a16z’s second State of Markets presentation, released in September 2026 and covering the first half of the year, points to a software market that has not collapsed but is demanding proof of durable growth. In figures reported by SaaStr from the presentation, horizontal software had a 2.7x median enterprise-value-to-trailing-revenue multiple, B2B firms less than a year old reached roughly 500–600% year-over-year growth in Stripe data, and 55% of U.S. VC-backed tech unicorns were in runway bands below two years.
Those are segment and cohort snapshots, not promises about any company’s valuation, growth or survival. The official a16z summary supports the broad shift toward profitability and the “prove it” interpretation; the detailed figures below are attributed to SaaStr’s account of the charts and the data sources it names.
What do the public-software valuation figures show?
1. Horizontal software was valued below infrastructure in the reported comparison
SaaStr’s review attributes the following H1 2026 median EV/TTM revenue figures to JPMAM data shown in the presentation. EV/TTM revenue compares enterprise value with revenue over the preceding 12 months.
| Public-company segment | Median EV/TTM revenue |
|---|---|
| Horizontal software | 2.7x |
| Vertical software | 4.6x |
| Consumer, commerce and transactional platforms | 4.0x |
| Security and identity | 6.8x |
| Cloud, data and AI infrastructure | 9.1x |
The comparison suggests investors were assigning a lower median multiple to broad, cross-industry software applications than to infrastructure and security categories. A segment median is not a valuation forecast for an individual business: growth, margins, retention, concentration and other company-specific factors are not captured by that single figure.
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2. Faster-growing public software companies commanded higher forward multiples
In a separate public-software chart summarized by SaaStr, companies growing revenue 20–40% traded at roughly 9–13x forward revenue, while companies growing 10–20% traded at roughly 4–5x. These are approximate ranges from the review’s account, and the comparison is about growth cohorts—not a guaranteed multiple available to a company that reaches a particular growth rate.
How did growth and profitability compare across public software?
3. Profitability was common, but 20% growth was not
The official a16z summary and SaaStr’s review both describe about 75% of public software companies as profitable, while only about 30% grew at least 20%. The market therefore combined a large profitable-company share with a relatively small high-growth share. The figures describe public software companies in the presentation’s analysis, not startups generally.
4. The reported growth distribution puts the median near 12–13%
SaaStr describes the latest public B2B growth distribution as having stabilized around the following approximate percentiles:
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| Position in reported distribution | Year-over-year growth |
|---|---|
| 25th percentile | High single digits |
| Median | 12–13% |
| 75th percentile | 20–22% |
| 90th percentile | 29–30% |
Percentiles show how companies in the covered distribution compared with one another; they are not targets or forecasts. In particular, the median growth figure and the separate finding that about 30% grew at least 20% describe different cuts of the public-software data.
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5. The 500–600% growth figure applies to very young B2B firms
Stripe payment data, as described by SaaStr, showed B2B companies less than a year old reaching about 500–600% year-over-year growth by early 2026. That figure should not be applied to established companies: firms at least a year old were at about 19% growth around January 2026 and later recovered to about 24%. The age groups and dates matter; the very high number belongs to the under-one-year cohort, not to B2B businesses across the board.
6. More than half of the covered unicorns had under two years of runway
For U.S. VC-backed tech unicorns in 2026, SaaStr’s account of SVB data puts 26% in the zero-to-one-year runway band and 29% in the one-to-two-year band.
| Reported runway band | Share of covered unicorns |
|---|---|
| 0–1 year | 26% |
| 1–2 years | 29% |
| Combined under two years | 55% |
Runway is the time a company can continue operating before it needs additional financing or another source of cash, based on its financial position and spending. The reported bands indicate financing pressure, not that all companies in them will fail or that any particular company will raise successfully.
The same review says 42% of the unicorns grew 0–20% and 15% were shrinking. It also notes that the margin categories in the chart add up to only about 25% with positive margins, which does not support the slide label “Mostly Profitable.” That discrepancy is a reason to distinguish the chart’s listed figures from its headline wording.
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SaaStr says recently funded startups were growing about 60–70%, compared with 15–30% for startups at scale. This is a comparison between groups as characterized in the review, not evidence that raising capital itself caused faster growth. The reported faster growth also came with deeper losses among recently financed companies, so growth alone does not establish a stronger financial position.
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What do the venture-fund returns and AI metrics add?
8. Carta-derived returns for 2024-vintage funds were widely dispersed
SaaStr attributes the following net IRR distribution to Carta data on 2,773 venture funds with roughly $119 billion in committed capital, as of Q1 2026:
| 2024-vintage fund position | Net IRR reported |
|---|---|
| 90th percentile | 40.5% |
| Median | -3.3% |
| 25th percentile | -14.6% |
These are population-level figures for the specified fund vintage and dataset, not a forecast for a particular fund or a measure of what an individual investor will earn. Net IRR also should not be read as a simple annual account return: it is a fund-performance measure, and the cited summary does not provide enough methodological detail to interpret the figures beyond the reported distribution.
9. AI adoption was broader than measured business impact
The presentation’s evidence, as summarized by a16z and SaaStr, suggests a gap between adopting AI and formally measuring its business effects. a16z says nearly 30% of S&P 500 companies reported some quantifiable AI impact, but about 2% reported a tracked metric. It also puts the share of U.S. households paying for an AI service at about 2% as of April 2026. Separately, SaaStr attributes to McKinsey data the finding that 20% of organizations cited AI cost as a constraint. These figures refer to different populations and measures; the household figure is not a corporate adoption rate.
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10. Entry-level headcount share moved differently across AI-adoption groups
Data attributed to Revelio Labs and Ramp showed a change in entry-level headcount share starting 24 months after AI adoption of +1.15 percentage points among high-intensity AI adopters and -0.52 percentage points among low-intensity adopters. The comparison is an observed change across adopter groups; it does not by itself establish that AI caused the difference or show what happened to entry-level employment in every company.
How should investors read these takeaways?
The figures describe different markets and measurement windows: public-company valuation and growth cohorts, Stripe payment-data company-age groups, a U.S. VC-backed unicorn sample, venture-fund vintages and AI-adoption comparisons. They should not be combined into one universal measure of startup health. For a founder, investor or employee assessing a particular company, the relevant questions remain company-specific: whether growth is durable, whether margins and cash needs are manageable, and whether the business can demonstrate the value it claims.
a16z’s interpretation is that software was repriced as companies traded some growth for profitability—not that software is finished. David George, a16z general partner and leader of its Growth investing team, put the thesis this way: “There’s been no apocalypse for software, but there has definitely been a ‘prove it.’”
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