Eric Chin

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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AI isn't just powering software. BrightAI just raised $51 million to automate inspection and other hardware-based AI capabilities. As Alexa von Tobel, co-founder of Inspired Capital put it: “So many people are so focused on the future of digital AI, but we’re excited about this new layer of AI: the physical world AI.” We are in the early innings of a series of AI-fueled innovations! https://lnkd.in/enhmafvH

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Today Isembard announced a $50m Series A fundraise led by Union Square Ventures, less than a year after Notion Capital led their Seed round. 💥This is a company right at the intersection of AI & supply chain sovereignty & they’re exceeding even our most optimistic expectations. By the end of the year they will have 25 factories across the UK, US, Germany, France & Ukraine. 💥Isembard is rapidly scaling its network of AI-first factories for precision manufacturing at the same time as market demand is accelerating. Macro forces (reshoring, rising defence spend, concentration risk in Asia, and the growth of neo-primes) are structurally increasing demand for fast, local, flexible manufacturing that the current industry is unable to deliver on. 💥The innovation is in the way the company builds and franchises factories as a product with tightly integrated units of machines, software, robotics, & process intelligence that turn design files into certified components with speed & reliability. 💥Their AI software platform, MasonOS, connects all sites into a single operating system, replacing fragmented shops with standardised, high-performance industrial nodes & enabling real-time quoting, predictable delivery, & low defect rates. By owning the full production stack, Isembard delivers premium manufacturing performance with the scalability & flexibility of a software platform. 💥We are at the top of the AI hype cycle and there are understandable concerns about the ROI on the vast sums of money being invested into this new super-cycle. But, the ROI for Isembard is both clear and compelling. They will deliver components 10x faster and at 50% of the cost of current suppliers. They will also build a de-centralised, global network of factories to meet the growing demand for national or regional sovereignty. 💥This is a company with a clear vision to disrupt a massive, fragmented $1.8tn component manufacturing market. Today marks another huge stride towards that vision. And they are only just getting started. We’re thrilled to be on this journey with Alexander Fitzgerald & the team & we’re also excited to welcome Rebecca Kaden & USV into the investor base. Notion Capital Union Square Ventures Alexander Fitzgerald Rebecca Kaden Maximilian Eichler Stephen Millard Britt Mulder

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With RL techniques like GRPO, it’s become easy to fine-tune small models to outperform foundation models on vertical tasks. We’re open sourcing Osmosis-Apply-1.7B: a small model that merges code (similar to Cursor’s instant apply) better than foundation models. Links in comments! Using foundation models for high specificity, low complexity tasks like code merging can be overkill - instead, it’s better to use a specialized model instead for higher performance and latency. We fine-tuned the model on 100K real-world code commits where the model needs to merge a code edit snippet into an original code snippet. On the test dataset (subset of CommitPackFT), foundation model performance ranged between 0.77-0.93 reward score. In comparison, Osmosis-Apply-1.7B achieved a 0.98 reward score - while also being 3X-5X more affordable than the next cheaper model AND ~10X faster! Why this matters: the best AI agents use multiple models to ensure task specialization and reliability at scale - on top of cost and latency benefits as well. Links to the model and full write-up (with reward function, hyperparameters, etc.) are in the comments - reach out if you’re interested in using reinforcement learning!

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I’ve known David Meister for years and backed him at Sydecar, so I’ve seen his ability to build through complexity. Axiom Trust is taking on one of the most outdated parts of wealth infrastructure, trust administration, by pairing a regulated trust company with AI-native workflows. We're proud to invest again and congrats to the team on the launch!

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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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Hot take: lot's of #LP's are growing underwhelmed with traditional venture investing. 💰 Fees are high given the level of engagement with the company operators. ❓ Access is opaque - LP's are unclear on the portco operations and outcomes. Outcomes are scattered - power law investing can certainly work, but many feel that venture capital investing is just a numbers game. There's another way. I'm biased, but I think it's better: #VentureStudios as an asset class. They blend: 🤜 🤛 Control and ownership 💡 Thematic focus 👬 Partnership with operators Studios offer a fund-like structure — but with more alignment, higher value conversion and less noise. If you're interested in understanding more about the studio model, we have resources to share. Please reach out and let's connect!

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I’m lucky to work alongside colleagues like Richard Fruehauf, who constantly push my thinking. His latest piece unpacks why U.S. robotics and drone manufacturing faces uphill economics, and how public policy can help turn the tide. One key takeaway: 232 tariffs on robotics imports could help reset the economics that are holding back U.S. investment and production. It’s a sharp perspective on how policy can shape the future of advanced tech here at home. Well worth a read: https://lnkd.in/edMPTQPQ Richard Fruehauf, Robert J. Szczerba, Michael Lutzky, Edward Ovando, Karl Sanchack, Herman Herman, Jeff Legault, Phil English, Richard Fruehauf, Ryan Aument, Michael Guido, Sheila Fitzgerald Sterrett, David McCormick

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