Einat Metzer

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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It's an analogy I hear often: VCs have the same high-risk, high-reward DNA as gamblers. From the gold seeking 49ers at Sutter's Mill to the naming of the largest podcast in our space (“All-In”), there's an undeniable connection between gambling and venture risk-taking. There's a certain "go west" or gambling spirit inherent in building a company from the ground up and backing lots of early startups. Sometimes, you do have to shoot blind, relying on gut instinct about a founder's compelling vision and little else. But here’s where the analogy breaks down: in venture, we aren't simply throwing chips on a roulette table. Our "adventure capitalism" is governed by structure: A Clear Target: Even when we have few signals to evaluate at an early stage, we are aiming at a fast-growing market and/or a proven founder profile. We are not shooting wildly; we're aiming in the right general direction. Due Diligence: We conduct market sizing, reference checks, financial modeling, etc. This is the opposite of blind faith. Fiduciary Responsibility: This is the ultimate check on the "gambler" impulse. This is not our money. We are custodians of other people's money—funds entrusted to us by LPs (current and former Fortune 500 CEOs, successful founders, etc.). Our job is to be disciplined allocators of capital, not reckless adventurers. We are risk-takers, but successful VCs are calculated ones as well. #VentureCapital #VC #Startups

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The French quantum computing pioneer 🚀🚀🚀🚀 Pasqal has announced (https://lnkd.in/gj6cZAwD) its intention to enter the public market through a SPAC merger 🤝 with Bleichroeder Acquisition Corp. II. Expected to finalize in late 2026 📅, this deal values the enterprise at $2 billion 💰 and aims to secure over $600 million in funding 💸 to advance its neutral-atom technology ⚛️. The company, co-founded by Nobel laureate Alain Aspect 🏆, will use the substantial capital injection 💉 to scale its hardware production 🛠️ and pursue quantum advantage for global commercial clients 🌍. By listing on the Nasdaq 📈, Pasqal positions itself as a dominant international player 👑 with a growing portfolio of partnerships 🤲 and significant revenue momentum 📊. This strategic move marks a major milestone 🎉 for the firm as it transitions from a high-value startup 🌟 to a publicly traded leader in the emerging quantum economy 🔮.

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Project Vend reads less like a capability story and more like a governability story. Phase two improved because institutional scaffolding (tools, procedures, role separation) reduced predictable judgment failures—persuasion, compliance blind spots, boundary confusion, values drift. The board-level question isn’t “can an agent run a business?” but “what governance primitives must exist before we delegate authority.” Until incentives, escalation rights, and refusal boundaries are structurally enforced—not merely prompted—agents remain high-performing managers, not autonomous executives.

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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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Voice is a super interesting modality right now - maybe the first modality we're seeing move to open source models across a number of scale ups / enterprises. Reliability concerns, high costs, and open source model performance are pushing engineers to do their own fine tuning vs. relying on third-party vendors of proprietary models. Many of these orgs have already been collecting their own first-party data and now with third-party vendors like Extrian, David AI, etc they can train really high quality models. RL has been insanely hyped, but it's been unclear how long it will take scale ups and enterprises to actually lean in. Voice AI might be hitting that inflection point faster than expected.

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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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If you care about out-innovating China in the AI war, you need more efficient chips, not just brute force buildout for hot, inefficient chips and architecture. That’s why DCVC backed Mythic — up to 1000x the SWapC of NVIDIA’s 2028 roadmap for real time embedded and datacenter compute. Oh, and by the way, *made on American fabs with zero Taiwan supply chain dependency*… We can’t pour more concrete than China, and our grid interconnect workers don’t slave away under threat of being murdered for their organs. We need smaller, cooler, faster, smarter, more power efficient chips. Mythic is proving that *today* with live silicon.

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