Kevin J. S.

Windsurf’s Claude Cutoff Sparks AI Platform Drama Lon and Alex discuss why Anthropic abruptly pulled access to Claude for Windsurf, just as the startup awaits a rumored $3B OpenAI acquisition. Is this bold move by Anthropic just business, or a warning sign for devs betting on closed models? Key Points: • Windsurf (fka Codem) got less than 1 week's notice before being cut off from Claude • Raises platform risk concerns for startups using closed foundation models • Ironically, the move could accelerate the OpenAI acquisition #AI #Anthropic #OpenAI #Windsurf #Claude #Startups #PlatformRisk #VC #ThisWeekInStartups

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The biggest engineering problem that companies are trying to solve w/ large scale model training? It's all about how to make something work on thousands of machines as efficiently as it can work on two. Ceramic.ai's founder Anna Patterson shares her thoughts with Prateek Joshi on the Infinite Curiosity podcast, where she discusses opportunities in long context training in industries like pharmaceuticals and banking, and why the current training stack needs to be rebuilt. You can listen-in and watch the full episode here: https://lnkd.in/ggvQxN6h cc: Laura N Simon, Ph.D. Autumn Yuan, Riley Munks, Maggie Philbin, Hannah Colson

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Andreessen Horowitz writes about robotics in China but only from the perspective of industrial robots aka big bulk robot arms that you typically see in video of automotive manufacturing lines. While it's important to look at that space as well, industrial robots are task-specific, require the entire industry to install, integrate, program and maintain them. It is much more important to pay attention to the progress in Embodied AI field both in all kinds navigation and manipulation domains.

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SpaceX and COSCO show how a new industrial logic is emerging. Both took mature, capital-intensive systems (launch and shipping) and rebuilt them through the fusion of technology, capital, and state backing. One used that alignment to accelerate a frontier; the other to secure permanence. Together they mark the return of strategic scale. The next great opportunities lie in the same pattern: sectors once written off as too slow or too political: energy, logistics, compute, communications, finance, where technological leverage and state alignment now make true disruption possible Full story here: https://lnkd.in/eMe4iWae

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Uber Is Investing $100M to Power Robotaxis Uber is putting $100M+ into building a dedicated autonomous vehicle charging network, starting in the Bay Area, LA, and Dallas. It’s partnering with EVgo Inc. in the U.S. and Ionity in Europe, while expanding robotaxi operations with Waymo and WeRide. This isn’t just about EV charging. It’s about securing the infrastructure layer of autonomy and especially as Tesla Inc. ramps up competition. Autonomy is becoming an infrastructure game. #AutonomousVehicles #Robotaxi #EV #FutureOfMobility

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Waymo has logged over 200 million fully autonomous miles on public roads, from complex city streets to highways. And yet heavy-duty trucking autonomy trails far behind, even though intuitively it seems like an easier challenge. Turns out autonomous trucking is hard. Eyal Cohen, CEO of Humble Robotics, thinks the answer is a combination of a fresh start (taking advantage of vision language action models and today's perception stack) and a clean sheet design (with a truck built from the ground up for autonomy). It's a big, ambitious vision and we at Energy Impact Partners are excited to be along for the ride. I talked to Eyal about the autonomous electric trucking space, and what it might take to catch up to passenger AVs. Listen: https://lnkd.in/gt5i3nRw

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Seed Stage Venture Debt? Most early founders think venture debt is something you raise at the Series B or later. It is seen as something you layer on once the business is fully “bankable” and later stage. But what if this paradigm is limiting growth for early stage companies? And what if founders can utilize a flexible product at the early stages to reduce dilution? At Top Corner we see it differently: - We underwrite The Founder and to enterprise value, not formulaic metrics - We believe debt can work much earlier, typically right after product-market fit (Seed/Series A) - Our debt is designed to support founders, not constrain them - Our structures are flexible, with none of the traditional debt mechanics that weigh down early stage founders (financial covenants, MAC defaults or investor support clauses) - Our process is fast (think weeks, not months) - Our capital is significantly less dilutive than equity, but can be used just like a dollar of equity: to hire, build, experiment and scale When used for growth, debt can help teams hit more milestones and empower founders to raise their next round on stronger term, all while preserving meaningful ownership for both founders and early investors. A recent example: Top Corner partnered with NxLite in February 2025, providing Seed stage debt capital that allowed the company to acquire critical equipment and fully prepare its 45,000 squarer foot production facility prior to raising additional equity. NxLite produces advanced coatings for windows and transparent barriers that dramatically reduce energy consumption and support a more sustainable built environment. With this early capital, NxLite achieved production capacity to serve a strong roster of Fortune 500 enterprises, creating a strong foundation for its next phase of growth. NxLite closed a $9.2M Series A in September 2025 at a meaningful valuation increase. We’re excited to support David Mather and the NxLite team as they build on this momentum. https://lnkd.in/g-Fipd9K

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Sustained ~252–291 GiB/s of HBM memory throughput on H100s under decode load — essentially hitting the hardware roofline. This matters because HBM throughput, not FLOPs, is the real bottleneck in LLM inference. By keeping memory nearly fully saturated, I’ve unlocked far higher efficiency and throughput than standard engines. The result: 0.9–1.36M tokens/sec with ~0.1 ms first-token latency  #AI #LLM #GPURouter #H100 #Inference #CostEfficiency #Innovation #DeepLearning #AIInfrastructure #HighPerformanceComputing NVIDIA AMD OpenAI Google Shilpa Kolhatkar Keith Strier a16z speedrun

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**AI-Driven Demand Reshapes Apple's Supply Chain** Apple's unexpected surge in AI-driven demand for Macs has led to supply constraints for key products like the Mac Mini, Studio, and Neo. This development illustrates how technology trends can dramatically shift market dynamics. At ElevenX Capital, we recognize that understanding these shifts can guide our investment strategies in tech sectors that capitalize on emerging trends. How are you adapting your portfolio to these technological advancements? #investing #innovation #venturecapital #entrepreneurship

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Back from Milken Institute in Los Angeles with one theme impossible to ignore: conversations at the epicenter of California innovation kept coming back to Texas. Whether the focus was powering AI and data centers, expanding industrial capacity, or thinking through where the next phase of manufacturing growth will happen, Texas was consistently part of the conversation. What stood out to me was not a California vs Texas narrative. It was the degree of alignment around where energy availability, flexibility, and industrial momentum are creating real strategic advantage and where companies and long-term capital are increasingly looking as they think about the next stage of growth. It was a valuable few days of conversations with investors, operators, and builders across energy, infrastructure, and technology, including time at the iConnections Los Angeles Cocktails event hosted by Ron Biscardi and Rahul Moodgal. The Texas story is no longer regional. It is increasingly central to the national conversation around growth, power, and industrial competitiveness. #MilkenInstitute #Texas #Energy #AIInfrastructure #DataCenters #Manufacturing #IndustrialGrowth

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Proud to announce Santa Barbara Venture Partners’ investment in LA-based AI data insight company Rwazi, joining a $12M Series A alongside our Santa Barbara/LA friends at Bonfire Ventures plus Newfund and Alumni Ventures. This is our largest single investment to date. Rwazi replaces static market research surveys, panels and extrapolations with real-time, zero-party data obtained directly from over 2 million verified consumers across India, Brazil, Mexico, Africa, and many other emerging markets. There's no data like Rwazi's available to global brands in these markets, data collected using machine vision and an AI assistant. Clients use Rwazi's consumer insights to better understand who is buying what, for how much, from where, when, and why, helping multinationals expand into new markets and grow faster. The company has grown 20x in ~3.5 years on just $4M of funding with zero churn across its global brand clients like The Coca-Cola Company, Visa, Red Bull, PepsiCo, Colgate-Palmolive, Booking.com, and Nestlé. Their Net Promoter Score is a whopping 87. Congrats to an incredible CEO in Joseph Rutakangwa and an amazing team. Thanks to Dominic-Madori Davis and TechCrunch for the great coverage. (Link to the #TechCrunch article in the comments). And thanks to Jennifer Richard and Jim Andelman for your support during our process. #SeriesA #Fundraise #Tech #Innovation #CPG

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It is great to be working with RaviKiran Gopalan and Aditya Akella as they build Canyon Code, the workflow intelligence layer for heterogenous AI datacenters. Proud to see these fellow Indian Institute of Technology, Madras alums taking a research-led approach to tackle a large problem within enterprise AI today: abstracting the complexity of heterogenous datacenters to deliver agentic workflow level transparency. NewBuild Venture Capital co-led this pre-seed round alongside Cota Capital and Blackhorn Ventures.

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Working with NRF to pursue our patent strategy is an important step in recognizing that proprietary AI systems and workflows are becoming a new form of intellectual property for modern firms. Great to partner with George, John McBride, and the team at Norton Rose Fulbright as we work at the frontier of how AI can transform investment decision-making in venture capital. At Decasonic, we believe the next generation of companies will reinvent with AI workflows and systems how organizations operate, how leaders make decisions, and how teams collaborate with intelligent systems. In going through this streamlined process, it's clear to me that many more startups should clarify their IP strategy. Our internally developed AI platform, RLEN, reflects that philosophy. We are building AI not to automate tasks and flourish outcomes, in collaborative ways for human and AI teammates. This enables our investors to test ideas, explore alternative perspectives, and reach deeper insights faster when navigating uncertainty. More broadly, we are seeing trailblazing work across industries and cities as leaders experiment with how AI can reshape communities, companies, and countries. Leadership with AI forever transforms. We are still in the early innings. Appreciate the partnership and collaboration from the Norton Rose Fulbright team as we continue inventing and innovating. Special shoutout to my colleague Abdul Al Ali who's listed as a co-inventor.

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Fusion power startup Inertia strikes a deal with Lawrence Livermore National Laboratory to commercialize fusion energy, marking one of the largest public-private partnerships in lab history. https://lnkd.in/ekj-GFZz

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My investment framework centers on 7 layers. CASIMIR fits into the first layer which is the Observation Layer. Out of 22 billion IoT devices on earth and ~10k satellites in space in 2026 we may have a 6 billion (and growing) ultra low power devices on earth and space that can one day run on MicroSparc 👍🏼 -> Eternal power chip Harold “Sonny” White + Capital Factory #HardThings #deeptech

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You can have groundbreaking science, elegant engineering, and even early customer interest. But without this one thing, you don't have an investable business. After two decades running IP strategy projects and now leading UC Berkeley's Deep Tech Innovation Lab while building the Berkeley Gateway Accelerator, I've watched countless brilliant technologies die in the valley between breakthrough and business. The problem isn't what most technical founders think it is. It's not about having better tech. It's not about getting more funding. It's not even about finding product-market fit. The fatal flaw shows up much earlier—and it's almost always the same mistake. In my latest piece, I answer questions from a Taiwanese entrepreneur about what really separates deep tech ventures that scale from those that stall. Including one provocative suggestion for Asian ecosystems that has nothing to do with technology. Read the full conversation, linked below. #DeepTech #Innovation #Startups #IntellectualProperty #VentureCapital

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Skilled labor is the bottleneck in industrial ops. Elevāt's platform basically tries to “productize the expert” using machine signals + docs + prior fixes. Now they have $12M to scale. Congrats to them. In industrial AI, the tech is usually the easy part....adoption is the battle. Curious what you think is the real gating factor:  -- trust (techs believe it’s right and won’t get them hurt),  -- workflow integration (it fits into CMMS/ticketing/parts + works offline/on-device), OR  -- incentives (the field team is rewarded for using it vs “just fixing it their way”). Which one kills deployments most often in your experience?

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Here's a counterintuitive truth: if you get sued for patent infringement, you should consider yourself lucky. It means you've built something valuable enough that competitors are worried. Most founders swing between two extremes: IP naive (ignoring patents completely) or IP obsessed (building patent fortresses instead of businesses). Both kill startups. After 20+ years in IP strategy, investing in deep tech startups, and now building a deep tech startup accelerator, I've seen the pattern repeatedly. The founders who succeed understand patents as competitive intelligence tools and business enablers—not substitutes for finding customers. New post breaks down the IP strategy spectrum and why the sweet spot matters more than most founders realize. 👉 https://lnkd.in/g2x5ssyJ

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San Diego-based Manifold (AI Detection and Response platform) has raised $8 million in seed funding; lead backer Costanoa Ventures, along with several institutional investors including Cherry Ventures, Rain Capital, Modern Technical Fund, and angel investors.

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