Jody Shapiro

Project Vend reads less like a capability story and more like a governability story. Phase two improved because institutional scaffolding (tools, procedures, role separation) reduced predictable judgment failures—persuasion, compliance blind spots, boundary confusion, values drift. The board-level question isn’t “can an agent run a business?” but “what governance primitives must exist before we delegate authority.” Until incentives, escalation rights, and refusal boundaries are structurally enforced—not merely prompted—agents remain high-performing managers, not autonomous executives.

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Worth reading: Bessemer Venture Partners Roadmap: Data 3.0 in the Lakehouse Era https://lnkd.in/gEFgpNJe My personal sense is that the paper reflects a clear evolution toward tighter dependence on CRUD (Create, Read, Update, Delete) principles and Systems of Record (where the authoritative version of the data lives). It (in my mind) highlights how modern architectures increasingly rely on authoritative, unified datasets to fuel AI and real-time analytics. As data systems mature, the paper (in my view) highlights the growing tension between the need for a single source of truth (accurate, consistent, system-of-record data) and faster access and lower latency for decision-making. ipso facto: This shift underscores a trend where data infrastructure must balance governance and fidelity (CRUD + SoR) with performance and responsiveness, leading to innovations like lakehouses that serve both ends of this spectrum. This (for me) also highlights that data observability will likely grow in importance over time, as will data interoperability... To summarise: Faster Processing at Lower Cost = Greater Insight. An enjoyable read and insightful. PS. Advantage Apache Arrow

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The old SaaS pricing playbook doesn’t work for AI. Madhavan Ramanujam and I partnered with Emergence Capital and Jake Saper to write a new one—built for where AI is going, not just where it is today. We lay out what state-of-the-art AI pricing looks like now—and what it will look like: ✅ Hybrid pricing (seats + usage) is the current best practice 🎯 Outcome-based models are the future—pricing tied directly to impact 💰 The best AI companies already capture 25–50% of the value they create As autonomy and attribution improve, outcome pricing will go from rare to expected. Founders who move early will win. Full read here 👉 https://lnkd.in/gwp6tDtp

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Seth Levine nailed it! The concentration problem isn't just a GP pain point... it's choking innovation. When capital pools around the same 20 funds, emerging managers and founders get shut out. Love that he's using his platform to call this out. Foundry has backed 50+ emerging managers. That's the diversification the ecosystem needs. This is exactly why Carta exists — making capital allocation visible and accessible. Capital Evolution hits at exactly the right moment. Thanks for having us Daniel Dart. Team Carta loves the community of FUTURE TITANS you have built 🚀

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For all founders, especially tech founders, here are a few key insights on how to elevate your pitch. Definitely worth a read and some thought... Treeo VC https://lnkd.in/emM8RK_4

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Ontologies remain one of the most effective ways to ground AI memory, keeping extracted knowledge anchored to existing structures and adaptable through feedback. As memory systems evolve, their role remains essential, and a core focus at Cognee.

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I rarely make predictions, but... No company founded after 2026 will organically grow to more than 10k FTEs. The leverage of rapid iterative decision loops by teams using best-in-class AI has reached escape velocity over traditional economies of scale.

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Wondering what Forward Deployed Engineers actually are and when it makes sense to use them? I recently had a great conversation with CJ Gustafson on FDEs, and how we use them at Invisible Technologies. We spoke specifically about FDEs as a GTM choice, how the economics change when engineering is pulled forward into the sales motion, pricing models, metrics to measure health of business and lots of other things. Check out more on the latest Mostly Media newsletter. If you're not subscribed, highly recommend subscribing. Link in comments.

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GPUs have gotten faster, and models have gotten larger. But storage is still stuck in the past. Modern AI workloads need instant access to huge datasets. Instead, teams waste time on slow retrieval, idle GPUs, and costly pipelines. Archil is changing this. It delivers object-storage scalability at block-storage speeds. The platform allows for seamless, high-performance access to massive datasets. By eliminating cold starts and accelerating throughput 30X, Archil enables teams to fully utilize their infrastructure and move faster on training, analytics, and AI deployment. Archil’s founder Hunter Leath brings rare technical depth and operating experience from building Amazon EFS and optimizing Netflix’s cloud performance. This is a team building the foundational layer that AI workloads desperately need. I’m thrilled to lead Archil’s seed at Felicis with Nancy Wang and support their mission to reinvent cloud storage for the AI era. https://lnkd.in/gWueXhzz

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Hailey Trier's journey (from modeling cognition to decoding the heart’s magnetic rhythm) is more than a personal narrative. It’s a glimpse into a broader shift reshaping science and engineering across industries: using synthetic data not as a proxy, but as a platform. At SandboxAQ, we’re not chasing data for data’s sake. We’re building tools to ask better questions before the data even exists. In healthcare, this means accelerating device development in data-scarce environments. But here’s the nuance: synthetic data is powerful because it forces a systems-level understanding. You don’t just generate plausible numbers; you also have to encode the physics, the physiology, the probability distributions behind the reality you’re modeling. You have no other choice but to build with intent. What excites me most is where this is going: the convergence of domain-specific simulation, physics-informed ML, and large foundation models. Not just building better tools, but smarter questions. #SyntheticData #Healthcare #LifeSciences

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The question I hear daily from partners, investors, and industry experts: What actually matters most to a hyperscaler? Co-packaged interconnects are deployable now. That’s why everyone’s excited and, frankly, in denial. Deployable is not the same as future-proof. Power density, serviceability, and rigidity don’t go away; they compound. The uncomfortable truth: short-reach interconnects and rack design are about to change. Different environments, different constraints. Fixed assumptions break fast at scale. So what’s the real priority: shipping something that works today, or building infrastructure that survives tomorrow? #wireless #aiinfrastructure #datacenter #hyperscaler #mmwave #6g #interconnects #server #serverrack #aws #google #azure #oracle

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Big milestone for Firmable. We’ve raised $14m Series A led by Airtree. Sales has moved through a few big waves: intuition-led, CRM-led, data-led. We’re now entering the next one – intelligence-led sales. The opportunity isn’t just better data. It’s turning that data into clear direction and action, without adding more work for sales teams. That’s what we’re building at Firmable: a foundation of trusted external data, layered with intelligence that helps sellers know who to focus on and when. Led by Airtree, this round supports our expansion across Asia and into the US – and accelerates the build-out of AI agents that take the admin work off sales teams so they can focus on what they do best. Proud of the team, grateful to our customers and investors. We’re just getting started. Read the exclusive in the AFR. https://lnkd.in/gr66uknb Leigh Jasper | Tara Salmon | Karthik Venkatasubramanian| Chester Thompson| Chath Widanapathirana

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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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The latest episode of "Where AI Works" just aired. I was honored to discuss AI, technology, and business in an interview by Serguei Netessine, Sr. Vice Dean of Innovation at The Wharton School. We covered topics including strategies for new AI business models, shifts to AI platforms, and the cultural divide between AI adopters and skeptics. You can find the full podcast here: https://lnkd.in/gCjQnRhU #AI #Strategy #Businessmodels #innovation #Wharton  

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We chose a good subject for our march Autotech Council meeting, and after this year's CES, it seems even more obvious. Our meeting on the AI Defined Vehicle will show how it's a natural next step after the Software Defined Vehicle phase that we've been in Tesla launched the Model S in 2012. Now, most carmakers have SDVs. It's time to layer on the AI in user-facing roles, and in invisible functions managing the vehicle.

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🗓️This Week at Anthropic 1️⃣ Releases Claude Opus 4.6 with 1M-token context and stronger agentic enterprise capability 2️⃣ Expands Claude Cowork plug-ins, accelerating AI-driven workflow automation across knowledge work 3️⃣ Nears $20B+ funding round at ~$350B valuation, extending compute and talent runway 🚀🚀🚀

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A.I. Clouds are not all the same. Justin Gage of Amplify Partners shares, in explicit detail, how Modal is just built different, and why it's a mistake to think of them as an "inference provider". https://lnkd.in/gmaYQZtT

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AI isn't constrained by capital, it's constrained by power. The solution? Bring power closer to where compute happens. I’m excited to share that one of the companies I’m working with, PowerLattice, has emerged from stealth to do just that, by bringing power directly into the processor package. The company has developed the industry's first power delivery chiplet that combines miniaturized, on-die magnetic inductors with a vertical design and programmable software layer -- all easily integrated into existing SOC product designs. This reduces total compute power needs by more than 50%, effectively doubling performance. Truly a major breakthrough. Congratulations to Peng Zou, Sujith D., Gang Ren and their entire team!

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