> **来源：[研报客](https://pc.yanbaoke.cn)** # Morgan Stanley Global Macro Forum # The AI Capex Acceleration: Reading the Signals from 4Q Earnings February 9, 2026 Vishwanath Tirupattur - Chief Fixed Income Strategist | Strategist Brian Nowak – Lead US Internet Analyst | Equity Analyst Keith Weiss - Software Equity Research Analyst | Equity Analyst Stephen Byrd – Global Head of Thematic Research and Sustainability Research | Equity Analyst Andrew Pauker - US Equity Strategist | Equity Strategist Vishwas Patkar - Head of US Credit Strategy | Strategist Lindsay Tyler - TMT Credit Research Analyst | Credit Analyst MORGAN STANLEY & CO. LLC Morgan Stanley does and seeks to do business with companies covered in Morgan Stanley Research. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of Morgan Stanley Research. Investors should consider Morgan Stanley Research as only a single factor in making their investment decision. For analyst certification and other important disclosures, refer to the Disclosure Section, located at the end of this report. # Brian Nowak # Lead US Internet Analyst # Keith Weiss # Software Equity Research Analyst # Stephen Byrd Global Head of Thematic Research and Sustainability Research # The Compute Supply-Demand Mismatch Why we believe that the demand for compute is likely to significantly exceed the supply: - Google exec forecast compute needs are $\sim 3x$ the Morgan Stanley projected compute CAGR for NVIDIA. In one year, weekly token usage has increased by $\sim 2,200\%$ . Average AI task complexity is increasing at a non-linear rate. - AI adoption broadening. Figure 2: Weekly token volume by model type. Stacked bar chart showing total token usage by model category over time. Dark red corresponds to proprietary models (Closed), orange represents Chinese open source models (Chinese OSS), and teal indicates open source models developed outside China (RoW OSS). The chart highlights a gradual increase in OSS token share through 2025, particularly among Chinese OSS models beginning in mid-year. # AI Adoption: Value Creation TAM as a % of 2026e Adjusted Pre-Tax Income The magnitude of AI adoption benefit "TAM" is nearly evenly split between Agentic (software-based) AI and Embodied (AI in the physical world, primarily robotics) AI. In total, this TAM is $>25\%$ of the S&P 500 2026e adjusted pre-tax income. AI Value Creation TAM & % of 26 Pretax Income Adj. Source: Morgan Stanley Research estimates # Non-Linear AI Improvement and “Crossing the Human Threshold” OpenAI recently released an analysis of the percentage of tasks where AI tools perform the task better than humans: - Anthropic's Claude Opus 4.1 performed the best, with superior performance relative to humans on $48\%$ of human tasks. - We would expect these results to escalate rapidly in 2026, following a $\sim 10\mathrm{x}$ increase in computational power used to train the frontier LLMs for the big 5 US players. GDPval win rate: performance on economically valuable tasks Source: OpenAI, Morgan Stanley Research # Our “Intelligence Factory” Model: Key Takeaways We have developed an in-depth analysis of the economics of LLM usage, built from the ground up and incorporating all data center costs associated with generating AI solutions for customers. Key takeaways from our "Intelligence Factory" model: (A) the ROIC for the "neoclouds" is likely to be attractive; (B) the margins for LLM developers that sell access to their models are attractive (at $\sim 60\%$ ); (C) given the rapid pace of chip technology improvement, we project especially rapid declines in the cost of intelligence, with average token prices potentially falling by $>70\%$ as we shift from the Blackwell to the Rubin GPU; and (D) we are able to flex the assumptions to understand the regional impacts of important cost differences, such as power costs and the cost of data center construction. An example of an Intelligence Factory: a $\sim 250$ megawatt data center using Blackwell GPUs, with power costs of $100/megawatt-hour, running GPT-4o queries, would in our model generate margins to a leading US LLM developer of $\sim 60 \%$ Where our model can be flexed and where the results are fascinating: power cost differentials globally are actually only a small driver of the cost of intelligence, while differences in active LLM parameters used for a query can have a big impact. # Andrew Pauker # US Equity Strategist # Fundamental Tailwinds Remain in Place for Large-Cap Tech Source: FactSet, Morgan Stanley Research # EPS Revisions Breadth Improving in Tech Subgroups, and High Capex-to-Sales Factor Continues to Outperform Earnings Revisions Breadth Technology Factor Performance: Capex-to-Sales (High vs. Low) Source: FactSet, Compustat, Morgan Stanley Research # Vishwas Patkar # Head of US Credit Strategy # We Expect IG Bond Issuance to Hit a New Record in 2026 (\(2.25trn) # AI capex financing, M&A, and rising corporate optimism are key drivers We forecast a sharp rise in credit issuance, especially IG Source: Dealoric, PitchBook | LCD, Morgan Stanley Research forecasts AI-related issuance a big part of the increase in IG supply <table><tr><td></td><td>2020</td><td>2021</td><td>2022</td><td>2023</td><td>2024</td><td>2025</td><td>2026e</td><td>‘26/’25 ($)</td><td>‘26/’25 (%)</td></tr><tr><td>Total Gross Issuance</td><td>1,945</td><td>1,610</td><td>1,277</td><td>1,283</td><td>1,615</td><td>1,810</td><td>2,250</td><td>+440</td><td>24%</td></tr><tr><td>Total Maturities</td><td>700</td><td>668</td><td>630</td><td>661</td><td>724</td><td>780</td><td>940</td><td>+160</td><td>21%</td></tr><tr><td>Total Calls & Tenders</td><td>362</td><td>417</td><td>274</td><td>176</td><td>238</td><td>393</td><td>310</td><td>(83)</td><td>(21%)</td></tr><tr><td>Total Net Issuance</td><td>883</td><td>525</td><td>373</td><td>446</td><td>652</td><td>637</td><td>1000</td><td>+363</td><td>57%</td></tr><tr><td>Total M&A</td><td>145</td><td>215</td><td>120</td><td>155</td><td>230</td><td>205</td><td>246</td><td>+41</td><td>20%</td></tr></table> <table><tr><td colspan="10">Non-Financial</td></tr><tr><td>Gross Issuance</td><td>1335</td><td>903</td><td>636</td><td>740</td><td>927</td><td>1090</td><td>1510</td><td>+420</td><td>39%</td></tr><tr><td>Maturities</td><td>350</td><td>334</td><td>342</td><td>378</td><td>389</td><td>442</td><td>585</td><td>+143</td><td>32%</td></tr><tr><td>Calls / Tenders</td><td>264</td><td>319</td><td>162</td><td>66</td><td>78</td><td>217</td><td>175</td><td>(42)</td><td>(19%)</td></tr><tr><td>Net (Maturities, Calls, Tenders)</td><td>721</td><td>250</td><td>131</td><td>296</td><td>457</td><td>431</td><td>750</td><td>+319</td><td>74%</td></tr><tr><td>Hyperscaler + AI Adjacent issuer</td><td>86</td><td>83</td><td>59</td><td>36</td><td>44</td><td>165</td><td>400</td><td>+235</td><td>142%</td></tr><tr><td colspan="10">Financial</td></tr><tr><td>Gross Issuance</td><td>610</td><td>707</td><td>642</td><td>543</td><td>692</td><td>720</td><td>740</td><td>+20</td><td>3%</td></tr><tr><td>Maturities</td><td>350</td><td>335</td><td>288</td><td>283</td><td>335</td><td>338</td><td>355</td><td>+17</td><td>5%</td></tr><tr><td>Calls / Tenders</td><td>98</td><td>98</td><td>112</td><td>110</td><td>161</td><td>176</td><td>135</td><td>(41)</td><td>(23%)</td></tr><tr><td>Net (Maturities, Calls, Tenders)</td><td>162</td><td>275</td><td>242</td><td>150</td><td>195</td><td>206</td><td>250</td><td>+44</td><td>21%</td></tr></table> # We See Modestly Wider Spreads in IG by Year-End on Accelerating Issuance Strong demand, good macro data, and the MBS rally have supported tight spreads for now We think the playbook is similar to 1997/98 or 2005; credit underperforms, but not "end of cycle" <table><tr><td></td><td>Capex YoY</td><td>Capex Ex-Energy YoY</td><td>M&A YoY</td><td>M&A (% of GDP)</td><td>US 10yr (YE)</td><td>Core PCE (YE)</td><td>US U-3 Unemplly.</td><td>S&P Return (%)</td><td>Yearly Spread Change (bp)</td><td>IG Excess Return (%)</td></tr><tr><td>1997</td><td>7%</td><td>5%</td><td>40%</td><td>24%</td><td>5.7</td><td>1.9</td><td>4.9</td><td>33.3</td><td>13.3</td><td>-0.3</td></tr><tr><td>1998</td><td>9%</td><td>10%</td><td>38%</td><td>32%</td><td>4.6</td><td>1.5</td><td>4.5</td><td>28.6</td><td>49.9</td><td>-2.4</td></tr><tr><td>2005</td><td>14%</td><td>9%</td><td>31%</td><td>29%</td><td>4.1</td><td>2.3</td><td>5.1</td><td>4.9</td><td>16.3</td><td>-1.2</td></tr><tr><td>Avg. ('97-98, '05)</td><td>11%</td><td>8%</td><td>35%</td><td>29%</td><td>4.6</td><td>2.0</td><td>4.9</td><td>22.3</td><td>26.5</td><td>-1.3</td></tr><tr><td>2026e</td><td>9%</td><td>10%</td><td>20%</td><td>21%</td><td>4.1</td><td>2.6</td><td>4.6</td><td>12.3</td><td>19.0</td><td>-0.7</td></tr><tr><td>Diff</td><td>-1%</td><td>2%</td><td>-15%</td><td>-7%</td><td>-0.6</td><td>0.6</td><td>-0.3</td><td>-10.0</td><td>-7.5</td><td>0.6</td></tr></table> Source: Bloomberg, Dealogic, Morgan Stanley Research forecasts; Note: As of December 2025. Capex is for the Russell 3000, M&A is global announced M&A. For 2026, we use Morgan Stanley forecasts for announced M&A and technology capex. # Lindsay Tyler # TMT Credit Research Analyst # For ORCL, GPUaaS Presents Sizable Revenue Opportunity and Sizable Funding Needs ORCL is positioning itself to benefit from compute demand, reflected by its RPO and revenue targets Entering this investment cycle, ORCL is mid-BBB across ratings agencies, with two on negative outlook Source: Company filings, Moody's, S&P, Fitch, Morgan Stanley Research; Note: GPUaaS stands for GPU as a Service. # ORCL's CY26 ~\(50bn Funding Plan Is a Positive Step but in a Multi-Year Effort Peak forecast leverage and debt remain elevated (including leases) even with benefit of $25bn new equity With equity colleagues, we estimated $>$ 150bn needs over 2.5 years, implying \~\$100bn for co. to address Source: Company filings, Bloomberg, Moody's, Morgan Stanley Equity & Credit Research estimates; Note: ORCL's fiscal year-end is in May, with FY26 ending May 2026. # Cautious View on ORCL Credit Given Cash Needs, Lease Growth, Counterparty Risk We recommend buying 5Y CDS protection and contend it could approach 200bp We recommend selling select bonds; risks to trade include views on cost/GW, monetization, financing Source: Bloomberg, Morgan Stanley Research # Vishwanath Tirupattur # Chief Fixed Income Strategist # Morgan Stanley Research AI Capex Estimates Revised Higher Hyperscaler cash capex estimated at $740bn+ in 2026, and over$ 900bn in 2027 Source: Bloomberg BEST, Morgan Stanley Research estimates # Key Takeaways Takeaways from large-cap 4Q earnings: The leading scaled companies with the most data, reach, and ability/willingness to invest are seeing the benefits of their flywheels, and the gap between them and the smaller players across the tech space is likely to widen faster than expected even 35 days ago. Based on what we heard from the large-cap tech companies, we have revised our AI-related capex estimates higher for 2026 and 2027. - AI capex and monetization: We continue to believe that the demand for compute is likely to exceed the supply significantly. Average AI task complexity is increasing at a non-linear rate and AI adoption broadening. ROIC for the "neoclouds" is attractive, as are margins for LLM developers that sell access to their models (at $\sim 60\%$ ). Given the rapid pace of chip technology improvement, we project especially rapid declines in the cost of intelligence, with average token prices potentially falling by $>70\%$ as we shift from the Blackwell to the Rubin GPU. Equity strategy: Fundamental tailwinds remain in place for large-cap technology companies and EPS revisions breadth is improving in technology subgroups. Despite questions around whether the market is beginning to enforce capex discipline in a more structural way, we show that the high capex/sales factor continues to outperform in both tech and the overall market. - Credit: We expect IG bond issuance to hit a new record in 2026 (\(2.25trn), driven by Al capex financing, M&A, and rising corporate optimism. We see modestly wider spreads in IG by year-end on accelerating issuance. We think that the playbook is similar to 1997/98 or 2005; credit underperforms, but not "end of cycle". We have a cautious view on ORCL credit given cash needs, lease growth, and counterparty risk. # Appendix: ORCL Credit Trades ORCL Credit Trade Valuation Methodology and Risks - Lindsay Tyler <table><tr><td>Trade</td><td>Entry Level</td><td>Entry Date</td><td>Current Level</td><td>Rationale</td><td>Risks</td></tr><tr><td>Buy ORCLCP 5Y CDS</td><td>+57bp</td><td>9/28/25</td><td>+156bp</td><td>Credit profile deterioration, driven by cash funding needs and increasing lease liabilities related to new data centers, may warrant Moody's downgrade & bondholder CDS hedgingLender hedging may be a key driver of CDS tooCDS could approach 200bp when looking at other large structure situations where there has been ratings risk</td><td>• Constructive updates on RPO/growth and financing clarity in the coming months• TL As may not ultimately serve a significant role in ORCL funding• Basis sellingHedge unwinds upon construction loan syndication• Profit taking in outright shorts via CDS by fast money</td></tr><tr><td>Sell ORCL 5.2 9/26/35Sell ORCL 5.95 9/26/55</td><td>+167bp+208bp</td><td>1/23/26</td><td>+156bp+200bp</td><td>Credit profile deterioration, driven by cash funding needs and increasing lease liabilities related to new data centersView ~200bp and ~250bp as possible spread widening targets considering large media/cable structure comps</td><td>• Additional visibility on long-term monetization and delevering beyond FY28-end• Issuance cadence may be different than our assumptions</td></tr></table> Source: Bloomberg, company filings, Morgan Stanley Research # Disclosure Section # Mortgage Backed Securities (MBS) and Collateralized Mortgage Obligations (CMO) Principal is returned on a monthly basis over the life of the security. 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