Catherine Wu

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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The end of human drivers? Tried Tesla Robotaxi in the Bay Area for the first time, 8.8 miles from Meta SF to Daly City station for $18. Unlike Waymo, Tesla’s Robotaxi can take the freeway. The ride was smooth, and the decision-making felt far closer to human than “bot-like.” What surprised me most: it’s just a regular Model Y. With the built-in rear screen, any Tesla could potentially be activated as a Robotaxi. That’s scalability at an entirely different level. Waymo might have an early lead, but Tesla’s vision-only approach gives it massive coverage and faster rollout potential. If this scales, Uber, Lyft, and even car ownership itself could be disrupted faster than expected. This is exactly why I quit my job to go all-in on ElasticDash We’re entering a new industrial revolution, the real question is: will you shape it, or be forced to adapt?

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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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Forbes published my latest article on what many investors may be getting wrong about robotics. Humanoid robots look like the future. But investing in humanoid hardware may not be. Manufacturing asymmetry is real. Competing with Asian supply chains is not a typical venture risk profile. Outside of companies like Tesla or Amazon, most hardware startups, even unicorns, will face extreme pressure. The long term U.S. advantage is far more likely to sit in embodied AI software and the intelligence layer, not in assembling robot bodies. In robotics, the control point will not be the factory. It will be the mind. https://lnkd.in/gmcVtCa5

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🔥 Headline: Cerebras Shatters Fundraising Records As AI Capital Frenzy Hits New Peak -- Cerebras’ Landmark Funding Signals Investor Conviction -- - Cerebras, a Silicon Valley-based artificial intelligence chipmaker, secured a monumental 1.1 billion US dollars in fresh funding—one of the largest rounds for any AI company this year. - The funding round, led by Saudi Arabia’s Public Investment Fund and other heavyweight backers, underscores a strategic global race for AI infrastructure dominance. - Cerebras specializes in building some of the world’s most powerful AI chips designed to challenge Nvidia’s stronghold—a bold positioning that investors clearly believe has scale potential. -- AI Infrastructure Is Becoming A Geopolitical Asset -- - This round reflects a growing trend: AI infrastructure is now treated as national economic security, not just a commercial bet. - Sovereign wealth funds, including Saudi Arabia’s PIF, are aggressively backing companies that could help nations reduce dependency on US chip suppliers. - Cerebras’ technology, which focuses on training large language models at unprecedented speeds, aligns with government ambitions to secure domestic AI capabilities. -- Market Dynamics: Competitive Pressure Intensifies -- - Nvidia’s dominance is being challenged on two fronts—cost efficiency and speed—where Cerebras aims to differentiate through its wafer-scale chips. - Investors are positioning Cerebras as a key alternative supplier, anticipating that demand for high-performance AI chips will far outstrip current supply. - The funding sets the stage for faster product rollouts, deeper R&D investment, and potential long-term supply contracts with global AI players. -- Implications For Investors And Startups -- - This scale of investment in a single company will likely redirect capital flows toward AI hardware, signaling that investors see long-term infrastructure value, not just application hype. - Startups operating in complementary spaces—such as AI software optimization, model training, and energy-efficient compute—stand to benefit from ecosystem expansion around Cerebras. - For institutional investors, this move reinforces that the next wave of AI winners may emerge from the infrastructure layer, not just consumer-facing applications. Reference: https://lnkd.in/eqdh-Ra6. #ArtificalIntelligence #AutonomousAgents #LLMAgents #AgenticWorkforce #AgentApplications #AIProductivity #AIAgentNewsletter #AgenticAI #AgenticWorkflows #AIAutomation #AIAgents #AgenticAI #FutureOfWork #Innovation #AIInvestment #AIBusiness #AIBank

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BaseFrame (YC W26) helps hardware teams find mistakes in their project designs and explore tradeoffs early. Anshul and Vaibhav were roommates at Berkeley and faced these issues themselves when working on robotics startups in the past. Their vision is to help hardware teams bring breakthrough products to market faster, leaner, and with data-driven compromise. To make this work, BaseFrame’s specialized AI agents validate your scope against industry best practices to surface gaps and areas for improvement. They then generate a Bill of Materials by parsing datasheets, manufacturer data, and past designs made by the team and give comprehensive comparison tables and citations. No more 15 tabs of datasheets clogging your workflow. Teams can branch designs, manage versions, and test multiple architectures simultaneously to highlight tradeoffs in cost, price, and lead time. Congrats Anshul Paul and Vaibhav Agrawal on the launch! https://lnkd.in/e6xnmnm2

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