Greylock Partners

Partnering with early-stage entrepreneurs since 1965. 100+ IPOs. 250+ acquisitions.

[Greylock Partners @GreylockVC]

Watch the full episode https://t.co/vqCIc1HYFY

[Greylock Partners @GreylockVC]

Founders learn to obsess over product-market fit. Greylock Partner @jerrychen says to look just as hard at who should write the first check. He calls it founder-VC fit.

In 2018, Jerry read a technical blog about M3, an open source project built at Uber, led by engineers Rob Skillington @roskilli and Martin Mao @martin_c_mao. At the time, they hadn’t even decided to start a company.

What followed was a five-month courtship.

Jerry was trying to convince Martin and Rob to take his investment. As first-time founders, they were diligencing him just as closely.

Martin sent him an 85-page business plan to see who would really do the work, understand the technology, and align with where they wanted to take it.

Rob and Martin eventually started @chronosphereio, with M3 as its technical foundation and Jerry wrote the company’s first check.

Seven years later, the company was acquired by @PaloAltoNtwks for $3.35B.

For founders, choosing the early investor can shape the company’s trajectory for years. The relationship deserves as much diligence as the financing.

Full story in the comments.

[Greylock Partners @GreylockVC]

Congratulations to @Tenzai_Labs and @resolveai on being named to the 2026 #Disruptors60 by @GreenfieldVC, unveiled today at @TechCrunch Disrupt.

Great to see the teams recognized among the world’s most groundbreaking AI startups, and excited to see what they build next.💪

[ResolveAI @resolveai]

We’re honored to be named to the @techcrunch #Disruptors60 Class of 2026!

From 17x faster root cause identification at MSCI to 87% less time to root cause at DoorDash, our team is building something that’s delivering real results.

Thank you to our team, customers, and partners!

[Cylake @CylakeAI]

Cybersecurity has always been asymmetric: attackers only need to find one opening, while defenders have to protect everything that matters. Now, AI is changing the scale and speed of that equation.

@chandna, Partner at @GreylockVC and Cylake board member, believes we're entering an era of machines fighting machines, where increasingly sophisticated attacks powered by “bad AI” will require equally capable “good AI” on defense.

In Episode 6 of Inside Cybersecurity, Asheem joins @renebonvanie to discuss what AI means for the cybersecurity stack, why truly AI-native architectures need to be built differently, and what three decades in cybersecurity have taught him about the people and companies that successfully navigate major technology shifts.

🎧 Watch Episode 6 of Inside Cybersecurity: https://t.co/0iVHlcvpjW

[Greylock Partners @GreylockVC]

Watch the full episode https://t.co/SUg0C5D4aA

[Greylock Partners @GreylockVC]

At an early-stage startup, great engineering candidates are asking more than “What are you building?”

They want to know: “Why did you choose to build this?”

Abby Hart from Greylock’s Core Talent team calls this the “pre-island” story: what happened before the company, what pulled the founder toward the problem, and why that story can help candidates build enough conviction to take the risk of joining early.

More in the latest episode of In Practice on early-stage hiring.

[Tenzai @Tenzai_Labs]

Sweet! Honored to be named to the #Disruptors60 Class of 2026 curated by @GreenfieldVC at @TechCrunch Disrupt.

Building the most powerful AI hacker in the world is a mission we're proud of.

Thank you to our team, and to the customers and partners who helped us get here.

See the full class: https://t.co/LN25bJez0j

[Greylock Partners @GreylockVC]

@baseten has scaled extremely fast across revenue, usage, and team size.

At ELC Annual 2026, Greylock Partner @CorinneMRiley joins Baseten co-founder @amiruci to talk about what happens when AI usage grows this quickly: flattening the cost curve, maintaining reliability, and making the right engineering tradeoffs as the organization scales.

📍Oct 13, 3:30–4:00 PM PDT.

[Shreya Shekhar @_shreya_s]

“The answer to a scientific breakthrough is rarely found in one place. One of the biggest bottlenecks is the fragmentation of data and context across experiments, simulations, images, and production systems.”
- @catherinehyeo, co-founder of @altaratech

Watch the talks from last month's AI for Science Symposium on @GreylockVC's youtube channel, linked in the comments!

[Vineet Edupuganti @vineete_5]

Most of AI Twitter read the OpenAI breach of Hugging Face as an alignment story. It was also a preview of what every hacker will be able to do in a few months.

In July, a swarm of 700 AI agents broke into Hugging Face and fired off more than 17,000 actions, gaining admin control of multiple internal clusters.

Swarms like this will shred cyber defenses built for human attackers at every company, hospital, government, and power plant.

Today we're launching the defensive counterpart. Cogent Attack Path Analysis finds the routes an agent swarm would take into your company, so you can close them first.

[Brandon Hill • Vori.com @IamBrandonHill]

Everyone is racing to build personal AIs that can shop for us.

But agents can only buy what they can see. https://t.co/l62DYEFuqG

[Bipul Sinha @bipulsinha]

"Will AI cause Armageddon?" is the wrong question.

The discussion about the threats that AI might pose has taken on an apocalyptic tone: will it extinguish life on Earth? Will it destroy humanity? It won’t, but it poses some far more real dangers to us all—right now, not in some imagined future. In the next couple of years, we are likely to face a new wave of rampant, pervasive cyberattacks and cybercrime.

As the recent series of attacks by AI agents show, we no longer need to speculate about whether AI can conduct cyberattacks. It can. The important question now is what happens when attacks that once required skilled human beings can be conducted by machines, at machine speed and machine scale.

For most of cybersecurity’s history, we have built defenses around an unstated assumption: the attacker is human. A person—or a team—has to inspect a system, find a weakness, exploit it, see what happens and decide what to do next. That takes time. Detection therefore buys time. Defenders spot an intrusion and respond before the attacker gets much further.

Frontier AI attacks that assumption.

An agent does not need to sleep. It can inspect code, probe a network, hit a barrier, devise another approach and try again. Make a thousand copies and they can work against a thousand targets at once. Allow agents to share what they learn and hacking begins to change from a human-scale activity into a machine-scale one.

At Rubrik, we are seeing part of this transformation firsthand. Through our participation in Anthropic’s Project Glasswing, one lesson became clear. AI is not necessarily creating new vulnerabilities. It is collapsing the cost and time required to discover them.

That changes the economics of cybersecurity.

Modern software may contain thousands of small imperfections. Most lead nowhere. A few, combined in the right sequence, open a path through a supposedly secure system. Historically, finding those chains required talented people, lots of time and some luck. Frontier models can increasingly do the searching themselves, discovering paths through complex systems that humans might never find.

This is what makes complacency about the new threat so dangerous.

In 1998 a group of hackers appeared before Congress and warned that the rapidly expanding internet contained vulnerabilities capable of producing enormous disruption. There was no single cyber Armageddon. Instead North Korea crippled Sony Pictures. WannaCry raced through more than 200,000 computers in roughly 150 countries, disrupting hospitals and factories. NotPetya began as a Russian attack on Ukraine and caused about $10 billion in damage worldwide. Ransomware shut down Colonial Pipeline and disrupted fuel supplies across the American east coast.

It would be strange to look at that history and conclude that the warnings were overblown. The catastrophe did not arrive as one spectacular event. Cyber insecurity became a permanent condition.

Now imagine that condition at machine speed.

That is why “Will AI cause Armageddon?” is the wrong question. A hospital whose systems are encrypted, a bank that loses control of its identities, or a company that cannot recover its data doesn’t care much whether civilization remains intact. The question is whether AI can make attacks faster, cheaper, more sophisticated and vastly easier to replicate.

We are beginning to get the answer.

Cyber-defense therefore has to change as well. For years the industry has concentrated on prevention and detection: keep the attacker out and, failing that, spot him quickly. Both remain essential. But when attacks operate at machine speed, detection may no longer buy enough time. Intrusion, exploitation and damage can begin to collapse toward the same instant.

Companies must assume that some attacks will succeed. They need to be able to recover just as fast.

The answer is not less AI. It is more AI on the defensive side: continuously hunting vulnerabilities, watching identities and data, detecting abnormal behavior, patching weaknesses and helping restore compromised systems. Machine-speed attacks require machine-speed defense.

There has always been an asymmetry in cybersecurity. An attacker has to be right once. A defender has to be right every time. AI makes that imbalance more dangerous because attackers do not need permission, compliance reviews or perfect systems. They need only find one opening—and now they can search for it continuously, cheaply and at an enormous scale.

Criminal groups and hostile governments will use this technology. They would be foolish not to.

The first generation of cybersecurity was built to stop human beings sitting at keyboards. That world has ended. The next attack may be launched by thousands of agents, moving at a speed no human team can match.

If the hackers are becoming machines, our defenses must become machines too.

[Corinne Marie Riley @CorinneMRiley]

Feels like every week in robotics there’s a new ‘this is the GPT-3 moment for robotics 🤖’ announcement. 

We brought on @GeneralistAI CEO & Co-Founder @peteflorence on @GreylockVC Change Agents to dig into what’s going on at the frontier of robotics models.

We covered the company’s latest Gen 1.5 model, few-shot learning, training robots on different embodiments, and the milestones towards a more generalized physical model. 

Timestamps:
00:51 The inception of Generalist
04:13 Long-term goal of the company
05:47 Parallels and differences between robotics and language models
12:05 Key differentiation in 1.5 Gen model
12:48 Robot vs banana
14:49 Emergent capabilities not explicitly trained for
17:13 Cross-embodiment and the importance of hands
22:26 Research vs working with customers
24:58 Beyond VLA vs world model
28:50 Future-looking milestones

Some of the top takeaways:

- One-shot and few-shot learning emerged without being trained for. Gen 1.5 can learn a new task from a single demonstration, and Pete compares it to the GPT-3 moment in language. In one example, a robot taught to sweep a cube into a bowl with a brush used a banana instead. Given a dustpan, it held the pan with one hand, swept with the other, then tipped the cube into the bowl. Neither behavior was explicitly trained, and Pete sees this as a signal of where the model's generalization is headed.

-Generalizing to new hands remains a challenging problem for cross-embodiment. Physical hardware doesn’t stay static, and so cross-embodiment - the ability of a physical AI model to adapt to different hardware systems - is vital for success. 

-Customer deployments are a valuable source of research inputs. Generalist actively partners with their customers for feedback, which they use to inform their research and make real-world evaluations.

-Generalist doesn't think in terms of "VLA vs. world model." Pete helped create early VLAs and has worked on world models, but he argues the goals matter more than the label, and the team is trained to think in a first-principled way when considering new research directions.

Watch the full episode at the link in the comments. Thank you to @peteflorence for joining us!

[Greylock Partners @GreylockVC]

Hosted by @baseten, Spiros Xanthos (@spirosx) from @resolveai and Greylock's @CorinneMRiley sit down to talk what it takes to out-execute the labs in the full SDLC.
💙 + 💛 + 💚🩷

[Seth Rosenberg @SethGRosenberg]

Sharing our thesis for co-leading the Series A in Parallax, the AI energy company

https://t.co/fMD0ZrOCEd

[Tenzai @Tenzai_Labs]

So Tenzai is #1 on @Hacker0x01's business VDP leaderboard - for the second quarter in a row.

Here's the news: this time we did it using an open-weight model anyone can download. Same result as the frontier model, ~30% cheaper per report, and only 3.5% false positives.

And here's the real news: These models are highly capable to power offensive AI operations at scale. Attackers can and will build malicious agents with these models.

The write-up: https://t.co/7l7itzgVOJ

[Corinne Marie Riley @CorinneMRiley]

the most interesting thing happening in AI x code today?

speaking with @spirosx about production engineering, AI SREs, and how @resolveai has been out-executing the labs in the SDLC

thanks @baseten for hosting

https://t.co/28qwbgfgiD

[Greylock Partners @GreylockVC]

@dwanehamilton Watch the full episode https://t.co/p7lN8T48WF

[Greylock Partners @GreylockVC]

Just like founder-led sales, early-stage hiring requires founder-led recruiting.

For founders coming from big companies, that means unlearning a recruiting playbook built around brand pull and recruiting infrastructure.

Dwane Hamilton (@dwanehamilton) from Greylock’s Core Talent team breaks down what founder-led recruiting looks like in practice: building trust directly, staying persistent, and making the process much more personal.

Link in the comments.