Founders who used to work at a tier 1 or tier 2 venture fund had about 3 times the odds of landing a lead investment from such a fund as otherwise similar founders, according to an analysis Deedy Das, a partner at Menlo Ventures, posted on X. Founders with an MBA had 0.60 times the odds.
That puts the top signal at 5 times the odds ratio of the MBA signal, by our arithmetic (3.00 divided by 0.60). The chart says the data cover 4,836 US startups from 2021 to 2024, from Harmonic.
Business Pill · POWER LAW
A short explainer on power laws, the idea that a few outcomes account for most of the results. It teaches the general idea only and says nothing about Das’s chart.
The key insight: As we read the chart, the strongest single predictor of a top-fund lead is having worked inside such a fund: an odds ratio of 3.00, five times the 0.60 for founders with an MBA, by our arithmetic.
Das wrote that he “looked at ~60 attributes across 5000 startups and these were the 15 strongest”. His chart is titled “Founder signals that predict a tier 1โ2 lead” and gives each signal as an “Odds ratio vs. otherwise similar founders”.
The footer reads “Logit, 59 signals + controls”. The chart does not define tier 1 or tier 2, list the controls or say how founders were matched to companies.
After former staff of tier 1 to 2 funds at 3.00, the highest odds ratios are for former Tesla or SpaceX employees at 1.83, Technion or Tel Aviv University graduates at 1.81, and founders whose last company had a tier 1 to 2 lead at 1.73, which the chart marks as weaker evidence.
Further down are founders who worked at a company backed by a tier 1 to 2 fund at 1.58, a computer science degree at 1.45, a top university at 1.41 and former Google employees at 1.39.
Odds ratio of a tier 1-2 venture lead against otherwise similar founders, by founder signal, as printed in the chart Deedy Das posted on X on 31 October 2026 (data: Harmonic, 4,836 US startups, 2021-24). Redrawn by FourWeekMBA; the 5x is our arithmetic.
What Was Posted on X
Which startups are most likely to get an investment from top tier VCs
I looked at ~60 attributes across 5000 startups and these were the 15 strongest.
#1 is being from a top tier fund. MBA is a negative signal
Deedy Das, a partner at Menlo Ventures, posted this chart on 31 October: odds ratios for 15 founder signals and a tier 1 to 2 venture lead, from Harmonic data on 4,836 US startups, 2021 to 2024. Former staff of tier 1 to 2 funds show 3.00 and founders with any MBA 0.60. The post presents the analysis as Das’s own work.
The Signals That Lower the Odds
Four of the 15 signals have odds ratios below 1. A PhD is 0.75, a fintech company 0.74, an oldest founder aged 50 or over 0.66, and any MBA 0.60. An odds ratio of 0.60 means odds about 40% lower than for otherwise similar founders, by our arithmetic.
The chart prints how many founders carry each signal. The MBA group is 1.2k, the PhD group 1k and the group from tier 1 to 2 funds 183.
Pedigree by Employer
Former Tesla or SpaceX staff show an odds ratio about 1.3 times that of former Google staff (1.83 against 1.39), and former fund staff about 2.2 times (3.00 against 1.39), by our arithmetic.
Harvard, a top-company alum and a former scaleup employee sit between 1.36 and 1.38, all marked as weaker evidence.
The Business Lever: Who Gets the Lead Check
A lead investment from a top fund sets the price and terms of a round. On this chart the strongest single predictor of getting one is having worked inside such a fund, ahead of any employer or degree.
The Structural Read
As we read the chart, the top three rows mix employers and schools: former fund staff at 3.00, former Tesla or SpaceX staff at 1.83 and Technion or Tel Aviv University at 1.81, while former Google staff, at 1.39, sit just below a computer science degree at 1.45.
As we read the chart, four signals lower the odds, and the MBA, at 0.60, is the lowest of the 15 shown.
As we read the footer, these are associations from a logit model with 59 signals and controls on Harmonic data, posted by Das on X, not a published study.
Deedy Das, partner at Menlo Ventures, on X, 31 October 2026
“I looked at ~60 attributes across 5000 startups and these were the 15 strongest”
Three Implications
FOR FOUNDERS The chart gives an odds ratio of 3.00 for former staff of tier 1 to 2 funds and 0.60 for any MBA, each against otherwise similar founders.
FOR FUNDS Three of the five highest-ranked rows involve a tier 1 to 2 fund or a company it backed, one of them marked weaker evidence; former fund staff number 183 in the data.
FOR READERS OF THE DATA The chart reports odds ratios with 95% ranges, not probabilities, and marks four rows as weaker evidence.
The framework says “The job of any operator, investor, or builder is to know which layer they are operating inside and which layer the binding constraint is rotating toward next.”
As we read it, Das’s chart is one investor’s attempt to measure which founder backgrounds attract a top-fund lead. The framework is the owner’s argument and does not discuss this chart.
This is Das’s own analysis, shared on X, not a peer-reviewed study. The chart gives odds ratios, not probabilities, and an association, not a cause. Counts for two rows are cut off in the image. We did not see the underlying data or code.
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In a chart Deedy Das posted, founders from tier 1 to 2 funds had 3.00 times the odds of a top-fund lead and MBA founders 0.60 times, a 5-fold gap by our arithmetic, across 4,836 US startups from 2021 to 2024.
A note on sourcing. Figures come from a post on X by Deedy Das, a partner at Menlo Ventures, and the chart attached to it, read on 31 October 2026. The chart names Harmonic as the data source. Ratios between odds ratios are our arithmetic. We did not contact Das.
Short explainer · Ex-VC staff 3x, MBAs 0.6x
A vertical news Short summarising this story. Figures come from the chart Deedy Das posted on X (data: Harmonic); our arithmetic is labelled.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
