Jonathan Goldenstein

"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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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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We first backed Kavitta Ghai and the Nectir team at seed in 2024. They raised $1.4M from VCs on our show, and then deployed across the California Community College system (2.1M students, 116 colleges). Today they announced a $12.5M Series A led by Rethink Impact. We followed on. If you haven't been paying attention to what's happening in AI and education, this is worth your attention. Nectir is the FERPA compliant AI infrastructure layer purpose-built for schools. The results from peer-reviewed research: 20% increase in GPA. 13% rise in final scores. 36% boost in intrinsic motivation to learn. Students are also staying enrolled and finishing their degrees. This isn't ChatGPT for homework. It's a personalized learning partner that knows your textbooks, assignments, and lectures. Plus it gives schools safe AI infrastructure for everything from FAFSA advising to career coaching. The vision is all 50 states. Every institution in the US is going to need safe AI infrastructure. Nectir is building it. Proud to be backing Kavitta and the team twice.

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Some news: Finch has raised a $20M Series A, led by Satish D. and Meera Clark at Redpoint. We’re grateful to welcome them as partners alongside Alfred Lin at Sequoia Capital, Matthew Mulvey at Liquid 2 Ventures, and generational operators like Tony Xu, Christopher Payne, and Jason Boehmig backing Finch. This funding helps us move closer to our mission: ensuring every American has access to the counsel they deserve. More than 75% of Americans can’t access counsel when they need it. At the same time, law firms are buried in admin work that limits their capacity to serve more clients. Finch is the first AI-powered pre-litigation team for personal injury, combining exceptional legal operators with AI agents to handle cases from intake to outcome. We help plaintiff firms say yes to more cases, without additional overhead. In the last 6 months, we’ve grown more than 10x. Firms we’ve worked with have tripled their case volume, secured seven-figure settlements, and expanded into new markets. We are a team of talented, curious, customer-centric operators, recently adding folks like Taylor Welch, Thea Feeney, Stacey Matheson, Jasmine Harris and (soon) Kelly Jacques, Rachel Fuld Cohen & Alex Gouchtchina. If you’re looking to shape the future of legal tech, we’re growing fast and hiring across the board. And if you’re an attorney ready to serve more clients, book a demo in the comments below. Let's get to work.

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Some founders are building companies. Others are building companies while navigating immigration systems, legal uncertainty, and invisible rules most people never see. Lotti's latest piece captures what it really takes to build in the U.S. as an immigrant founder, and why those experiences often produce exceptional builders. Full post linked in comments.

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