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a16z’s second State of Markets, released in September 2026 and covering the first half of the year, points to a market that still rewards growth—but expects companies to demonstrate durable economics. David George, a16z’s growth-investing lead, summarized the shift this way: “There’s been no apocalypse for software, but there has definitely been a ‘prove it.’”
The takeaways below draw on Jason Lemkin’s SaaStr review of the September presentation and the datasets that review attributes to JPMAM, Stripe, SVB, Carta, McKinsey, Revelio Labs and Ramp. a16z’s public summary independently supports the broad picture of software profitability rising as fewer companies grow quickly, but it does not publish all the detailed charts, definitions or sample construction described in the review. The figures should therefore be read as reported segment, cohort and population comparisons—not as independently audited forecasts for any particular company or fund. a16z described the presentation as containing more than 100 charts; SaaStr called the version it reviewed a 90-slide deck. Those are different counts and may refer to different versions or formats.
1. Horizontal software had a lower reported trailing-revenue multiple than infrastructure
For the first half of 2026, the review reports a median enterprise value-to-trailing-12-month-revenue (EV/TTM revenue) multiple of 2.7x for horizontal software. The figures below are segment medians attributed to JPMAM data in the presentation; they describe public-market valuations at that time, not a valuation forecast for an individual company.
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|---|---|
| Horizontal software | 2.7x |
| Cloud, data and AI infrastructure | 9.1x |
| Security and identity | 6.8x |
| Vertical software | 4.6x |
| Consumer, commerce and transactional platforms | 4.0x |
The spread suggests investors were valuing broad-use business applications less richly than the infrastructure categories in this comparison. It does not establish why any one company received its multiple: the review does not provide enough chart methodology or company-level detail to isolate causes such as growth, margins or product mix.
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2. Faster-growing public software companies traded at higher forward multiples
In a separate public-software chart, the SaaStr review says companies growing revenue 20–40% traded at roughly 9–13x forward revenue, while companies growing 10–20% traded around 4–5x. These are approximate cohort ranges reported from the presentation, not the same measure as the H1 trailing-revenue segment medians above. The comparison supports a practical reading of “prove it”: profitability matters, but growth still appears to command a premium when it is present.
3. Profitability was common among public software companies; 20%-plus growth was not
a16z’s account of public software says about 75% of companies were profitable, while only about 30% grew at least 20%. The same broad proportions appear in the SaaStr review’s summary. The figures describe the public-software population, not startups generally. Taken together, they show that profitability had become widespread without making high growth typical.
4. Public B2B growth was concentrated below the fastest-growing cohort
The SaaStr review describes the latest public B2B growth distribution as having stabilized around the following approximate rates. Percentiles indicate a position in that distribution; they are not growth targets or a prediction for a particular business.
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|---|---|
| 90th percentile | About 29–30% |
| 75th percentile | About 20–22% |
| Median | About 12–13% |
| 25th percentile | High single digits |
The distribution helps put the 20%-plus threshold in context: growth at that level was associated with roughly the upper quarter to third of public B2B companies, rather than the typical company.
5. Stripe data showed a sharp growth difference by B2B company age
In Stripe payment data as summarized by SaaStr, B2B firms less than a year old reached roughly 500–600% year-over-year growth by early 2026. Firms at least a year old were growing at about 19% around January 2026, then recovered to about 24%.
These age cohorts are not interchangeable. The very high rate applies to businesses less than a year old in the Stripe data; it should not be generalized to mature B2B firms or treated as a typical startup growth benchmark. The review does not supply enough methodological detail to infer how much of the gap reflects cohort composition, small starting revenue bases or other factors.
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6. More than half of the reported unicorns had less than two years of runway
For U.S. venture-backed tech unicorns in 2026, the SaaStr review attributes the following runway figures to SVB data. The first two bands together account for 55% of the unicorn population shown in the chart.
| Reported measure | Share of U.S. VC-backed tech unicorns |
|---|---|
| 0–1 year of runway | 26% |
| 1–2 years of runway | 29% |
| Growth of 0–20% | 42% |
| Shrinking | 15% |
| Positive-margin categories combined | About 25% |
The positive-margin categories in the chart add up to about a quarter, which does not support the slide’s “Mostly Profitable” label as reported by the review. This is a different population from public software companies, so its margin figures should not be compared directly with the separate public-company profitability proportion.
7. Recently financed startups reportedly grew faster than startups at scale
The review says startups that had recently raised were growing at roughly 60–70%, compared with about 15–30% for startups at scale. It also characterizes the recently financed group as accepting deeper losses, but does not provide a specific loss figure in its summary. The comparison is a reported cohort pattern, not evidence that a new financing round causes faster growth or that a particular startup will achieve those rates.
8. Carta’s 2024-vintage fund data showed a wide spread in reported net IRR
For 2024-vintage venture funds, the SaaStr review reports net internal rates of return from Carta data as of Q1 2026. It describes the dataset as covering 2,773 funds and roughly $119 billion in committed capital.
| Position among 2024-vintage funds | Reported net IRR |
|---|---|
| 90th percentile | 40.5% |
| Median | -3.3% |
| 25th percentile | -14.6% |
The gap between the reported upper and lower parts of the distribution is a reminder that a vintage-wide statistic conceals substantial fund-to-fund variation. It is not a return prediction for an individual fund, and the review’s summary does not establish how any specific fund will perform.
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9. AI cost was a reported constraint for a minority, while measured impact remained limited
The review attributes to McKinsey data in the presentation the finding that 20% of organizations cited AI cost as a constraint. In a separate a16z account of adoption and impact, nearly 30% of S&P 500 companies reportedly described some quantifiable AI impact, while about 2% reported a tracked metric. a16z also said about 2% of U.S. households paid for an AI service as of April 2026.
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These measures cover different populations and are not directly comparable: one concerns organizations citing a cost constraint, another corporate reports of impact or metrics, and the third household payments. Together they support a16z’s characterization of adoption as broad but shallow, rather than proving that AI was already producing widely measured financial results.
10. Entry-level headcount share moved in opposite directions by AI-adoption intensity
Data attributed in the review to Revelio Labs and Ramp showed a change in entry-level headcount share beginning 24 months after adoption. Among high-intensity AI adopters, the reported change was +1.15 percentage points; among low-intensity adopters, it was -0.52 points.
This is a comparison of headcount share across adopter groups after the stated interval, not a count of jobs created or eliminated, and not proof that AI adoption caused the difference. The review’s summary does not provide enough detail to establish the underlying sample construction or other factors that might affect the comparison.
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