harvest period. 2) A divergence exists in market expectations. BlackRock's "Micro is Macro" concept highlights how AI investments by a few firms already impact macro trends. The S&P 500 market-cap weighted index outperformed the equal-weight version by 8% YTD, suggesting concentrated AI benefits for tech giants. Morgan Stanley projects the S&P 500 to reach 7800, betting on continued dominance of big tech. Conversely, J.P. Morgan and Goldman Sachs anticipate AI红利global spillover, especially to emerging markets (10.9% expected returns), Europe (7.1%), and Japan (8.2%), fueled by a weaker dollar and global infrastructure upgrades. The divide centers on whether AI红利remains concentrated in U.S. tech or disperses globally.

After reviewing the 2026 trend outlook reports from five top institutions—a16z, Goldman Sachs, J.P. Morgan, Morgan Stanley, and BlackRock—two key insights have been distilled:

1) Forget the Bubble, the AI Industry is Entering an Accelerated Investment Phase

Morgan Stanley provided a staggering figure: AI infrastructure capital expenditure is projected to reach $3 trillion, with less than 20% currently deployed.

What does this mean? Hyperscale cloud providers like Amazon, Google, Meta, Microsoft, and Oracle are pouring massive investments into building data centers, purchasing GPUs, and expanding power infrastructure—but this is just the beginning.

However, J.P. Morgan offered a more cautious perspective on the actual benefits of widespread AI adoption, suggesting that it will only boost profits for some companies in the short term, helping giants optimize their earnings narrative. Achieving the transformative productivity gains from AI will take many more years.

In essence, 2026 will be another year of frenzied spending on AI, but it remains an investment phase, far from the harvest season.

2) U.S. Stock Concentration Dividends vs. Spillover to Non-U.S. Markets: Which Side Are You On?

BlackRock introduced the concept of “Micro is Macro,” arguing that the AI investments of a few companies already have macro-level impact.

Data shows that year-to-date in 2025, the equal-weighted S&P 500 has risen only 3%, while the market-cap-weighted version of top tech companies has surged 11%. This 8% gap may be attributed to the concentration of AI dividends.

On this front, Morgan Stanley is the most aggressive, setting a target of 7800 points for the S&P 500—a 14% increase from current levels—based on the sustained strengthening of the profitability of the tech giants.

However, J.P. Morgan believes that as the U.S. dollar weakens, AI dividends will spill over into global supply chains, projecting an annualized return of 10.9% for emerging markets, higher than the 6.7% for U.S. large-cap stocks. Goldman Sachs also sides with the spillover effect, giving emerging markets the same 10.9% projection and suggesting opportunities in Europe (7.1%) and Japan (8.2%).

Simply put, these are two entirely different bets: BlackRock and Morgan Stanley are betting that AI dividends will continue to be monopolized by U.S. tech giants, while J.P. Morgan and Goldman Sachs are wagering that AI is a global infrastructure upgrade, with benefits diffusing to non-U.S. markets worldwide.

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Domande pertinenti

QWhat is the projected AI infrastructure capital expenditure by 2026 according to Morgan Stanley, and what does it indicate about the current deployment?

AMorgan Stanley projects AI infrastructure capital expenditure to reach $3 trillion by 2026, with current deployment at less than 20%. This indicates that major cloud providers like Amazon, Google, Meta, Microsoft, and Oracle are in the early stages of investing heavily in data centers, GPUs, and power facilities, signaling a period of accelerated investment rather than immediate returns.

QHow does J.P. Morgan assess the short-term impact of AI adoption on corporate profits?

AJ.P. Morgan believes that the large-scale adoption of AI will only provide a short-term boost to some corporate profits in the near term, primarily helping giants optimize their earnings narrative. It will take many years for AI to achieve a qualitative leap in productivity and deliver substantial returns.

QWhat is the 'Micro is Macro' concept proposed by BlackRock, and how is it reflected in the stock market performance?

ABlackRock's 'Micro is Macro' concept suggests that the AI investments of a few companies already have macro-level influence. This is reflected in the stock market where the equal-weight S&P 500 index rose only 3% year-to-date in 2025, while the market-cap-weighted version, dominated by top tech companies, rose 11%, indicating an 8% gap driven by AI concentration红利 (dividends).

QWhat are the differing views between Morgan Stanley and J.P. Morgan regarding the geographical distribution of AI红利 (dividends)?

AMorgan Stanley bets that AI红利 will continue to be monopolized by U.S. tech giants, setting an ambitious S&P 500 target of 7800 points. In contrast, J.P. Morgan believes that AI红利 will spill over to the global supply chain as the U.S. dollar weakens, forecasting a 10.9% annualized return for emerging markets, higher than the 6.7% for U.S. large-cap stocks.

QWhich institutions side with the view that AI红利 will globalize and benefit non-U.S. markets, and what are their specific forecasts?

AJ.P. Morgan and Goldman Sachs side with the view that AI红利 will globalize. J.P. Morgan forecasts a 10.9% annualized return for emerging markets, while Goldman Sachs also predicts 10.9% for emerging markets, along with 7.1% for Europe and 8.2% for Japan, anticipating that AI-driven infrastructure upgrades will benefit markets outside the U.S.

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Bitcoin Mining Farms Are Becoming AI Factories

Bitcoin mines are transforming into AI factories. This shift is driven by the convergence of three key assets from the previous crypto cycle: infrastructure, talent, and capital. Crypto mining companies like Crusoe, CoreWeave, and Bitdeer are repurposing their core competency—securing power, land, and grid connections in remote locations—to build data centers for AI clients. These firms are signing multi-billion dollar, long-term contracts with companies like Anthropic, AWS, and Microsoft, as AI's demand for reliable, high-capacity compute surpasses the profitability of Bitcoin mining. Simultaneously, crypto entrepreneurs and engineers are applying their skills to new AI ventures. Examples include OpenSea's co-founder launching OpenRouter (an AI model aggregator), and former Coinbase engineers building Fal.ai (a generative media infrastructure platform). Their experience in building scalable, global software networks translates effectively to the AI space. Furthermore, capital accumulated during the crypto boom is now fueling AI. Figures like Jed McCaleb (co-founder of Ripple) funded Voltage Park, a large-scale GPU cloud provider. Notably, some crypto investments, like FTX's early bets on Anthropic and Cursor, have generated astronomical paper returns, demonstrating how high-risk crypto capital flowed into AI before it became mainstream. The transition is not just about repurposing hardware, but about redirecting critical resources—power infrastructure, distributed systems expertise, and venture funding—to the next technological frontier: artificial intelligence.

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Morpho Launches Fixed-Rate Product Midnight: Lenders and Borrowers Set Their Own Rates, Ending the Era of Algorithmic Interest Models On-chain lending has grown to $60 billion but remains minuscule compared to traditional finance's $200 trillion annual credit volume. Morpho identifies the lack of fixed rates and maturity dates as key bottlenecks. Institutions need predictability, not the passive floating rates set by algorithmic models. Midnight allows lenders and borrowers to directly quote rates, set terms, and become price makers, not takers. Fixed-rate lending is now viable due to cheaper, faster blockchains and the entry of institutions demanding control and certainty over returns, costs, and duration. Morpho Blue previously gave users control over risk; Midnight adds control over interest rates. Past attempts at on-chain fixed-rate lending failed primarily because they were built on top of floating-rate pools (creating unpredictability) or lacked sufficient active participants. Midnight avoids these pitfalls as a standalone primitive with fixed rates at its core, built upon Morpho Blue's existing large and active user base. Midnight offers distinct value: institutions gain predictable term structures and full control; fintech companies can offer tailored fixed-rate products; lenders/borrowers achieve predictability and efficiency; and curators can now differentiate by configuring both risk and interest rates. Morpho Midnight is not a replacement for Morpho Blue. The Morpho network will now feature two complementary market structures: floating-rate/open-term (Blue) for flexibility and fixed-rate/fixed-term (Midnight) for predictability. Liquidity can flow between them. The launch will be gradual, prioritizing security. Initially, it will support direct lending on Base network with one trading pair (cbBTC/USDC) and limited maturity dates. Advanced features like auto-rollovers will be introduced later.

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