Portfolio Growth
How MassChallenge's portfolio has scaled from 93 startups in 2010 to 4,486 across 16 cohort years. "Cohort year" = the year a startup entered an MC program, not its founding date. A company can appear in multiple programs but is counted once, using its earliest cohort year.
Companies per Cohort Year
Green bars = 2010–2021 cohorts. Amber = 2022+ (recent cohorts with increasing sector focus). MC scaled rapidly during 2015–2021 with global program expansion, then right-sized for deeper vertical focus.
Cohort Funding ($B per cohort)
Total funding raised by all companies in each cohort — not funding raised in that calendar year. Older cohorts show higher totals because their companies have had more time to raise follow-on rounds. The 2010 cohort alone accounts for $3.1B (Ginkgo Bioworks, Flywire, RelayRides).
Cumulative Funding Over Time ($B)
Running total across all cohorts. The step-ups in 2010, 2014, 2016, and 2019 correspond to cohorts that produced outsized fundraisers, not to market conditions in those years. Amber segment marks the 2022+ cohorts.
The Power Law: Funding Concentration
This is the defining feature of any venture portfolio. The top 1% of funded companies (24 startups) account for 45% of all funding. The top 10 account for 32%. The top 100 account for 68%. The curve below shows cumulative funding share as you move from the highest-funded company to the lowest. If you're using portfolio averages to benchmark, you're benchmarking against a number that describes almost no one.
📋CB methodology note: Crunchbase funding totals include all financing instrument types — equity, grants, debt, SAFE notes, crowdfunding — not just equity rounds. Only 53% of companies (2,397 / 4,486) have a recorded funding_total in CB; the $27.1B figure reflects disclosed funding and likely understates actual capital raised. This is consistent across the portfolio and across all articles in this series.
Challenge Areas
MassChallenge classifies startups into five Challenge Areas using LLM analysis of pitch text + Crunchbase descriptions. This retroactive classification applies to all 16 cohort years — so the data shows what sectors companies were always working in, not when MC formalized the verticals. 2,835 companies map to the 5 CAs; 1,382 are Cross-Industry and 269 are Industry 4.0 — legacy categories from MC's pre-transition model.
Carta 2024 Benchmark
MassChallenge: 41% of startups have a female founding team member across the portfolio. Carta's 2024 Annual Equity Report found that only 5.3% of VC capital goes to women-only founding teams. Different measurements (MC = at least one female founder on team; Carta = capital share to women-only teams), but the gap is striking: MC's portfolio is 7.5× more inclusive by participation than the broader venture ecosystem is by capital allocation.
Total Funding by Challenge Area ($B)
HC&LS leads at $9.2B — driven by biotech companies that raise large later-stage rounds. Finance has the highest average funding per company ($12.7M) despite the fewest startups, reflecting capital-intensive infrastructure builds in fintech.
CB Active Rate by Challenge Area
Raw Crunchbase operating_status — not the cohort-adjusted composite. All five CAs show >89% raw active rates. Security leads at 93.9%, though newer cohorts dominate its composition, inflating the raw rate. See the Survival tab for the adjusted model.
Portfolio Share — 5 Challenge Areas
Of the 2,835 companies classified into the 5 CAs, HC&LS represents 45%. Note that the remaining 1,651 companies (Cross-Industry + Industry 4.0) are not shown in this donut — they appear in the legacy block below.
HC&LS Subsector Breakdown
1,283 healthcare startups span 5 subsectors. MedTech is largest by count (509), but Biotech drives the most funding ($5.0B) — 54% of all HC funding from 21% of companies. Mental Health has the highest female founder rate (60%).
Cross-Industry & Industry 4.0 — Legacy Categories
Before the Challenge Area transition, 1,382 startups were classified as Cross-Industry and 269 as Industry 4.0 — totaling 1,651 companies with $6.6B in funding and a combined 80% CB active rate. These span edtech, consumer, manufacturing, general SaaS, and companies that don't map cleanly to a single vertical. As MC's classification matures, some may be reclassified into Challenge Areas.
Funding Pipeline
How MC alumni progress through venture funding stages — from first capital to Series A and beyond. Only 6.7% of the portfolio has reached Series A+, which is consistent with the broader startup ecosystem where the vast majority of early-stage companies remain at seed or are bootstrapped. This tab shows both the funnel and the timing.
📊Reading the funnel: Each row shows how many companies have reached at least that stage. The percentage is relative to the full portfolio (4,486). "Series A+" is identified via Crunchbase's last_equity_funding_type field — any company whose most recent equity round was Series A or later. This is a broader and more accurate filter than funding_stage, which CB often leaves null.
4,486
100%
Total MC alumni (CB-matched)
4,042
90%
Have any funding date recorded in Crunchbase
2,397
53%
Have a disclosed funding total in Crunchbase
1,477
33%
Classified as Seed stage by Crunchbase
300
6.7%
Reached Series A+ (last equity round = Series A, B, C…)
12
0.3%
IPO (publicly traded)
Graduation Gap: Years from MC Cohort to Series A+
For the 283 companies with valid timing data (Series A+ reached, with known funding date after cohort year), how long did it take? The median is 4 years. Finance and Security graduate fastest (3–3.5 yr median), while HC&LS and Food take 5 years, reflecting longer R&D and regulatory timelines in those sectors.
4 yr
Overall Median
n = 283
Median Gap by Sector (Years to Series A+)
Sample sizes in parentheses. "Other" = Cross-Industry (n=62) + Industry 4.0 (n=15) legacy companies. Low n in Finance and Security reflects both fewer total companies in those CAs and shorter graduation timelines — not missing data.
Distribution of Graduation Gap (All Sectors)
Most Series A+ graduates take 3–8 years from MC cohort entry to reach that milestone. The 5–8 year bucket is the largest (110 companies), reflecting the reality that regulated-industry startups face longer development cycles than pure software.
Median Gap by Cohort Year
⚠️Time-in-market artifact: 2022+ cohorts (amber) show lower median gaps because these companies simply haven't had enough time to reach later stages. A company entering in 2023 can only have a max gap of ~2 years. This is a ceiling effect, not evidence that newer cohorts graduate faster.
Survival & Methodology
Operating status across the portfolio, the composite model behind the 59% active rate, and why we don't use CB's raw 88%. This is where we show our work — including the parts that aren't flattering.
⚠️Why we don't trust CB's 88% active rate. Crunchbase's operating_status field only updates when a closure is publicly reported. If a startup quietly shuts down — no press release, no SEC filing — CB continues to list it as "active." For a 15-year portfolio with predominantly early-stage companies, this lag inflates survival dramatically. The 2010 cohort shows 54% "active" on CB after 15 years — BLS data suggests ~25% of all establishments survive that long.
88%
Raw CB Active Rate
3,932 / 4,486 — inflated
59%
Composite Model
2,654 est. — cohort-adjusted
50%
BLS 5-Year Survival
all US establishments
34%
BLS 10-Year Survival
1994 cohort study
How the Composite Model Works
We classify every company into one of three buckets, then apply cohort-adjusted assumptions to the uncertain bucket.
BUCKET 1 · CONFIRMED ACTIVE
1,750 companies (39%)
Funded since Jan 2022 OR reached Series A+ OR publicly listed. Recent market signals confirm they're operating.
BUCKET 2 · CONFIRMED CLOSED
554 companies (12%)
CB operating_status = closed. Confirmed failures with a public record of shutdown.
BUCKET 3 · UNCERTAIN
2,182 companies (49%)
CB says "active" but no funding since 2022, no Series A+, not public. These are the companies CB is likely wrong about.
Graduated Dead Rates
For the uncertain bucket (49% of the portfolio), we apply graduated "assumed dead" rates calibrated by cohort age. A 2010 company with no funding signal in 14 years is far more likely to be dead than a 2023 company with no signal in 2 years.
Rates: 90% for 2010–11 → 85% for 2012–13 → 80% for 2014–15 → 75% for 2016–17 → 65% for 2018 → 60% for 2019 → 50% for 2020 → 45% for 2021 → 35% for 2022 → 25% for 2023 → 15% for 2024 → 10% for 2025.
This produces a portfolio-wide ~59% active rate — consistent with what we'd expect from a 15-year early-stage portfolio, benchmarked against BLS establishment survival data (50% at 5 yrs, 34% at 10 yrs) and Stripe's published survival curves.
The Gap: Raw CB Rate vs. Composite Model by Cohort Year
This is the core argument — why the 88% headline number is misleading. The shaded area between the two lines represents companies CB lists as "active" that our model estimates are actually defunct. The gap is widest for the oldest cohorts (37pp for 2010–11) and narrows to near-zero for recent cohorts — exactly what we'd expect if CB's lag is the primary distortion.
🔍The 49% uncertain bucket is the story. Nearly half the portfolio sits in a grey zone — CB says they're active, but they have no funding signal since 2022 and haven't reached Series A. Our model doesn't call all of them dead — it applies graduated rates from 90% (2010, 15 years of silence) to 10% (2025, less than a year in market).
Company Status by Cohort Year
Three buckets stacked to 100%. Note how the uncertain bucket (amber) dominates older cohorts — these are the companies where CB's lag matters most.
Dead Rate Applied to Uncertain Bucket
The graduated assumption: higher rates for older cohorts where time without market signals accumulates. A 15-year-old company with no funding, no Series A, no IPO is overwhelmingly likely inactive.
BLS Benchmark: US Establishment Survival (1994 cohort)
The Bureau of Labor Statistics tracks survival of all US business establishments — restaurants, retail, services. Our composite model's ~59% portfolio-wide rate implies MC alumni roughly double the BLS 10-year benchmark of 33.6%. Note: BLS tracks a broader, arguably easier-to-scale population than MC's regulated-industry portfolio.
📚Peer-reviewed context: Accelerator graduates show a +23 percentage point survival advantage over non-accelerated startups (Regmi et al., 2015). A 2025 meta-analysis in the Journal of Technology Transfer found that vertical accelerators outperform generalist programs on survival — consistent with MC's strategic shift toward Challenge Areas. Even with our conservative model, MC's ~59% composite rate for a 15-year portfolio suggests performance consistent with the accelerator advantage documented in the literature.
Full Cohort Breakdown — Every Number Behind the Model
Amber rows = 2022+ cohorts. The "Gap" column shows how many percentage points CB overcounts by for each cohort year — the cumulative distortion across the portfolio.
Benchmark Sources
BLS Business Employment Dynamics (Table 7): All new private-sector establishments. 50% survive 5 years, ~34% survive 10 years. Broadest benchmark available.
Stripe Press (2020): Delaware incorporations show ~60% active at 5 years — more comparable to tech startups than BLS all-sector.
Regmi et al. (2015): Meta-analysis finding accelerator graduates show +23pp survival advantage over non-accelerated startups.
Neither number is "right." CB's 88% is almost certainly too high. Our 59% involves assumptions that could be too aggressive for some cohorts and too generous for others. The truth is between them. Showing both is more honest than showing either alone.
Demographics
Founder diversity across the portfolio. "Female founding team" means at least one founder identifies as female — this is a team-level measure, not a "female-founded" label. BIPOC data is self-reported and has significant coverage gaps in earlier cohorts. We show the data honestly, including where it's thin.
Carta 2024 Benchmark
MC portfolio: 41% female founding teams (1,807 / 4,377 with data). Carta reports ~14% of startups on its platform have a female founder. These are the most comparable available benchmarks, though they measure slightly different things (MC = self-reported at application; Carta = cap table data).
41%
Female Founding Teams
1,807 / 4,377 known
53%
BIPOC Founding Teams
1,533 / 2,902 known
2,902
BIPOC Data Coverage
65% of portfolio
4,377
Gender Data Coverage
98% of portfolio
Female Founding Team % by Cohort Year
Gender data is available for 98% of the portfolio. The jump between 2012 (4.3%) and 2013 (39.2%) reflects a change in MC's data collection — not a real demographic shift. From 2016–2021, female representation stabilized at 47–53%. The post-2022 decline (to 25–38%) may reflect the shift to more technical Challenge Areas like Security, or changes in applicant pool composition.
BIPOC Founding Team % by Cohort Year
Only shown where n ≥ 100 respondents. Before 2015, BIPOC data coverage was too thin to be meaningful (4–28 respondents per cohort). From 2017 onward, coverage exceeds 55% of each cohort. The BIPOC rate ranges from 44–65%, with a notable peak in 2022 (65.5%, n=385). Grey bars show sample size for context.
Female Founding Team % by Challenge Area
HC&LS leads at 46.9%, driven by Mental Health (60%) and Care Delivery (56%) subsectors. Security is lowest at 29.6%. In the published HC&LS deep dive (Article 3), we found that female founding teams have reached funding parity in 3 of 5 healthcare subsectors — and surpass it in Mental Health, where female teams raise 3× the median ($3.39M vs. $1.14M).
💡The parity finding: In 3 of 5 Challenge Areas, female founding teams raise at or above the median funding of non-female teams. The gap is concentrated in Food (2.2× male advantage) and Security (1.6×). In HC&LS — the largest CA — female and non-female teams are at near-perfect parity. This challenges the blanket narrative that female founders universally raise less.
Female vs. Non-Female Median Funding by CA
Grouped bars show median funding for female founding teams (purple) vs. non-female teams (grey) in each Challenge Area. HC, Climate, and Finance all show female parity or advantage. Food and Security show gaps — possibly reflecting capital-intensity differences in sub-sectors where female founders concentrate.
HC&LS Subsector: Female Funding Gap
The HC story is even more striking at the subsector level. Mental Health: female teams raise 3× the median ($3.39M vs. $1.14M). MedTech and Care Delivery also show female advantage. Biotech flips: male teams raise 2.2× more, reflecting that the most capital-intensive subsector also has the lowest female representation (42%).
BIPOC vs. Non-BIPOC Median Funding
Portfolio-wide, non-BIPOC teams raise more ($1.20M vs. $830K). But HC&LS flips this: BIPOC founding teams out-raise non-BIPOC ($1.50M vs. $1.25M). The portfolio-wide gap is driven by Food ($365K vs. $1.08M) and Finance ($1.41M vs. $4.56M). BIPOC data coverage is 65% of the portfolio — strongest in 2020+ cohorts.
📋Methodology note: "Female founding team" = at least one founder self-identified as female at application. "BIPOC" = at least one founder self-identified as Black, Indigenous, Hispanic/Latino, Asian, Middle Eastern, Native Hawaiian/Pacific Islander, or multiracial. Both are team-level, binary flags. Data is from MC internal records, not Crunchbase. BIPOC data was not collected systematically before ~2015, and coverage varies by program and geography. Funding comparisons use median (not mean) to reduce the effect of outlier mega-rounds. Sample sizes shown — interpret small-n sectors with caution.
Cohort Composition
How the sector mix has shifted over 16 years. This heatmap is the clearest visualization of MC's strategic transition: from a broad, industry-agnostic portfolio to one concentrated in five Challenge Areas. Each cell shows the number of companies in that sector × year intersection. Color intensity scales with company count.
📊The transition in two numbers: Cross-Industry was 59% of the 2011 cohort (58 of 98 companies). By 2024, it's 10% (30 of 294). Meanwhile, HC&LS grew from 13% of 2011 (13 companies) to 32% of 2024 (93 companies).
Sector × Year Heatmap
Amber column headers mark 2022+ cohorts. Rows are ordered by total portfolio size. The diagonal pattern — Cross-Industry fading as the five CAs grow — is the data signature of MC's strategic shift. Industry 4.0 appears as a distinct row; it was a transitional category that peaked in 2019–2020.
Cross-Industry Share Over Time (%)
The declining share of Cross-Industry companies is the quantitative story of MC's transition. The inflection point starts around 2017–2018, accelerates in 2022, and becomes dominant in 2024–2025. This isn't about companies being reclassified — it reflects actual changes in which startups MC accepted into programs.