Jeff Grimes

The Headline family is experiencing incredible momentum, and today our portfolio company DeepIP is announcing a $25M Series B, bringing total funding to $40M.  This is the kind of company that doesn’t just add AI to a workflow, but it sets the standard for an entire category. The momentum speaks for itself: 1️⃣ 10x ARR growth in < 18 months 2️⃣ Trusted by 400+ law firms and leading corporate IP teams globally 3️⃣ 2M+ chats on the DeepIP platform We’ve had the chance to back FX (Francois-Xavier) L. and Edouard D., leading their Seed round with Headline. Watching them take on a problem as complex and high-stakes as patent work has been genuinely impressive. And working with them has been an absolute pleasure: these 2 are coupling very high ambition and humility. Tech built in Europe, but opening the US office very early and FX starting a life in NYC with his family with 0 hesitation in order to build a global category leader in the IP industry. What convinced me early is still what stands out today: patent teams don’t need another standalone AI tool. They need something that fits the way the work actually happens: documents, long-running matters, multiple stakeholders, and zero room for sloppy context handoffs. Big congrats to the whole DeepIP team + Korelya Capital, Balderton & Serena Proud that we’re doubling down at Headline and continuing the support for what’s next!

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Exciting to see the incredible progress from Peptone, a company we’ve backed since the early days at Walden Catalyst Ventures. Peptone just announced Peptron-O, developed in collaboration with NVIDIA — a groundbreaking AI engine that brings ensemble-first structure prediction to the disordered proteome. This is a major scientific and technological milestone, targeting one of the most complex and previously inaccessible areas of biology: intrinsically disordered proteins (IDPs). By shifting the paradigm from static models to dynamic ensembles, Peptron-O opens new doors for drug discovery — especially in areas like oncology and neurodegeneration, where IDPs play critical roles. We’re proud to have supported Peptone on their mission to build the infrastructure for next-gen molecular physics. Their work is a great example of how deep tech can transform life sciences. Read more about the announcement: 👉 https://lnkd.in/eygnKQ67 #AI #Biotech #DrugDiscovery #DeepTech #LifeSciences #WaldenCatalyst #Peptone #NVIDIA #PeptronO with: Kamil Tamiola, Ph.D. Francis Ho Shankar Chandran Nicolas Autret Andy Kau Roni Hefetz Victoria Slivkoff

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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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🚀🚀 AI is rewriting the rules of startup success. In our latest Crunchbase piece, Noah Lin and I dive into how we @ defy.vc have seen AI-first companies scale smarter with value model tactics—leveraging predictive power and network effects to reshape industries. From defying traditional metrics to building data moats, these strategies are a game-changer for founders. 💡 Stay tuned for a deeper follow-up where we’ll share actionable tactics for founders to scale their AI ventures! Full article 👇 #AI #Startups #Innovation #VentureCapital

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And we're off! Our first annual conference SCULPT just kicked off with three of our biggest feature launches yet: 𝗦𝗰𝘂𝗹𝗽𝘁𝗼𝗿: Your AI GTM co-pilot. Build live workflows or table analyses in natural language conversation to save hours of manual work. (Try "Build me a list of CEOs at mid-stage healthcare companies in SF along with their work email" or "Analyze my list of closed-lost deals and tell me why accounts didn't buy.") 𝗔𝘂𝗱𝗶𝗲𝗻𝗰𝗲𝘀: Removes the 50K row limit. Sync millions of records, combining signals, enrichment, and CRM data in a single table for precise targeting. Create highly specific segments by layering multiple data types without stitching together separate tables. 𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝗿: Go from signal to campaign in minutes. Send native email campaigns without leaving Clay, with data and bulletproof deliverability baked in. These launches are some of our biggest this year, and we hope they will help you come up with, experiment and execute your best growth ideas faster than ever. Read the full feature details in our post linked below.

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Today's fastest teams look nothing like those of the past decade. We are see seeing many AI-first startups completely rewrite how companies are organized. Early themes are are seeing: 1. Broader roles designed around outcomes, not processes In the AI era, process work is automated. So early roles don't “own” a lane, they own an outcome. e.g. Head of Marketing becomes "Head of Growth" 2. Team design that facilitates company-wide innovation Innovation happens fastest when everyone can bring their full skill set to any project. Broad roles, no lanes, in-person, fluid across product + GTM + ops. Streamlines building without handoffs. 3. Mindset: the AI-first operating psychology In the pre-AI era, founders hired for functional kill. Now you hire for taste, adaptability, and the willingness to reinvent how they work. We plan to write much more about this in the coming weeks. But here's the new startup blueprint as we're seeing it today: https://lnkd.in/dsvQnHNi

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The data doesn't lie - brilliant deep tech founders outside Silicon Valley consistently face structural headwinds that have nothing to do with their technology or vision. Too many breakthrough innovations stall because founders can't find investors who understand long development cycles, lack access to operators who've navigated complex regulatory paths, or struggle to build the interdisciplinary teams hard tech requires. The science is there. The ambition is there. We're building the infrastructure to connect them properly. Details coming soon. 

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The AI boom didn’t quite break on compute; it broke on supply, labor, and lead times. Behind-the-scenes stories of overprovisioning and, ironically, a shortage of thousands of people who still need to plug, test, and re-plug glass to keep “autonomous” systems alive. I don't know, maybe AI will fix this, since humans are clearly the rate limiter.

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