The 2026 software selloff has partially reversed. The diligence questions it raised have not.
Advisory firms evaluating a venture or growth equity manager should ask how the manager measures AI exposure company by company: where each company's value comes from, how much revenue is priced per seat, which customers it serves, whether AI is funding growth or only cost cuts, how positions are marked, and whether the manager's own AI claims can be substantiated.
Key takeaways
The S&P 500 software and services index lost about $830 billion in market value between January 28 and February 4, 2026, according to Reuters, after new AI agent tools raised questions about which software businesses could be displaced.
Public software valuations have recovered only partially: Software Equity Group reports that the median EV/TTM revenue multiple of its SEG SaaS Index fell from 5.7x in 2Q25 to 3.2x in 2Q26.
Operating results have not kept pace with the market narrative: McKinsey's State of AI survey, published August 2026, found that 37% of respondents attribute any EBIT impact to AI, essentially unchanged from the prior year.
No industry-standard questionnaire addresses portfolio-level AI exposure; ILPA last updated its Due Diligence Questionnaire in 2021.
AI claims are an examination focus: the SEC Division of Examinations' FY2026 priorities state that staff will review registrants' representations about AI capabilities for accuracy.
Why are advisory firms asking managers about AI exposure now?
Public software valuations were repriced quickly in early 2026. The S&P 500 software and services index lost about $830 billion in market value in the week ending February 4, 2026, according to Reuters, after the release of new AI agent tools. The market was not marking down software as a category so much as asking, company by company, which product an AI agent could replace.
Public software multiples have recovered only part of that decline. Software Equity Group reports that the median EV/TTM revenue multiple of its SEG SaaS Index was 3.2x in 2Q26, compared with 5.7x in 2Q25.
Private markets have followed a different pattern. The PitchBook-NVCA Venture Monitor Q4 2025 First Look, published in January 2026, reported that AI companies received more than half of the $512.6 billion in global venture deal value in 2025. Carta reported a down-round rate of 11.4% in Q1 2026, comparable to 2019 and 2020. Public software multiples reset sharply in 2026, while private round pricing largely did not, and that gap is where manager judgment and disclosure matter.
Corporate operating data has not yet confirmed the scale of the market move. McKinsey's State of AI in 2026 survey, published August 25, 2026, found that 37% of respondents attributed any EBIT impact to AI, essentially flat year over year. Markets repriced AI risk faster than company results have confirmed it, which makes AI exposure a diligence question rather than a settled verdict.
3.2x
SEG SaaS Index median EV/TTM revenue, 2Q26 (5.7x in 2Q25)
Software Equity Group, 2Q26
>50%
Share of $512.6B in 2025 global VC deal value that went to AI companies
PitchBook-NVCA, January 2026
37%
Survey respondents attributing any EBIT impact to AI
McKinsey, August 2026
What should advisory firms ask a venture or growth equity manager about AI exposure?
Allocators do not yet have a standard instrument for assessing portfolio-level AI exposure. ILPA last updated its Due Diligence Questionnaire in 2021. AIMA reported in September 2025 that 29% of surveyed institutional investors already included generative AI questions in their manager questionnaires, but those questions focus on a manager's own use of AI, covering oversight, data privacy, and intellectual property, rather than on how exposed the underlying portfolio companies are. The six questions below are designed to address that gap.
Six questions for a venture or growth equity manager
Where does each portfolio company's value come from, and would that source of value hold up against a capable AI alternative? A credible answer names the companies whose value rests on durable sources, such as proprietary data or trusted customer relationships, and the companies whose value does not.
What share of portfolio revenue is priced per seat, and how is that share changing at renewal? A credible answer quantifies the share of portfolio ARR priced per seat and the trend in seat counts at renewal.
How does each company's customer mix affect the timing of AI-driven pressure on retention? A credible answer segments retention by customer segment and cohort and identifies which material contracts renew in the next 12 to 24 months.
Are portfolio companies using AI to expand what they deliver to customers, or primarily to reduce headcount? A credible answer gives specific examples of capacity redeployed to serve customers, supported by operating metrics.
How are software positions marked relative to the 2026 reset in public software multiples? A credible answer presents the public comparable set and its date, and explains any software mark that has not moved since 2025.
Which AI-related statements in the manager's own materials could it substantiate for an SEC examiner? A credible answer is documentation: the analysis behind each AI-related statement, available on request.
What does a strong set of manager answers look like?
No manager will have complete answers to all six questions, because the evidence on AI's effect on software economics is still forming. What distinguishes a credible response is specificity at the company level, a willingness to name the positions that are exposed, and consistency among the manager's AI narrative, its pricing data, and its marks. A manager who describes every holding as an AI beneficiary is offering a narrative rather than an analysis.
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Is there a standard due diligence questionnaire for AI exposure?+
No standard questionnaire currently addresses portfolio-level AI exposure. ILPA last updated its Due Diligence Questionnaire in 2021. AIMA reported in September 2025 that 29% of surveyed institutional investors include generative AI questions in manager questionnaires, but those questions focus on a manager's own use of AI rather than the exposure of its portfolio companies.
Why is per-seat pricing a central AI risk in a software portfolio?+
Per-seat pricing ties revenue to customer headcount. If AI enables customers to run a function with fewer people, vendors billing per user can lose revenue even when the product remains in use. A Bain & Company analysis of more than 30 incumbent SaaS vendors found that none had fully moved away from seat-based pricing as of October 2025.
Does AI exposure mean a venture portfolio should avoid software?+
AI exposure, on its own, does not argue against software. The evidence points to dispersion within software rather than a sector-wide verdict. Companies whose value rests on proprietary data, trusted customer relationships, network effects, or high-stakes judgment face different risks than narrow, seat-priced workflow tools. The relevant question is how a manager distinguishes between the two.
What distinguishes a credible manager answer on AI exposure?+
A credible answer is specific at the company level. The manager names which positions are exposed and which are not, quantifies revenue priced per seat, presents the public comparables and dates behind its marks, and documents the analysis behind each AI-related statement in its materials. A portfolio described entirely as AI beneficiaries warrants follow-up.
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