A deep analysis of the foundation model landscape, where the real moats are being built, and why we believe the best AI investments of this decade will happen at the infrastructure-application boundary rather than in the models themselves.
Read Article →Buyers are skipping Google and asking ChatGPT, Claude, and Perplexity for vendor recommendations — and LLM traffic converts at 30% vs. 5% for traditional SEO. A six-step playbook for AI founders on how to build visibility in the AI search era, from prompt targeting to Wikipedia strategy to technical LLM parsability.
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The metrics, milestones, and narrative frameworks that separate AI companies that successfully raise Series A from those that don't. A practical guide built from our portfolio's experiences and what top-tier Series A funds tell us they are looking for.
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As foundation models expand up the stack and CIOs shift to "build" strategies, which B2B software segments will survive? We examine the first-mover vs. fast-follower debate, vertical AI as a defensible category, and what the historical record actually tells us about who wins technology transitions.
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Why data infrastructure is the most critical and underappreciated investment category of the AI era. We explore the full stack from data collection and labeling to vector stores, data pipelines, and observability platforms that make enterprise AI possible.
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PMF is the most overused and most poorly defined phrase in startups. We define it rigorously — consistent customer success, measured with a leading indicator before churn data arrives — and explain why the distinction between early revenue and genuine PMF is the most important call an AI founder makes.
Read Article →After years of pilot projects and proof-of-concepts that never reached production, enterprise AI deployment is finally accelerating. We break down the four structural changes that are driving this shift and what it means for founders building in this space.
Read Article →The conventional wisdom that AI safety research and commercial AI development are in tension is wrong. We make the case for AI safety as the most important emerging investment category in technology, with a $50B+ addressable market that most VCs are systematically underestimating.
Read Article →How Harvey ($100M·2024), Glean ($200M·2023), Sierra ($110M·2024), and Cognition ($175M·2024) are proving that enterprise AI has crossed the chasm from promising pilot to mission-critical deployment — and the five patterns that separate winners from failed pilots.
Read Article →Why Weights & Biases ($200M·2022), Hugging Face ($235M·2023), and Scale AI ($1B·2023) validate the ML infrastructure thesis — and how we identify the next generation of picks-and-shovels companies that will define how AI is built this decade.
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