Matt Dyor

Voice is a super interesting modality right now - maybe the first modality we're seeing move to open source models across a number of scale ups / enterprises. Reliability concerns, high costs, and open source model performance are pushing engineers to do their own fine tuning vs. relying on third-party vendors of proprietary models. Many of these orgs have already been collecting their own first-party data and now with third-party vendors like Extrian, David AI, etc they can train really high quality models. RL has been insanely hyped, but it's been unclear how long it will take scale ups and enterprises to actually lean in. Voice AI might be hitting that inflection point faster than expected.

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Yesterday, I posted about venture capital returning in 2026, but in a much more concentrated way. February alone saw $189 billion in startup funding globally, the largest month of venture investment ever recorded. But 83% of that capital went to just three companies: OpenAI, Anthropic, and Waymo. At the same time, venture firms are still raising new funds and allocating capital to early-stage companies, even in a more disciplined market. Capital still exists. But investors are approaching early-stage deals with much greater discipline. Which raises an important question founders should ask themselves: What signals tell investors that a company is ready for capital? Over the years, working with founders preparing to raise, I’ve noticed investors tend to look for the same practical indicators before moving forward. Signs that the company behind the idea is structured to grow responsibly. Here are some of the signals investors quietly evaluate before making early-stage bets. If you’re a first-time founder navigating fundraising or planning a round soon, join us for our upcoming masterclass on March 17th, where we will cover the 3 hidden traps in term sheets that could cost you control. Save your seat: https://lnkd.in/gPaPpsFC

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Odin Dynamics, Inc write-up by Matthew Bernard. The story of how Amir and Jon came together to build something of National importance. It's worth the read. https://lnkd.in/eXq8zySA Tareyton Venture Partners

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Skilled labor is the bottleneck in industrial ops. Elevāt's platform basically tries to “productize the expert” using machine signals + docs + prior fixes. Now they have $12M to scale. Congrats to them. In industrial AI, the tech is usually the easy part....adoption is the battle. Curious what you think is the real gating factor:  -- trust (techs believe it’s right and won’t get them hurt),  -- workflow integration (it fits into CMMS/ticketing/parts + works offline/on-device), OR  -- incentives (the field team is rewarded for using it vs “just fixing it their way”). Which one kills deployments most often in your experience?

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Welcome Natan Linder, Co-Founder and CEO of Tulip Interfaces on this episode of 🔥The Lantern 🔥 Tulip Interfaces helps manufacturers build production systems that put people first with no code, low code, and other AI tools to turn operational knowledge into apps, workflows, and continuous improvement. 🔍 What happened in 2025 as the team heads into 2026? 🔎 Tulip wrapped their largest-ever “Operations Calling” conference with 700+ attendees to end 2025. Their platform is processing north of 620 million automations, 70 million application-based processes, and 35 million station hours. They're about to cross 30 billion contextualized data points! They also acquired the talented Akooda (acquired by Tulip) team, led by Yuval Gonczarowski, bringing enterprise AI search and deep learning expertise into their platform to help customers sift through massive operational datasets. In January, they announced a $120M fundraise led by Mitsubishi, that values Tulip at $1.3B. 🤔 Why does manufacturing need specialized AI? 🤔 Unlike software engineers who can freely experiment with their AI agents, real world operations require more rigor. Downtime and/or destroyed raw materials costs millions. Tulip is building AI copilots, composer products, and full agentic frameworks that are safe for the factory floor in regulated industries like pharma, medical devices, and aerospace. 🧠 What has Natan learned from building Tulip? 🧠 Work very close to customers. Natan believes a startup's focus should be split into 80% on customers, 10% on markets, and 10% for hallucinations. That customer obsession drove their Akooda (acquired by Tulip) acquisition. Operations leaders are knowledge workers too and need AI verticalized for their specific needs to democratize access to manufacturing expertise and elevate operations teams. 🤝 How can we help Tulip scale? 🤝 Tulip is in full sgrowth mode heading into 2026. If you're an engineer passionate about manufacturing, AI, or hard tech, or know world-class operators who want to transform how physical products get made, reach out! Boston is building the future of intelligent manufacturing and Tulip is leading the charge. The Lantern is brought to you by MGMT Boston, Rho, & Invest Northern Ireland – Americas this month

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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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