Sam Zaid

Don't Build a Better Wheat Farm" - Why Defensibility Stakes Are Higher in Deep Tech Just published a new piece on my "Ignore the Confusion" blog, building on thoughtful insights from Eric Ver Ploeg at Tunitas Ventures about startup defensibility. Eric's core thesis: Too many startups pitch like wheat farmers - "huge TAM, slow incumbents, growing market, domain expertise" - but fail to think through long-term defensibility until it's too late. From a deep tech perspective, the stakes are even higher: ** Unlike software, deep tech founders must commit to defensibility strategies from day one - their funding depends on it ** Patent vs. trade secret decisions are often difficult to reverse and shape your entire competitive strategy ** Even "picks and shovels" providers (the tools that make industries more efficient) become commodities without proper moats The key insight that resonates: Defensibility can't be retrofitted. Whether you're building software or deep tech, your moat must be architected into the business model from the start. Thanks to Eric Ver Ploeg for sharing these insights on startup strategy and letting me build on his framework from a deep tech lens. Read the full post: https://lnkd.in/dEj_iF-Q #DeepTech #StartupStrategy #Defensibility #VentureCapital #Innovation

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"Build the event you wish existed" That's what we at Monadical did last week at #NYTW. We wanted an AI Engineers discussion for Engineers. There's always a lot of events out there for VCs to network, or for startups to learn about G2M, but very little on lessons learned from actual engineers in the field. So that's the event we hosted. Big thank yous to our awesome panel, Roy Pereira, Ben Cohen and Corey J. Gallon. Here's the key takeaways and the AI tools we're using. 🚀 All three panelists independently called AI Agents the most transformative LLM application they’ve used. They specifically called out Claude 3.5 Sonnet for its accuracy and reliability. 🪨 We dug into how LLMs are “jagged”, not general. They can be shockingly good at some tasks and completely fail at others. Everyone agreed: good evaluations are critical (and hard.) 🧪 Corey noted how public benchmarks and reality are two different things. Most public evals are saturated or gamed. ♊ Ben emphasized that AI projects are actually two projects: building the tool and building the evaluation process. 🧱 We explored how falling dev costs may impact startup defensibility and labor demand. Roy shared that founders are already shifting strategies in response. ⚒️ In a world of daily AI launches, the panel discussed how they decide what’s worth attention, and what’s just noise. They called out tools like Goose, Aider, Claude Code, and Monadical’s own Cubbi, which helps run agentic workflows safely in dev environments. (links in the comments). CTA: What would you want to hear in an AI Applied Engineering talk you attended?

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Camber Creek led the $3.7 million Seed round for TwinKnowledge, which is tackling an important problem in the built environment. Construction projects get delayed and rework is required because of discrepancies across construction documents, submittals, requests for information, design, and other plans. TwinKnowledge’s AI Agents absorb structured and unstructured project data, surfacing and preventing conflicts before the work begins. We’re excited about our partnership with CEO Ivan Panushev and his team. #construction #AEC #AI #technology https://ow.ly/mBYX50VtaVG

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I had the opportunity to speak to the CEOs of the startups funded by Shift4Good, which is investing in sustainable transportation. In full disclosure, I chair their impact committee. I argued that firms should think about scaling deep (building stronger relationships in existing communities), rather than simply thinking about scaling fast (wide geographical spread). Research by Suntae Kim and Anna Kim shows that scaling deep can build resilient organizations and communities. The standard Silicon Valley orthodoxy is to scale up and fast, which arises often because of investor pressures. Many people do not question this orthodoxy, and yet it comes at a cost. There are numerous firms that sit in a graveyard of companies that scaled too fast and failed. I could sense the skepticism from many of the CEOs. Yet, one CEO and cofounder, Sriram Kannan, of Routematic agreed with me. I had the privilege to talk to him again and he was able to articulate so clearly how the scaling deep model works, partly by contrasting how scaling deep model of Routematic contrasts to the scaling up model of Uber. The fundamental principles of scaling deep are: 1. Grow organically from profits, not from investor cash 2. Master one market before opening up others 3. Focus on relationships, not transactions I think my biggest epiphany was that scaling deep does not mean slow growth or weak profits. Routematic has been able to achieve 8-10% month-over-month growth in revenues and has largely been profitable since it began offering its transportation services. Uber took almost 15 years to be profitable. I captured these ideas in the Forbes article I posted today, which was fun to write and taught me a lot. This is one of the many ideas I am exploring to see how systems thinking is encouraging a different way of thinking--where businesses can contribute to and benefit from stronger communities and a natural environment. As always, I welcome your reactions and provocations. Ivey Business School at Western University, Innovation North, Network for Business Sustainability (NBS), Kavitha Ramachandragowda, Ameya Uchil, Sebastien Guillaud, Thierry de Panafieu, #systemsthinking, #ScalingStrategy #SustainableGrowth #StartupStrategy #ProfitableGrowth #Entrepreneurship #Leadership #ImpactInvesting #SustainableTransportation https://lnkd.in/gM74UmYM

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Vertical AI feels like a horserace 🏇 but after stepping back and thinking about the current state of market, I think what's equally unique is you have three different models competing at once. I think about it through this lens: 1️⃣ AI-enabled SaaS: the familiar path Take a proven vertical category and supercharge it with AI. Clear ROI, legible GTM, easy for VCs to understand. But real questions around differentiation and long term moats. 2️⃣ AI Roll-Ups: PE gets a facelift Acquire businesses, deploy AI to improve unit economics, and own the data + operations. Powerful if executed well, but operationally and financially complex. Big open questions around end state valuation. 3️⃣ AI-native industry disruptors: Become the thing Don’t sell software. Don’t buy incumbents. Build a fundamentally new version of the industry from scratch. Highest upside, but probably the highest casualty rate. You have to choose the model where the physics match the market. Each model has different capital needs, metrics, risk profiles, and founder skill requirements. I did a deep dive on each model 🤿 : ▪️where each model wins (and fails) ▪️how data dynamics actually differ across them ▪️why valuation expectations may diverge more than people expect ▪️and where I think the biggest outcomes will come from... As always, if you are building, investing, or just trying to make sense of vertical AI landscape, I always love to jam. Full post here: https://lnkd.in/g9a_9rtj

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Self-driving cars are no longer experimental. They're becoming a catalyst for how and where people choose to live and work. With autonomous vehicles moving from pilot programs to scaled urban deployment, we're entering a new geography of labor. Real estate is starting to feel the pressure to adapt. Waymo alone has seen its U.S. monthly active users grow tenfold since 2022, reaching nearly 1 million across just three cities. Today, it already represents 3% of Uber's and 7% of Lyft's U.S. market shares, according to Goldman Sachs and Sensor Tower. As Brad Hargreaves at Thesis Driven has outline in a recent article (Autonomous Vehicles Are a Moral Imperative), cities may push back, but driverless fleets keep scaling. Boston is trying to slow-roll Waymo while operating zones in places like Phoenix, San Francisco, and Los Angeles expand - usage and service miles continue to climb. Concurrently, labor dynamics have undergone fundamental changes. Data from the New Geography of Labor Markets shows that employees hired since March 2020 are far more likely to live more than 50 miles from their workplace, especially high earners in their 30s and 40s. These aren't outliers. They represent a core segment of the knowledge economy that is untethering itself from traditional hubs and redefining the value of commute time. This convergence of automation and distributed work is a wake-up call for the real estate industry. Mobility has always shaped demand, but the rules are shifting. Car-as-a-service means the vehicle becomes a productive workspace. Physical distance becomes less of a constraint. What draws someone to a building or district can no longer rest solely on proximity. That means the real estate we build must do more. They need to deliver activation, productivity, and destination value. The built environment has to earn the journey. Autonomous mobility expands choice, and real estate must answer with relevance.

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I've got enough data points now to call it a trend... We have officially moved on from a seed stage AI infra fever-dream into the NGMI stage. There are hundreds of startups working on every single problem and opportunity... and even if you were lucky enough to pick a category that is durable, there's not enough differentiation. Oh and the hyperscalers are working on those problems too... The smart folks are quickly realizing that either one or a few winners are going to take the category or that nobody is and that you're on a fools errand and better find a soft-landing yesterday. It doesn't mean that you've built something without value, it's just that you accidentally showed up to the NFL combine straight from high school. So my advice, and one that I've given to multiple founders at this point, is to either start operating with the aggressiveness of a face-eating leopard or get out now because reality is coming for you and there is no escape.

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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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