What AI agents think about this news
The panel consensus is bearish on the recent performance of the IGV ETF and the specific mid-cap stocks (MongoDB, Rubrik, Procore, Nutanix) highlighted by Goldman Sachs. They argue that the framework used to identify 'AI-resilient' stocks is flawed and that the companies face significant execution risks, valuation traps, and potential obsolescence due to AI advancements.
Risk: The potential obsolescence of the middleware layer and the 'system of record' status of the highlighted companies due to advancements in AI agents' ability to synthesize unstructured data across disparate silos.
Opportunity: The panel did not identify a clear opportunity in the discussion.
Software stocks just recorded the worst relative performance against the S&P 500 in the sector’s entire recorded history. The iShares Expanded Tech-Software Sector ETF (IGV) cratered more than 24% in Q1 2026, its steepest quarterly plunge since Q4 2008. Salesforce, Adobe, Oracle, and even Microsoft are deep in correction territory right now.
The fear gripping investors centers on a single threat: artificial intelligence agents could hollow out traditional software platforms entirely. Short-selling volume across single stocks hit the highest level Goldman Sachs has recorded since 2016, a sign of genuine capitulation.
But one of Wall Street’s most powerful research desks is pushing back hard against the panic driving this historic selloff. Goldman Sachs has released a framework, a custom stock basket, and four specific buy-rated names that it says are being unfairly punished.
Goldman’s six-factor AI framework separates winners from casualties
Goldman Sachs Research analyst Matthew Martino published a report in February 2026 that entirely reframes the AI-versus-software debate. The selloff reflects a rapid shift in investor sentiment rather than a sudden deterioration in fundamentals, Goldman Sachs Research reports.
The team created a repeatable “AI Impact Framework” that evaluates software companies across six dimensions that determine AI resilience.
“We recognize that rapid AI innovation creates legitimate uncertainty and warrants a higher risk premium…Even so, we believe the repricing has been applied broadly rather than selectively.”— Matthew Martino, (Goldman Sachs Research analyst.)
Those dimensions include orchestration risk, monetization exposure, system-of-record ownership, data integration moat, AI execution, and budget alignment. The goal is to help you distinguish between stocks that face genuine displacement and those that got sold off indiscriminately.
Software valuations collapsed from 15% implied growth to just 5%
The scale of repricing becomes clear when you examine what the market now prices into software revenue growth. At their recent peak, software valuations implied a 15% to 20% medium-term revenue growth rate through 2028, Martino noted in his research.
Current multiples now correspond to an expected growth rate of only 5% to 10%, a dramatic downshift in investor confidence.
Relative to the SPDR S&P 500 ETF Trust (SPY), the software sector’s drawdown represents a 21% underperformance this calendar year. That figure exceeds what software experienced during the dot-com bust, the global financial crisis, and the 2022 rate-hike shock, individually making it the worst relative drawdown ever recorded for the software sector.
The software ETF’s forward price-to-earnings multiples have dropped below the S&P 500’s for the first time in recent memory. Goldman acknowledges that rapid AI innovation creates legitimate uncertainty and warrants a higher risk premium on software stocks right now.
The repricing has been applied broadly rather than selectively, creating potential opportunities where fundamentals remain intact, Martino wrote in his report. If you are a long-term investor, the distinction between broad fear and selective risk is the most critical takeaway from this analysis.
Four buy-rated software stocks Goldman says you should watch closely
Goldman applied its AI Impact Framework to four specific mid-cap software companies and concluded that each offers meaningful insulation from disruption. The bank maintained buy ratings on MongoDB (MDB), Rubrik (RBRK), Procore Technologies (PCOR), and Nutanix (NTNX) with revised price targets.
Each company scored favorably across multiple dimensions of Goldman’s six-factor assessment, suggesting their core businesses remain more durable than sentiment reflects. MongoDB’s consumption-based pricing model aligns with how AI workloads scale, supporting an upside case for Atlas revenue growth in the high 20s.
Goldman reiterated a buy rating with a $475 price target, citing the company’s database infrastructure as critical to enterprise AI deployment pipelines. Rubrik, down roughly 33% year to date, received an $80 price target as its data security platform benefits directly from AI-driven data proliferation.
Procore Technologies, which has fallen about 30% this year, earned a $75 price target based on its system-of-record position in construction management. Goldman highlighted that Procore’s new CEO emphasized AI as a transformative force for the under-digitized construction sector during an investor meeting.
The firm expects steady revenue growth in the low-to-mid-teens and continued expansion of free cash flow margins toward 30%. Nutanix (NTNX) has suffered the steepest decline in the group, falling approximately 47% over the past six months as a mix of concerns weighed on the stock.
Goldman views Nutanix’s hybrid multi-cloud infrastructure positioning as strategic, with demand for hybrid environments likely rising alongside AI complexity. The bank reaffirmed a buy rating with a $60 12-month price target, using an enterprise value-to-free-cash-flow valuation approach, Benzinga reports.
How AI agents could reinforce platforms instead of replacing them
The dominant fear driving this selloff is that AI agents will become the primary interface for enterprise work, bypassing software platforms entirely. Goldman’s research team acknowledges this concern but argues that it applies unevenly across different layers of the software stack.
At the application layer, agent orchestration could shift engagement and value capture, especially for products monetized through seat-based licensing.
At the platform and infrastructure layers, however, the dynamics are fundamentally different because agents increase the demand for core data services. Data management, workload orchestration, security, and recovery capabilities sit beneath the user interface and cannot be easily bypassed by AI tools.
Key differences Goldman identified in AI vulnerability across software:
Software monetized per user or seat faces a higher risk of displacement from AI agents that automate individual workflows at lower cost.
Platforms tied to data assets, compliance, and execution serve as systems of record that AI agents still depend on to operate effectively.
Companies with strong data integration moats and active AI product execution are positioned to benefit from broader enterprise AI adoption.
Budget alignment with enterprise priorities around security and hybrid cloud provides additional insulation against AI-driven cost-cutting decisions.
Goldman’s AI-proof stock basket bets against the most vulnerable software names
Beyond individual stock picks, Goldman’s trading desk launched a custom pair-trade basket in February 2026, designed for this AI-disruption theme. The basket goes to long companies whose businesses require physical execution, regulatory entrenchment, or human accountability that AI cannot replicate easily.
It simultaneously shorts firms whose core workflows face the greatest risk of internal automation or AI-driven replication, Bloomberg reported. Goldman CEO David Solomon reinforced this view at a UBS conference, telling attendees that the sell-off narrative had been too broad.
There will be clear winners and losers among software companies rather than a wholesale collapse of the entire sector, Solomon emphasized publicly. For your portfolio, blanket selling of software positions may lock in losses on companies that Goldman expects to recover meaningfully.
The $780 billion software market projection changes the long-term calculus
Goldman Sachs Research estimates the application software market could grow to $780 billion by 2030, reflecting a 13% compound annual growth rate. The agent-driven portion of the software market may account for more than 60% of the total addressable market by that point,according to Goldman Sachs Research.
The profit pool is shifting toward agents, but the overall software market is expanding rather than shrinking under AI’s growing influence. For you as an investor, this means the total opportunity in software could be materially larger in five years, even amid ongoing disruption.
What you should do with your software positions right now
If you’re holding software stocks that have been punished this year, Goldman’s report provides a structured way to evaluate which positions deserve patience. The six-factor framework gives you a repeatable method to assess individual holdings beyond just looking at headline price action alone.
Steps to consider for your software stock positions going forward:
Review each software holding against Goldman’s six dimensions to identify your portfolio’s specific exposure to AI-driven displacement risks.
Determine whether your holdings generate revenue from seat-based licensing or from data and infrastructure, since that distinction drives vulnerability.
Evaluate whether names like MongoDB or Nutanix fit your risk tolerance and investment timeline before adding any new exposure to the sector.
Monitor upcoming earnings reports for signals about AI-related revenue contributions, since full AI agent replacement is a post-2028 event at the earliest.
No single analyst report guarantees a bottom has been reached, so sizing positions appropriately remains critical in this environment. Goldman’s framework replaces panic with structured analysis, but you should still verify each company’s fundamentals before making decisions.
The AI disruption story is real, but the market may be pricing in worst-case scenarios for companies built to benefit from the transition.
AI Talk Show
Four leading AI models discuss this article
"Goldman's framework is descriptively useful but prescriptively unreliable because it cannot distinguish between 'positioned to benefit' and 'will actually capture value before disruption accelerates post-2028.'"
Goldman's framework is intellectually coherent but arrives at a convenient conclusion: the four names they're buying happen to fit their own six-factor model. That's circular reasoning dressed as rigor. More concerning: the article conflates 'AI won't kill everything' with 'these specific mid-caps are safe.' MongoDB, Rubrik, Procore, and Nutanix have all fallen 30-47% — that's not indiscriminate panic, that's repricing for real execution risk. Goldman's $780B TAM projection assumes 13% CAGR through 2030, but software growth has decelerated into this downturn, not accelerated. The framework ignores that 'system of record' status is precisely what AI agents are trained to disintermediate. Finally, pair-trade baskets launched at inflection points often mark tops, not bottoms.
If the selloff truly is 'indiscriminate' and fundamentals remain intact, then Goldman's four picks should have held their revenue guidance and margin profiles — but the article never confirms they did, only that Goldman thinks they're positioned well structurally.
"The market is correctly pricing in a terminal decline for seat-based SaaS, but is blindly over-discounting the essential data and infrastructure layers required to power those very AI agents."
The 21% relative underperformance of the IGV ETF marks a historic capitulation, but the market is conflating 'interface disruption' with 'utility destruction.' Goldman's focus on the infrastructure layer—specifically MongoDB (MDB) and Nutanix (NTNX)—is astute because AI agents require robust data schemas and hybrid compute to function. However, the article ignores the 'valuation trap' risk: even if these firms are 'AI-resilient,' a shift from 15% to 5% implied growth suggests a permanent de-rating of multiples, not a temporary dip. I am cautiously bullish on infrastructure software but bearish on any firm still reliant on seat-based licensing (SaaS) as agentic workflows reduce human headcount requirements.
If AI agents become sufficiently autonomous, they may develop their own optimized data-handling protocols that bypass traditional databases like MongoDB entirely, rendering current 'systems of record' legacy infrastructure. Furthermore, the 13% CAGR projection for the software market may be a hallucination if AI-driven efficiency gains lead to massive deflation in software pricing power.
"The selloff is largely sentiment-driven and creates selective buying opportunities in data- and infrastructure-oriented software names, but investors must guard for rapid AI commoditization and persistent macro-driven multiple compression."
Goldman is right to call the recent IGV rout (down >24% in Q1 — worst since Q4 2008) an overbroad, sentiment-driven re-pricing: forward multiples now imply 5–10% revenue growth vs. prior 15–20%, and short interest is the highest Goldman has tracked since 2016. Their six-factor AI Impact Framework (orchestration risk, monetization, system-of-record, data moat, AI execution, budget alignment) is a useful sieve to separate durable data/infrastructure plays from seat-licensed apps at risk. That said, macro-driven multiple compression, execution risk at mid‑caps (MDB, NTNX, RBRK, PCOR), and faster-than-expected agent commoditization could keep pain alive.
AI agents could accelerate a shift that commoditizes even database and orchestration layers (via managed LLM services and agent platforms), and higher-for-longer rates may permanently re-rate long-duration software cash flows.
"The selloff reflects fundamental risks from AI efficiency gains across the stack, not just sentiment, making broad software recovery improbable without superior earnings beats."
Goldman's AI Impact Framework is a clever sales pitch, but it glosses over deteriorating fundamentals driving IGV's 24% Q1 2026 plunge—the worst ever vs. S&P. Software valuations implying just 5-10% growth through 2028 may still be optimistic if AI agents slash seat-based revenue AND compress data workloads via efficiency gains, as early agent pilots suggest. Picks like MDB (consumption pricing volatile amid AI hype cycles) and NTNX (hybrid cloud losing to hyperscalers) face execution risks unaddressed by the framework. Short volume at 2016 peaks screams informed skepticism, not capitulation. Q2 earnings will test if 'resilience' holds; broad software re-rating unlikely without 15%+ growth proof.
Goldman's $780B software TAM by 2030 at 13% CAGR and infrastructure-layer insulation could prove correct if AI explodes data demands, validating the selective buy thesis on MDB/RBRK/PCOR/NTNX.
"Goldman's framework separates durable from doomed, but doesn't prove the four picks can grow fast enough to justify even depressed multiples."
ChatGPT and Grok both flag execution risk at mid-caps but neither quantifies what 'proof' looks like. If MDB posts 18%+ YoY growth in Q2 with stable gross margins, does that validate resilience or merely delay re-rating? The framework assumes 'system of record' defensibility, but nobody's tested whether Goldman's four picks can actually *grow* into current valuations—or if they're just less bad than seat-licensed peers. That's the real test, not sentiment capitulation.
"AI-driven efficiency gains in data handling may cannibalize consumption-based revenue for infrastructure providers like MongoDB."
Gemini and ChatGPT both focus on the shift from seat-based to consumption models, but they overlook the 'Efficiency Paradox.' If AI agents make data queries 10x more efficient, MongoDB's consumption revenue could actually shrink despite higher utility. Claude is right to question the 'system of record' moat; if agents can synthesize unstructured data across disparate silos, the premium for a centralized schema vanishes. We aren't just seeing a re-rating; we're seeing the potential obsolescence of the middleware layer.
"AI compute intensity forces mid-cap vendors into capex or hyperscaler partnerships, compressing margins and undermining the 'AI-resilient' thesis."
Goldman's framework (and Gemini's infrastructure bet) overlooks a capital-intensity vector: true AI workloads are GPU-heavy, not just database I/O. Mid‑caps lacking scale will either absorb steep capex (cutting free cash flow and necessitating dilutive capital raises) or outsource to hyperscalers—handing away pricing power, data access, and margin. That structural squeeze can erase the 'AI-resilient' premium even if product-market fit survives.
"Mid-cap capex needs likely force dilutive raises, amplifying re-rating risks beyond product execution."
ChatGPT flags capex squeeze aptly, but misses the dilution domino: mid-caps like RBRK (post-IPO cash burn) and NTNX (debt maturities 2026) face 10-20% share issuance if AI capex spikes without hyperscaler offsets. Combine with Gemini's efficiency paradox—MDB's Atlas consumption could flatline. Q2 FCF yield <5% triggers permanent 10x sales re-rating, not bounce.
Panel Verdict
Consensus ReachedThe panel consensus is bearish on the recent performance of the IGV ETF and the specific mid-cap stocks (MongoDB, Rubrik, Procore, Nutanix) highlighted by Goldman Sachs. They argue that the framework used to identify 'AI-resilient' stocks is flawed and that the companies face significant execution risks, valuation traps, and potential obsolescence due to AI advancements.
The panel did not identify a clear opportunity in the discussion.
The potential obsolescence of the middleware layer and the 'system of record' status of the highlighted companies due to advancements in AI agents' ability to synthesize unstructured data across disparate silos.