CHAPTERS
- 0:00 – 2:28
Why “non-consensus investing” can be dangerous (and what the tweet really meant)
Martín explains the viral tweet: being proudly non-consensus can be a red flag because you might be missing something, and because startups depend on future capital. He clarifies he’s not advocating “consensus investing,” but arguing that ignoring market consensus is risky—especially given his belief that early markets are more efficient than people assume.
- •Non-consensus can mean you’re wrong or missing key information
- •Startups often need follow-on funding; markets can veto survival
- •Martín’s academic peer-review analogy: great work still needs acceptance
- •Core clarification: awareness of consensus matters; chasing consensus doesn’t
- •Claim: early-stage markets are “pretty darn efficient”
- 2:28 – 3:36
Seed vs. Series A viewpoints: non-consensus early, consensus later
Leo largely agrees that companies eventually need consensus, but argues many of his best seed investments began as non-consensus because proof points were scarce. The upside of getting in early is that once a company becomes obviously good, valuations can move so fast that later investors capture smaller multiples.
- •You eventually need broader investor buy-in to keep companies funded
- •Many top seed deals look non-consensus before clear proof exists
- •Valuations can re-rate extremely fast once traction appears
- •Non-consensus isn’t about genius insight; often it’s about timing/proof
- •Stage matters: pre-seed/seed has more uncertainty and more upside
- 3:36 – 6:04
Anecdotes vs. real consensus: “hard round” isn’t the same as “market disagreement”
The group challenges common narratives that big winners were non-consensus simply because fundraising was difficult at some point. Martín argues many cited examples had elite founders, known spaces, YC signaling, and consistently high pricing—suggesting they were not truly non-consensus in the market-wide sense.
- •Defining “consensus” is slippery; people talk past each other
- •A tough raise doesn’t necessarily imply the market disagreed on value
- •Examples like Anduril/Scale: elite founder + known space ≠ non-consensus
- •High median pricing across rounds can contradict ‘non-consensus’ claims
- •Focus should be on company quality, not getting a ‘deal’ vs other investors
- 6:04 – 11:06
Hot rounds, follow-on momentum, and how to measure market efficiency
They explore whether hot rounds predict future success, and whether investor demand is itself a useful signal. Martín and Leo discuss doing correlation and basket analyses (e.g., number of term sheets, speed of follow-ons) to test whether “hotness” reflects underlying quality or just herd behavior.
- •Peter Thiel quote: fast/higher up rounds may be a positive signal
- •Hypothesis: the best predictor of a hot round is that the prior round was hot
- •If round momentum persists, that suggests inductive market efficiency
- •Basket analysis beats cherry-picked anecdotes (compare cohorts over time)
- •Investor perception can create value even when business execution lags
- 11:06 – 13:08
Founder fundraising dynamics: the hidden cost of being labeled non-consensus
Erik and Martín emphasize that founders feel real pressure: they must appear fundable to VCs to ensure runway and follow-ons. Martín notes founders’ DMs strongly agreed with the tweet, describing the tension between being non-consensus in product and consensus in fundraising narratives.
- •Founders must raise again within ~18–24 months; perception matters
- •Being publicly ‘passed on’ can hurt future fundraising
- •Founders: need product-market alpha, but must sell a consensus story
- •Investor pattern-matching drives founder frustration and anxiety
- •Different audiences interpreted the tweet through identity/status lenses
- 13:08 – 17:26
Indigestion vs. starvation: when consensus capital becomes harmful
Leo argues non-consensus fundraising can force healthy frugality, while consensus rounds can lead to weak diligence and reckless spending. Martín agrees, adding his belief that many companies fail from ‘indigestion’—raising too much too easily—citing the 2021 era as likely a major capital wipeout cohort.
- •Hard-to-raise companies often become cash-efficient by necessity
- •Consensus/hot markets can create “house of cards” fragility
- •Weak diligence failure mode: ‘brand-led’ markups with little verification
- •Martín’s maxim: companies often fail from indigestion, not starvation
- •2021 mega-rounds (e.g., huge Series Bs) likely produced heavy losses
- 17:26 – 19:54
Cycles and the AI craze: efficiency on average, bubbles at the edges
They reconcile two truths: markets can be broadly efficient over time, but still show bubbles and neglected sectors during cycles. Martín points to AI attracting speculative capital while strong non-AI infra struggles; Leo references dot-com vs. 2010-era vintages as evidence that sentiment shifts strongly affect returns.
- •Two persistent failure modes: bubbly consensus and undue pessimism
- •AI cycle: speculative raises in unclear models; good ‘off-theme’ companies starve
- •Despite hype, leaders like OpenAI/Anthropic show real demand signals
- •Venture vintage data: dot-com median funds bad; 2010 era rewarded contrarians
- •Market efficiency may improve on average even as extremes persist
- 19:54 – 23:19
Martín’s startup story: exuberance, downturn, then consensus returns
Martín recounts his own company’s fundraising journey: early term-sheet frenzy, a 2008 crash where funding vanished, then renewed interest as signs emerged, culminating in a major acquisition that later proved highly valuable inside the acquirer. The story illustrates how market sentiment, timing, and emerging signals interact—and how early exuberance can look irrational but still align with eventual outcomes.
- •2007: hot seed at a high price (10M post) before clear direction
- •2008 recession: couldn’t raise; near-bankruptcy; harsh investor feedback
- •Recovery: hot round returns as signs of life appear; later rounds become very hot
- •Acquisition looked expensive on current metrics but proved strategic long-term
- •Interpretation ambiguity: luck/overexuberance vs. market sensing latent potential
- 23:19 – 26:19
How non-consensus becomes consensus: milestones, follow-on narratives, and deep tech
Martín presses Leo on what bridges the gap from a lonely seed to broad validation. Leo explains he underwrites milestone-based de-risking: early checks fund progress that makes a later, larger round plausible—especially in deep tech where the ‘asset’ often won’t be fully working by Series A.
- •Key question: do you bet on business working soon, or on milestones that attract capital?
- •Deep tech: product often still being built at Series A/B; milestones matter most
- •Fundraising plans must match realistic next-round expectations (10M vs 50–100M)
- •Harder bet: needing a ‘top 5%’ mega Series A after a small seed
- •Investors must model what future investors will require to say yes
- 26:19 – 28:20
AI vs. deep tech/humanoids: growth speed, weaker moats, and valuation hype
Leo notes AI companies can grow faster than the old ‘triple-triple-double’ playbook, but durability and moats feel more uncertain. They discuss hype cycles in defense, bio, and robotics (especially humanoids), where valuations can surge without corresponding fundamental changes, affecting opportunity cost and investor discipline.
- •AI: unprecedented growth speed, but outcomes may be less durable due to weak moats
- •Deep tech cycles: defense valuations jumped post-conflict without fundamental shift
- •Humanoids: extreme hype and capital concentration makes new entrants harder
- •Opportunity cost framing: invest in hyped sector at 40 or quieter sector at 15?
- •Consensus areas often get implicitly avoided due to pricing and crowding
- 28:20 – 33:38
TAM traps and unit economics: why “infinite markets” distort venture thinking
Martín and Leo critique the tendency to justify any valuation with a massive TAM (e.g., ‘human labor is trillions’). Martín argues unit economics and standalone business viability must anchor investment theses, citing autonomous vehicles as an example where enormous spend hasn’t yielded venture-grade economics for most startups.
- •Huge TAM can make ‘any price’ seem rational, warping decision-making
- •Unit economics as the discipline that prevents hype-driven investing
- •Autonomous vehicles: $100B invested, yet economics may not beat Uber-like benchmarks
- •Two styles: invest for standalone scale vs. invest for likely acquisition outcomes
- •Picks-and-shovels can be a better way to play uncertain unit economics
- 33:38 – 43:07
Outcome expansion, fund size mechanics, and why prices may still be ‘too low’
Erik and Martín argue that because outcomes are now orders of magnitude larger, investing at higher prices can still deliver venture-like returns, and being in the winner matters more than bargain pricing. This leads to a debate about whether prices are constrained less by intrinsic value and more by fund mechanics and LP capital access—echoing SoftBank/Tiger-era experiments.
- •Bigger outcomes can justify paying ‘seed-like’ multiples at Series A/B prices
- •Martín: top returns imply some winners were underpriced even at high valuations
- •Constraint may be fund mechanics/LP capital, not lack of opportunity
- •SoftBank/Tiger tested ‘bigger checks/higher prices’; results were mixed for many reasons
- •Key claim: for top outcomes, price and ownership matter less than access to the winner
- 43:07 – 50:25
Venture identity vs. market reality: efficiency, cost of capital, and why venture isn’t dead
Erik reframes the debate as ‘competitive vs. non-competitive rounds’ and ‘working vs. not working’ rather than identity-laden consensus language. Leo warns a fully consensus world becomes a cost-of-capital game where the lowest required return wins, while Martín argues more venture capital is broadly positive because it funds growth and creative destruction over incumbent preservation.
- •If markets get more efficient, firms that can’t win deals will struggle
- •Consensus language is tied to VC identity; better frames: competitiveness and traction
- •Fully consensus markets risk devolving into cost-of-capital competition
- •Martín: public markets prioritize predictability; venture uniquely funds innovation
- •Ecosystem lens: more capital + competition can accelerate breakthroughs (e.g., healthcare)
- 50:25 – 55:51
What they’ll test next: cohort pricing vs. outcomes, and stage-based differences
Martín outlines a planned data analysis: compare winners vs. non-winners by relative pricing versus stage medians, and determine whether most returns come from companies priced above median. They close by acknowledging that check size and stage shape the feasibility of non-consensus investing, and briefly discuss whether multi-stage firms have structural advantages in seed for certain founder profiles.
- •Planned metrics: (1) were winners priced above/below median by stage? (2) where did returns concentrate?
- •Shared takeaway: don’t over-index on price arbitrage; missing great companies is costly
- •Stage and check size matter: large-check investing makes non-consensus harder
- •Multi-stage advantage at seed is strongest for proven founders and obvious spaces
- •Agreement to revisit once numbers are computed
