Benchmark

We're teaming up with @cerebras to build the fastest possible inference. Coming soon to Amazon Bedrock, we’re delivering inference performance an order of magnitude faster than what’s available today by connecting AWS Trainium3 for compute-intensive prefill with Cerebras CS-3 to power decode. Learn more about the partnership. go.aws/3Pzcota

$550M Series D led by @Accel. $5.55B valuation. One year into our U.S. expansion, we’re doubling down: accelerating across America and building AI with the lawyers who use it every day. Grateful to our customers, partners, and team. More: legora.com/blog/series-d

BREAKING: Swedish start-up @WeAreLegora has raised a $550M Series D funding round at a $5.55B valuation to fuel US growth. CEO & Co-Founder @MaxJunestrand says: “Over the past year, the pace of adoption in the U.S. has exceeded our expectations, as leading firms and in-house teams move decisively from experimentation to embedding AI across their organisations". The round was led by @Accel with participation from existing investors @benchmark, @BessemerVP, @generalcatalyst, @ICONIQCapital, @Redpoint, and @ycombinator.

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Benchmark’s fund strategy 1:14:14 Benchmark’s history of non-traditional VC rounds 1:17:56 Is the 20% ownership model outdated? 1:19:20 Chetan’s rebirth as a consumer investor 1:22:39 What Benchmark looks for in founders 1:25:01 AI coding and AI software gross margins

"Everyone's talking about continual learning. That's entirely where this space is going to go." The Applied Compute platform is architected around that premise: build memory and intuition from fragmented data across your entire org, train reasoning models directly on top of it, and close the loop. A model is just one piece. An agent is where it runs, what tools it has, how permissions and auth are handled, how humans guide and instruct it, and the observability around it all. Every interaction should be treated as a training signal so the system can compound over time. Thanks for having us @tbpn

We partnered with @mercor_ai to post-train custom models on high-quality expert data from fields like law, investment banking, and consulting. Our latest model ranks #1 on the APEX-Agents leaderboard in corporate law and #4 overall. Domain-specific post-training on high-quality, organization-specific data can systematically close the gap between general AI competence and expert-level reliability, making capable enterprise agents practical and affordable for knowledge-intensive industries. appliedcompute.com/case-studies/m…