🟢 [E4077] GS recommends geographical diversification as an attractive strategy for managing AI disruption risk. TMT exposure is typically lower in non-US equity markets and tends to be more hardware-focused, with such markets also offering more capital-heavy sector opportunities. Within European Financials, LSEG and CVC stand out as attractive opportunities.

🟢 [E4073] Mueller-Glissmann argues AI-driven rotation from capital-light to capital-heavy sectors may continue. TMT became much larger share of S&P 500 since GFC while capital-heavy sectors underperformed partly due to lack of inflation. If AI disruption concerns persist, investors may demand higher equity risk premium, potentially resulting in de-rating of equities vs bonds, with bonds potentially benefiting from AI-driven disinflation.

🟢 [E4062] Ben Snider argues investors should not expect a V-shaped rebound in AI-disrupted stocks. Historical precedent from newspaper stocks (down ~95% over 5 years in early 2000s) shows share prices stabilize only when earnings stabilize. Tobacco stocks in late 1990s declined 50%+ and troughed only as litigation settlement reduced uncertainty. Disproving disruption narrative is difficult when near-term earnings haven't even begun to weaken.

🟢 [E4088] GS expects more dispersion as AI produces more winners and losers, supporting strategies that buy individual stock and sell index options, and long/short hedge funds in equity and credit. Despite disruption concerns, underlying deployment outlooks are constructive, and for Opportunistic Credit managers, most see opportunity in the current backdrop.

🟢 [E4064] GS recommends selectivity rather than binary bets on software's survival or collapse. Borges is focused on fast followers and firms with defensible moats, recommending ServiceNow, Salesforce, HubSpot in applications; Snowflake in infrastructure; and Cloudflare, Palo Alto Networks, CrowdStrike in cybersecurity. Hotchkiss and Martino see value in Vertical Software, Data Infrastructure, and Physical-to-Digital firms with moats being key differentiator.

🟢 [E4063] Recent focus on AI disruption risk has triggered rotation toward 'real world' assets with perceived AI insulation. Asset-heavy stocks have sharply outperformed asset-light stocks. GS recommends balancing cyclical exposures with defensive equity positions as broader market remains vulnerable if growth proves less robust than expected or industry-specific AI fears spread to labor market disruption concerns.

🟢 [E4075] GS finds 10% rise in oil prices would be especially negative for Türkiye and oil importers across Asia, while oil exporters Brazil and Russia likely biggest beneficiaries. GS raised FY26 average core CPI inflation forecast for Japan to 2.1% (from 1.6%) reflecting higher energy prices. GS lowered EA growth forecast and raised EA inflation forecast reflecting higher energy prices amid US-Iran conflict.

🟢 [E4074] GS macro section notes oil exports via Strait of Hormuz have ground to near halt based on satellite data. GS base case scenario models 5-week Hormuz disruption. Oil disruptions related to US-Iran conflict pose downside risks to growth and upside risks to inflation. GS lowered 2026 growth forecasts for Saudi Arabia, Oman, Qatar, Bahrain, Kuwait, and UAE to reflect disruptions and falling non-oil economic activity.

🟢 [E4109] The report catalogues numerous enterprise AI tools emerging from multiple providers including Anthropic Claude Cowork, OpenAI Frontier, Microsoft Copilot, Google Gemini, and open-source OpenClaw. Poonen argues software companies may shift from price per user to price per outcome models, though tracking outcomes is challenging. Companies failing to defend pricing must expand and improve products or see revenue compression.

🟢 [E4058] Rick Sherlund describes a transformative platform shift requiring complete re-architecture of legacy systems. Over 10,000 venture-backed AI-native companies are poised to disrupt the ~$400bn global SaaS market. Traditional SaaS relies on hard-coded workflows and menu-driven interfaces, while AI-based systems feature conversational interfaces with LLM intelligence orchestrating autonomous agents—a fundamental change comparable to the shift to relational databases or cloud computing.

🟡 [E4078] The report presents divergent views on moat durability. Borges argues moats buy incumbents time and they can leverage domain experience for better AI outcomes. Sherlund is more skeptical—moats will buy time but 'aren't insurmountable—AI will very likely erode them over time.' Both agree incumbents must prove they can leverage moats to deliver outcomes, which 'hasn't happened yet in a broad enough way.'

🔴 [E4079] GS CRE Services analyst Julien Blouin argues AI disruption concerns for CRE services are 'significantly overstated.' The physical nature of CRE ecosystem provides meaningful insulation versus digitally-native industries. Historical precedents (listings platforms, virtual tours, automated valuation models) show CRE tech adoption unfolds over years/decades and didn't structurally disintermediate brokerages. Transaction volumes still 44% below 2021-2022 peaks.

🔴 [E4083] European transport, infrastructure, and construction sector faces relatively low AI disruption risk. Incumbents benefit from large physical networks, blue-collar workforces, physical gateways, warehouses, and network effects difficult for AI-native entrants to match. Asset-light logistics companies like DSV are positioned to benefit from AI integration into ongoing digitization, expecting mid-to-high-single-digit annual productivity gains from 2027 with operating margin rising from 10% to 12% by 2030.

🔴 [E4057] Gabriela Borges argues AI won't 'eat' software because AI is software—code designed to perform tasks. The total addressable market will grow enough to offset competition. The key question is whether incumbent software leaders can capture AI upside through domain experience and platform advantages, or whether AI-native entrants capture the new opportunity while incumbents retain only the shrinking 'system of record' function.

🟢 [E4084] Credit sector performance has become correlated with AI automation exposure. IG spreads show R²=0.15 correlation with AI automation exposure, HY spreads R²=0.22, and leveraged loan prices R²=0.16. Insurance, brokers/asset managers, and software show largest spread widening, while energy, metals/mining, and railroads show tightening. Higher dispersion dynamic will persist as AI capabilities continue improving.

🟢 [E4081] European private credit managers face greater AI disruption risk than traditional asset managers. Fund performance is more sensitive to dispersion and unlike buyout funds, private credit managers cannot offset losses with investments into 'winners.' Wealth managers and investment platforms experienced AI-related selloff after Altruist launched AI-powered tax planning tool, raising concerns about fee compression from automated advice tools.

🟢 [E4080] Insurance sector faces AI disruption with P&C personal lines and micro business brokers most vulnerable as competitors leverage AI to simplify lower complexity products. OpenAI incorporated insurance services directly into ChatGPT, sparking significant selloff. Auto insurers face multiple contraction from autonomous vehicles and could see revenue disruption from AI-based distribution platforms. Commercial (re)insurers are relatively safe from revenue disruption.

🟢 [E4061] Information Services sector has de-rated sharply since mid-2025 on AI disintermediation fears. Median NTM P/E in GS Info Services coverage fell from 32x in June 2025 to 20x currently. Companies most vulnerable are those with limited proprietary content, weak client workflow integration, and insufficient AI investment—examples include FDS, HRB, and RHI (white-collar temp staffing getting ~50% revenue from bookkeeping, admin support, and call centers).

🟢 [E4060] The 'AI scare trade' has spread beyond software to multiple sectors. Eric Sheridan identifies digital advertising and gaming as most exposed to AI disintermediation, with AI-powered tools enabling $170bn shift from traditional to digital channels, $114bn ad creative automation opportunity, $25bn ad tech intermediary consolidation, and $161bn agency disruption potential.

🟢 [E4059] Sanjay Poonen warns that legacy software companies must 'surf this tsunami, or it will demolish you.' The key risk is pricing pressure—companies unable to sustain current price per user must either expand product offerings or see revenue growth and terminal value compressed. At the core of every SaaS application is CRUD functionality that AI has potential to radically disrupt.

🟢 [E4056] The software re-rating reflects not just slower growth expectations but rising uncertainty about terminal value and business model durability. Ben Snider notes that markets are 'questioning software moats and business models' with a return to first principles questions about the industry's value proposition. This differs from 2022's rate-driven selloff as it concerns fundamental disruption risk.

🟢 [E4055] Goldman Sachs analysts argue that new agentic AI tools are triggering a sharp re-rating of the software sector. The iShares Expanded Tech-Software Sector ETF (IGV) has declined ~17% YTD and 26% from fall 2025 highs. Software sector NTM P/E has fallen from 35x in late 2025 to 22x currently—the lowest since 2014. The 10 largest IGV stocks have lost nearly $800bn in market cap since January 2026.

🟡 [E4085] The report presents Borges' argument that some recent price action has 'likely gone too far.' Two areas allow bullishness: 1) incumbents can leverage domain experience for better AI outcomes, and 2) potential for more companies to manage to GAAP earnings providing valuation support. The perception vs reality gap should narrow for majority of software firms, but stabilization requires 'the numbers to contradict the narrative' through clean beat-and-raise quarters.

🟢 [E4086] Vertical Software (Guidewire, Tyler Technologies, Veeva Systems) will likely remain core 'systems of record' for their industries because: (1) data and context embedded are unique to complex industry-specific networks and essential for AI; (2) AI gives opportunity to enhance connectivity between complicated workflows. Management teams that actively integrate AI are better positioned to limit value abstraction.

🟢 [E4065] Software accounts for 16% of the broadly syndicated loan (BSL) market versus only 5% of IG/HY bond indices. Nearly 40% of technology leveraged loan supply since 2021 has been tied to sponsor-backed M&A and LBO financing. Software-exposed loans have seen secondary prices fall to lowest levels since April 2025. HY software spreads have widened from tighter than index to roughly double index spreads.

🟢 [E4067] BDCs face meaningful software exposure with software loans representing ~22% of gross loans at industry level. Direct lending to software-oriented companies accounts for 4% of firmwide base management fees on average. BDC equities have traded at wider discounts to NAV while credit spreads have widened in correlation with software exposure (R²=0.23). Stress analysis suggests 5-15% mark-to-market impact would affect firmwide base fees in low single-digits.

🔴 [E4066] Shamshad Ali argues software loan impairment is unlikely to catalyze a turn in the credit default cycle. First, the macro backdrop remains benign—history suggests defaults coincide with rather than predict the broader cycle. Second, lower interest rates and improved funding conditions should provide incremental relief to floating-rate borrowers. Some impairment is 'hard to avoid' but not sufficient for systemic stress.

🔴 [E4068] Alexander Blostein and Michael Vinci argue Alternative Asset Managers' firmwide software exposure is manageable at ~7% of total AUM on average. Software PE investments average only ~6% of total management fees. Software loans are mostly senior secured and short duration with strong underlying EBITDA and cash flow. Probability of liquidity event or forced de-leveraging in direct lending is low due to moderate leverage and substantial dry powder.

💬 [E4111] Alternative Asset Managers stocks have declined ~27% YTD, bringing valuations below post-Liberation Day lows with NTM P/E (net of SBC) at ~15x. This reflects concerns about software exposure across private equity and private credit as well as fears that weaker investment performance could hamper growth. However, GS sees the selloff as overdone given firmwide software exposure averaging only ~7% of AUM.

🟢 [E4070] Microsoft is positioned as best in GS software coverage to benefit from compounding AI product cycles, beginning with AI compute leadership extending to Copilot and agent orchestration. Beneath Microsoft Copilot's surface is a highly nuanced graph showing how knowledge workers collaborate, built from years of observing enterprise workflows. Microsoft's vertical integration across platform, infrastructure, and application layers allows optimization of enterprise work.

💬 [E4076] GS European Financials analysts note market structure firms like LSEG and DB1 face varying AI exposure. LSEG's stock declined since mid-2025 on AI disruption fears, but immediate revenue risk appears limited—over half of earnings come from regulated capital raising, trading venues, or indices. ~70% of LSEG Workflows segment revenues are trading-related. LSEG is leader in real-time data which is difficult for AI to replicate.

🟢 [E4072] To manage AI innovation and inflation risks in portfolios, GS recommends rotating from capital-light to capital-heavy businesses — telecoms, industrials, and utilities that AI is unlikely to disrupt. After aggressive rotations YTD, GS favors being more selective. They see diversification benefits from defensive styles: low volatility stocks, dividend aristocrats, and infrastructure.

🟢 [E4071] GS identifies AI disruption as creating structural shifts in sector valuations. Asset-heavy vs asset-light indexed performance shows asset-heavy outperforming in risk-off periods. Defensive styles (low volatility, dividend aristocrats, infrastructure) materially outperformed on risk-adjusted basis during Tech Bubble burst. GS recommends geographical diversification as TMT exposure is lower in non-US markets with more hardware focus and capital-heavy opportunities.

🟢 [E4087] Data Infrastructure (Snowflake, MongoDB, Rubrik) will benefit from AI as a 'force multiplier.' As organizations deploy AI copilots and agents, they will create and query more data, demand faster response times, and tolerate less downtime. This raises the value of the foundational data layer that determines whether AI can run reliably and securely in production.

🟡 [E4069] Christian Mueller-Glissmann notes ongoing anxieties around hyperscalers' massive AI capex spend. The big spenders are becoming more capital-heavy, which might weigh on ROE in the medium term. However, tech sector's ROE has nearly doubled since the GFC, with TMT commanding largest weight within US equities comparable to Tech Bubble levels but with substantially greater earnings contribution than then.

🟢 [E4082] Microsoft is positioned as best positioned in GS coverage to benefit from compounding AI product cycles, beginning with leadership in AI compute and extending to Copilot and agent orchestration at application and platform layers. Microsoft's domain experience graph showing how enterprise knowledge workers collaborate represents a powerful moat from years of observing workplace behavior.