Building something great is hard. The question is: which hard are you willing to choose? That’s something @DWeisburd and I dug into on the latest episode. David chose podcasting as his hard. Three years ago, he committed to building How I Invest for at least a decade - before he knew whether it would work. There were no guarantees. Just the grind of booking guests, navigating compliance, building credibility, and showing up episode after episode. That commitment eventually turned the podcast into something much bigger: a network of investors, entrepreneurs, relationships, and venture deal flow. And that’s what I found most interesting about our conversation... The podcast wasn't just building David’s network. It was building David. We talked about: → The power of “information alpha” and “relationship alpha” → Why some emerging managers have a winner mindset—and others don't → Why founder-product fit matters → How relationships compound over time → And why the best opportunities often come from playing the infinite game My biggest takeaway: You can’t make building something great easy. But you can choose a hard worth doing. Then you have to stay in the game long enough for the compounding to begin. Big thanks to David for joining the pod.

Doing a speaking role at a @sydecario investor event today. There’s a great community of people coming together to provide liquidity into the venture market. I believe that the private markets are going to increasingly fill the void created by the lack of access to the IPO market. There’s a parallel here to the LBO market where firms trade amongst themselves to unlock capital. Theory - more liquidity is coming.

the surprise conclusion of the machine consciousness debate will be that people are machines.

I have raised my hand to help the Post Exit Founder chapter in NYC plan some events. We hosted some of the members at the @Interplay office for lunch. A pic below. The group is jam-packed with people that can be driving change and improving society. I think what really motivated me to volunteer was the hope that we could get a few more ex-founders back in the fray driving change. If you’ve got ideas on how to leverage this community. Ping me.

Science non-fiction

Most people only see the front end of fintech. The app. The card. The payment. But underneath that experience is an enormous amount of infrastructure and some of fintech’s biggest opportunities are hiding there. @RoryOReilly, cofounder and CEO of @KnotAPIs, joined me on the pod to talk about how Knot is rewiring fintech and transforming financial connectivity. We dive into: - Why keeping a card “top of wallet” is harder than it sounds. - The infrastructure required to connect financial institutions with thousands of merchants. - Why security and compliance are foundational, not afterthoughts, in fintech. - Where blockchain and stablecoins could fit into the financial stack. - Rory’s unconventional path from selling shoes with his family to building fintech companies with his brother. What I particularly enjoyed about this conversation is that Knot is tackling a problem most consumers don't even realize exists. The biggest innovations in fintech may not be the products we see, they may be the infrastructure quietly making everything work. Special thanks to Rory for joining me on the pod.

Robots don't usually fail because they don't work. Poor system integration and mismatched ROI expectations cause more than half of deployment failures. Inadequate change management adds another ~20%. The tech works. The deployment doesn't. That gap is a software problem. @navya_prab maps where physical AI's second wave is opening up: interplay.vc/blog/physical-a…

Main Street is planning to hire like it's 2020. The S&P is earning like it's 2021. Both are true right now. In May, the NFIB Small Business Optimism Index fell to 95.3, its ninth straight month below its 52-year average. Buried inside: a seasonally adjusted net 9% of small-business owners plan to add jobs in the next three months — down four points from April, and the lowest reading since May 2020. Two months later, the top-line labor number caught up. June nonfarm payrolls came in at just 57,000 — roughly half the 115,000 consensus, with downward revisions to the prior two months on top. Meanwhile, the other economy is having its best year in half a decade. S&P 500 companies are on pace for their strongest quarterly earnings growth since 2021, concentrated in the AI-adjacent names that drive capex and revenue simultaneously. → Small-business hiring plans: net 9% (lowest since 2020) → S&P 500 Q1 2026 earnings growth: strongest since 2021 The takeaway isn't that Main Street is broken. It's that the two economies are running on different fuel. Big-cap earnings are being pulled forward by an AI capex cycle that concentrates spending in a narrow set of buyers and sellers. Small-business hiring is being throttled by pricing pressure, labor costs, and demand uncertainty that doesn't show up in an index dominated by ten names. Any thesis that ignores which economy a portfolio company sells into is a thesis running on the wrong data. Full analysis → interplay.vc/blog/two-econom… #SmallBusiness #NFIB #Economy #VentureCapital #AI #Interplay

The manufacturing story on your feed says two things: factories are humming, or factories are dying. Neither is what the federal data actually shows. Employment is up 333,000 workers since 2015. It's also down 305,000 since January 2023. Output is 2% below its 2015 baseline. It's also up 3.4% off its October 2024 bottom. Both facts, same series. The 2015–19 productivity gap is closing right now: output per hour is up 3.9% since early 2023, on a workforce that's shrinking. And it's closing in specific segments the aggregate hides. High-tech equipment output is up 11.1% year over year. Consumer goods is down 1.2%. The average tells you nothing. With capacity utilization 2.5 points below its long-run norm, the industrial-tech pitch that sells "more units per hour" is selling into a factory that already has room to run harder. What buyers are actually paying for right now is different. We pulled the federal data. Full read: interplay.vc/blog/manufactur…

Joined @TheFullRatchet to talk AI demand curves, startup moats, seed strategy, and building internal AI tools. Always a pleasure chatting with Nick. Check out the full episode here: fullratchet.net/514-ais-effe…

Every founder I know is asking the same question: “How should we actually implement AI across our company?” The reality is that every playbook will require customization, but there are clear patterns beginning to emerge. In this episode, Chetan Narain, Cofounder of Pepper, joins me and my colleague, Interplay Senior Partner @kevinwtung , to share how Pepper is integrating AI across its products and organization — and the lessons they’ve learned along the way. We discuss: - Where vertical SaaS ends and vertical AI begins - AI agents and guardrails - Organizational redesign - How to move fast without creating chaos If you're building a company and thinking beyond AI features toward AI transformation, I think you'll enjoy this conversation.

This is cool.

The hands of Tesla Optimus will be better than this

What happens when technology evolves faster than the venture capital model built to fund it? In this episode, I sit down with my longtime friend @jefielding , Cofounder and General Partner of @EverywhereVC , to explore how venture capital is adapting to a world where competitive advantages can disappear faster than ever. We discuss what it takes to raise a pre-seed round in today's market, why traditional assumptions around defensibility are being challenged, and how AI is reshaping the economics of software companies. Jenny also shares lessons from her time as a Managing Director at Techstars and explains why Everywhere Ventures built a community-driven approach to investing — leveraging founder networks and real-time market insight to identify breakout companies at the earliest stages. Whether you're a founder raising capital, an investor navigating a changing market, or simply curious about where technology is headed, this conversation offers a practical look at how venture is evolving for the next generation of companies. Special thanks to Jenny for joining the show and sharing her perspective on the future of venture.

OpenAI took the first formal step last month toward the most anticipated tech listing since the dot-com era. It sits on an $852B private mark from its March round and has signaled ambitions to price the IPO north of $1 trillion. The valuation is the "easy" part — justifying it isn't. Set the valuation against the fundamentals and you get the defining tension of the filing: → Revenue is genuinely extraordinary — roughly $25B annualized as of early 2026, up from ~$20B at the end of 2025. → But the same business runs a 33% gross margin and is on track to burn ~$27B of cash in 2026, rising toward ~$63B in 2027. Internal forecasts pointed to a $14B loss in 2026 alone. That gap — between fast-climbing revenue and even faster-climbing cash burn — is what needs to be justified. A deck can frame a widening loss as investment in a category-defining platform — Amazon did exactly that. But an S-1 is a legal document. It has to reconcile an $852B mark with disclosed losses, a compute-cost structure that scales with usage, and partner economics — including Microsoft's revenue share — that most public investors will read closely for the first time. For founders, the lesson isn't "don't burn capital." It's that the story you tell in a deck and the story an S-1 forces you to tell diverge sharply the moment losses have to be quantified in a table. OpenAI is about to run the highest-stakes version of that exercise in public. What the market decides the unpriceable is worth will reset the comp set for every AI company behind it. Full analysis → interplay.vc/blog/pricing-th… #OpenAI #IPO #VentureCapital #AI #AIEconomy #TechIPO

It might be an understatement to say that time heals all wounds. We instinctively reduce stories of old to fantasy - a part of some other reality. Objectively this isn't true, it's just a fallacy of the human mind. 250 years ago people endured awful tragedy to deliver this holiday to the Americans reading this. People ran directly into gunfire, were impaled, starved and burned alive.  People lived the rest of their lives without husbands and sons. It was an ugly business, but the people of the time valued their freedom so greatly that they died willingly. In doing this they started a global movement. Before this revolution - roughly zero humans lived in a democracy. Today close to half of all humans live in a version of one. Americans - imagine what it would take for you to opt in for that now, risking your life and your family. Enjoy your BBQ, laugh and be happy, but privately, in the back of your mind, marvel at what they did. And then think about the folks in other countries who are still fighting this battle.

Socks are the #1 most requested item at homeless shelters. Dave Heath turned that single fact into @BOMBAS . The brand has donated over 200 million items of clothing and built a billion-dollar business along the way. What makes Dave a builder worth studying isn't just the scale. It's the discipline behind it. He reverse-engineered an exceptional product from a mission. He learned to test before betting. And he protected the brand as it grew. In this episode, I sit down with Dave to break down: • How he reverse-engineered an "exceptional product" from a donation mission and brought athletic-sock innovation to the mass market. • The Shark Tank effect, going from $800K to $2M in revenue in the six weeks after airing. • The expensive lesson of expanding into adjacent products too fast and the MVP-testing discipline that replaced it. • Radical-ish transparency. • The self-awareness to evolve his own role as the company scaled and how he screened his successor for humility • How Bombas is approaching AI. Big thanks to Dave for coming on the pod and sharing the playbook (and the mindset) behind Bombas.

The LBO market has had a private capital liquidity option for years. LBO firms would just sell to other LBO firms. In my view this is the VC market catching up.

America just stopped building offices for people. In April 2026, US data-center construction ran at a $50.7 billion annualized rate, passing general office construction at $43.8 billion for the first time on record. This is the cleanest single-chart refutation of the "services-led growth" narrative we have seen. The next investment dollar is no longer funding a software-company office in Austin. It's funding a hyperscale data-center campus (the giant cloud-compute halls run by Amazon, Microsoft, Google, and Meta) in northern Virginia, Phoenix, or central Texas — and the entire industrial supply chain underneath: electrical switchgear, transformers, liquid cooling, specialized cleanroom build-outs, concrete, copper, steel. Two things are converging. Raw computing power is now the binding constraint on every frontier tech company. And the office category itself is shrinking in real terms after a generation of remote/hybrid work and a wave of conversions of mid-tier ("Class-B") offices into apartments. The compute curve is doubling every five years; the office curve has flatlined. The opportunity for industrial and AI-infrastructure founders is wider than the headline. Anything that touches the data-center supply chain — grid interconnection, electrical equipment, liquid cooling, prefab construction, on-site power, fiber, security, system commissioning — sits on a multi-decade tailwind that does not depend on AI models continuing to get better. Even if the AI software layer becomes a commodity, the compute infrastructure has been built and someone has to run it. The $50.7B number being quoted is in the spotlight, but the real insight is the crossover. Read more here: interplay.vc/blog/america-st… #industrialAI #datacenter #venturecapital #infrastructure

The Semiconductor Industry Association (SIA) reports March 2026 chip sales hit $99.5B — up 79.2% YoY. Q1 totaled $298.5B. The industry is on track for the first $1T year in semiconductor history, ~2 years ahead of the most aggressive 2024 forecasts. The number matters less than the shape of the curve. For 40 years, semis were textbook cyclical: 18–24 month up-cycles (PCs, mobile, cloud), then 12–18 month corrections. Investors learned to underwrite the cycle, not the trend. The 2024–2026 data shows the cycle didn't correct. It accelerated. March's +79% follows a +28% comp in March 2025 — this isn't a base-effect bounce. One end-market explains nearly all of it: AI infrastructure. HBM is sold out through 2027. The constraint is no longer demand — it's fab capacity, packaging, and power. For VC, the takeaway isn't about chip companies themselves. It's that the cost of running AI — training and inference — is falling faster than any prior compute shift, because the underlying chip supply is compounding at 70%+ annually. That cost decline flows downstream to every company building on top of it. In the cloud era, infrastructure costs flattened within a few years; here, they're still dropping. Anything built on this base inherits that. The first trillion-dollar year isn't the story. The story is that it no longer looks cyclical. interplay.vc/blog/the-chip-i…