25 articles in this tag
Ask an LP what colour fintech is and they'll say blue. In this data it isn't even fintech's most common colour — and across the whole sample, blue runs at roughly half the Fortune 500's rate, the single largest gap of any colour measured. Read as a market-structure signal, not a stock-picking one.
We built a verification pipeline to keep the wrong logo out of a 499-company dataset. The two errors that got through were photographs of cars. Here, that same pipeline runs the sharpest test the data can support — logo colour against which well-funded companies later shut down — and the honest answer is a clean, well-powered null.
We built a dataset of 499 heavily-funded startup logos, verified every match by hand, pre-registered our exclusion rule before looking at the data — and watched our own headline finding weaken under its own scrutiny. Here's how the pipeline works and what actually held up.
A pitch deck's score isn't fixed — it's the same evaluation dimensions read through a different weighting for every fund. An angel, a growth investor, and a family office can score the identical deck differently and each still be right, because an investor's thesis is mechanically a re-weighting, not an opinion.
We ranked Y Combinator's 2010–2019 companies by how often they died, sector by sector. Social startups died at ~58%; recruiting startups at ~15% — a four-fold spread. The ranking isn't random: two forces explain almost the whole gradient, and both are things a founder can see before they build.
Every accelerator tells founders B2B is the safer bet. We checked the outcomes of about 1,200 Y Combinator companies from batches old enough to have an answer. B2B companies died at 27%; consumer companies at 43%. But survival and returns turn out to be different instruments — and the advice quietly conflates them.
Five practitioner lessons from building a ~6,900-company venture-outcome graph — identity is the real bottleneck, the dangerous errors are the plausible ones, survivorship hides in track records too, the founder is the unit not the company, and consensus isn't correctness.
Airbnb's rejection emails, Stripe's narrow wedge, Coinbase against consensus, Kyle Vogt's second act, and the thousand companies with no name. Ten real, public, verifiable arcs — and the patterns they share with an outcome dataset of ~6,900 accelerator companies.
We built an outcome graph of ~6,900 accelerator companies — every Y Combinator batch and publicly announced Startmate cohorts — and linked each to what happened next. Three lessons: failure is the training data everyone throws away, selection and success are different instruments, and the outcome is the only signal that doesn't flatter you.
Venture returns follow a power law — roughly half of investments lose money and a handful return most of the profit. AI changes how cheaply you can rank companies, not the shape of that distribution. Why the right move is to let the machine raise the floor and keep the ceiling human.
Not all VCs evaluate startups the same way. We mapped 4,988 investors with detailed thesis statements to 9 distinct archetypes — each linked to a philosophical tradition — based on how they weight team, product, traction, and financials. The Oracle bets on people. The Empiricist demands data. The Stoic guards against risk. Find out which archetype your target investor is.
Every VC has a thesis. Most also have an anti-thesis — sectors, stages, and business models they will not touch. We analyzed 7,800+ investor thesis statements to map the explicit rejection patterns that founders rarely see.
Who actually leads rounds in ANZ? We analyzed 4,988 investor profiles with detailed thesis data (of 9,000+ total active investors) to map the lead-follow dynamic — the single most important structural factor in Australian startup fundraising that founders rarely understand.
We mapped 4,988 investor thesis statements (from 9,000+ total active investors) by sector and stage to reveal where VC capital concentrates — and where the white space is. SaaS and fintech are the most crowded. Climate and deep tech have the highest capital-per-investor ratio. Interactive heatmap inside.
Technology adoption curves are compressing with every generation. From the telephone to TikTok to ChatGPT to OpenClaw — and what each acceleration means for how venture capital works.
$5.1B invested. 61% went to AI. Victoria beat NSW for the first time. We cross-referenced the State of Funding report with 197 NuScore evaluations to find what actually drives investment decisions.
Digital operating models eventually cross and surpass traditional ones as scale grows. That crossing point has now arrived in venture capital — with documented evidence from 2020 to 2026.
How does VC evaluation differ by city? We analyzed 4,956 investor thesis statements across 12 global tech hubs. Melbourne VCs mention founders and team 24.1% of the time — 5.6x more than Berlin (4.3%). Full ranking of all 12 hubs with team-to-product ratios.
64.8% of VC deals return 0-1x. Only 4% return 10x+. The data from PitchBook and Cambridge Associates shows why deal screening volume is the most underrated edge in venture capital.
VCs say they bet on teams. Our data from 7,800+ investor theses, 249 scored evaluations, and 162 real VC deal memos shows conviction and traction — not team — actually drive their decisions.
Solo GPs and emerging managers need speed, not complexity. Here's how NUVC's new VC dashboard compresses 2 hours of morning pipeline review into 5 minutes — with keyboard shortcuts, AI morning briefs, and conviction scoring that actually maps to your IC process.
Everyone now has access to ChatGPT, Claude, and Gemini. But the gap in outcomes between investors using these tools is widening — not closing. Here's what separates the ones getting real leverage from the ones still copy-pasting decks.
If two founders can build an AI-native platform with an AI agent workforce, what happens when a solo GP applies the same leverage to fund management? The AU/NZ VC ecosystem is about to find out.
Our AI analysed 298 decks from companies worth $116B combined. Here's what the data says about detecting outlier potential, conviction archetypes, and the signals that predict which startups become unicorns.
Most founders waste months pitching the wrong investor type. Here's how to identify whether you need an angel, a VC, a syndicate, or an accelerator — and the specific approach that works for each.
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