Florian Pestoni

Ali Dastjerdi CEO of Raylu shared his story on Startup Mindsets and the complex world of AI and how to work with LLMs. It was quite a fascinating conversation: listen on Spotify for the full episode: https://lnkd.in/gbvwirUd How are you using LLMs and has it been to your benefit? LMK in the comments! #StartupLife #Founders #Entrepreneurship #BuildInPublic #StartupJourney #Startuplife #VentureCapital #SeriesA #SaaS #GrowthMindset #BusinessStrategy

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I had a wonderful time joining Alec Crawford on the AI Risk Reward podcast. We spoke about how AI should enhance rather than replace human creativity, what it means to build ethical, human-centered AI systems, and the future of work. We even discussed how AI acts as a mirror to our collective unconscious. 🎧 Listen to the full episode here: https://lnkd.in/g6Y9b9Yp ▶️ Watch on YouTube: https://lnkd.in/grtJ3FC6 #AI #GenerativeAI #Creativity #AIethics #AIinBusiness #Podcast

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Just back from TDK Ventures 100X 2026 Conference where I served on a panel. A few insights from the morning session. THE KEYNOTE: “100X is not an invention. It enables an ecosystem” -- Chuck Mattera Chuck Mattera (former CEO of Coherent, now CEO of Avalanche Thinking) argued that no 100X has ever been a single breakthrough, but a chain: transistor, IC, microprocessor, CMOS scaling, SoC, AI,. The question isn't what you invented — it's what becomes possible to compound. Building an ecosystem is like playing Go: the board is visible to everyone, the opportunities are not. And manufacturing is where compounding starts: making things creates learning, scale compounds learning, learning compounds speed. Bring manufacturing back and you build the ecosystem that learns fastest.  I couldn’t agree more. MY PANEL: FROM MODELS TO MOTION — AI ENTERS THE PHYSICAL WORLD Moderator: Modar Alaoui, Panelists: Les Karpas (NVIDIA Inception), Claire Delaunay (OPALIN), Ben Burchfiel (Walden Robotics) I think my panel agreed on more things than we disagreed on. There seemed to be consensus that there is no single winning embodiment.  The moat is shifting to whoever owns a data engine (which is not the same as owning data) and whoever can learn from the customer feedback cycle. I shared where I'd invest for the next decade: data engines and the application-specific robotic foundation models under them. In hardware, components like actuators, hands and tactile sensing. Models move fast; their advantage decays. Hardware moats are slow and structural: actuators are 25-50% of BOM, and ~90% of permanent magnets are made in China. We need to solve supply chain issues. What we underestimate: simulation, non-visual sensing, and safety as its own software category. The next 100X, in my view: omni-models — robots that take in force, acoustic, tactile and chemical data and tell us in natural language what they sense – replacing judgment, not just hands. AI RUNS ON POWER Panelist Jim Messina's numbers were sobering: opposition to data centers has gone from ~45% to 71% in months, 240 cities have moratoriums, and 10 of the 15 biggest congressional races are running ads on them. Tarun Raisoni's counterweight: someone in a lab at IIT or MIT or Berkeley will figure out how to train the model on 1/100 the power. Both can be true — and the industrial base we build for data centers will serve far more than AI. Thanks 🌱🤝🌍 Nicolas Sauvage, Qianran (Katherine) He, PhD, David Delfassy and Starry Wang for another great 100X. 🌱🤝🌍 #Robotics #PhysicalAI #AI #100X

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Our World Fund start-up IQM Quantum Computers just became a unicorn, raising $320m – the largest ever quantum Series B outside the US. 🚀 When our co-founder Daria first pitched quantum computing as a climate deal for our climate VC World Fund, I have to admit that I wasn’t immediately convinced. But she was right. Why? Because quantum has the potential to cut 7Gt of CO₂ a year, according to McKinsey & Company – an 18% reduction in global emissions. #IQM has already shown this potential in its collaboration with Volkswagen on next-gen battery chemistry that across all market applications is a pathway that could mitigate up to 1.4Gt of emissions annually by 2035. We backed #IQM in 2022, leading their €128m Series A. Today they’re scaling globally, with a roadmap to error-corrected systems and a strong push into the US. The link between compute and climate wasn’t obvious then. It’s obvious now. And we’re proud to be part of this journey. Full story in the comments.

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Humiliating Poll Reveals the Full Scale of Trump’s Global Reputation Disaster https://lnkd.in/gVTN8PZU Donald Trump’s claim that he made the U.S. “respected all over the world” has gone up in flames with new polling that reveals the scale of his global reputation disaster in his second term. A Gallup poll conducted in 2025 across more than 130 countries found median approval of U.S. leadership dropped from 39 percent in 2024 to 31 percent in 2025. At the same time, disapproval rose to a record-high 48 percent. The same survey found China surpassed the U.S. in global approval ratings, with a median of 36 percent approving of Beijing’s leadership, compared with 31 percent for Washington. Gallup said the five-point gap marked the widest lead China has held over the U.S. in nearly two decades. AI Found 31 Shocking Parallels Between Trump and Roman Emperor Nero  https://lnkd.in/g4wi2Q8b Nero was a deeply unstable, narcissistic, erratic ruler who exhibited troubling signs of severe personality disorder and psychosis. Dive into AI’s 31-point comparison of Trump and Nero — exposing unsettling similarities in how both men used power, attacked critics, and undermined institutions.

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Genspark announced its $275M Series B and the launch of its AI Workspace. The platform moves beyond traditional assistance by producing complete deliverables from a single intent, using a coordinated system of models, tools, and datasets. This reflects a broader shift in enterprise AI toward systems that can execute, not just generate text. Full details on our blog.

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Lessons from YC: Culture Isn’t a Slide Deck YC didn’t just change how we built products. It changed how we worked together. Before YC, “culture” was something we’d figure out later. We thought it meant perks, team dinners, or a list of values on the wall. But inside YC, culture meant one thing: how fast your team can move without breaking. That’s when it clicked. Culture isn’t what you say. It’s what you tolerate. If someone cuts corners and you let it slide, that’s your culture. If someone owns their mistake and you celebrate it, that’s your culture. We started treating culture like product — build, test, iterate. That's when we realized small habits create the real DNA: how you communicate under pressure, how you make decisions, how you handle bad days. YC pushed us to grow up as a team, not just as founders. Because a product might attract customers but culture is what keeps them.

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Big news from the Gray Line portfolio — I’m thrilled to announce our lead investment in Archive Intel's $6.3M Series A! Compliance has historically been a tax on innovation. Archive Intel flips that script. Their AI-powered platform consolidates archiving, marketing review, and supervision — cutting manual review by up to 85% and reducing false flags by 99%. More than 500 wealth management and financial services firms and 7,000 users are already on board. Under Larry Shumbres's leadership, the team is redefining how regulated firms operate — not by layering on more tools, but by finally unifying compliance into one intelligent system. We’re proud to partner alongside Social Leverage and Garuda Ventures as Archive Intel scales into the category leader it’s clearly on track to become. https://lnkd.in/gqsTPQhA

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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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AI success that scales comes from systems design. Specifically it’s how systems learn and how they develop expertise over time. As we’ve built our AI Operating System (AIOS), we’ve made deliberate architectural choices that don’t show up in demos, but compound into learning advantages. It helps us scale over firms that have built individual agents. Three aspects of what Abdul and I found out over the past two years: 1) Memory as the foundation of learning. Most AI systems treat context as disposable. We treat it as cumulative. Our architecture centers on persistent, evolving memory that enables continuous learning across interactions, personalization at the OS level and compounding context that adapts performance over time. Without memory, there is no real learning and the system needs to repeat many steps. 2) Model layers as pathways to expertise. We’ve moved away from single agents on monolithic models toward modular, layered systems: - Routing to specialized models, fine tuned to develop domain-specific expertise - Orchestration layers that route tasks to the right “expert”, human or AI - Continuous evaluation and learning capabilities that upgrade without breaking the system Expertise emerges not from one agent doing everything, but from systems that know which intelligence to apply, when. 3) Multiplayer AI and reinforcement learning from experts. The next frontier is smarter learning loops. We’re building toward a multiplayer paradigm where: - Networks of experts (human + AI) provide reinforcement signals - Systems learn from real-world judgment, not just static data - Expertise is encoded, refined, and scaled across the network Most teams are still optimizing for benchmarks and surface-level capabilities. But durable advantage in AI will come from systems that learn continuously, architectures that compound expertise, and feedback loops that improve with scale The companies that win will build systems that get smarter, more specialized, and more valuable with every interaction.

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