Jeff Grimes

Lucas and Thomas founded Deepnight (YC W24) to replace analog night vision systems with software. Their models are able to render images like the one on the right at 90fps on commodity hardware.

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2 years ago today, A16Z announced they put $100M into Pinecone, as everyone and their mother tripped over each other to invest in vector databases. Given how little standalone vector DBs are valued now, the hype cycle is wild to look back on. Quick timeline below: Late 2022: • ChatGPT launches. • Early RAG architectures emerge. • First buzz around Pinecone, Weaviate, Milvus, Chroma. Q1 2023: • Massive demand surge for vector search. • Vector DBs seen as critical infra. • Heavy VC excitement (“new database category!”). Q2 2023: • Peak hype. • Pinecone raises $100M, Weaviate raises $50M. • Every AI infra diagram has a vector DB layer. Q3 2023: • Saturation hits. • Postgres, Redis, MongoDB add native vector search. • Clouds bundle it. Q4 2023: • Hype fades. • Vector search becomes a commodity. Early 2024: • Market tightens. • Attention shifts to agents, fine-tuning, orchestration.

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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I'm thrilled to share that Clay is one of 6 companies to join OpenAI’s new Frontier program. Tomorrow's best companies will run on AI across every function. Clay is already the creative tool powering growth teams at Cursor, Ramp, Figma, and OpenAI itself. Now we're making that same power available to enterprises through OpenAI's new platform. This partnership will allow us to help more companies use OpenAI's models for growth, sales, marketing, and RevOps workflows to find and expand their best customers. Here are some ways Clay and OpenAI help GTM teams use AI to grow together: - RevOps and GTM engineering teams can turn their CRM into a living entity: one that automatically refreshes, dedupes, and surfaces accounts that are ready to buy - Sales teams can score companies showing buying signals, pull verified contacts, generate personalized messaging, and sync everything to their CRM. - Marketing teams can build targeted audiences and ship relevant creative campaigns, from ads to landing pages to email I'm grateful to be building the future of GTM alongside OpenAI and this incredible group of companies -- Harvey, Sierra, Abridge, Ambience, and Decagon Learn more about OpenAI's Frontier Partner Program here: clay.link/kp6KIJy

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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 best founders don’t just build products; they reframe the problem. Ryan Alshak has done exactly that with Laurel - turning time from a background task into a source of insight, alignment, and leverage. IVP is leading Laurel's Series C to help build the system of record for how work actually happens. Read more from Allie Garfinkle: https://lnkd.in/gweNiwsc

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Vibe coding products like v0 (Vercel) / Bolt / Lovable have created ~$100M ARR in 6 months. Prediction: this will grow to $1B in the next year for this suite of tools. There are two market dynamics happening simultaneously giving us a glimpse into the future: 1) Consumer phenomenon: vibe coding is trending on TikTok, and a brand new market of creators is emerging. This is akin to the number of microsoft excel users around the world, but way cooler. You can produce a site or app in seconds with no need to write code. 2) Future of enterprise software: with the ability to seamlessly prompt a user interface, enterprise platforms like Salesforce and ServiceNow will see their rigid UIs open up and be personalized for each users own preferences. My bet is 30%+ of UI of each of these will be user generated.

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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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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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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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