Seed. Early. Growth. We invest across consumer and enterprise and have partnered with thousands of inspiring entrepreneurs over the past two decades.
Menlo Park, CA
Joined January 2010
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Redpoint retweeted
Thrilled to co-lead @fractile_ai's $720M Series C and get to work with @goodwin_ml and the incredible team.
Agents are running longer on bigger models and larger contexts. They’re bottlenecked by memory.
Fractile is the chip-first frontier AI company built to break that, stacking DRAM directly on logic for speed and scale. Over many whiteboards in Bristol, it became clear that Fractile’s full-stack approach is unlike anything else in silicon. You won’t find a more talented and fast-moving team. Couldn’t be more excited for everything ahead!
We’re delighted to announce that Fractile has raised $720M in a Series C led by @thrivecapital @foundersfund @lightspeedvp and @redpoint, with further participation by brilliant new and existing investors.
Congratulations to @goodwin_ml and the entire @fractile_ai team on raising a $720M Series C! We’re proud to co-lead the round alongside Thrive Capital, Founders Fund, and Lightspeed.
As agents take on longer tasks and reasoning models think longer, what limits the most capable AI systems is increasingly not what they know but how fast they can produce useful work. Fractile is rebuilding the hardware underneath frontier AI to change that. Its processors interleave memory and compute, so large models can generate output dramatically faster across very long contexts.
Walter made this bet in 2022, as a doctoral student at Oxford, well before most of the industry saw it coming. He has since built a team of semiconductor veterans spanning the full stack, from transistor-level circuit design to the software that serves models in the cloud. In every conversation, we were struck by how directly the company’s roadmap is tied to where frontier AI is heading.
Fractile is a chip-first frontier AI company, building the foundation for what AI systems will be able to do next. We couldn’t be more excited to back Walter and the team on this journey.
Redpoint retweeted
"Let them cure cancer" may be the new "let them eat cake": why Jev's creator, @CompleteSkeptic, thinks some AI valuations are miscalibrated:
Why hasn't AI automated more work? What's holding coding agents back? And are the labs' valuations built on the right expectations?
@CompleteSkeptic, co-founder and CEO of @typesafeai, was the perfect person to discuss this and more. Diogo coauthored OpenAI's InstructGPT paper, and a few weeks before we recorded, TypeSafe launched Jev, a "system one" model that returns structured decisions instead of text.
It was a ton of fun to discuss this with Diogo.
The conversation includes:
0:00 Intro
1:14 The Early Bet on RLCD
4:40 Built for Humans vs Computers
6:39 Roadmap & Model Limitations
11:04 Mental Models of Models
16:07 Do LLMs Have a Place in 10 Years?
17:32 New Model Shapes
21:24 Problems Still Unsolved
27:00 The Tyranny of the KV Cache
31:21 Rethinking Coding Agents
34:40 Multimodality
37:52 Images, Games & Computer Use
39:36 Could This Have Been Built 5 Years Ago?
42:01 Benchmarks & Copycat Releases
44:12 Fundraising
45:27 Quickfire
Listen here:
YouTube: youtu.be/ham8dROHCrc
Spotify: bit.ly/4kyeNBQ
Apple: bit.ly/3WppmgK
Redpoint retweeted
How does a corporate card startup become a $60B AI product factory?
In our first on-camera interview a decade in the making, Ramp co-CEO Eric Glyman (@eglyman) answers just that question.
Today, @tryramp is best known for its corporate cards, or maybe its marketing stunts with the likes of Kevin from ‘The Office,’ Tony Hawk and a 70-minute Broadway musical about paying bills.
But Eric says his $60B startup, NYC's most valuable, feels less like a fintech and more like a productivity company driven by one simple question: If a business spends $100 to make $200, how can Ramp improve that ratio?
Nearly 100,000 companies use Ramp today for everything from expenses to managing AI spend. But Eric says that when he zooms out to the millions who still aren't customers, Ramp feels painfully early still.
On The Upstarts Podcast, Eric shares why he’d rather run Ramp like Amazon than Google; why Ramp hires for proof of work, not credentials, from a 14-year-old intern to a 28-year-old CTO; and how being the first customer for Anthropic’s Claude Code and Cognition’s Devin led Ramp to build its own AI tools.
Plus: The real story of Paribus, his first startup with co-founder Karim Atiyeh (@karimatiyeh); what they learned at Capital One; and a warning for Instinct and Muse.
This season is presented by @Atlassian and @chargebee 🫡
CHAPTERS
00:00 Introduction
01:51 What Ramp Actually Does
03:27 From Gym Charges To 1% Of U.S. Corporate Payments
05:13 Glass, Inspect And Building AI Tools For Itself
07:45 An AI Sales Assistant Before ChatGPT
09:45 How Ramp Decides What Products To Ship
11:14 First Customer For Claude Code And Cognition’s Devin
13:09 Why Ramp Wanted To Build Like Amazon, Not Google
15:42 Hiring For Proof Of Work: The 14-Year-Old Intern
18:43 A .300 Batting Average And ‘Growth Without Fear’
21:17 What Ramp’s Founders Learned At Capital One
24:00 The $1,000 Flight Refund Behind Paribus
26:34 Bots, A Cease-And-Desist, And A Lesson For Instinct And Muse
32:00 Marketing 101: Bill Pay The Musical And Kevin In A Glass Cube
36:01 Business Books And Helping People Do Their Life’s Work
39:03 How A 28-Year-Old CTO Catalyzed A Co-CEO Change
43:55 ‘Still So Early’: How Ramp’s Founders Stay Hungry
55:26 John Wooden And Why Ramp Competes Against Itself
Thanks for watching!
Redpoint retweeted
Starting today, every engineer who joins Serval begins with a weeklong, $10,000 paid vacation. We’re building the next generational company. We need generational talent to see it through.
Our mission has always been to empower people across every team to turn their understanding of a problem into working, trustworthy automations. Making that function inside the largest enterprises takes world-class engineering.
That’s what you’d be joining us to do. There are foundational decisions still to make, and the engineers making them will shape the future of how big we can build this company.
We want you to arrive rested and ready to do the best work of your life. So your first week is a chance to get away.
And if a @getserval employee refers you, both you and that employee receive $30,000, on top of the vacation.
Come build with us: serval.com/careers
Redpoint retweeted
Why hasn't AI automated more work? What's holding coding agents back? And are the labs' valuations built on the right expectations?
@CompleteSkeptic, co-founder and CEO of @typesafeai, was the perfect person to discuss this and more. Diogo coauthored OpenAI's InstructGPT paper, and a few weeks before we recorded, TypeSafe launched Jev, a "system one" model that returns structured decisions instead of text.
It was a ton of fun to discuss this with Diogo.
The conversation includes:
0:00 Intro
1:14 The Early Bet on RLCD
4:40 Built for Humans vs Computers
6:39 Roadmap & Model Limitations
11:04 Mental Models of Models
16:07 Do LLMs Have a Place in 10 Years?
17:32 New Model Shapes
21:24 Problems Still Unsolved
27:00 The Tyranny of the KV Cache
31:21 Rethinking Coding Agents
34:40 Multimodality
37:52 Images, Games & Computer Use
39:36 Could This Have Been Built 5 Years Ago?
42:01 Benchmarks & Copycat Releases
44:12 Fundraising
45:27 Quickfire
Listen here:
YouTube: youtu.be/ham8dROHCrc
Spotify: bit.ly/4kyeNBQ
Apple: bit.ly/3WppmgK
Redpoint retweeted
Danila Shtan is the CTO of @nebiusai, the AI cloud racing AWS, Google and Microsoft.
Danila has been with the company since the beginning. On November 1, 2023, Nebius launched its first public cloud with about 1,600 H100s. That same day, Danila told the engineering team to throw the entire stack away and start from scratch.
Today, Nebius is a Nasdaq-listed AI cloud with a $17.4 billion deal with Microsoft, a Meta agreement worth up to $27 billion, and a $2 billion investment from NVIDIA.
Danila is betting Nebius can grow fast enough to fight the hyperscalers as equals before they turn their full attention to AI cloud. He thinks the market is barely scratching the surface.
0:00 Intro
1:25 What Differentiates AI Clouds
6:16 Nebius’ Origin Story
10:15 Bringing New Nvidia Chips Online
14:55 What Different Customers Want
19:58 Latency vs. Cost
21:53 Disaggregated Prefill & Decode
24:59 Inference’s Low-Hanging Fruit
28:31 What Nebius Should Productize
30:11 The Tavily Acquisition
31:56 How Data Centers Differ
34:29 Bottlenecks in the AI Buildout
37:03 How New Workloads Shape Clusters
39:17 Why Hyperscalers Didn’t Win This Market
43:39 The Future of Neoclouds
45:42 What Gets Absorbed by AI Hyperscalers
49:43 OpenAI x Baseten
52:07 Quickfire
Listen here:
YouTube: youtu.be/qJkcjURcAe8
Spotify: bit.ly/47OFCKJ
Apple: bit.ly/4f6hwPk
Congratulations to @jakeserval, Alex McLeod, @TatianaBirgisso, and the entire @getserval team on raising a $150M Series C at a $3B valuation, their third round in 12 months. We’re honored to have backed this team since their Series A.
Legacy IT service management tools track employee requests, but people still do the actual work of resolving them. Serval’s AI agents build the automations behind employee support, then resolve requests against them instantly. IT, HR, finance, and legal teams can automate their own work inside one governed platform that plugs into the systems they already use. Jake and the team bring a proven record of execution from Verkada and have attracted top engineers from Semgrep, Meta, and Uber.
Their approach is resonating with some of the most demanding companies around, including Live Nation, Notion, Ramp, and Lucid Motors. Serval has doubled ARR every quarter since its Series B and recently onboarded two Fortune 20 customers to deploy automations across their entire organizations.
We’re excited to be along for the ride as Jake, Alex, Tatiana, and the team build the platform enterprises run on. 🎉
We were early believers in @getserval and the team @jakeserval has built. This Halloween, we decided cheering from the sidelines wasn’t enough.
Congratulations to Jake Stauch, @AlexAtServal, @TatianaBirgisso, and the entire Serval team on raising a $150M Series C at a $3B valuation, their third round in 12 months.
Redpoint retweeted
Great to be back on Unsupervised Learning with @jacobeffron and @patrickachase!
We talked about the day-to-day knife fighting of getting GPUs, why we contribute a lot to open source, and how our most experienced engineers are the biggest agent-maxxers.
Is the compute market still the Wild West? What will happen to GPU prices over the next few years? And how will the inference market evolve?
@bernhardsson, co-founder and CEO of @Redpoint portfolio company @modal, was the perfect person to discuss this and more. Modal has crossed a $1B revenue run rate and recently raised $750M at a $15.75B valuation.
A few takeaways from my conversation with Erik, alongside @patrickachase:
▪️ Why compute won’t become a commodity
GPU capacity varies in disk, networking, location, and especially reliability. That variation makes a true exchange hard to build, so much of the market still runs on late-night WhatsApp groups and hallway deals at events like GTC. Erik expects it to get somewhat more efficient over time, but to stay structurally messy.
▪️ NeoClouds, NeoLabs, and the cost of capital
Erik disagrees with the idea that NeoClouds are going to die, and he sees them as sound businesses built on leasing hard assets against locked-in contracts. Their real disadvantage is cost of capital, which is why the successful hyperscalers all sit inside cash-rich public companies. NeoLabs carry a different risk. GPU deals often require only about 30% upfront, so a lab that raises $1B can commit to far more compute than it can pay for today. If funding tightens, Erik expects some NeoLabs to resell capacity and start to look more like NeoClouds.
▪️ Where durable advantage lives in inference
Erik sees pure token serving as a race to the bottom, with routers already commoditizing the underlying providers. Better kernels and smarter KV caching still matter, but those techniques spread quickly. He believes the more durable edge comes from offering a cluster of adjacent building blocks.
▪️ Who is actually adopting coding agents
Erik expected junior engineers to adopt agents first, but he has seen the opposite. For senior engineers, agents are like having 10 interns. Give one a full POSIX test suite and it can write a new file system in three days. He also sees training data creating a new kind of incumbency in software, with developers defaulting to frameworks like React because agents already know them well.
▪️ Why GPU prices may go up
Erik changed his mind on this 48 hours before we recorded. After reading Anthropic’s S1, he concluded that the largest buyers of compute are financially strong enough to keep acquiring capacity, which points to higher prices over the next 12 months.
Full Unsupervised Learning Episode 👇
0:00 Intro
1:26 The Compute Market Today
5:16 Modal’s Path to LLM Inference
10:02 Building the Hard Infrastructure
12:32 Unsolved Problems in Inference
16:07 Chips & Model Sizes
21:21 What Wins in Inference
24:56 Prioritizing a Multi-Product Roadmap
30:20 Agents as Modal’s Users
33:19 The Next Big Bet After Sandboxes
38:58 Robotics & Multimodal Inference
41:04 Scaling From $2M to $1B
42:51 NeoClouds & NeoLabs
48:15 RL as a Service
51:40 Quickfire
YouTube: youtu.be/HJDekXH-h5c
Spotify: bit.ly/4dZ0O4e
Apple: bit.ly/4inZJnE
Redpoint retweeted
Is the compute market still the Wild West? What will happen to GPU prices over the next few years? And how will the inference market evolve?
@bernhardsson, co-founder and CEO of @Redpoint portfolio company @modal, was the perfect person to discuss this and more. Modal has crossed a $1B revenue run rate and recently raised $750M at a $15.75B valuation.
A few takeaways from my conversation with Erik, alongside @patrickachase:
▪️ Why compute won’t become a commodity
GPU capacity varies in disk, networking, location, and especially reliability. That variation makes a true exchange hard to build, so much of the market still runs on late-night WhatsApp groups and hallway deals at events like GTC. Erik expects it to get somewhat more efficient over time, but to stay structurally messy.
▪️ NeoClouds, NeoLabs, and the cost of capital
Erik disagrees with the idea that NeoClouds are going to die, and he sees them as sound businesses built on leasing hard assets against locked-in contracts. Their real disadvantage is cost of capital, which is why the successful hyperscalers all sit inside cash-rich public companies. NeoLabs carry a different risk. GPU deals often require only about 30% upfront, so a lab that raises $1B can commit to far more compute than it can pay for today. If funding tightens, Erik expects some NeoLabs to resell capacity and start to look more like NeoClouds.
▪️ Where durable advantage lives in inference
Erik sees pure token serving as a race to the bottom, with routers already commoditizing the underlying providers. Better kernels and smarter KV caching still matter, but those techniques spread quickly. He believes the more durable edge comes from offering a cluster of adjacent building blocks.
▪️ Who is actually adopting coding agents
Erik expected junior engineers to adopt agents first, but he has seen the opposite. For senior engineers, agents are like having 10 interns. Give one a full POSIX test suite and it can write a new file system in three days. He also sees training data creating a new kind of incumbency in software, with developers defaulting to frameworks like React because agents already know them well.
▪️ Why GPU prices may go up
Erik changed his mind on this 48 hours before we recorded. After reading Anthropic’s S1, he concluded that the largest buyers of compute are financially strong enough to keep acquiring capacity, which points to higher prices over the next 12 months.
Full Unsupervised Learning Episode 👇
0:00 Intro
1:26 The Compute Market Today
5:16 Modal’s Path to LLM Inference
10:02 Building the Hard Infrastructure
12:32 Unsolved Problems in Inference
16:07 Chips & Model Sizes
21:21 What Wins in Inference
24:56 Prioritizing a Multi-Product Roadmap
30:20 Agents as Modal’s Users
33:19 The Next Big Bet After Sandboxes
38:58 Robotics & Multimodal Inference
41:04 Scaling From $2M to $1B
42:51 NeoClouds & NeoLabs
48:15 RL as a Service
51:40 Quickfire
YouTube: youtu.be/HJDekXH-h5c
Spotify: bit.ly/4dZ0O4e
Apple: bit.ly/4inZJnE
We’re proud to continue our partnership with @barrald, @crcolgrove, @glen_taka, and the entire @hexanalytics team as they raise $100M at a $1.2B valuation and launch Hex 4.0.
Teams now build dashboards and reports with coding agents and custom tools. Since data work no longer happens in one place, results often get passed around with no context, permissions, or provenance. A chatbot bolted onto legacy BI won’t fix that.
We’ve backed Hex since their Series A in 2021, and the team has kept pushing the analytics category forward ever since. Hex 4.0 is a from-scratch rebuild of their platform that treats everything as code and adds the context and controls teams need to trust what agents produce.
We’re excited to keep backing Barry, Caitlin, Glen, and the Hex team as they build what comes after BI 🎉
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Redpoint retweeted
Over the past 2.5 years, the Legora team has constantly redefined my bar for moving at AI speed and building exceptional AI products. Spend any time with them and their customers and it’s clear that Legora is a truly special company with an insanely exciting future ahead. A huge congrats to the team on this milestone!
We have raised a $550M Series E at an $8.8B valuation, led by Atreides.
Every major existing investor is back in, and we’re welcoming Baron Capital, Capital Group, Fidelity, Glade Brook and J.P. Morgan. We picked investors who think in decades. Several of them work with law firms and legal teams every day, so they can see where the work is heading.
Lawyers are moving from AI assistants to agents that do multi-step work. The money goes there. We’re growing our teams across North America, Europe, Latin America and Asia-Pacific, and going deeper on the Legora aOS.
Thank you to the 1,000+ people at Legora, and to the 140,000 legal professionals who use what we build.
I say this a lot because I believe it: it’s the best time in history to be a lawyer. We're thankful to be a part of it.
Full story: legora.com/blog/series-e
Congratulations to @bernhardsson, @akshat_b, and the entire @modal team on their $750M Series D! We’re honored to continue our partnership.
Erik started Modal to solve his own problem. While building Spotify’s music recommendation system, he found that roughly 90% of the work went into the infrastructure underneath it rather than the system itself. So he set out to build the infrastructure layer he wished he’d had, with its own file system, container runtime, scheduler, and networking on a capacity pool that spans 20+ clouds. Thousands of developers now use Modal, and Erik is still driven by the same thing that got him started: “building something that people like.”
We first reached out to Erik years before investing, after his post on the future of the cloud went viral on Hacker News. Having partnered with Erik and Akshat since their Series A and doubling down in every round since, we’ve seen how early they spot what developers will need next. Today, Modal has crossed $1B in run-rate, more than tripling in the five months since their Series C and growing over 15x year over year.
We’re excited to keep backing Erik, Akshat, and the Modal team as they build high-performance AI infrastructure for the next generation of software 💚
Redpoint retweeted
Meet Mistral Large 4, aka Le Chonk.
• 1T parameters, natively multimodal. 49B active.
It is the best open weights model from US or Europe on aggregated benchmarks.
• State-of-the-art on critical workloads, including cyber defense, manufacturing and finance and it surpasses closed frontier models on visual grounding.
• Forged in Europe end-to-end and is deployable from Europe via our own Mistral Cloud infrastructure.
• Available to all via API today. Working with cybersecurity partners privately.
Open weights release end of October.
🤖 Made with AI
Redpoint retweeted
When is harness optimization enough? When does post-training make sense? What will determine who becomes the new AI hyperscaler?
@ypatil125, co-founder of @appliedcompute, was the perfect person to discuss this and more.
Yash worked on Codex at OpenAI before leaving to co-found Applied Compute with fellow OpenAI researchers Rhythm Garg and Linden Li. Sixteen months in, the company is a unicorn, and is serving enterprises including DoorDash and Cognition.
We cover:
0:00 Intro
1:42 The Case for Owning Your Intelligence
4:35 OpenAI x Baseten and the Multi-Model Future
7:19 Can Post-Training Beat Frontier Models?
9:03 What Data Stays Out of Distribution
11:51 Data Efficiency and Continual Learning
14:31 When Updating Model Weights Makes Sense
16:01 Online Training in Practice
18:16 Lowering the Cost of Post-Training
19:48 Does Every Company Need Its Own Model?
24:06 RL in Non-Verifiable Domains
26:59 How Applied Compute Picks Customers
28:46 Building an Inference Business
34:43 The Case for a New AI Hyperscaler
38:21 Building RL Environments
41:33 Quickfire
YouTube: youtu.be/QeiYSLBCwD4
Spotify: bit.ly/4y6EJrz
Apple: bit.ly/4j1okRj
"Everybody is looking at how we can squeeze the most juice possible out of the chips that exist in the world today."
@ericabrescia joined Bloomberg this week to talk about where value in the AI ecosystem goes next, specifically the inference stack.
As more AI moves into production, Erica is seeing private companies across inference grow at astronomical rates. She sees innovation happening across the entire inference stack, from the chip level with new memory-optimized architectures to inference engines and new model architectures. She's also a big believer in a heterogeneous future from an accelerator perspective, creating investment opportunities across each layer of the stack.
She expects that growth to continue for both inference serving providers and the companies building the technology that enables inference, whether it runs on neoclouds, hyperscalers, or inside enterprises themselves.
Congratulations to Kevin Mandia, Travis Lanham, Evan Peña, David Slater, and the entire @ArmadinSecurity team on their $255.5M Series B! We're excited to partner with them as they enter this next chapter.
AI has compressed the time between vulnerability disclosure and periodic penetration tests can't keep pace.
Armadin deploys a swarm of specialized agents that think like a skilled adversary. They chain low-severity weaknesses into validated attack paths so security teams can shut them down before anyone exploits them. Seven months after emerging from stealth, Armadin is already running these campaigns in production for Fortune 500 enterprises and government customers.
Few teams know the attacker's playbook better. Kevin founded Mandiant, which Google Cloud acquired for $5.4B, and helped expose the 2020 SolarWinds supply chain attack. Evan spent 13 years at Mandiant, where he helped build and later led a red team that grew to more than 200 consultants. Travis and David both come from Google Cloud Security Operations, where Travis was the technical lead who scaled the business from launch and David architected a data platform that processes trillions of events per day.
We're honored to back Kevin, Travis, Evan, David, and the Armadin team as they put AI-powered offense to work for defenders. 🎉
More on Armadin: armadin.com/blog-posts/inves…
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Congrats to @StephanEwen and the @restatedev team on their $20M Series A! We backed Restate at seed and are thrilled to keep building with Stephan, Till, Igal, Ahmed and the rest of the Restate team.
Software has always had to survive interruption, but AI agents raise the stakes. Agents run longer, call models and APIs, use tools, and change direction mid-task. When something breaks, there is far more to preserve. It's no longer only the execution that needs to survive, but the state and communication around it.
Restate's founders created Apache Flink, which became foundational infrastructure for real-time data systems. With Restate, they built durable execution on a purpose-built distributed log, so execution, state, and communication live in one lightweight system with no external database required.
It's already running in production. Restate handles all durable orchestration for Replit Agent, and hundreds of companies, from leading AI companies to the Fortune 500, rely on it for mission-critical workloads.
We're proud to continue our partnership with Stephan and the Restate team as they make durability a property of the infrastructure itself. Let's go, team 🚀!
