Ali Yahya

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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Foundation models just crossed another ground-breaking threshold. Last week, Anthropic released Opus 4.6. Founders are telling me it unlocked production-ready code end-to-end. These systems now exhibit product taste: the ineffable sense of “right” that people insisted AI would never have. This isn’t an incremental improvement. It’s something different entirely. At IVP, we’re thrilled to have invested in Anthropic’s most recent financing. My colleagues Somesh Dash, Shravan N., and I are looking forward to supporting Dario Amodei, Krishna Rao, James Black, Eoin Donohoe, and the incredible team at Anthropic in the years ahead. Anthropic delivers something rare: frontier research that expands what’s technically possible, elegant products that make it genuinely useful, and an approach enterprises trust. We believe Anthropic will be an era-defining company — and we’re excited to be part of what comes next.

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Congratulations to Yann LeCun and the AMI Labs team. Following Fei-Fei Li’s World Labs, this is another major capital signal that serious money is moving into 𝐧𝐨𝐧-𝐋𝐋𝐌 paradigm. AMI Labs, just raised $1.03B at a $3.5B pre-money valuation to build AI around world models rather than LLMs. And it changed something deeper: the AI race is no longer just an LLM race. It has split into three camps. 1) 𝐓𝐡𝐞 𝐋𝐋𝐌 𝐂𝐚𝐦𝐩 The bet is simple: scale text prediction. LLMs demand massive data, compute, and energy as an ongoing requirement. Hallucinations are architectural. Periodic retraining is mandatory. The weaknesses are structural: lacks reliability, adaptability, predictability, and accountability. These systems are frozen after training, cannot learn continuously and brittle in the real world. That is why so much of today’s AI industry looks like scaffolding built around static models: wrappers, guardrails, orchestration layers, patches, and human babysitting. In many cases it is productivity theater with no real intelligence. 2) 𝐓𝐡𝐞 𝐖𝐨𝐫𝐥𝐝-𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐦𝐩 This is where Yann LeCun’s AMI Labs and Fei-Fei Li’s World Labs sit. The bet here is to move beyond text into multimodal world prediction: learning from video, audio, space, and sensor data to support reasoning, planning, and eventually robotics and other physical-world systems. This is a real break from traditional LLM thinking. But it still appears to remain within the pre-trained-model paradigm: train → deploy → retrain Smarter than text prediction, yes. But not yet a full break from model-centric AI. This camp simulates a better model of the world by improving the representation of reality, but they do not create a system that truly learns. 3) 𝐓𝐡𝐞 𝐒𝐞𝐥𝐟-𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐈 𝐂𝐚𝐦𝐩 This camp starts from a different premise. Real intelligence is the ability to continuously learn, adapt, and apply what is learned across changing contexts. That means persistent memory, reasoning, understanding, dynamic world models, and autonomous adaptation through experience. These systems self-improve with experience. The result is compounding intelligence, not just faster or more fluent answers. This is developmental intelligence that starts with foundational knowledge, then develops upward through experience, without consuming planetary-scale data, compute, and energy. None of this can be brute-forced, it's the hardest and the least crowded path. This is the real paradigm shift: compounding ROI, no endless retraining, far lower resource requirements, and a cleaner path to real intelligence. AMI Labs is more ambitious than LLMs, but real intelligence comes from continuous learning and compouding capabilities through use in production. 𝐋𝐋𝐌𝐬 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞. 𝐖𝐨𝐫𝐥𝐝 𝐦𝐨𝐝𝐞𝐥𝐬 𝐬𝐢𝐦𝐮𝐥𝐚𝐭𝐞. 𝐒𝐞𝐥𝐟-𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐜𝐨𝐦𝐩𝐨𝐮𝐧𝐝. That is the emerging stack. The real AI race has only just begun.

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Yann LeCun, the former chief AI scientist at Meta and a Turing Award winner, just raised $1.03 billion in seed funding for a new startup called Advanced Machine Intelligence Labs, or AMI. The round values the company at $3.5 billion and is believed to be the largest seed round ever raised by a European company. Investors include Nvidia, Temasek, and Jeff Bezos' Bezos Expeditions. LeCun has been one of the most vocal critics of the technology dominating the AI industry. His argument is that large language models, the systems behind ChatGPT, Claude, and Gemini, are fundamentally limited because they only learn from text. They can generate fluent language, but they do not understand the physical world. AMI is built around a different approach called world models. Instead of predicting the next word in a sequence, these systems learn from video, audio, and sensor data to build abstract representations of how the real world works. The underlying architecture, called JEPA, is a framework LeCun first proposed in 2022. The company plans to spend its first year entirely on research and development, with target industries including healthcare, robotics, and manufacturing. The founding team is drawn almost entirely from Meta's AI research organization. A billion dollars in seed funding for a company openly challenging the direction nearly every major AI lab is moving in. Most of the industry is betting on making language models bigger. LeCun is arguing the entire approach has a ceiling and that the next generation of AI will need to learn the way humans do, through experience with the physical world rather than text alone.

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So many genAI applications would be better and more impactful if only models were faster. So many applications don't exist because genAI models aren't fast enough. But that's changing with the introduction of dLLMs. I'm so impressed and excited by the work the Inception team has done.

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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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Building a DevTool? These 40+ VCs actively invest in the space-- who are we missing? If you are building developer infrastructure, tooling, platforms, or workflows, these funds have deep technical DNA and consistent interest in dev-first products. A strong place to start. 🚀 Foundational Infrastructure Investors Andreessen Horowitz 1752vc (Formerly Pegasus) CRV Accel Menlo Ventures SignalFire Point Nine SIERRA Ventures 🧠 Early-Stage, Pre-Seed & Founder-Led Funds Afore Capital Precursor Ventures Hustle Fund Founder Collective Bling Capital Great Oaks Venture Capital Flybridge 🧱 Infrastructure, Data & Systems-Focused Initialized Capital Tribe Capital Khosla Ventures Insight Partners General Catalyst Energy Impact Partners 🌍 Platform, Network & Global Funds BoxGroup Global Founders Capital FJ Labs Crosslink Capital Merus Capital Presence Capital 🎮 Vertical & Thematic Specialists (Dev, Security, Gaming, Tooling) 8-Bit Capital BITKRAFT Ventures Konvoy Ballistic Ventures Elementum Ventures ⚡ Accelerators & Hybrid Platforms Techstars Eniac Ventures 645 Ventures Recursive Ventures Uncork Capital Streamlined Devtools win when distribution, community, and product-led growth align. These firms understand that motion. Bookmark this if you are building for developers 👇 #venturecapital #VC

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Yann LeCun's new startup AMI Labs just raised $1.03B seed round, w/o any product yet, to build AI that learns how the world works, not just how language works. While most of the AI world is racing to build bigger language models, Yann LeCun is betting on a different future. LLMs predict the next word. 'World models' aim to predict the next state of reality. If this works, the implications are massive: Robotics that understand physics AI systems that plan and reason using fundamental laws Machines that can simulate the world before acting in it The AI race may be quietly shifting from language intelligence → world intelligence. This can be a massive leap.

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