Peter Fenton

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Peter Fenton

Peter Fenton

@peterfenton

GP @benchmark. Director: @ExaAILabs @mercor_ai @SierraPlatform @ollama @ClickHouseDB @Sorare @sema4ai @airtable @digits @TigerDatabase @LabsCockroach @Docker

Katılım Nisan 2007
1.3K Takip Edilen48.4K Takipçiler
Peter Fenton retweetledi
Chetan Puttagunta
Chetan Puttagunta@chetanp·
Turner and I covered a wide range of topics including the history of software and why the AI application vs SaaS shift mirrors the SaaS vs On-Prem disruption. Plus: investing in AI applications, the dynamics in the AI application market, and more. Hope you will listen!
Turner Novak 🍌🧢@TurnerNovak

New @ThePeelPod with @chetanp We talk Manus, the history + future of software, why incumbents should make big AI acquisitions, why investors are begging for AI companies to go public, and inside @Benchmark’s latest investing strategy. Thanks @Numeral and @FlexSuperApp for sponsoring this episode. 0:08 Inside the $2.5B Manus acquisition 6:24 Manus' three main use cases 11:08 Taking heat on Twitter 15:10 Starting to tweet about software in 2018 22:50 The history of application software 29:15 Benchmark’s 25x Fund 7 31:33 How incumbents got too dominant by 2020 31:48 Going all-in on AI software in 2022 39:31 Why Benchmark didn’t invest in the AI labs 40:48 How cloud companies beat on-prem incumbents 44:33 Why AI companies will beat legacy cloud incumbents 50:04 SaaS companies should make big AI acquisitions 57:35 Why incumbents have not bought more AI companies 1:04:43 Public markets are starving for AI companies 1:10:14 Inside Benchmark’s fund strategy 1:14:14 Benchmark’s history of non-traditional VC rounds 1:17:56 Is the 20% ownership model outdated? 1:19:20 Chetan’s rebirth as a consumer investor 1:22:39 What Benchmark looks for in founders 1:25:01 AI coding and AI software gross margins

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Chetan Puttagunta
Chetan Puttagunta@chetanp·
If you haven't listened to this conversation between @btaylor and @jaltma yet, I couldn't recommend it more. If you are building in the AI space right now, consider this required listening. Truly exceptional content. 10/10.
Jack Altman@jaltma

This week, on Benchmark's new podcast Uncapped 😂, I sat down with @btaylor, founder of Sierra and Chairman of OpenAI. He's easily one of the most impressive people I’ve met in tech or in general. We talked about AI and the saaspocalypse, the unique considerations of building an AI native / agent company, whether young or experienced founders have the advantage right now, Codex and OpenAI ads, and much more. Learned a ton from Bret, hope you enjoy. (0:00) Intro (0:20) The Saaspocalypse and systems of record (12:34) Sierra's landscape (17:05) Outcome-based pricing (24:22) The rapid evolution of AI support technology (28:21) Young founders vs. experienced founders (34:12) What comes next beyond support (38:47) Codex and the future of software engineering (51:49) OpenAI and advertising (54:59) Working with investors and boards

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Everett Randle
Everett Randle@EverettRandle·
The thing that becomes obvious when you hear Bret speak is how much of a business/tech historian he is and how valuable being steeped in those learnings from the past is when building a generational business for the future
Jack Altman@jaltma

This week, on Benchmark's new podcast Uncapped 😂, I sat down with @btaylor, founder of Sierra and Chairman of OpenAI. He's easily one of the most impressive people I’ve met in tech or in general. We talked about AI and the saaspocalypse, the unique considerations of building an AI native / agent company, whether young or experienced founders have the advantage right now, Codex and OpenAI ads, and much more. Learned a ton from Bret, hope you enjoy. (0:00) Intro (0:20) The Saaspocalypse and systems of record (12:34) Sierra's landscape (17:05) Outcome-based pricing (24:22) The rapid evolution of AI support technology (28:21) Young founders vs. experienced founders (34:12) What comes next beyond support (38:47) Codex and the future of software engineering (51:49) OpenAI and advertising (54:59) Working with investors and boards

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Sam Altman
Sam Altman@sama·
Happy for my brother. An absolute triumph for Benchmark.
Jack Altman@jaltma

I’m really excited to share that I’m joining Benchmark. The past two years as a full time investor have been the most rewarding of my career. I really love venture capital, which is not something I ever imagined I’d say when I was kid, but here we are. I love new ideas and being part of a team with a mission. I love getting to be there for people who are struggling towards goals they really care about. I love learning from people who are better CEOs than I ever was. I love the texture of the work, the competition, and the way the job lets you invest in relationships. I love it so much that I’ve even turned into a little venture nerd with a podcast who goes around harassing great investors and founders, trying to learn as much as I can as fast as possible. I’ve certainly learned what I care most about, and what kind of investor I want to be. What I’ve realized is that I love investing at the Series A, when there’s enough going on that an investor can be useful but not so much that you can’t have an impact. I think there are many amazing ways to practice venture, it’s just the way that most speaks to me. And as I came to realize that, I started to think about how to best set myself up to do that craft as well as possible. It became clear to me there is nowhere better for this than Benchmark; the way they’re structured, their principles, their overall approach to investing, and their track record all create an environment that I believe will let me do my best work as an investor and help founders the most I possibly can. As I’ve gotten to know the team at Benchmark I’ve come to admire so much about each of them. Peter is truly playing his own game. A lot of what he says sounds like poetry at first, but as the ideas roll around in your head for a while you realize how much depth they have. I first heard about Eric many years ago from my friend Saji at Benchling while I was building Lattice, who described him as the most amazing board member and attributed him with a lot of the company’s success. That’s the kind of partner I want to be one day. Chetan is brilliant and truly thinks for himself; I’ve realized over time what a courageous guy he is. And then there’s my friend Ev, whose skills complement mine and who I just love to be around. I can’t wait to have him as a partner in crime. When given the chance to work with this group I just knew I had to go. One of my motivating north stars with Alt Capital was to build a firm and be a partner that I most would have wanted as an entrepreneur. Although I haven’t gotten everywhere I want to be yet, I’m proud of the work so far. And now I’m excited to build on that work at Benchmark, where I hope to increase my rate of learning and get armed with the power of a partnership so I can help founders reach their dreams even more. Thank you to the companies who’ve let me invest with them at Alt Cap. I’m keeping all my board seats and supporting everyone just the same as before. Thank you to the LPs who’ve backed me as well. I am so excited about the portfolio we have and am grateful I can stick with all those companies. And finally thank you to my teammates, Bala, Vivek, and Nate. Bala took a bet on me and started investing with me before it was remotely obvious, and we’ve been able to grow so much figuring it out together as investors. I credit Nate with helping Alt start feeling like a firm. He joined us from First Round over a year ago and made everything run smoothly. And while Vivek joined just a little while ago, even in the short time we’ve worked together he’s had a meaningful impact on how we think and invest. They’re all joining Benchmark with me. So pumped for this chapter.

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Peter Fenton retweetledi
Will Bryk
Will Bryk@WilliamBryk·
We just launched the fastest search engine in the world. Why the AI ecosystem needs this: AI Agents now use multiple tool calls within their tasks. When the end-to-end task needs to be fast (like seconds), then any underlying web search tool calls need to be near instant. Exa Instant is on average ~180ms. It's faster than Google’s consumer search, and up to 15x faster than other search APIs. It’s being used in realtime chat products, coding agents, and voice apps. We always planned for Exa to get ludicrously fast. This required some major architectural changes to get here and optimizations at every layer of the stack (GPU kernels, TCP networking, etc). Our engineering/research teams moved mountains for this. And it's only the start. AI agents will continue to get faster each month. Web search needs to keep up. Excited to see what you (and your agents) build with instant access to the world's knowledge 🫡
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Exa@ExaAILabs

Introducing Exa Instant: the first sub-200ms search engine. Faster than Google, it's custom built to power realtime AI products like chat and voice.

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Andrew Feldman
Andrew Feldman@andrewdfeldman·
Just one month after announcing our partnership with @OpenAI, we’re launching our first model together: OpenAI Codex-Spark, powered by @cerebras. Codex-Spark is built for real-time software development. In coding, responsiveness is the product. It is not a nice to have. Codex-Spark is optimized for targeted code edits, logic revisions, and frontend iteration. It gives developers near-instant feedback so they can stay in flow. Powered by the Cerebras Wafer-Scale Engine, it runs at over 1,000 tokens/s. That speed fundamentally changes the experience. We did not build this to win a benchmark. We built it so developers could move faster. I’m proud of how quickly the OpenAI and Cerebras teams have brought this to life. This is what fast execution looks like - deep engineering collaboration, rapid iteration, and shipping real products developers can use today. We are just getting started. When inference is fast, entirely new markets open up. We plan to lead that shift with our partners at OpenAI.
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Harry Stebbings
Harry Stebbings@HarryStebbings·
The story of Legora is absolutely nuts. A couple of kids from Sweden decide to upend the AI legal market. Raise $50K in angel funding and the journey begins. @ycombinator beckons and with the advice of @gustaf they raise a stellar “seed” round from @chetanp @benchmark In a matter of weeks, they raise a Series A from @loganbartlett @redpoint Then, the first board meeting comes and they tell their VCs, one thing, we will not be selling anything for the next 6 months. NOTHING. Today, less than two years later: $2BN valuation $200M+ raised from top VCs $50M+ ARR (latest reported) 300 employees in US and Europe The best shows are art (storytelling) and science (frameworks). This has both off the charts. Spotify 👉 open.spotify.com/episode/0I1UkH… Youtube 👉 youtu.be/ZjwrXqjr59A Apple Podcasts 👉 podcasts.apple.com/us/podcast/20v… My 6 takeaways with @MaxJunestrand 👇 Timestamps: 00:00 Intro 02:56 Why Does Everyone Think Harvey When They Hear Legal AI? 08:31 Why OpenAI is Toast? Switching to Anthropic! 14:57 24 Months: Which Foundation Models Will Win? 22:19 Lessons Scaling from Europe into the US 29:43 Why Seat Models Are Not Dead in SaaS? 34:04 How to Use Competition To Drive a Fire in Your Team? 40:26 Is Legal AI a Winner-Take-All Market? How Does It End? 52:41 The Future of Law Firms: Do Juniors Get Fired? 59:55 How We Raised $200M and 3 Rounds with No Deck 01:01:33 Quick-Fire Round: Best Advice, Closest Mentor, Biggest Mindset Shift
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Joshua Xu
Joshua Xu@joshua_xu_·
The HeyGen avatar skill is available for Claude Code now. I was playing around with Remotion + HeyGen to make a talking video. It works great! Here’s how you can do it: - Install the remotion skill - Install the heygen avatar skill: npx add-skill heygen-com/skills - Set up your HEYGEN_API_KEY (we offer free API credits if you have a HeyGen account) - Start prompting Check out the example video made with HeyGen + Remotion + Claude Code.
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Eric Vishria
Eric Vishria@ericvishria·
.@benchmark co-lead the initial round for Cerebras 10 years ago. Over the following 5 years, the team amazed, delivering the technological marvel of a wafer-scale chip, the system to heat and cool it, and more recently the software layer that allows giant fleets of Cerebras systems to work together for very large MoE models. But even more impressive, is they just never fucking quit, despite kissing death like 3 times, getting made fun of for unusual early customers, and getting passed over by virtually every respected semi investor (who have all converted now). The team knew, IF they could stay alive, it was just a matter of time…. In tech, speed ultimately wins, and nothing is close to as fast as Cerebras.
Andrew Feldman@andrewdfeldman

@OpenAI and @Cerebras have signed a multi-year agreement to deploy 750 megawatts of Cerebras wafer-scale systems to serve OpenAI customers. This has been a decade in the making. Deployment begins in early 2026, and when fully rolled out, it will be the largest high-speed AI inference deployment in the world. OpenAI and Cerebras were both founded in 2015 with radically ambitious goals. OpenAI set out to build the software that would push AI toward general intelligence. Cerebras set out to rethink computing hardware from first principles. Our teams met as far back as 2017. We shared ideas, early work, and a common belief: there would come a point when model scale and hardware architecture would have to converge. That point has arrived. ChatGPT set the direction for the entire industry. It showed the world what AI could be. Now we’re in the next phase - not proving capability, but delivering it at global scale. The history of technology is clear on one thing: speed drives adoption. The PC industry didn’t operate at kilohertz. The internet didn’t change the world on dial-up. AI is no different. As models grow more capable, speed becomes the bottleneck. Slow systems limit what users can do, how often they engage, and whether AI becomes infrastructure or remains a novelty. Cerebras was built for this moment. By keeping computation and memory on a single wafer-scale processor, we eliminate the data-movement penalties that dominate GPU systems. The result is up to 15× faster inference, without sacrificing model size or accuracy. That speed changes product design, user behavior, and ultimately productivity. For consumers, it means AI that feels instantaneous. For the economy, it means agents that can finally drive serious productivity growth. For Cerebras, 2026 will be a defining year. With this collaboration with OpenAI, Cerebras’ wafer-scale technology will reach hundreds of millions - and eventually billions - of users. We’re proud to work alongside OpenAI to bring fast, frontier AI to people around the world. This is what a decade of long-term thinking looks like.

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Will Bryk
Will Bryk@WilliamBryk·
Building a search engine might be the last software problem that AI solves. That's because it's extremely hard to perfectly search through exabytes of unstructured data. In this blog post, we detail just one part of our search stack - the data processing framework. We explain why we built it from scratch to optimize for the special workloads a modern search engine requires. Very proud of @nityasnotes who wrote a beautiful blog post and @hubertyuan_ for designing a beautiful system :)
Exa@ExaAILabs

What does it take to store the web as a database? exa-d is our internal data framework that orchestrates declarative typed dependencies, sparse updates with precise granularity, efficient and parallel execution across scaling compute, and more. exa.ai/blog/exa-d

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Brendan (can/do)
Brendan (can/do)@BrendanFoody·
2025 wrapped -Mercor grew revenue by 4658% -we went from paying out $2M / month to $2M / day -users increased from 217K to 3.4M -interviews increased from 88K to 1.5M -referrals increased from 1198 to 1.9M
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Peter Fenton
Peter Fenton@peterfenton·
The manus team has forever raised the bar of product execution in the AI era, seeing this clearly and nurturing it motivates everything we do @benchmark. Heroic, intense, multi-theatre warrior against xenephopia — really a totality of venture partnership by @chetanp
Chetan Puttagunta@chetanp

Manus was acquired by Meta. This remarkable group of people created a product that was the fastest ever to go $0 to $100M ARR and changed how consumers used AI to get things done. It was an extraordinary privilege for me and Benchmark to have invested in this incredible company pre-launch earlier this year and to have been on the board with Red and Pan. To @Red_Xiao_, Pan, @peakji, @hidecloud, CZ, and Henry: thank you! You are an amazing group of founders with a unique combination of brilliance, resilience, grit, vision, execution, and kindness. You’ve built one of the greatest technical teams in the world. You and your team will do great things at Meta. There’s so much more to share about this company and I’m sure we will over time. For now, I’m overwhelmed with the feeling of awe and gratitude. Thank you!

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Manus
Manus@ManusAI·
$0 → $100M ARR in 8 months. Since we launched in March: -147 trillion tokens processed -80M+ virtual computers created -Total revenue run rate over $125M Thank you to everyone building with us. manus.im/blog/manus-100…
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Bret Taylor
Bret Taylor@btaylor·
Sierra uses 15+ frontier and open source models for low latency tool calling and decision-making, precision classification, long-context reasoning, and empathy/tone. We call this a constellation of models, and it’s a key ingredient to the state of the art performance of agents built on Sierra sierra.ai/blog/constella…
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Peter Fenton
Peter Fenton@peterfenton·
Ah that’s simply the starting point…soon we won’t engage with org charts but with a relational interface
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Peter Fenton
Peter Fenton@peterfenton·
This astonishing $100M milestone points to the real story—the human story. My third journey with @btaylor from the beginning has given me a front-row seat to his arc of growth. He and @claybavor embody decades of personal evolution, intersecting perfectly with the most explosive force in technology, likely ever
Bret Taylor@btaylor

Sierra just hit $100M in ARR, just seven quarters since we launched in February 2024. @claybavor and I are very grateful to our customers and proud of the Sierra team, who has redefined the meaning of intensity and craftsmanship. I have never had this much fun in my career. (Photo is Clay signing the contract that crossed the $100M mark) sierra.ai/blog/100m-arr

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Tony Zhao
Tony Zhao@tonyzzhao·
The table-to-dishwasher task is the classic nightmare scenario for roboticists: Long-horizon, highly dexterous, precise, whole-body manipulation combined with delicate, transparent, reflective, and deformable objects. Yet Memo handles it so naturally and elegantly.
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Harry Stebbings
Harry Stebbings@HarryStebbings·
I am so bored of hearing podcasts with guests that have done the podcast tour. Benchmark added Ev Randle as their latest GP. This is his first public appearance as a Benchmark GP. - Why Margins Matter Less in AI - Why Mega Funds Will Not Produce Good Returns - OpenAI vs Anthropic: What Happens and Who Wins Coding - Investing Lessons from Peter Theil & Mamoon Hamid My 7 takeaways with @EverettRandle 👇 YouTube: youtu.be/xs7bhb3NEFc Spotify:  open.spotify.com/episode/2uKZoZ… Apple: podcasts.apple.com/us/podcast/20v… Timestamps: 00:00 Intro 01:46 Biggest Investing Lessons from Peter Thiel, Mary Meeker and Mamoon Hamid 14:07 OpenAI Will Be a $TRN Company & OpenAI or Anthropic: Who Wins Coding? 24:12 Why We Should Not Focus on Margin But Gross Dollar Per Customer 30:41 Why AI Labs are the Biggest Threat to AI App Companies 41:15 Do Benchmark Fire Founders? If so… Truly the Best Partner? 55:48 People, Product, Market: Rank 1-3 and Why? 59:14 Why the Mega Funds Have Just Replaced Tiger 01:04:33 GC, Lightspeed and a16z Cannot Do 5x on Their Funds… 01:22:10 Single Biggest Threat to Benchmark
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Applied Compute
Applied Compute@appliedcompute·
Generalists are useful, but it’s not enough to be smart. Advances come from specialists, whether human or machine. To have an edge, agents need specific expertise, within specific companies, built on models trained on specific data. We call this Specific Intelligence. It's what we're building at Applied Compute. We unlock the latent knowledge inside a company, use it to train custom models, and deploy an in-house agent workforce that reports to your team. We work with sophisticated companies that have already captured early gains from general models, like @cognition, @DoorDash, and @mercor_ai. They’re pulling even further ahead with proprietary in-house agents that don’t need to wait for the next public model release. Together, we are building and validating models and agents in days instead of months, achieving state-of-the-art performance on customer evals. Our team has high density and low latency. Our founders all worked on different parts of this problem while they were researchers at OpenAI — @ypatil125 as a key member on the agentic software engineer effort (Codex), @rhythmrg as a core contributor to the first RL-trained reasoning model (o1), and @lindensli as a core contributor on ML systems and infrastructure for RL training. Two-thirds of the team are former founders, and everyone brings a deep technical background, from top AI researchers to Math Olympiad winners. We are backed by $80M in funding from Benchmark, Sequoia, Lux, Elad Gil, Victor Lazarte, Omri Casspi, and others. With their support, we are growing the team, scaling deployments, and bringing to market the first generation of agent workforces built on specific models. In short: 1. We are building Specific Intelligence for specific work at specific companies. 2. That will power in-house agent workforces to support their human bosses. 3. That in turn will unlock AI’s full potential through humanity’s greatest engine of progress: thriving corporations in a free market.
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