Alper Tekin

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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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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Wow! Fascinating article Andrew Antos & Nischal Nadhamuni about how the Klarity platform does knowledge capture! A total disruptor indeed for how consultants traditionally did this work! Capturing not just what happens but why it happens has always been the missing link in enterprise AI, and Klarity ’s approach to surfacing human reasoning through its intelligent platform is a real step forward. The #AI Advisor’s ability to traverse vast context graphs, uncover cross-team patterns, and turn knowledge into actionable insights shows how innovation in knowledge capture can truly transform enterprise decision-making.

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Excited to announce our investment in IntuigenceAI, building the AI System of Action and copilot for process manufacturing. Moe Tanabian helped define the last generation of industrial AI, and now the Intuigence team is building what comes next. We’re fired up to be leading their $10M Seed round. One of the biggest transformative opportunities for AI lies in the physical industries — multi-trillion-dollar markets long dominated by legacy giants and sector consultants, not modern software companies. That’s been due to a mix of proprietary, unstructured data, complex implementation needs, and distinct cultural dynamics. With AI, this dynamic is shifting quickly. The white space for startups to build flexible, scalable AI systems — with fast time-to-value — is compelling, and their success is critical for these sectors to manage a retiring workforce and rising demand. Intuigence is unlocking this opportunity in process manufacturing, starting with downstream oil & gas and chemicals. Their platform augments engineering teams — chemical, process, controls, mechanical, and more — increasing margins, reducing emissions, and improving safety. From refineries to chemical plants, the world's largest industrial operators are turning to Intuigence to help run their facilities with greater safety, speed, and insight. The customer pull is remarkable. We’re humbled to join the journey and support the mission. Read more about why we're excited about Intuigence in the first comment below. Scott Brady

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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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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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"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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