Charles Hudson

Don't Build a Better Wheat Farm" - Why Defensibility Stakes Are Higher in Deep Tech Just published a new piece on my "Ignore the Confusion" blog, building on thoughtful insights from Eric Ver Ploeg at Tunitas Ventures about startup defensibility. Eric's core thesis: Too many startups pitch like wheat farmers - "huge TAM, slow incumbents, growing market, domain expertise" - but fail to think through long-term defensibility until it's too late. From a deep tech perspective, the stakes are even higher: ** Unlike software, deep tech founders must commit to defensibility strategies from day one - their funding depends on it ** Patent vs. trade secret decisions are often difficult to reverse and shape your entire competitive strategy ** Even "picks and shovels" providers (the tools that make industries more efficient) become commodities without proper moats The key insight that resonates: Defensibility can't be retrofitted. Whether you're building software or deep tech, your moat must be architected into the business model from the start. Thanks to Eric Ver Ploeg for sharing these insights on startup strategy and letting me build on his framework from a deep tech lens. Read the full post: https://lnkd.in/dEj_iF-Q #DeepTech #StartupStrategy #Defensibility #VentureCapital #Innovation

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Trying something new this week - a studio industry roundup with my take on what matters. The studio model is no longer an experiment being run in a handful of places. It is infrastructure being built across geographies, sectors, and institutional types. 5️⃣ stories from the past seven days:  1️⃣ OSS Ventures hit a EUR 40M first close on a EUR 75M follow-on fund -- capital raised specifically to back the 30 companies their studio created since 2019. The "build then fund" lifecycle is maturing. B'More Venture Studio with a similar thesis: studio operations first, then dedicated capital to scale what's working.  2️⃣ Delta40 raised $20M from 54 investors across 13 countries to launch Africa's first integrated venture studio and fund. The LP base is almost entirely DFIs and foundations. Impact capital is discovering the studio model as a deployment vehicle, something I seeing across geographies (conversations with Michael Bob Starr and Damias McDonald this week alone) 3️⃣ Start Holdings launched Start.vc out of NYC and Barcelona after a $200M EV infrastructure exit. Another operator-turned-studio-founder. This is the pattern we see quite often in our work at 9point8 -- experienced operators like Jason Goldsmith who've built and exited deciding the studio model is how they want to build next.  4️⃣ UBC HATCH admitted 10 new deep-tech spinouts for Winter 2026 -- mostly climate tech. University venture builders are quietly becoming one of the fastest-growing segments. Follow Evan Allen and Jaimie Testai to keep track of its rapid evolution.  5️⃣ The INVEST Act is advancing through the Senate. If passed, it expands qualifying VC fund size from $10M to $50M and loosens general solicitation rules for accelerator and university events. For studio-funds -- and for folks in the The Venture Studio Forum community tracking policy -- this could be the most significant capital formation legislation since the JOBS Act. If this roundup format is useful, I'll keep it going. What other noteworthy news hit your desk this week, and what stood out to you?

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Thrilled to share the launch of Criticality, a $65M fund built to back founders solving hard, high-leverage problems at the intersection of technical complexity and real-world urgency. Criticality builds on the foundation we’ve laid at Antler - applying our conviction-driven approach to companies at Seed & Series A. The fund is sector-flexible but applies a deep tech lens to each opportunity, targeting non-obvious, high-leverage problems from the earliest stages of company formation. Grateful to the New Mexico State Investment Council for their support in anchoring Criticality. More to come soon from Cash Allred & I.

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"We've become so insular in who we talk to and listen to, and I didn't realize how much that was keeping me in tunnel vision and tunnel thinking." Seth Levine, Co-Author of Capital Evolution and Partner at Foundry, talks about seeking out more diversity of thought on Tech Scenes Unplugged. #venturebacked #vcbacked #venturecapital #leadership #ceo #founder #operatingsystem #peakOS #foundermode

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Talking to users and thoughtfully integrating their needs and wants was woven through everything we did at IDEO—threaded through every prototype and product decision. Reading my colleagues Joanne and Leo's latest work brought that to mind. They describe a future where research runs continuously, with AI agents recruiting participants and instantly surfacing insights. Synthetic personas, carefully trained on consumer signals, mirror human behavior closely enough to extend live studies and compress timelines from weeks to hours. Today user research mostly runs in fits and starts, prohibitively expensive and locked away in dedicated project cycles when it could be a constant companion, seamlessly integrated into the product development process. Joanne and Leo lay out the full vision here: https://lnkd.in/gEUBSG_J

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Vertical AI feels like a horserace 🏇 but after stepping back and thinking about the current state of market, I think what's equally unique is you have three different models competing at once. I think about it through this lens: 1️⃣ AI-enabled SaaS: the familiar path Take a proven vertical category and supercharge it with AI. Clear ROI, legible GTM, easy for VCs to understand. But real questions around differentiation and long term moats. 2️⃣ AI Roll-Ups: PE gets a facelift Acquire businesses, deploy AI to improve unit economics, and own the data + operations. Powerful if executed well, but operationally and financially complex. Big open questions around end state valuation. 3️⃣ AI-native industry disruptors: Become the thing Don’t sell software. Don’t buy incumbents. Build a fundamentally new version of the industry from scratch. Highest upside, but probably the highest casualty rate. You have to choose the model where the physics match the market. Each model has different capital needs, metrics, risk profiles, and founder skill requirements. I did a deep dive on each model 🤿 : ▪️where each model wins (and fails) ▪️how data dynamics actually differ across them ▪️why valuation expectations may diverge more than people expect ▪️and where I think the biggest outcomes will come from... As always, if you are building, investing, or just trying to make sense of vertical AI landscape, I always love to jam. Full post here: https://lnkd.in/g9a_9rtj

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