Matt McIlwain

Venture Capitalist and senior leader at Madrona Venture Group

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Key positions and views

The current market is defined by a stark valuation dichotomy, with undervalued public SaaS companies and overvalued private AI firms [1, 17].

Venture capital LPs are facing a critical liquidity shortage, or 'DPI problem,' which needs to be resolved through increased M&A and IPO activity [3, 15].

The AI application layer is undergoing rapid, disruptive turnover, and will ultimately consolidate around a few dominant players in each category [18, 24, 29].

The foundational model lab category is generally overfunded and overvalued, suggesting better investment opportunities exist elsewhere in the AI stack [23].

A long-term, thesis-driven approach, like Madrona's 2016 shift to 'intelligent applications,' is crucial for navigating the hype and volatility of the current AI market [20].

Podcast consensus on McIlwain

Points of consensus

▶The AI application market is experiencing extreme disruption and high turnover, evidenced by the fact that 27 out of 40 companies on the Intelligent Application 40 list were replaced in a single year [18, 29].Apr 2026

▶Madrona Venture Group is actively investing from its tenth fund, a $770 million vehicle focused on early-stage companies [9, 21].Apr 2026

▶Echodyne, a Madrona portfolio company, is providing its advanced radar technology for security at all World Cup stadiums this year [8, 10].Apr 2026

▶Anthropic has a deep, ongoing collaboration with Goldman Sachs, with engineers embedded for over nine months to build a new wealth management onboarding system [11, 16].Apr 2026

Points of debate

▶McIlwain highlights a major market dichotomy: established public software companies like ServiceNow and Atlassian trade at record low multiples, while private AI companies raise capital at extremely high valuations [1, 17].Apr 2026

▶He identifies a critical 'DPI problem' where venture capital LPs lack cash returns [15], while simultaneously predicting that a near-term increase in M&A will begin to solve this liquidity issue [3].Apr 2026

▶He believes the foundational model lab category is overfunded and overvalued [23], yet also recounts his firm's decision to pass on investing in a leader in that category, OpenAI, at what was a lower valuation [28].

▶He notes that large private equity firms like Thoma Bravo are struggling after acquiring SaaS companies at high prices [2], which contrasts with the hyper-growth narrative of his own firm's portfolio companies like Echodyne, which grew bookings from $20M to $340M in three years [27].Apr 2026

Key themes

▶The Great Valuation DivideApr 2026

McIlwain frequently contrasts the depressed valuations of public software companies with the sky-high valuations of private AI firms. He points to examples like ServiceNow trading at record lows while AI unicorns raise massive rounds, creating a challenging environment for investors to navigate.

This valuation gap suggests a market dislocation where future growth expectations for AI are being priced in aggressively, creating both significant opportunities and risks for venture capitalists who must decide where to place their bets.

▶The AI Application Layer ShakeoutApr 2026

McIlwain emphasizes the rapid disruption and consolidation occurring in the AI application space. He cites the high turnover on the Intelligent Application 40 list as evidence of this volatility and predicts that most categories will consolidate to just two or three winners.

For investors, this signals that backing a company in a crowded AI application category is a high-risk, high-reward proposition, where market leadership is fragile and long-term dominance is far from guaranteed.

▶Venture Capital's Liquidity CrisisApr 2026

A core concern for McIlwain is the 'massive DPI problem' where Limited Partners (LPs) are not seeing cash returns from their venture investments. He connects this to a slow M&A and IPO market, which has trapped capital in late-stage private companies.

This focus on DPI highlights a shift in the venture landscape from prioritizing paper markups to demanding real, distributed returns, which will likely increase pressure on VCs to find exit opportunities for their portfolios.

▶Madrona's 'Intelligent Application' ThesisApr 2026

McIlwain details his firm's strategic pivot in 2016 to focus exclusively on 'intelligent applications' infused with data and machine learning. This thesis is exemplified by successful portfolio companies like Echodyne and the sale of Statsig to OpenAI, demonstrating a long-term commitment to the AI-native ecosystem.

Madrona's early and disciplined focus on a specific AI thesis appears to be a key differentiator, allowing them to build deep expertise and potentially outperform in a crowded market, even if it means passing on seemingly obvious deals like OpenAI.

Source episodes

Sentiment over time

Not enough data for timeline

Changes over time

2013

McIlwain notes the origin of the Lean programming language at Microsoft Research, a technology later central to the thesis of portfolio company Axiom [12].

2016

Madrona Venture Group, where McIlwain is a key figure, strategically shifted its investment thesis to focus exclusively on 'intelligent applications' infused with data and machine learning [20].

Summer 2022

McIlwain reveals that Madrona Venture Group made the significant decision to pass on investing in OpenAI's round at a $25 billion valuation [28].

~3 years prior to 2026 outlook

McIlwain provides a baseline for portfolio company Echodyne's growth, noting it had $20 million in bookings [27].

Last year (relative to 2026 outlook)

He highlights Echodyne's massive growth, with bookings soaring to $340 million [27]. He also notes the high turnover on the Intelligent Application 40 list, with 27 new companies appearing [18, 29].

Present (2026 Outlook)

McIlwain characterizes the current market as having a 'massive DPI problem' for LPs [15], a stark valuation dichotomy between public and private tech [17], and an overfunded foundational model category [23].

Suggested prompts

How does McIlwain's observation of a 'DPI problem' influence Madrona's investment and portfolio management strategy for Fund 10? &nearr;Given Madrona's decision to pass on OpenAI, what specific criteria does McIlwain likely use to evaluate foundational model vs. application layer investments? &nearr;McIlwain predicts consolidation in AI application categories. What early indicators might he look for to identify the likely two or three winners in a given space? &nearr;How does the thesis behind Axiom (AI as a verifier for math and code) align with Madrona's broader 'intelligent applications' strategy that began in 2016? &nearr;

Key concepts

Venture Capital Market Dynamics 1 ep AI Application Layer 1 ep Market Valuations 1 ep Madrona Venture Group 1 ep DPI (Distributions to Paid-In Capital) 1 ep Portfolio Company Performance 1 ep Intelligent Application 40 1 ep AI Foundational Models 1 ep AI Verification & Logic 1 ep

Notable quotes

“how can the price of some of these great software companies, ServiceNow, Atlassian, Monday.com, be at such record low multiples in the public market? And then you're seeing the kinds of valuations and amounts of capital that are happening in the private markets.”

Matt McElwain · 2026 Private Capital Outlook

“our university endowments and foundations, the family offices that are our investors, they've got a massive DPI problem. Distributions to paid in.”

Matt McElwain · 2026 Private Capital Outlook

“of last year list compared to the prior year there were 27 out of the 40 that turned over”

Matt McElwain · 2026 Private Capital Outlook

“Part of the conjecture is that an AI mathematician can be generalized as a verifier, not just of a mathematical proof, but of other things like software code.”

Matt McIlwain · Axiom Math at The Montgomery Summit | Fireside Chat

Report last updated: Apr 21, 2026

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