Vispi Daver

Been listening to this amazing conversation with Andrej Karpathy on Dwarkesh Patel podcast: Andrej Karpathy on The Cognitive Revolution: https://lnkd.in/gQ-kzGCt It’s a long one, but totally worth it — Andrej is such a fantastic educator. I’ve only made it through the first 1.5 hours, but there are already so many gems: 🧠 On Reinforcement Learning: RL sucks — it’s like "sucking supervision through a straw.” But it's the best we have. Such a good analogy and evocative imagery. 💡 On the future of models: The best future models will learn to extract the cognitive core of intelligence — separating reasoning and generalization from the rote memorization that defines current LLMs. You can already see glimpses of that shift as smaller models begin outperforming larger ones. 🤖 On Agentic AI: He compares agentic AI to compilers — tools that make humans better programmers of thought. Will we get to AGI? No definitive answer, but I like the framing. I'm still only 70% through the episode, but it’s packed with insights like these. I’ll share more takeaways as I go — this one’s definitely worth a listen.

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🚀 Agentic Automation is redefining enterprise workflows! Discover the 5 core capabilities powering this shift—AI Copilots, self-healing automation, cross-platform execution & more. 👉 Read now #AgenticAutomation #RPA #AI https://lnkd.in/duqsGKS4

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AI Is Rewriting the Rules of Go-to-Market (GTM) As someone who’s spent years aligning strategy, systems, and outcomes across product, engineering, and GTM teams — this article by Dave Birckhead really hits home. In his latest Full-Stack Growth post, “Why AI Requires a New Kind of GTM Role and Org Structure,” Birckhead explains why traditional functional silos — marketing ops, sales ops, customer success ops — can’t keep pace in the AI era. AI doesn’t thrive in isolation. It thrives on shared context, unified data, and connected workflows. Most enterprise AI projects fail not because the models are weak, but because the systems they live in are fragmented. The takeaway: AI value is created when organizations reimagine entire workflows that span the customer journey — using AI as connective tissue, not as point solutions. Birckhead makes a compelling case for a new leadership role: Head of GTM Systems — the orchestrator of data, tools, and AI workflows across marketing, sales, and customer success. The payoff? ✅ Faster innovation ✅ Higher ROI on AI investments ✅ Seamless customer experience ✅ Shared measurement across the funnel Just as SaaS created Marketing Ops and RevOps, the rise of AI now demands GTM Systems Leadership — a discipline that connects, aligns, and scales the way growth truly happens. 📖 Full article here: https://lnkd.in/gU2fa5Jn By Dave Birckhead, Full-Stack Growth (Oct 14, 2025) #AI #GoToMarket #ProductOperations #GTMSystems #RevOps #DigitalTransformation #Leadership #Strategy #Innovation #FutureOfWork #OperationalExcellence #ProductManagement #SalesOps #MarketingOps #CustomerSuccess #B2BLeadership

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To make enterprise agents work reliably, what do you need? semantic contracts, explicit rules? Both, but it’s only half the picture. The other half is the layer that runs enterprises but rarely shows up cleanly or coherently in systems of record: decision traces. These are the exceptions, overrides, precedents, and cross-system context that live in Slack threads, deal-desk conversations, escalation calls, and, most often, in people’s heads. You can’t reliably reconstruct them from your CRM or ERP. Rules tell an agent what should happen in general (“use official ARR for reporting”), but they fall apart on edge cases (“a VP approves a discount on a Zoom call”) where the action is logged but the reasoning is lost. When that happens, agents don’t know what they’re allowed to do next, or why. Systems-of-agents startups have a structural advantage because, when they sit in the execution path, they can see what inputs were gathered, what policy was evaluated, what exception route was invoked, who approved, and what state was written. If you persist and link those traces, you get what my partners Jaya and Ashu call a “context graph”: a living, queryable record of decision traces that links decision events to the why behind them. Our bet is that context graphs will power a new generation of startups and become the core asset behind the next trillion-dollar platforms, especially in workflows where “the AI decided” is not an acceptable answer.

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