Oliver Roup

💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ

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Lots of news out of Hetz Ventures portfolio today - Nimble announced their $47M Series B, led by Norwest with participation from Databricks Ventures. We've been proud to back Uriel Knorovich, Menachem Salinas and the entire Nimble team since early on, turning the live web into trusted, decision-grade data for AI agents and mission-critical workflows. Tonic Security launched the Tonic Mobilization Coordinator, the industry's first agentic remediation orchestrator. It automatically drives exposure remediation end-to-end, moving security teams from knowing they have risk to actually reducing it, continuously and at scale. Well done Sharon Isaaci, David Warshavski and Gregory Ainbinder. Anima launched their UX Design Agent on Product Hunt today: https://lnkd.in/demUMrUX. Designers and product teams can now go from prompt, Figma file, or live website to on-brand, production-ready frontend code in a single shot. Deepchecks released Know Your Agent (KYA), their answer to evaluation in the agentic era. Borrowing from the KYC framework in financial services, it gives teams a structured way to understand how their agents actually behave across complex multi-agent workflows. #proudseedinvestor https://lnkd.in/dyGTAHcg

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New Uncovered Bookmarks is out. This one covers our Airtree Studios launch (featuring Tim Davis from Modular and a conversation on non-technical operators now shipping code with Saahil Bijlani, Renee Zhang and Jason Zhou), plus some things I've been reading on data centres building their own power plants, Stanford turning papers into AI agents, and why enterprise SaaS might have more moat than markets give it credit for.                                                                                                      https://lnkd.in/g-Xh2pbE

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Just rewatched Paz's presentation at CMX — one of the more thoughtful breakdowns I’ve seen on scaling community. She shared how she used Intros AI 🤝 to automate different types of member introductions, making it easy to create meaningful connections without manual coordination. One line stuck with me: “We connected hundreds of people across 12 countries in minutes.” As community building evolves, so should our tools. Automating logistics isn’t about removing the human — it’s about making space for deeper, more intentional engagement.

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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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As I'm leaving AWS re:Invent in Vegas, I’m reminded of a counterintuitive truth: competing with the public clouds is often really good business VCs love to ask, “Why wouldn’t the incumbent do this?” But in markets this large (and evolving this quickly) that’s rarely the right question. The real questions are: Who can assemble the best team, get to market fastest and with the most focus? And where are there opportunities to partner with the biggest players rather than compete? We’ve seen this pattern repeat across the ecosystem: -People assumed Vercel and AWS were destined to compete. Today, they’re strategic partners with CEO Guillermo Rauch in the keynote -Snowflake vs. Amazon Redshift. Now, AWS is one of Snowflake’s strongest distribution channels -In Datadog’s early pitches, the concern was, “If it’s just monitoring AWS, why not use CloudWatch?” Fast forward to now, and Datadog is now $50B+ public company We’re going to see the same dynamics play out with OpenAI and Anthropic. These are becoming foundational platforms. But that doesn’t preclude incredible companies from being built on, next to, and even around them. Some will look competitive. Many will end up as partners. And the biggest outcomes often start in those gray zones

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