Uncapped with Jack AltmanInside the Mind of the Investor Who Backed Josh Kushner, Peter Thiel, and Marc Andreessen | Ep. 34
CHAPTERS
TrueBridge’s vantage point: Fund-of-funds, Midas data, and the job of picking investors
Jack sets context on Mel Williams and TrueBridge’s role as a venture fund-of-funds with deep ecosystem visibility, including early backing of top firms and data work supporting the Forbes Midas List. They frame the episode around how an LP picks venture managers versus how VCs pick companies.
- •TrueBridge’s scale and positioning in venture (fund-of-funds, multi-cycle perspective)
- •Early exposure to firms like Founders Fund and Thrive
- •Why LP decision-making differs from VC decision-making
- •Goal of the conversation: read on venture markets and investor selection
2025 venture sentiment: Early AI wave, real adoption, but frothy pricing at the front end
Mel describes optimism driven by AI’s long-run opportunity horizon and evidence of adoption at scale. At the same time, he sees frothiness—especially at the earliest stages—where credibility-driven rounds can clear at high valuations before PMF is proven.
- •AI seen as a 10–15 year return-generating wave
- •Adoption signals are strong, reinforcing excitement
- •Environment feels frothy overall, especially in early-stage AI
- •Founder ‘credibility’ can substitute for product evidence in pricing
Valuation inversion: Early-stage hotter than growth, and why multiples look ‘healthier’ later
They discuss how some growth rounds are being priced at more reasonable revenue multiples compared to prior peaks, while early-stage rounds can be aggressively priced. Rapid AI-driven revenue growth complicates conventional multiple-based valuation frameworks.
- •Growth-stage multiples can resemble public-market comps more than peak-private froth
- •Early-stage rounds often price in success before PMF
- •AI companies can grow 1→5/7 ARR faster than past SaaS norms
- •Faster growth makes standard multiple heuristics less reliable
Outside AI: More rational markets, attractive valuations, and milestone-based capital staging
Mel argues that non-AI categories look notably less frothy, with more disciplined step-ups tied to progress and fundamentals. While AI dominates activity, there remain compelling opportunities in other sectors at more reasonable entry points.
- •Market feels more attractive outside AI than within it
- •Valuations and terms more grounded in evidence and milestones
- •Founders and company quality remain strong beyond AI
- •AI comprises a large share of venture activity, creating demand/supply imbalance
If things go wrong: PMF failures, capital overhang, and ‘carnage + massive value creation’
Mel’s core risk is that many heavily funded startups won’t reach product-market fit, leading to painful write-downs and failures. Yet he believes both outcomes can coexist: significant carnage alongside unprecedented value creation, echoing past cycles like dot-com.
- •Main risk: high-valuation, high-capital companies failing to find PMF
- •Expectation of significant failures over the next decade
- •Simultaneous belief in larger-than-ever category-defining winners
- •Venture remains (and is increasingly) power-law driven
Why winners are bigger now: lower marginal costs, faster adoption, and incumbents fully awake
They explore why winner-take-most dynamics may be intensifying: software scales cheaply, buyers adopt faster, and enterprises are proactively experimenting. Consumers also onboard quickly (e.g., ChatGPT), accelerating early traction and compounding advantage.
- •Lower marginal cost of software increases scalability of winners
- •Enterprises are actively buying/trying AI—few incumbents are ‘asleep’
- •Consumer adoption cycles are faster than previous eras
- •Faster ramp amplifies power laws and signaling effects
Talent and signal concentrate at giants: OpenAI/Anthropic/Meta as magnets vs startups
Jack notes recruiting has shifted: top talent now views leading AI labs and major tech as compelling even relative to hot startups. Mel ties this to the magnified value of ‘signal,’ which pulls capital, talent, and customers faster than before.
- •Top companies now compete aggressively for startup-minded talent
- •Signal strength accelerates talent aggregation and customer pull
- •Examples extend beyond AI labs (SpaceX, Anduril)
- •Implication: increased concentration of advantages at the top
Platform snowballs vs emerging edges: Why TrueBridge likes both ends of the barbell
Mel explains TrueBridge’s barbell preference: premier platforms that can win across stages, and distinct individuals with proprietary early-stage access. He argues growth-stage venture can be attractive risk-adjusted return if done with the right firms, while early-stage requires unique angles to compete.
- •Barbell approach: elite platforms + differentiated individuals
- •Growth-stage (C/D/E) can be attractive with the right partners
- •Platforms have ‘right to win’ across the lifecycle with capital and brand
- •Early-stage winners need proprietary access and a differentiated approach
The long tail problem: brand advantages, legacy software drift, and who gets hurt
Mel expresses concern for the long tail of venture firms and for legacy software companies that can’t pivot to AI. As signaling becomes stronger, top brands gain structural advantages that may squeeze mid-tier firms and slow adapters.
- •Worry about long-tail venture firms in this cycle
- •Brand/signal advantages tilt outcomes toward top firms
- •Legacy software companies that can’t pivot to AI are at risk
- •Market dynamics may widen dispersion of returns
Consensus vs contrarian: ‘10% are the signal’ and the rest chase it
Reacting to the idea that good investing is increasingly consensus investing, Mel argues it’s both: most capital chases heat, while a small set of exceptional firms/people create the signal. Those signal-creators can be both contrarian early and dominant in competitive rounds later.
- •Most investors chase signal; a small minority create it
- •Examples of signal: Sequoia, Thiel, Kushner, top-tier platforms
- •Top firms can be contrarian and still win consensus battles
- •Signal creation becomes a source of compounding advantage
Fund size debate: venture math vs techno-optimism, and what actually breaks funds
Mel reconciles Josh Kopelman’s ‘physics of venture math’ with Marc Andreessen’s view that winners will be enormous. Fund size matters, but the more predictive risk factor is rapid step-ups in fund size relative to firm capability and conviction, especially when concentration requirements jump.
- •It’s harder to 10x multi-billion funds, but winners can still drive outcomes
- •Data shows size matters, but isn’t the only determinant of returns
- •Biggest risk is doubling+ fund size faster than capabilities evolve
- •Large funds require larger check sizes and higher concentration into winners
What makes exceptional investors: first-principles contrarianism + conviction to concentrate
Mel identifies two standout traits of elite investors: contrarian/first-principles thinking and the willingness to bet big when evidence emerges. They discuss how top-performing funds often end with extreme NAV concentration in a few breakout winners.
- •Trait #1: contrarian or first-principles approach (not following signal)
- •Trait #2: conviction—‘push chips in’ as winners emerge
- •Concentration is psychologically hard but historically correlated with top performance
- •Example referenced: Thiel doing deals others avoided
Why seed still matters: platforms struggle at seed, and TrueBridge focuses on people over markets
Mel argues many platform firms historically struggle to invest effectively at seed due to signaling and downstream conflicts, making specialist seed managers valuable. In selecting seed managers, TrueBridge emphasizes people—track record, unique angle, proprietary access, and personal brand—rather than trying to pick markets.
- •Seed offers important exposure and can be return-accretive
- •Platforms often cycle in/out of seed due to structural challenges
- •Selection criteria: judgment/track record, unique angle, proprietary deal flow
- •Seed is people-driven; avoid locking into ‘market bets’ over long horizons
Decision case studies: early bets that worked, and misses corrected later
Mel shares examples of non-obvious early commitments (Amplify Partners, Emergence) and the firm’s most consequential early relationship with Founders Fund. He also describes missing First Round’s first fund due to portfolio construction and passing on a16z fund I before investing in fund II once the platform thesis was clearer.
- •Affirmative ‘hard’ decisions: backing Amplify and Emergence early
- •Best unconventional decision: investing early in Founders Fund’s institutional era
- •Miss: passed on First Round fund I due to concentration constraints
- •Recovery dynamic: invested in a16z starting fund II after platform evidence emerged
Why mediocre venture firms survive: fragmented LP base, luck vs skill confusion, and long feedback loops
They explain the durability of venture firms: capital comes from many sources, many LPs struggle to separate luck from skill, and performance evidence takes years. By the time results are clear, managers may have already raised multiple additional funds.
- •Highly diversified LP supply base sustains many firms
- •Most LPs misattribute outcomes because luck vs skill is hard to parse
- •TrueBridge relies on deep network reference checks to assess real contribution
- •Long feedback cycle lets weaker firms raise follow-on funds before truth emerges
TrueBridge’s own concentration strategy: fewer managers, forced ranking, and capital-constrained discipline
Mel outlines TrueBridge’s evolution toward a more concentrated manager roster, shrinking core managers over time while increasing allocations to the best. They actively force-rank managers annually and remove ones displaced by higher-conviction allocations, better newcomers, or manager-specific issues like team changes or strategy drift.
- •Portfolio concentration increased from ~18 managers to ~11–12 over time
- •Annual force-ranking keeps the bottom of the roster ‘at risk’
- •Reasons to exit: allocate more to top managers; add better new entrants; manager-specific deterioration
- •Preference for capital constraints to enforce hard choices and preserve returns
Advice for aspiring LPs: build an authentic network and ‘follow the signal’ before trying to be it
Mel’s key advice is that LPs are only as good as their networks—relationships drive access, diligence, and insight. He recommends that newer LPs follow strong signal rather than trying to create it prematurely, since becoming signal requires long experience and pattern recognition; the cost of missing early funds is comparatively lower for LPs.
- •Network quality is the primary edge for LP performance
- •Make relationships authentic and personal to improve diligence and access
- •Early-career LPs should generally follow signal; being signal is hard
- •Becoming signal takes time, cycles, and accumulated pattern recognition
