Thrilled to announce KP22, our twenty-second venture fund with $1 billion to back early stage companies, along with $2.5 billion in growth funds to back high-inflection, category-defining businesses — $3.5 billion in total. AI is reshaping every industry from the ground up, faster than anything seen before. This is the moment to build. Grateful to our team, and the founders and LPs who’ve trusted us over the years, and looking forward to partnering with the next generation of history-making companies. kleinerperkins.com/perspecti…

Today, @tryprofound is launching Aim, the first background agent purpose-built for marketers. For months, we've been obsessed with one problem: dashboards tell you what's happening, but not how to act on the data. Aim is the agent harness designed specifically for marketing. Aim is trained from scratch on Profound's proprietary data, grounded in our research, and understands how marketers get work done. Aim analyzes your AI Search data, Prompt Volumes, competitive insights, and Knowledge Base to surface the opportunities worth acting on. It finds the anomalies that matter so you can spend your time on what moves the needle. Every Project comes with a data-backed brief and recommended tasks. From there, you can: • Chat with Aim to refine the plan • Deploy the custom Agent in one click • Track progress automatically Aim is your newest teammate keeping you moving in the right direction, working 24/7.

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So sad to hear about Om’s passing. He was a beautiful, warm, kind and deeply thoughtful soul. May his memory be a blessing. om.co/2026/06/24/1966-2026/

Inference-time model routing based on legal practice area dramatically improves agent performance. So do other types of "blended intelligence": - collaborative model teams, - advisor-executor patterns, - and model routing based on inferred user preference. While these methods appear promising, they come with substantial risks and eval challenges. We're experimenting with all of them at Harvey. Read more from our Head of Legal Research @ItsJulioPereyra:

Massive

@kleinerperkins is thrilled to lead the Series A of @sailresearchco and I'm pumped to have joined the board. Agents executing complex work end to end is the ultimate promise of AI, but they are also extremely token hungry. With exploding token volumes, latency stops being the bottleneck. Cost and throughput become everything. Businesses simply cannot afford their inference bills in the age of agents and we're already seeing signs of the tokenmaxxing era running into budget caps, leaving tons of demand for intelligence unmet. @neilmovva and @blintzbase are two of the most precocious builders I've met and are building Sail to meet the moment. They are building *the* inference platform for long horizon agents that will enable every business to run their agents with maximum efficiency and lowest cost. In addition, they have built world class observability (Sail Voyages) and a stateful sandbox (Sailboxes) positioning Sail to be the one place where you operate and run your most critical agents.

We're at a breakthrough moment in AI. The most direct path to its promise is AI that accelerates its own research, and @mirendil is building exactly that. A frontier lab rebuilt from scratch around AI R&D. This team has made mind blowing progress in the short few months they’ve been working together using their own systems. @bneyshabur, @HarshMeh1a @shayan_, @tararezaeikh and the Mirendil team have a rare mix of research depth, scale experience, and the instinct to ship. Expect(ing) big things. We’re proud to have been there from day one. kleinerperkins.com/perspecti…

Today, I’m excited to formally announce @mirendil with my amazing co-founders Harsh Mehta, Shayan Salehian, and Tara Rezaei! We’re fortunate to work with @a16z and @kleinerperkins, who led our seed round of $200M, followed by a major investment from NVIDIA, among others. Mirendil exists to accelerate science and technology, and through them, to help solve humanity's most pressing problems. Self-accelerating AI R&D is the most direct path to delivering on AI's broader promise, which is why we believe the most important application of AI is AI itself. Get this loop right, and it compounds. It fundamentally changes the rate of progress itself across all domains. We believe this capability should be democratized. It should be used to power all scientific efforts trying to innovate at the frontier. There are far more important problems—and broader ones—than any single lab can take on, so more groups should be able to pursue them. This pulls concentration of power away from a few labs: businesses and science labs can own their AI and infrastructure, keep their margins, and control their own destiny instead of ceding it all to a single AI lab. We’re a small team with a singular focus. Our founding team consists of 20 researchers and engineers from frontier institutions including Anthropic, xAI, Google DeepMind, and OpenAI, united by a passion for science and a drive to build the technologies that move it faster. If you want to build the system that builds systems, join us! @HarshMeh1a, @shayan_, @tararezaeikh

The single biggest impediment in AI isn't absolute intelligence or coding ability. It's deeply understanding large repositories of knowledge that every person and every company has. Our repositories will explode in size, with AI agents writing much of them. This is a two-way street: we need to understand what our AI generates, and AI needs to better understand us. A breakthrough in memory is needed – gradients need to be taken, and our team is the right one to take the big swing.

Have been excited about this team for a long time! Memory is one of the next big unlocks in AI. Thrilled to partner with this exceptional group @dan_biderman, @EyubogluSabri, @realJessyLin, @jxmnop, @scott_linderman, and Chris.

We just launched Business Banking* at @Rippling ... it’s going to simplify the daily lives of a lot of people in finance and HR.🧵

Harvey partnered with @appliedcompute to train a legal agent. We optimized each part of the agent stack, including the eval loop, agent harness and compaction, and post-trained the underlying GLM-5.1 model using reward signal from Harvey's Legal Agent Benchmark (LAB). Check out more in the agent-training deep dive below. Kudos to @nikogrupen, @ItsJulioPereyra, @rhythmrg, @jacob_dphillips, and @raymondmfeng for leading this effort - more to come, with lots of opportunity to push the frontier with GLM-5.2.

If we’ve learned anything this past week it’s that GLM is a strong base for customization. Together with @appliedcompute, we focused on GLM-5.1 and have results that are a great example of what full-stack agent optimization looks like —> post-training harness verifier. Real potential to push the Fable frontier now with GLM 5.2. Great working with @ypatil125 @rhythmrg & team!

Tagged along with @MollySOShea and @gabepereyra to talk all things tokens, benchmarks, models, and research at @harvey. And also why there are Funko Pop replicas of Gabe, Winston, Julio, and myself in the Harvey speakeasy (h/t @katieburkie).

Was great to speak with @MollySOShea and @nikogrupen about Harvey's 13x token usage growth since January, synthetic data for our Legal Agent Benchmark, and our plans to train legal foundation models at Harvey Labs:

Model strategy for @harvey: We are working on the first model in our legal foundation model series, inspired by @cursor_ai's Composer. Two goals: 1. Allow us to serve frontier intelligence across our product surface areas at an affordable price and a strong security posture. 2. Create the foundations for law firms to build their own specialized models and own their own intelligence. The model series will focus on complex client matters that span months and take dozens of associates. The agentic system will learn to control legal tech tools, sub agents and ask for help from frontier models or human partners, much like a senior associate. We’ve open sourced benchmarks for evaluating our initial post training work that represents work done by associates and in-house lawyers. We are scaling these significantly using synthetic and human pipelines as well as building private evals for firms. Open sourcing this data has allowed us to quickly validate the feasibility of post training open weight models for legal work. With our research partners we’ve already shown promising results post training open source models to approach frontier performance: 1. @baseten - novel compaction strategies for analyzing large data rooms. 2. @FireworksAI_HQ - matching frontier performance by using frontier as an advisor. 3. @appliedcompute - improving performance and reducing cost of large scale review tables. 4. @trajectorylabs & @nvidia - sovereign continual learning over client matters. We plan to continue to invest heavily in working with research partners and open sourcing our data, models and research as much as possible. We believe open research in legal will be important to building trust in the frontier ecosystem. We are also scaling our research team. Harvey Labs is our internal research group, responsible for pushing the frontier of legal intelligence and working closely with labs, research partners, and academia to bring the frontier of agent research into Harvey. Labs is run by @nikogrupen and @ItsJulioPereyra - Niko worked on multi-agent RL at Google Brain and Julio clerked and worked in BigLaw. We believe this pairing is crucial for building frontier legal AI systems. Together they have already made significant progress in scaling our data and training efforts. The long term goal of Harvey Labs is to contribute to the research and infrastructure required for the legal industry to create a frontier ecosystem. We believe that the best version of legal super intelligence is one where each law firm, enterprise and government owns their own specialized version. We are hiring for Harvey Labs across the post training, agent and data stack and open to acquiring talented teams / neolabs in this space. If interested please DM me.

So proud. The team cooked on this one.

Ripping indeed! 🚀 Great one @winstonweinberg @MollySOShea

AI agents are the new interface between consumers and the web and are fundamentally changing how people discover products, make decisions, and interact with brands. Every brand will have to adapt. @thejamescad, @dbabbs and the @tryprofound team saw it early and turned that conviction into a category. Great work by the @kleinerperkins Studio team capturing the early founding story.

We released Sonic-3.5 and Ink-2, the #1 streaming models for text to speech and speech to text you can use in your voice agents today. New architectures enable new frontiers for speed and quality. We're now the only provider to have #1 models for both speaking and listening.