More About Matt
Matt Bornstein is a partner at Andreessen Horowitz focused on AI, data systems, and infrastructure. He serves on the boards of Replicate and Udio, and also works with Tabular (acquired by Databricks), Cursor, Hex, Mistral AI, Character.ai, Pinecone, Labelbox, and others.
Prior to a16z, Matt was a member of seed-stage venture capital firm Blumberg Capital, where he led investments in companies like Pachyderm, SigOpt, and SmithRx. He also worked on the business operations team at LinkedIn, served as a management consultant at the Monitor Group, and co-founded two bootstrapped startups.
Matt holds an MBA from Harvard Business School and a BS in mathematics from Brown University.
Latest Content
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Labelbox CEO Manu Sharma explores data labeling and evaluation in AI, from early supervised learning to today’s reinforcement learning loops.
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The third batch of a16z Open Source AI Grants focuses on better understanding the capabilities of frontier LLMs and testing their boundaries.
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dbt Labs founder and CEO Tristan Handy discusses the rise (and plateau) of the modern data stack and role of AI in analytics and data engineering.
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The a16z Infra teams explores how LLMs are reshaping software development — from its potential as a new programming abstraction to its practical impacts.
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Hedra is an AI video company focused on character animation, making it easy to generate video scenes from an image and audio track or text script.
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a16z Infra partners Guido Appenzeller, Matt Bornstein, and Yoko Li discuss and debate one of the tech industry's buzziest words right now: AI agents.
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Replicate CEO Ben Firshman and a16z partner Matt Bornstein discuss the art of building productss — from PaaS to AI — that appeal to software developers.
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Cursor is a fork of VS Code that’s heavily customized for AI-assisted programming, with loads of features designed to integrate AI into developer workflows.
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In this episode of the AI + a16z podcast, a16z partners Guido Appenzeller and Matt Bornstein discuss the state of the generative AI market, about 18 months after it really kicked into high gear with the release of ChatGP...
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Naveen Rao of Databricks joins a16z's Matt Bornstein and Derrick Harris to discuss where we're at in terms of large language model (LLM) adoption.
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Distributional is building a platform for robust and repeatable testing of AI and machine learning models so teams can push to prod confidently.
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a16z announces its second batch of a16z Open Source AI Grant recipients. This cohort focuses mainly on LLMs and visual AI models.
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Mistral is at the center of the open source AI developer community. This is the most promising path to achieve robust and trusted AI systems.
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生成AIの領域で、ごく初期段階の技術スタックが生まれている。無数のスタートアップがこの市場に押し寄せ、基礎モデルの開発、AIネイティブのアプリケーション開発、そして立ち上げに必要なインフラ／ツール提供を行っている。
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a16z announces its Open Source AI Grant program, which will support a small group of open source developers through grant funding.
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A reference architecture for the LLM app stack. It shows the most common systems, tools, and design patterns used by AI startups and tech companies.
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A curated list of resources we’ve relied on to get smarter about modern AI, including generative AI, LLMs, and transformer models.
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The generative AI boom is compute-bound and, as a result, a predominant factor driving the industry is simply the cost of training and inference.
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We’re leading a $100 million Series B round in vector database provider Pinecone, to support its vision of becoming the memory layer for AI applications.
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Understanding what’s in an image — one of the simplest cognitive tasks for most humans — is a stubbornly difficult problem for artificial intelligence systems to solve.
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Every so often, we meet founders with unusually strong conviction about solving systems problems. They’ve often lived through the pain of bad software, diagnosed the cause of their suffering, and made it their mission to...
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It’s very rare to see a new building block emerge in computing. If aliens landed on earth and decompiled our software, every app would look roughly the same: some combination of networking, storage, and compute. The way...
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We're starting to see the very early stages of a tech stack emerge in generative artificial intelligence (AI). Hundreds of new startups are rushing into the market to develop foundation models, build AI-native apps, and...
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There's much more to generative AI than heroic avatars and tongue-in-cheek art. The potential applications are widespread, from drug discovery and therapy to writing, game development, education, ecommerce, and more. Las...
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Why AI models will replace artists long before they'll replace programmers
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Much has been discussed, written, and reconfigured around the modern data stack – and for good reason, it’s one of the most significant shifts in data architecture of our time. But as close followers of its evolution, on...
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As an industry, we’ve gotten exceptionally good at building large, complex software systems. We’re now starting to see the rise of massive, complex systems built around data – where the primary business value of the syst...
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To help data teams stay on top of the changes happening in the industry, this article reviews an updated set of data infrastructure architectures.
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This first appeared in the monthly a16z enterprise newsletter. Subscribe to stay on top of the latest in enterprise & B2B.
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AI has enormous potential to disrupt markets that have traditionally been out of reach for software. These markets – which have relied on humans to navigate natural language, images, and physical space – represent a huge...
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At a technical level, artificial intelligence seems to be the future of software. AI is showing remarkable progress on a range of difficult computer science problems, and the job of software developers – who now work wit...