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a16z’s September 2026 State of Markets presentation points to a public software market that still rewards growth—but expects companies to prove it. The review’s headline comparisons are striking: horizontal software had a reported median trailing-revenue multiple of 2.7x, Stripe data showed very young B2B companies growing more than 500% year over year, and 55% of U.S. VC-backed tech unicorns were in runway bands below two years.
The presentation covers the first half of 2026. a16z partner David George’s announcement describes more than 100 charts; SaaStr’s detailed review describes a 90-slide deck. Those counts may refer to different versions or ways of counting. The chart-level figures below are reported in the SaaStr review, which names the underlying data sources; a16z’s accessible summary independently supports the broad shift toward profitability and the “prove it” interpretation, but does not expose every chart’s definitions or sample construction.
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1. Horizontal software had the lowest reported trailing-revenue multiple
For the first half of 2026, the median enterprise value-to-trailing-12-month-revenue multiple for horizontal software was 2.7x, according to JPMAM data cited in the SaaStr review. It was below each of the other reported public-market segments:
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|---|---|
| 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 |
These are segment medians, not forecasts or guaranteed valuations for a particular company. The comparison suggests that investors were assigning different prices to different kinds of software businesses; it does not, on its own, establish why any individual company trades at a particular multiple.
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2. Faster-growing public software companies traded at higher forward multiples
In a separate public-software chart summarized by SaaStr, companies growing revenue 20–40% traded at roughly 9–13x forward revenue. Companies growing 10–20% traded at about 4–5x. These are approximate ranges from the review’s account of the chart, not a valuation rule for private fundraising or future exits.
Together with the segment comparison, the ranges underline the presentation’s “prove it” theme: software was not valued as one undifferentiated category, and growth cohorts had notably different multiples.
3. Profitability was common, but 20% growth was not
a16z’s account and SaaStr’s review both report that about 75% of public software companies were profitable, while only about 30% were growing at least 20%. The figures describe a market where profitability had become widespread even as rapid growth was less common.
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That combination helps explain a repricing without implying that software itself was finished. David George, a general partner at Andreessen Horowitz who leads its Growth investing team, put the distinction this way: “There’s been no apocalypse for software, but there has definitely been a ‘prove it.’”
4. Public B2B growth varied sharply by percentile
The latest public B2B growth distribution in SaaStr’s account was concentrated in the low teens at the median, with substantial differences between slower- and faster-growing companies:
| Position in the growth distribution | Reported growth |
|---|---|
| 25th percentile | High single digits |
| Median | 12–13% |
| 75th percentile | 20–22% |
| 90th percentile | 29–30% |
The review characterizes these percentiles as having stabilized. They are a distribution of reported company growth, not a target that every B2B business should expect to reach.
5. Very young B2B companies grew far faster than more mature ones in Stripe data
Stripe payment data described by the review showed B2B companies less than a year old reaching roughly 500–600% year-over-year growth by early 2026. That exceptional figure applies to the youngest cohort; it should not be read as a typical growth rate for established businesses.
For B2B firms at least a year old, the reported rate was about 19% around January 2026, then recovered to roughly 24%. The contrast is between different company-age cohorts, not a like-for-like forecast for an individual startup as it matures.
6. More than half of the unicorns in the cited U.S. sample had under two years of runway
For U.S. VC-backed tech unicorns in 2026, the SVB data reported in the review placed 26% in the 0–1 year runway band and 29% in the 1–2 year band. Together, those two bands account for 55% below two years of runway.
The same review reports that 42% of these unicorns grew 0–20% and 15% were shrinking. It also notes that the margin categories shown in the chart add to about 25% with positive margins. On those figures, the slide’s “Mostly Profitable” label is not supported by its own table; the margin breakdown should not be paraphrased as a majority being profitable.
7. Recently financed startups reportedly grew faster than startups at scale
SaaStr’s review says recently funded startups were growing at about 60–70%, compared with 15–30% for startups at scale. It also describes the recently financed group as accepting deeper losses. The comparison is reported at the group level: it does not establish that a new financing round caused faster growth or predict the result for a particular company.
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8. 2024-vintage venture fund returns showed a wide spread
Carta-derived figures in the review show substantial dispersion in net IRR for 2024-vintage venture funds:
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| Position | Net IRR |
|---|---|
| 90th percentile | 40.5% |
| Median | -3.3% |
| 25th percentile | -14.6% |
The reported dataset covered 2,773 funds and roughly $119 billion in committed capital as of Q1 2026. These are fund-population figures, not a prediction of an individual fund’s return or a startup’s odds of raising capital.
9. AI adoption was broad, but its measured impact remained shallow
The review reports that 20% of organizations in McKinsey data cited AI cost as a constraint—evidence that cost was a concern for a minority, not that it was irrelevant to adoption decisions.
a16z’s account describes broad but shallow adoption in company reporting: nearly 30% of S&P 500 companies reported some quantifiable AI impact, while about 2% reported a tracked metric. Separately, about 2% of U.S. households paid for an AI service as of April 2026. These measures track different populations—large public companies and U.S. households—and should not be treated as interchangeable adoption rates.
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Data attributed to Revelio Labs and Ramp in the review showed a change in entry-level headcount share beginning 24 months after adoption: +1.15 percentage points among high-intensity AI adopters and -0.52 points among low-intensity adopters. The comparison is an observed difference between adopter groups. It does not by itself establish that AI caused either change or explain the broader direction of hiring.
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