Jeff Clavier

Honored and excited to partner with Drew Baglino and the incredible team at Heron Power as they re-imagine the electric grid for the modern world. Power in America is not zero sum. We deserve access to cheap and abundant electricity and we should win the AI race. And we must build the capacity to manufacture critical technology here at home. When faced with existential challenges, we deploy technology against them. And we win.

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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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I’ve been working on the theory of why small, shippable factories are the perfect inversion to the centralized prefab factory model. It all collapsed into one line of math (credit to Gilles Retsin): Factory Efficiency = Usage × (Throughput × Product Value ) / ( CapEx + Deployment OpEx) The idea is that the closer you can drive Usage to 100 % while keeping the denominator tiny, the faster the flywheel spins.  A shippable microfactory tackles this by primarily focusing on a high Usage factor. Because the microfactory can be packed up afterwards and reused, its CapEx gets amortized over multiple projects, effectively raising its overall Usage across the year. If one project alone doesn’t fully occupy the factory, it can simply roll over to another job to maintain high Usage. Its CapEx is often an order of magnitude lower than a centralized factory (< $1M vs. $10-50M min), so the breakeven throughput is more achievable on a small pipeline of work. The real breakthrough here is portability. There is near-zero stranded capital and almost no idle time. To flesh this theory out, I wrote 3000 words on the topic for Brad Hargreaves and Thesis Driven. The article breaks down: - why “fixed factories, shipped goods” is being inverted to shipped factories, fixed goods - what a sub‑$1 M robotic cell does to CapEx per home versus a £45 M off‑site plant - early production/cost data on a shippable microfactory from Mollie Claypool, Gilles Retsin, Sam Baker and the Automated Architecture (AUAR) team - the hybrid reality of robots tackling the heavy structural components and human crews assembling faster and cheaper - technical constraints to pull this model off and future considerations on the optimal business model (own vs. rent) Major shoutout to Gilles Retsin for helping me pull this together. https://lnkd.in/eRTnbZPa

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"Build the event you wish existed" That's what we at Monadical did last week at #NYTW. We wanted an AI Engineers discussion for Engineers. There's always a lot of events out there for VCs to network, or for startups to learn about G2M, but very little on lessons learned from actual engineers in the field. So that's the event we hosted. Big thank yous to our awesome panel, Roy Pereira, Ben Cohen and Corey J. Gallon. Here's the key takeaways and the AI tools we're using. 🚀 All three panelists independently called AI Agents the most transformative LLM application they’ve used. They specifically called out Claude 3.5 Sonnet for its accuracy and reliability. 🪨 We dug into how LLMs are “jagged”, not general. They can be shockingly good at some tasks and completely fail at others. Everyone agreed: good evaluations are critical (and hard.) 🧪 Corey noted how public benchmarks and reality are two different things. Most public evals are saturated or gamed. ♊ Ben emphasized that AI projects are actually two projects: building the tool and building the evaluation process. 🧱 We explored how falling dev costs may impact startup defensibility and labor demand. Roy shared that founders are already shifting strategies in response. ⚒️ In a world of daily AI launches, the panel discussed how they decide what’s worth attention, and what’s just noise. They called out tools like Goose, Aider, Claude Code, and Monadical’s own Cubbi, which helps run agentic workflows safely in dev environments. (links in the comments). CTA: What would you want to hear in an AI Applied Engineering talk you attended?

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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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I’m thrilled to share that White Star Capital is backing AMI - Advanced Machine Intelligence in their historic $1.03bn seed round 🚀 - Led by the legendary Yann LeCun and Alex LeBrun. AMI isn't building another LLM, they're building World Models. Why this matters: *Beyond Text: Grounded in JEPA architecture to solve physical-world problems. *Global Powerhouse: A dream team from Meta FAIR, OpenAI, and DeepMind. *Sovereign Tech: A credible European-led alternative to US Big Tech. Excited to be part of this journey along with the rest of the WSC team Eric Martineau-Fortin, Matthieu Lattes, Bérénice Moustial and Victoria Pozzi Rocco Belforti. 🌍✨ https://lnkd.in/eiCKZKSQ #VentureCapital #AI

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As AI workloads eat up global computing supply, DRAM prices are surging like never before—creating major challenges for teams trying to plan out their data center spend. One of my portfolio companies, Mext, is addressing the largest cost component in the datacenter: server memory (DRAM). From being on the board of Redis for the last twelve years, I’ve learned a lot about the various aspects of this problem and approaches to solving them. Mext found a breakthrough, using new AI techniques, for dramatically reducing the amount of server-DRAM required to run applications, all while maintaining performance. It intelligently manages the server’s memory, keeping hot pages (i.e., those in use by applications) in fast DRAM and offloading cold pages (i.e., those less used) to a much less expensive memory tier (e.g., NVMe Flash). Key to the approach is ensuring cold memory pages that are about to be accessed by an application are back in DRAM before the application needs them or notices that they were gone. This is done without modifications to the application or the OS – so it can run in the cloud or on-premise. Mext thus allows applications to either run using less DRAM or keep their DRAM footprint but do more with it. They're hosting a webinar along with Fred Weber (former CTO of AMD) on Jan 22nd at 10am pacific time. You can register here: https://lnkd.in/gXe9GCeE

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I enjoyed joining Peter on Technoventure for a discussion about the next chapter of AI investing. As significant CapEx continues to flow into AI infrastructure, the opportunity we’re most focused on is how those investments translate into real enterprise productivity. The next wave of value creation will likely come from application-layer companies—teams using AI to solve concrete industry problems, streamline workflows, and deliver measurable outcomes. Founders who understand the mechanics and pain points of their verticals are well-positioned to build durable businesses. We’re seeing meaningful momentum in verticals including healthcare, financial services, and legal, markets where automation can materially change unit economics. Additionally, the challenges of data management and cybersecurity keep getting more complex and remain attractive investment opportunities. It is an exciting time. Thank you to Peter for the thoughtful conversation. I look forward to seeing you at The Montgomery Summit in March. 🎧 Episode here: https://lnkd.in/e7USCqUV

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