Goldman's AI Disruption Thesis: A Sector Rotation Signal for Institutional Capital

The selloff in software stocks is a rational response to a genuine threat. The core institutional concern is that AI agents could automate complex workflows, shifting the value chain away from traditional platforms. As Goldman Sachs analysts note, investors fear that if AI agents become the primary interface for executing work, established software platforms risk being relegated to passive data stores. This would erode their pricing power and strategic relevance, directly threatening the growth and profitability that have driven valuations for years.

Historical precedent underscores the severity of this risk. When an industry's earnings stability is threatened by disruption, the market can impose punishing, multi-year declines. The newspaper industry's experience provides a stark example. Between 2002 and 2009, as the internet grew, the share prices of newspaper stocks declined by an average of 95%. This decline only ended once earnings estimates bottomed, a pattern that suggests the current software rout could be prolonged if uncertainty persists.

The primary risk today is therefore one of prolonged uncertainty. This makes near-term earnings a critical signal of business resilience. While AI innovation introduces a higher risk premium, the market's broad application of disruption fears appears to have created a mispricing. The selloff reflects a rapid shift in sentiment rather than a sudden deterioration in fundamentals for all companies in the sector. For institutional capital, the opportunity lies in separating the signal from the noise, using a framework to identify which businesses are truly insulated from or even benefit from the AI transition.

The Macro Backdrop: AI-Driven Unemployment and Market Stability

The systemic risk from AI disruption extends far beyond corporate earnings. Goldman Sachs economists have flagged a tangible threat to economic stability, warning that AI-driven job displacement could raise the unemployment rate to 4.5% by year-end. This projection, from economist Pierfrancesco Mei, comes with a clear caveat: upside risks from faster adoption and larger displacement could add up to an additional 0.3 percentage points to that figure. The warning is grounded in visible early effects, with job growth slowing and turning negative in subindustries where AI is most ready for deployment.

This sets the stage for a "Great AI Scare" scenario, a term now circulating in market commentary. The Citrini Research paper outlines a potential path where AI capabilities improve, leading to white-collar layoffs, reduced consumer spending, and firms investing more in AI to cut costs-a feedback loop that could double unemployment and slash the total value of the stock market by one-third by 2028. While that is a two-year horizon, the mere articulation of such a scenario amplifies volatility and forces a reassessment of risk across all asset classes.

For institutional capital, the danger is twofold. First, a sharp rise in unemployment would compress the risk premium across equity markets. As economic uncertainty grows, investors demand higher returns for bearing risk, but a "Great AI Scare" could trigger a flight to safety that destabilizes the entire market. Second, this scenario directly challenges the stability of the growth and quality factors that have dominated recent performance. If AI accelerates structural job losses, the very economic expansion that supports premium valuations for high-quality companies could stall, forcing a broad-based rotation away from growth stocks and into more defensive or cyclical sectors. The macro backdrop is no longer a theoretical concern; it is a material tailwind for volatility and a potential catalyst for a sector rotation that would upend portfolio construction.

Sector Rotation: From Software to Physical Enablers

The institutional capital shift is now clear. As software valuations compress under disruption fears, money is flowing into the physical infrastructure that will power AI's rollout. This is a classic sector rotation, but with a structural twist: investors are turning to 'old economy' sectors with tangible assets, like data centers, which are foundational to AI adoption.

These industries-once dismissed as 'value traps' due to low returns and overcapacity-have been starved of capital spending since the Global Financial Crisis. Now, with AI driving a surge in demand for compute and power, they are back in the spotlight. The rotation reflects a deliberate search for more tangible, less discretionary revenue streams that are less exposed to the workflow automation risks facing software. While hyperscalers race to build AI capacity, the supporting physical network is where the next wave of investment is concentrated.

This isn't a fleeting trend. The shift began in earnest last year, broadening the sources of market returns beyond tech. As Peter Oppenheimer, chief global equity strategist, noted, value stocks began to stage a recovery, particularly outside the US, highlighting the renewed benefits of diversification. The institutional focus is on sectors that maintain the physical infrastructure AI depends on, from utilities to industrial firms. The logic is straightforward: if AI is a multi-year capital expenditure cycle, the beneficiaries are the companies building and operating the essential hardware and energy systems.

The bottom line for portfolio construction is a recalibration of risk and return. The rotation away from software is a bet on reduced uncertainty and more visible cash flows. It signals that for now, institutional capital is prioritizing quality of earnings and capital allocation discipline over pure growth narratives. The search for physical enablers is a conviction buy in the tangible, a hedge against the volatility of the digital frontier.

Credit Market Implications and Private Credit Resilience

The sector rotation is now testing the credit markets, where the fears of AI disruption are rippling through private credit. As a key lender to the technology sector, this corner of the alternatives market is facing a direct stress test. Investors are reassessing the repayment risks of their loans, particularly exposure to enterprise software, as the threat to earnings power becomes more tangible. This has triggered a broader reassessment of exposure, redemption risks, and fundraising prospects, highlighting how macro and sector risks are converging in the non-traded credit space.

Against this backdrop, Goldman Sachs Private Credit Corp has demonstrated notable resilience. The unit has maintained a fourth-quarter redemption rate of 3.5%, a figure that stands well below the more than 5% for peers. This divergence is a critical signal for institutional capital. It underscores that in a period of heightened uncertainty, credit quality and asset backing are paramount. Goldman's lower redemption rate, coupled with strong December inflows 11% above the year-to-date average, suggests investors are seeking out managers with disciplined underwriting and a clearer view of disruption risk.

This performance highlights a key takeaway for portfolio construction: the importance of a manager's framework for navigating structural change. Goldman noted it has been assessing AI impact for years, even passing on its first deal due to AI concerns in late 2023. Its internal framework, launched in early 2025, focuses on businesses with structural advantages and incumbency moats. In a market where sentiment is volatile and flows are shifting, this disciplined approach to credit selection provides a tangible buffer. For institutional capital, the lesson is clear: resilience in private credit is no longer just about yield, but about the quality of the underlying collateral and the manager's ability to navigate a disruptive cycle.

Catalysts and Risks: What to Watch for Portfolio Rebalancing

The sector rotation and macro narrative now hinge on a few key forward-looking signals. Institutional capital must monitor these to confirm the sustainability of the shift away from software and assess whether the 'Great AI Scare' scenario is gaining traction.

First, near-term earnings for software companies will be the primary test of business resilience. As Goldman Sachs notes, near-term earnings results will be important signals of business resilience, though they may not disprove long-term disruption risks. A pattern of sustained profit declines or guidance cuts would validate the market's fears and likely prolong the sector's underperformance. Conversely, evidence of pricing power or margin stability could begin to challenge the narrative, potentially halting the rotation.

Second, the sustainability of the physical infrastructure investment wave must be tracked through capital expenditure flows. The rotation into sectors like utilities and industrial firms is predicated on AI driving a multi-year capex cycle. Institutional investors should watch for continued, disciplined spending on data centers, power grids, and related infrastructure. If this flow wanes, the rotation could be seen as a temporary flight to safety rather than a structural reallocation of capital.

Third, credit market conditions will provide a critical stress test. The fears of AI eroding software earnings are already rippling through private credit, where concerns have been compounded by renewed troubles at Blue Owl and a broader reassessment of exposure. A material widening in credit spreads for tech-related loans would signal a deterioration in financing conditions, making it harder for software companies to fund operations or innovation. This would tighten financial conditions and could force a broader market repricing.

Finally, the macro stability narrative depends on monitoring unemployment data and market volatility. Goldman Sachs economist Pierfrancesco Mei projects the unemployment rate could drift higher to 4.5% by year-end, with upside risks. Any acceleration in job losses, particularly in white-collar sectors, would be a direct signal that the 'Great AI Scare' scenario is materializing. This would likely trigger a flight to safety, compress risk premiums across equity markets, and force a broad-based rotation away from growth and quality stocks into more defensive assets.

The bottom line is that portfolio rebalancing is not a one-time event. It requires active monitoring of these four catalysts to determine whether the current rotation is a tactical shift or the start of a multi-year reallocation, and whether the macro risks are contained or escalating.