Series A Got Pickier About Product Categories
We classified 1,220 venture-backed startups into ten categories and tracked a decade of Series A rounds. The biggest shift was not AI rising. It was the generalist software bucket collapsing from 24.9% of Series A rounds to 9.6%.
The common reading of the 2022 to 2024 funding market is that AI ate everything. Our data does not support that, at least not in round count. AI/ML grew from 24.0% to 31.2% of Series A rounds between 2015-18 and 2022-24. That is a 7.2 point gain and the biggest category shift we measured, but it is not a reshaping of the market. Cybersecurity, Fintech, and Healthtech each grew alongside it.
The real rotation came from two places that get written about least. The generalist “Other” bucket fell from 13.5% of Series A rounds to 9.6%. Data Infra and DevTools/Infra, long-favored categories of the 2015-18 era, together gave up 12.8 points of share. Capital did not concentrate into AI. It concentrated away from categories that used to be the default software bet and away from companies that could not credibly claim a sector identity. This is the same dataset we used to map how long a Series A takes now; here we slice it by category instead of by time.
01The Dataset
The dataset is 1,220 US-based venture-backed companies. Every company had at least one funding event on record, and we pulled the full funding history for each: every seed round, Series A, Series B, and beyond, with dates and dollar amounts where disclosed. That produced 3,403 funding events. For this post, we focused on the 566 Series A rounds that cleared our classifier.
02How We Grouped the Companies
Industry labels in the source data were not useful. 86% of companies in the raw export tagged themselves as “information technology & services,” which is too broad to tell a story. We built a keyword-based classifier instead: each company has a list of self-reported tags like “machine learning platform” or “endpoint security,” and we mapped those tags against ten category dictionaries.
The dictionaries are deliberately narrow. A company only lands in “AI/ML” if its tags include phrases like “artificial intelligence,” “machine learning,” “deep learning,” or “generative ai.” Tagging a product as “predictive analytics” or “conversational ai” counts. Tagging it as “enterprise software” does not. Companies that did not match any category with at least two keyword hits landed in “Other.” 247 companies (20.2%) ended up in “Other,” which is itself a finding we return to in section 04.
03Share of Series A Rounds by Category
The first thing we wanted to see is whether the narrative of category rotation holds up. It mostly does not. Here are the ten categories ranked by 2022-24 share of Series A rounds.
Share of Series A rounds by category, grouped by calendar year of the round. Columns sum to 100%.
AI/ML and Fintech are the only categories with meaningful share gains (+7.2 and +5.9 points). Cybersecurity and Healthtech each added a few points. The biggest losers are Data Infra (-7.5), DevTools/Infra (-5.3), Other (-3.9), and Industrial/IoT (-3.2). The magnitudes do not support the framing of a market-wide rotation into AI. They support a slower story: the 2015-18 Series A default was a data-or-tooling company sold to other software teams, and that default lost its slot.
The 7.2 point shift in AI/ML between 2015-18 and 2022-24 matters, but so does what it displaced. A meaningful chunk came out of Data Infra, which lost 7.5 points of share partly because Data Infra companies rebranded themselves as AI/ML between 2019 and 2024. The category labels blurred as the underlying product did too.
04The Real Rotation: Out of Generalist Software
The Other bucket is not a huge row in the table. It dropped from 13.5% to 9.6% of Series A rounds, a 3.9 point loss. Taken alone, that number looks modest. Taken together with the 12.8 point loss across Data Infra and DevTools/Infra, a pattern shows up: the categories that lost share were the ones where “we make infrastructure for software teams” used to be a sufficient thesis.
These companies did not disappear. They either repositioned into AI/ML, repositioned into Cybersecurity, or failed to raise. The companies raising Series A now have sharper category identities than the ones raising a decade ago. Founders learned that “enterprise SaaS” is not a narrative, and investors learned that “enterprise SaaS” is not a thesis.
This is the signal operators should look at if they are building a company today. The AI tailwind is real but saturated. The Fintech tailwind is real but gated by regulation. The Cybersecurity tailwind is real but dominated by incumbents. What changed most is that raising a Series A as a horizontal software or developer-tools company got meaningfully harder. Not impossible. Forty-eight companies still cleared it in 2022-24 across Other, Data Infra, and DevTools. Meaningfully harder.
05Where Each Category Lands on Size and Share
Share tells you how many rounds happened. Round size tells you how much capital each company absorbed. The chart below plots both at once. The x-axis is the median Series A round-size multiple from 2015-18 to 2022-24. The y-axis is the change in each category's share of all Series A rounds over the same window. Bubble size is the number of Series A rounds each category logged in 2022-24.
Round-size multiple is the ratio of median Series A size in 2022-24 over median in 2015-18. Share change is percentage-point delta in each category's slice of all Series A rounds over the same window.
The chart has four quadrants and the story sits in the contrast between the top and bottom halves. AI/ML and Cybersecurity sit in the upper left: modest round-size growth (1.5x to 1.6x) but meaningful share gains. Data Infra and DevTools/Infra sit in the lower middle: bigger round-size growth (2.5x to 2.7x) but share loss. Each dollar of AI funding went to a new company; each dollar of Data Infra funding went to an existing category with fewer winners per year.
Fintech is the outlier on the right: a 12x jump in median round size, driven by a thin base in 2015-18 when Fintech medians sat at $1.5M. Once Fintech crossed a credibility line with growth-stage investors, round sizes reset hard. Healthtech and Industrial/IoT show the same pattern at lower magnitudes (5.2x and 4.1x): narrow-buyer categories that historically raised small rounds now raise normal-sized ones when they raise.
The broader point is that round-size inflation was not an AI phenomenon. Seven of ten categories more than doubled median Series A size. AI/ML grew in round count, not per-round size. If you hear “AI round sizes are out of control,” the truth is that Series A sizes are out of control, and AI happens to have the most rounds.
The number that does stand out is Vertical SaaS at $24.0M median for 2022-24, four times its 2015-18 value. That is a small sample, only two rounds in 2022-24, and should be read with caution. The direction is consistent with what we hear anecdotally: once a vertical SaaS company clears the “is this a real market” bar, investors fund it aggressively.
06Where the Old Playbook Still Works
In the previous post in this series, we identified 86 companies in the dataset that raised a seed round, did not raise again for at least three years, cleared $3M in annual revenue, and kept revenue per employee above $150K. We called them the efficient compounders: companies that took the path of building a real business instead of raising more capital. The share of each successive seed-year cohort taking this path has shrunk over time, from 11.5% of 2014-16 seeds to 3.1% of 2020-22 seeds.
That rate varies sharply by category.
Only categories with at least 30 companies shown. Climate/Energy and Vertical SaaS had zero efficient compounders in our dataset but sample sizes were too small to report.
The pattern inverts the progression-rate pattern. Industrial/IoT, which has the lowest raw progression rate, has the highest share of efficient compounders. Healthtech follows. Fintech and Cybersecurity sit in the middle. AI/ML and DevTools/Infra, the categories where capital compounds fastest, have the lowest efficient-compounder rates by a wide margin.
The reading is that enterprise sales cycles and specialized buyer categories still support the old playbook. Industrial companies selling to factories, health companies selling to hospitals, and fintech companies selling to regulated counterparties run slower sales motions that reward capital efficiency. AI/ML and DevTools sell into developer or business-user audiences where growth can compound monthly, which makes not raising economically irrational once the product-market fit signal is clear. The same category-level divergence shows up in revenue per employee: lean sales orgs win where buyers can self-evaluate, and the categories that dominate our efficient-compounder list are exactly the ones where that is hardest.
The non-raising path is not dead. It is category-specific. If you are building in a category with a twelve-month enterprise sales cycle, raising past seed is optional. If you are building in a category with a two-week product-led growth loop, raising past seed is structurally required.