“Henry was a key member of the engineering team at Tilt as an expert in payments, Tilt Pro, and data engineering. He lead numerous complex projects at Tilt and always delivered. He has proven himself capable of operating in an ambiguous environment, making high quality decisions, and moving fast. I can't think of a single challenge Henry was given that he didn't quickly excel. As a team member, Henry was always willing to help out others. He was quick to do anything he thought would benefit the business or coworkers, no matter the task. Henry was a great employee and someone I would love to work with again in the future.”
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Currently working on robotics and continual learning, particularly how agents learn…
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Dave Messina
I've looked at a lot of YC batches. The bio and health lineup in P26 is STACKED. https://lnkd.in/gQKxpe9t Adialante - Compact, mobile whole-body MRI to make cancer screening accessible at hundreds per scan FinalDose - A programmable drug that reads DNA inside the cell and destroys it if it's diseased, starting with previously undruggable cancer targets Voquill (YC P26) - An AI coworker for pathologists that learns your reporting style and produces sign-out ready reports in real time Clara - An AI primary care doctor with a licensed clinician reviewing every medical decision Lumius (YC P26) - Affordable, real-time 3D ultrasound, starting with vascular access
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Dave Lambert
Some of the best founders I know are rebuilding their CS workflows with GenAI. The RSCM team did a deep-dive with two RSCM founders (Ablorde Ashigbi from 4Degrees and Dakota R. Younger from Boon) on how they’ve rethought onboarding, bug triage, and knowledge sharing. Here’s a look at what they’ve done: - Built a queryable brain to bring up answers in seconds - Translated customer calls into feedback for the product team - Automated responses to inbound queries Founders, read this. Your team and customers need it: http://bit.ly/4lsFchy #AILeadership #Founders #CustomerSuccess #Startups #RSCMPortfolio #GenAI
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Ivan Landabaso
Zynga sold for $12.7 billion. Its founder says startups fail due to this: They try to innovate too early (unless its deep tech). Mark Pincus calls it the “All new fails” rule: 1/ Don’t start with “new”: Start with what’s already proven to work. Copy it legally, study it deeply. 2/ Proven means 10 out of 10 users say: “Yeah, that works.” If they don’t, it’s not proven. 3/ Founders fail because they skip the proven phase: They chase novelty before they’ve earned the right to innovate. 4/ The Zynga story began with poker: Same table, same felt, same gameplay, nothing original. 5/ Then they made it better: No downloads. Just click and play. Friction cut in half. 6/ “Better” means 10 out of 10 users agree: Not your team, not your investors, real users. 7/ Only after proven and better do you earn “new”: That spark that surprises and delights users emotionally. 8/ Zynga’s “new” was showing your friends’ faces: It made games social, not solitary. 9/ Forget MVP, build a minimum viable idea: A concept that hits emotional resonance before metrics. 10/ Seek true signal: You’ll feel it viscerally before you can measure it. 11/ Silicon Valley worships originality: But originality without resonance is noise. 12/ Better founders are great students first: They copy what works before they try to change it. 13/ Most products die from founder ego: They’re “new” before they’re useful. 14/ Users don’t care about novelty: They care about ease, trust, and delight. 15/ “Proven → Better → New just works. And it built a $12.7B company. 📩 Get ai + vc intel in your inbox via my newsletter Startup Riders, link under my name ☝️ 📌 Source: Mark Pincus on Why Most Startups Fail a16z #ai #llms #agent #startups #founder #vc
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James Green
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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Sandy Kory
40%+ of Seed and Series A funding in 2026 is flowing into $100M+ rounds, as Zachary DeWitt recently highlighted. The market is making a big bet on mega rounds. I'm taking the under on their performance--I'd bet heavily on an index of regular-sized seeds vs the megas. Here's what I think is happening. Ambitious founders surrounded by AI hype see Anthropic's and OpenAI's returns and think they need to do something similar. In their minds, this means raising huge rounds to fund NBA-level salaries for the most legible AI talent at Meta, Google, other AI labs, etc. The problem is that this approach is fundamentally derivative. This is not how Anthropic, OpenAI, or any of the other most disruptive AI companies started. Disruption requires doing something that experts dismiss. If a startup requires raising mountains of cash to hire legions of expensive experts, it might have some degree of success, but it will not be disruptive. Look, this is tricky. Anthropic raised giant early rounds and seems very mission-oriented, so I don't want to paint with too broad a brush here. That said, Anthropic's $124m Series A in 2021 was actually very unconventional in many respects, even in the context of a frothy 2021. Besides being derivative (and thus not disruptive), this approach also selects for mercenary over missionary culture. And all things being equal, companies with missionary culture outperform. Because if you really care about solving a problem, you're willing to make sacrifices and do non-consensus things—not just copy what worked for the last wave. So, why does this pattern persist despite these concerns? The most economically dominant point is that the investors doing the big rounds make a lot of money on management fees. When partners at funds enjoy massive compensation from management fees, investment performance becomes a "nice-to-have." To be fair, many of these partners are post-economic and are arguably more interested in hiring huge teams and building "platforms." The fees justified by mega rounds align nicely with the expense structure of empire-building. On the other hand, the smaller rounds are done by firms where the principals are making much less on management fees and have much more incentive to see a return on the capital via carry. The mega firms would surely retort that many of the smaller rounds are done by firms that are here today, gone tomorrow. And there's truth to that. Mega firms with billions of assets are inherently more stable. At the same time, those firms are fundamentally targeting much lower returns. It's trade-offs all the way down. So when I look at that 40%+ number, I see capital flowing into a pattern that selects for derivative thinking and mercenary culture, with an underlying incentive structure that ensures it keeps happening. And that's why I'm taking the under on the performance of that 40%.
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