Greylock Partners

1/ With a long history of partnering w/ founders from idea to IPO, we @GreylockVC are excited to announce 2 new updates to advance this mission: 1/ Fund 17, our new $1B early-stage fund 2/ Edge, a bespoke program to help founders initiate new companies bit.ly/45fLmcr

We evaluated @Tenzai_Labs AI hacker across six major CTF competitions designed for humans. Result: Top 1% performance, outperforming 125,000+ human hackers across different domains - web hacking, ai hacking, low level system hacking. We wanted to see what @Tenzai_Labs's hacking agent is really capable of in the most complicated and competitive environments, where to excel, one needs to solve increasingly difficult challenges. The results we achieved surprised even me. This is incredible evidence of what AI agents with the right harness can do and I expect it to only get better from now. blog.tenzai.com/tenzais-ai-hac…

When you start a fundraiser, every detail matters: how you tell your story, when to share, and how to keep the momentum going. That’s why we’re excited to introduce GoFundMe’s smart fundraising coach—a proactive, personalized AI experience that gives you clear next steps so you can build momentum with confidence. Give it a try today -> gfme.co/4dfEFie

Axiamatic, which offers an "agentic control plane" to manage large-scale enterprise transformations, emerges from stealth with $54M from Greylock and Bessemer (@lilymaelazarus / Fortune) fortune.com/2026/03/11/exc… #a260311p32" target="_blank" rel="nofollow noopener">techmeme.com/260311/p32#a26… 📥 Send tips! techmeme.com/contact

AI Agents can do a lot but what agents can't do is be a funny + quirky high school science teacher that goes to space to stop the astrophage and befriend new species for that, theres the Greylock screening of Project Hail Mary cc @GreylockVC

AI-native cybersecurity startups are here, and investors are paying attention. Consider Cylake, a company that just raised a $45 million seed round, led by @GreylockVC. The latest from @maddierenbarger 👇

Introducing @CylakeAI, built to deliver security entirely within the customer’s own sovereign environment, serving a market where cybersecurity innovation hasn’t kept up. Cylake is founded by cyber veterans Nir Zuk, Wilson Xu, and Udi Shamir. We at Greylock Partners have known @nirzuk for 30 years. When he started Palo Alto Networks out of a tiny office at Greylock in 2005, we had the privilege of being a founding investor and part of the company's journey from day zero. Read more on our investment from @GreylockVC's @chandna: greylock.com/portfolio-news…

[1/5] Everyone is asking whether AI will replace cybersecurity products. But the real question is whether our current security architecture can even support what AI requires. To understand the effect of AI on cybersecurity, let’s roll back the clock for more than a decade.

[1/5] The world’s largest and most regulated institutions demand state-of-the-art cybersecurity, but many cannot depend on products tied to the public cloud. We believe that shouldn’t exclude them from access to top-tier protection.

How has @ankrgyl been able to recruit both a strong eng and GTM team? "We take recruiting as seriously as we take customer obsession," says @ankrgyl. "Our customers get really involved in helping us recruit candidates."

Why do companies known for having taste like Stripe use @braintrust? "We were very specific about which customers we wanted to make a bet on, and make them really happy," says @ankrgyl. "We just roll up our sleeves and go all in."

Ankur Goyal has been building AI products long before ChatGPT. Now, as the Founder and CEO of @braintrust, which announced their $80M Series B last week, he's helping developers ship AI that actually works. Early Braintrust investor @saammotamedi sat down w/ @ankrgyl to talk about why evals are the foundation of AI product development, lessons learned when scaling GTM, Braintrust's customer & talent obsession, and more. youtu.be/U5OM6MRI9mw

Cogent Security Cogent Security @cogent_security announces their $42M Series A today, led by Enrique Salem at Bain Capital Ventures (BCV), with @GreylockVC doubling down following our seed financing last year. When @vineete_5, @snggeng, and @ThanosBaskous started Cogent at the Greylock San Francisco office, they were obsessed with a specific and stubborn bottleneck in cybersecurity: the gap between detecting vulnerabilities and actually fixing them. Security teams are drowning in alerts, tickets, coordination work, and ownership ambiguity. Meanwhile, attackers are increasingly automated and AI is accelerating both the speed and complexity of attacks. Human-only vulnerability management simply doesn’t scale anymore. Cogent's agentic AI platform for vulnerability management has purpose-built AI agents sit on top of existing security tooling, ingest signals across scanners and asset systems, determine what truly matters, assign the right owner, and drive remediation end-to-end. In just six months since launching from stealth, Cogent is already running in production across large Fortune 1000 environments and technology native customers like BILL, University of the Pacific, LangChain, Alteryx and many others. Customers are reducing the lifetime of critical vulnerabilities by ~97%. Cogent’s origin story is a special one for us and builds on our history of initiating companies with outlier entrepreneurs at Greylock. @evanreiser, who founded Abnormal AI at Greylock in 2018, first introduced us to Vineet and Geng — two of his key leaders at @AbnormalAI. Their third cofounder, Thanos, previously led infrastructure at Coinbase and earlier co-founded a company with Evan. Since starting, they’ve recruited one of the strongest AI teams in cybersecurity, including talent from Gemini/DeepMind, Tesla, Stripe, and more. With today’s Series A, Cogent is positioned to scale its agentic AI platform to even more of the Fortune 1000. Onwards!

In between major AI model breakthroughs, new models still ship, but they’re not fundamentally expanding what you can do with them. Braintrust CEO @ankrgyl says that’s exactly when open source starts to surge: "When new models come out, people forget about open source and forget about economics because it changes fundamentally what you can do." "Programming is a good example. The workflow today is completely different than it was a year ago. If you tried to use models from a year ago today, you wouldn't be able to do crazy stuff like OpenClaw or whatever people are building nowadays." "When models don't change at that speed, people optimize the performance on the use cases that they have." "If you look at usage on our platform, almost half the token usage is coming from open source models from a very small number of companies that have figured out how to optimize use cases really well."

We sent this note to our customers to let them know that Braintrust has raised a new round of funding, and thank them for their support. While the money is exciting, our focus hasn't changed: we're building Braintrust to help our customers ship quality AI products. In 2026, AI is moving to production but teams have never had less conviction about what will fail next. Our customers are building AI products that serve millions and simply need to work. If Braintrust makes their lives easier and their products better, I know we are doing our job. Thank you to @ICONIQCapital for leading our Series B, and to @a16z, @GreylockVC, @basecasevc, and @eladgil for doubling down. Thank you to the Braintrust team for all the incredible work you've done over the past year. And thank you to our customers, who have made this growth possible.

In two years, every new tech company will run on a CRM you can vibe code to fit your business. This CRM will not be built from scratch on a coding platform though. It will be built on top of managed infrastructure with complete data capture, indices designed for LLMs to understand the whole picture, clean APIs, curated UI frameworks designed for selling, enterprise-grade security, and come with 24/7 support. You’ll instruct the agent using natural language and it will write the code + run it for you. That’s what we’re building at Lightfield and today we’re announcing step two of our plan - code execution. You can now ask your agent to build programs, artifacts, and run complex analysis instantly. It does this by writing and running Python in a high performance sandbox using full customer memory — including every email, meeting, and note that Lightfield has captured — and reasoning across every relationship to deliver high quality work. Ask your agent to build a competitive battle card before a call tomorrow. It pulls positioning, objections, and win/loss patterns from real conversations. Ask it to flag every open deal where your champion's engagement has dropped or sentiment has shifted. It reads across every conversation and tells you where to focus. Ask it to build a pipeline review with charts and graphs for your board. It produces the whole thing in minutes. Here’s what we did with it this week: → We asked our agent to grade our sales team on discovery, rapport, and closing. It gave a structured scorecard with specific examples from real conversations. → Our GTM team asked the agent to build a plan to expand one of our enterprise customers. It pulled competitive threats, upsell paths, stakeholder mapping, and a phased execution plan — in minutes. → We used it to find every feature request from the last quarter that our engineering team has since shipped, and draft a personalized follow-up to each customer using their original words. It closed loops across dozens of accounts that would have taken days to track down manually This is the first step towards building any custom GTM workflow in natural language on top of what Lightfield knows about your business - a world model built from every single interaction your team has had with customers.

Our ability to measure AI has been outpaced by our ability to develop it. This evaluation gap is one of the most critical problems in AI. Today, we’re excited to announce the Open Benchmarks Grants - with a starting $3M commitment from @Snorkel + support from @HuggingFace @togethercompute @PrimeIntellect Factory HQ @harborframework & @PyTorch - aimed at supporting the academic and open source teams solving this. Today, two things are simultaneously true: - (1) AI agents are taking massive, quantifiable leaps forward in certain areas like coding, where they are rewriting how we do our work. - (2) Most of the world is still unsure of where/whether they can put agents in production for their unique use cases. The source of this dichotomy: our ability to measure AI has been outpaced by our ability to develop it – and this evaluation gap is one of the most critical problems to solve in AI. Public benchmarks are of course only one (limited) tool for measurement, and need to be balanced responsibly with others (no benchmaxxing!). But at their best, they become key guideposts for whole new vectors of AI research and development. We’re particularly excited to see progress along three core axes: - (I) Environment Complexity: E.g. domain-specificity, context complexity, multi-modality, tool complexity, human & agent interaction, world modeling, and more - (II) Autonomy horizon: Long horizon scope, non-stationary goals and environments - (III) Output complexity: Complex work products with nuanced trace- and output-level rubrics, new evaluations of trust and safety, and more But these are just our starting thoughts. We’re excited to see what the community comes up with, and even more excited to support! 🚀

"Any company that runs software, if you call their CTO today and say, 'What's your number one problem? What stops you from being more productive as an organization?' It's all the issues in production." @GreylockVC's @saammotamedi shares why he believes AI for prod can be bigger & more valuable than AI for coding, what the next 12+ months look like as AI for production becomes a defining category, why we bet on @spirosx and the @resolveai AI team, and more. resolve.ai/blog/Series-A-…

In 2023, 3 years before this chart, @will_lawrenceTO had a clear vision for how AI agents were going to automate compliance based on his experience at WhatsApp and Paxos Fast forward, and @brettonai has become the enterprise leader in AI for financial crimes Individual banks can spend $100-$300M *each* on human compliance analysts for poor results - including backlogged alerts, fines, and poor customer experiences Bretton AI has become the trusted provider for regulated financial institutions at scale, serving customers worth over $1 trillion in aggregate market cap Congrats on the $75M Series B, and welcome @rajeevdham and @SapphireVC to the team! fortune.com/2026/02/10/bre…

We’re excited to announce our $75M Series B and a new name that reflects our growing ambitions. Greenlite AI is now Bretton AI: the Agent Platform for Financial Crime. Bretton AI agents handle AML, KYC and Sanction reviews for Fortune 100s, global payment networks, and highly-regulated U.S. banks - including institutions regulated by the OCC, FDIC and Fed. We deliver higher quality and efficiency than BPOs, with the auditability regulators expect. What makes this work: - Agentic browsing that uses your existing tools, so you don’t need to migrate to benefit from AI - An agent-architecture designed for iterative, complex investigations - Bank-grade model risk management built for regulators and third-party audits Our Series B is led by Rajeev Dham at Sapphire Ventures, with continued participation from Greylock, Y Combinator, Thomson Reuters Ventures and Canvas Ventures. We’re also welcoming TIAA Ventures as a new investor. It’s literally Day 1 here at Bretton AI. If you want to work on hard problems at the intersection of AI and financial services, come build with us.