Tim Lenardo

Super exciting to see reports this morning that Waymo is in advanced discussions to raise $10–15B at a valuation near or above $100B, with Alphabet expected to anchor the round. That would be a major step up from Waymo’s prior ~$45B valuation in 2024 and a clear step change in investor confidence. Notably, Waymo has not raised this year, despite the broader AI funding frenzy. The size and structure of the round also make this feel like a step toward an eventual spinout or IPO. This matters because capital supercharges growth. Waymo is past the “does this work?” phase and firmly in “how fast can we scale?” mode. The company has already logged ~14M rides this year and expects to reach ~1M rides per week by late 2026 as it expands into markets like Miami, Dallas, and Philadelphia, including expanding onto freeways. At the same time, competition and consumer expectations are accelerating. In markets where Waymo operates, fully driverless rides are becoming normal rather than novel. Once people can reliably hail a car with no one in the driver’s seat, tolerance for “pilot programs” and distant timelines drops quickly. The race is no longer about whether robotaxis work. It is about who can scale them first, safely, and everywhere — a win for consumers who get cheaper, safer, and more reliable rides.

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Key takeaways: - The core shift is economic, not hype. LLMs turn “AI” into a recurring operating cost, so pilots quickly become capacity planning, governance, and ownership. - Power and timelines show up because production inference needs sustained compute capacity. When usage increases across the org, you run into procurement, security review, integration work, and infrastructure readiness challenges. - The winners are not the teams with the flashiest demos. The winners are the teams that convert compute into measurable outcomes inside a workflow with traceability and controls. - In 2026, “AI strategy” that is not tied to a specific workflow, KPI, and operating model is mostly theater. If you are building the application layer on top of the datacenter buildout, execution systems, workflow automation, data-layer tooling, compliance automation, I want to see what you are shipping. Founder intro: www.axion.ventures

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AI is changing what “scale” actually means. In CTech by Calcalist’s 2026 VC Survey, Haim Bachar and Lee Ben-Gal share how we’re seeing the scaling metric shift - from headcount growth to output per person. This isn’t about doing more with less. It’s about building companies differently from day one - and rethinking how success is measured. See the full article below 👇

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The big labs are expansive in their product ambition, especially since foundation models have largely improved in lockstep - in order to compete with them you have to do things they won’t which are: - building a very rich software ecosystem around a primitive - orchestration across multiple models - going insanely deep on product and growth for a narrow vertical domain

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Hot take: I don't think MCPs are here to stay. I think AI will be smart enough to use the tools and services we have today (API, SDK, UI) to do anything a human/dev can.

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Why every SaaS company should be paying attention to what happened at NVIDIA GTC!👀 Jensen Huang - "Every software company in the world needs an OpenClaw strategy." The user is changing. It's not always a human anymore - it's a developer with agents working alongside them, or doing the work entirely for them. That creates some urgent questions for anyone building software, especially for technical personas (developers adopt agents and new tech faster than anyone else) -- What does "active users" mean when half the usage is via agents? -- Who are you selling to when the evaluator isn't always human? -- Does seat-based pricing still make sense? & more... At Reo.Dev, we're watching this shift closely - and building for this new reality. Revenue intelligence for DevTools is changing every time we blink, and it's only going to get more interesting from here! LFG🚀💪

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CRV Security: Request for Startups I never know if this actually works for our friends over at YC but figured we'd try. Here's what we want to fund in 2026! 1. Golden Artifacts: Think Chainguard but more broad. Artifact attestation exists for open source. Almost nothing exists for internal software — especially the vibe-coded tooling now running in production. We want the company building cryptographic proof of secure software delivered from secure artifacts: who built it, how, and whether it was reviewed. If more things are being yeeted into the world via Claude Code (myself included), this feels like an issue. 2. MCP & Agentic Security: Agents are getting real credentials and taking real actions. The security posture of most orgs around this is basically zero. That changes fast. You'd never give an employee hardcoded API keys or write access to your email without supervision/trust. Why give it to agents? 3. AI Governance: Boards are asking CISOs to account for AI risk. CISOs have no good answer other than "Palo has a module" 4. Next-Gen Endpoint: CrowdStrike was built for a world of static binaries and human operators. AI workloads, cloud-native infra, and AI-assisted attackers need a new architecture. The category is ready to be reinvented. 5. Networking in the AI Era: Zero trust was designed for humans. What does network security look like when the entity requesting access is an agent? Nobody's really solved this. 6. Email Security + Next-Gen Phishing: LLMs have made spear phishing infinitely scalable. I've never truly understood why Abnormal and KnowBe4 aren't one company. Maybe this time it's different. 7. Frontier Security Lab: We'd back a credible, well-staffed lab focused entirely on red-teaming models and setting the evidentiary standard the industry needs as LLM built apps become the norm. 8. Dependency Security: That Actually Remediates Malicious and vulnerable dependencies are a top attack vector. The tooling is mostly noise — scanners that don't close the loop. The winner here ships fixes, not just alerts. 9. Critical Infrastructure Cyber: Data centers, satellites, power grids, undersea cables. The physical backbone of the internet is increasingly exposed and wildly under-defended. We have data centers in space, for God's sake. Surely we need better cyber for critical infrastructure? 10. PAM for the Modern Era Legacy: PAM was built for static roles, human users, on-prem directories. Cyberark was founded in 1999.....Agents, ephemeral workloads, and cloud-native infra have broken all of those assumptions. Is anyone rebuilding this from scratch? If you're building in any of these areas — or something we haven't thought of — reach out. james@crv.com

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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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SaaS lock-in has historically been built on two main kinds of friction: the cost and risk of migrating data at scale, and the muscle memory that comes from teams building workflows around a specific interface. Agents weaken the second kind. When users interact with an agent rather than the underlying system directly, the interface becomes less sticky. This affects SaaS broadly: any tool whose value lived primarily in its interface is now vulnerable. For systems of record, the threat runs deeper. As agents increasingly do the work, fewer people log into the CRM directly. The system of record shifts from being an application to being infrastructure. You don't need to rip out Salesforce to make it worth a fraction of what it is today. You just need agents sitting above it, capturing the value of the work being done. So what replaces friction as the moat? We believe it’s context: the organizational decision histories and reasoning patterns that accumulate as an AI system learns how a specific company thinks and operates. That's what we've been calling a context graph, and it's different from existing systems of record. Incumbent systems of record were optimized for a human workforce. They record outcomes, not the reasoning behind them. For a human workforce that carried that context in their heads, that was fine. Agents need to understand not just what a company has done, but how it thinks. Retrofitting a context graph onto a legacy architecture is a fundamentally different engineering challenge than building one from scratch. Incumbents will have to do it while protecting existing revenue, supporting existing customers, and reporting earnings every quarter. Startups carry none of that weight. We laid out our context graphs' thesis in December. February’s “SaaSpocalypse” is the market catching up to the same realization: https://lnkd.in/gP25Qvzt

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We need more entrepreneurs in Quebec/Canada who have the ambition to build category-defining companies (and at Telegraph Ventures, we aim to back these entrepreneurs with conviction before they become “obvious”). Mehdi Merai Ph.D.(c) & Gabriel De Lisi have that energy, grit and proven track record. AI-powered cloud optimization is/will be a massive opportunity for years to come, and JetScale AI is well positioned to dominate in that space. Proud to be a part of this journey and partner up with the fine folks at Diagram to help turn this vision into reality. Let’s get to work! 🚀

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