Rebecca Chiu

"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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In the San Francisco Bay Area, founders are relentless about growing and scaling their companies. They spot opportunity, scale through uncertainty, and create companies that redefine industries. Recently, we gathered a group of founders and tech leaders to discuss the intersection of company growth and long-term personal success. Eric S. Yuan, Founder and CEO of Zoom Communications, shared the inspiring story behind his company’s growth and eventual IPO. Eric reiterated that the founders who create the most impact don’t just plan for an exit – they prepare for what comes after. Too often, personal wealth planning lags company growth, and that gap can become costly at the most pivotal moments. The rest of the evening was focused on closing that gap. Our colleagues in Goldman Sachs Investment Banking, Pawan Tewari and Nick Giovanni, shared a candid outlook for M&A and IPOs in this environment, along with important steps founders should take to prepare for a strategic exit. We ended with a conversation around the key considerations for maximizing founders’ personal outcomes. Pre-transaction planning isn’t defensive - it’s opportunistic. Your company’s exit strategy and your family’s long-term planning work should not be done in isolation. When investment banking and private wealth work in sync, founders can cohesively optimize outcomes on all sides of the equation. To learn more about the practical “do’s and don'ts” for founders, we are happy to share our ‘Goldman guide’ for navigating wealth planning, business exists, and IPOs: https://lnkd.in/ewhEGjV3. Thank you, Eric, and to all the founders who joined us at The Battery in SF. On behalf of Goldman Sachs Private Wealth Management and Investment Banking, we look forward to the next conversation.

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Honored to have been invited to fal and Henry Shi’s Lean AI Leaderboard Celebration Dinner, celebrating some of the leanest and fastest-growing startups in SF. Here are some of the most overwhelming trends that were discussed among high-growth companies working with small, high-output teams: 1/ Headcount is no longer used as a success metric. As Henry Ward said, “Hiring means we failed to execute and need help.” Headcount is not a flex measure anymore. People aren’t asking “How big is your team?” anymore. They’re asking “How many customers do you have?” 2/ They're finding ways to leverage AI across every function. Every engineer uses Cursor or Windsurf. Every GTM leader is in Clay. The most efficient teams are built AI-first across the board. Thank you to Henry Shi, Burkay Gur, and Gorkem Yurtseven for putting this all together!

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A few weeks ago, we spent time in the San Francisco Bay Area meeting founders, operators, and investors who are building at the frontlines of this shift. Across conversations with people like Aliisa Rosenthal, former Sales Head at OpenAI, Emily Cohen, Head of Operations at Cognition, Malte Ubl the CTO at Vercel, and Alfred Lin, Partner at Sequoia Capital, one theme kept coming up in operating discussions: The real AI advantage is not intelligence. It is reliability. Teams are no longer asking, “Can AI do this task?” They are asking, “Can this system run every day, without supervision, and still deliver predictable outcomes?” That shift changes how products are built. In many of these companies, the conversation has already moved beyond models and prompts. The focus is now on: • How decisions flow across systems • How data moves in real time between teams • How errors are caught before they become operational problems • How workflows keep running even when conditions change In other words - AI is becoming part of the operating layer, not an add-on feature. That lens resonated strongly with me because construction has always been an execution-heavy industry. If you’ve spent time on live sites, you know the challenge isn’t a lack of effort or intent. Contractors, engineers, and site teams are professionals who manage enormous complexity every day. The friction usually comes from systems that don’t reflect how projects actually run - fragmented information, delayed visibility, and decisions that arrive too late. Small gaps compound quickly: • A material delay turns into idle labour • A missed update turns into rework • A cash timing mismatch turns into project stress None of these are intelligence problems. They are system coordination problems. What I saw in the Bay Area conversations was a clear direction: AI is being used to close these coordination gaps: quietly, consistently, and at scale. Not as a mere feature. As operational infrastructure. Over the past few months, we’ve been taking these ideas and pressure-testing them against the realities of the Indian construction ecosystem: across projects, teams, and workflows that run every single day. The goal is simple: - Build systems that reduce surprises - Improve predictability, and - Help teams focus on execution instead of firefighting On Thursday, we will share the next step in that journey for Powerplay. More soon.

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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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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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"We've become so insular in who we talk to and listen to, and I didn't realize how much that was keeping me in tunnel vision and tunnel thinking." Seth Levine, Co-Author of Capital Evolution and Partner at Foundry, talks about seeking out more diversity of thought on Tech Scenes Unplugged. #venturebacked #vcbacked #venturecapital #leadership #ceo #founder #operatingsystem #peakOS #foundermode

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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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