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Today, we’re humbled and excited to share something meaningful: an early look at the results from our first real-world study on how a foundational AI model for mental health can support people’s wellbeing - conducted in collaboration with New York University. This summer, over 10 weeks, participants used Ash on their own terms — at 2 a.m. when they couldn’t sleep, on lunch breaks, or during commutes. What we found was that Ash contradicted one of the most persistent criticisms of AI: that it isolates people. Instead, participants reported feeling more connected, more hopeful, and more supported, spending more time with others, and gaining, on average, one new person they could rely on. 72% reported less loneliness 75% felt more supported socially 95% made measurable progress toward personal goals Beyond social connection, users also reported meaningful changes in emotional wellbeing — with reductions in depression and anxiety comparable to those often seen in traditional forms of mental health support. Just as importantly, Ash excelled in all safety measures. It’s an early signal, but an encouraging one, that when designed thoughtfully, AI can be transparent, responsible, and deeply pro-human. Our mission remains simple: to help a billion people change their minds and lives, in the ways they want to. 🧠 Read more about the study in the link below. A special shout-out to a few of the paper authors: Thomas Derrick Hull, Pat Arean, and Matteo Malgaroli, PhD #ArtificialIntelligence #mentalhealth #DigitalMentalHealth

🚨 ANNOUNCEMENT 🚨 We’re growing our GTM team and hiring Enterprise Account Executives and Deployed Engineers! At Traversal, we believe the “root cause” of a great company starts with a product our customers love. That’s why we’ve remained steadfastly focused on proving that our AI SRE is highly accurate not only for alerts, but for real-world, complex incidents — because that’s the only thing that matters. In a market where many are sprinting to scale before the product is ready, we’ve taken the opposite approach — building a product with easy deployment, deep engagement, and clear repeatability across customers. Now, with proven accuracy and daily use inside some of the largest and most complex engineering organizations in the world, it’s time to grow our reach.   We believe we have an opportunity to completely redefine the observability market, and we’re looking for world-class GTM operators to join us. See open positions in the comments.

Excited to announce that Reducto has raised a $75M Series B led by Andreessen Horowitz, bringing our total funding to $108M. Less than two years ago, we released the first Parse API to blend traditional OCR with frontier VLM capabilities, and set a new standard for converting complex documents into LLM-ready data. We’ve pushed that frontier ever since alongside the best teams in AI as they build everything from document workflows to agentic applications that can read, edit, and act on unstructured data. Today, Reducto powers companies of all sizes — from leading startups like Harvey, Mercor, and Rogo to some of the world’s largest financial institutions, tech companies, and healthcare organizations. Reducto has now processed more than a *billion* pages for these incredible teams, and that number is continuing to accelerate each month. Our monthly processing volume has increased 6x since we announced our Series A just a few months ago. But we’re just getting started. Real-world impact from AI means working with messy, real-world data, and we have a lot of work ahead of us as we build the interface that unlocks that context. With this round we’ll be accelerating our work across model research and product capabilities. In the near term we’ll be announcing a series of product improvements that extend our capabilities even further to help our customers achieve near-perfect accuracy on their most challenging data. Just as importantly, we want to make Reducto available for everyone. Starting today we’re introducing a new flexible pricing structure, including a generous credit program for startups and researchers, for the next generation teams that want access to the same best-in-class infrastructure used by leading enterprises. I’m incredibly excited to partner with the a16z team and to welcome Jennifer Li to our board. I first met with Jennifer shortly after our seed round, and couldn’t imagine a better partner to add to our team for this next chapter. I’m also very excited to have our existing investors Benchmark, First Round Capital, BoxGroup, and Y Combinator double down in this round, and for all of their support in getting us to this point. Most of all, Raunak and I wanted to thank everyone that has helped get Reducto to where it is today. That includes friends and family, our investors, the entire team, and most importantly all of the customers that have trusted us along the journey. We said from day one we wanted to be their ingestion team, and everything we build is shaped by working alongside you. We can’t wait for everything that’s ahead. If you’re excited about defining how the world processes unstructured data, we’re hiring across engineering, sales, and more, and would love to have you join us. Our careers page and more information about the round is linked in the comments. More from us soon!

Today we're sharing the next phase of Reflection. We're building frontier open intelligence accessible to all. We've assembled an extraordinary AI team, built a frontier LLM training stack, and raised $2 billion. Technological and scientific progress is driven by values of openness and collaboration. The internet, Linux, and the protocols and standards that underpin modern computing are all open. This isn't a coincidence. Open software is what gets forked, customized, and embedded into systems worldwide. It's what universities teach, what startups build on, what enterprises deploy. Open science enables others to learn from the results, be inspired by them, interrogate them, and build upon them in order to push the frontier of human knowledge and scientific advancement. AI got to where it is today through scaling ideas (e.g. self-attention, next token prediction, reinforcement learning) that were shared and published openly. Now AI is becoming the technology layer that everything else runs on top of. The systems that accelerate scientific research, enhance education, optimize energy usage, supercharge medical diagnoses, and run supply chains will all be built on AI infrastructure. But the frontier is currently concentrated in closed labs. If this continues, a handful of entities will control the capital, compute, and talent required to build AI, creating a runaway dynamic that locks everyone else out. There's a narrow window to change this trajectory. We need to build open models so capable that they become the obvious choice for users and developers worldwide, ensuring the foundation of intelligence remains open and accessible rather than controlled by a few. Over the last year, we've been preparing for this mission. We’ve assembled a team who have pioneered breakthroughs including PaLM, Gemini, AlphaGo, AlphaCode, AlphaProof, and contributed to ChatGPT and Character AI, among many others. We built something once thought possible only inside the world’s top labs: a large-scale LLM and reinforcement learning platform capable of training massive Mixture-of-Experts (MoEs) models at frontier scale. We saw the effectiveness of our approach first-hand when we applied it to the critical domain of autonomous coding. With this milestone unlocked, we're now bringing these methods to general agentic reasoning. We've raised significant capital and identified a scalable commercial model that aligns with our open intelligence strategy, ensuring we can continue building and releasing frontier models sustainably. We are now scaling up to build open models that bring together large-scale pretraining and advanced reinforcement learning from the ground up. There is a window of opportunity today to build frontier open intelligence, but it is closing and this may be the last. If this mission resonates, join us.

Today we’re excited to share that Flai (YC S25) has raised a $4.5 million seed round led by First Round Capital with participation from Y Combinator, SV Angel, Liquid 2 Ventures, Innovation Endeavors, Antigravity Capital, RedBlue Capital, Pioneer Fund and many other terrific funds and angel investors. This funding fuels our mission to bring customers to automotive dealerships by handling all communication before they walk in the door. After visiting hundreds of dealerships and speaking with service advisors, BDC managers, GSMs, and GMs across the US, the pattern was clear: missed calls, full inboxes, long hold times, and customers who don’t try again. In fact, 70% of people who hit voicemail call a competitor within 30 minutes, translating directly into lost revenue for the dealership. Flai has already handled tens of thousands of customer calls and is live in dozens of dealerships. Some dealers have told us Flai has paid for itself on a single Sunday and we’ve generated millions in revenue in the last two months. For our biggest location, we’re generating about 150k in revenue each month. With this funding, we’re pushing the product to new levels to build the AI communications platform for auto dealerships where every customer interaction outside of the store is taken care of.

Today, Ekin Dogus Cubuk and I are excited to introduce Periodic Labs. Our goal is to create an AI scientist. Science works by conjecturing how the world might be, running experiments, and learning from the results. Intelligence is necessary, but not sufficient. New knowledge is created when ideas are found to be consistent with reality. And so, at Periodic, we are building AI scientists and the autonomous laboratories for them to control. Until now, scientific AI advances have come from models trained on the internet. But despite its vastness — it’s still finite (estimates are ~10T text tokens where one English word may be 1-2 tokens). And in recent years the best frontier AI models have fully exhausted it. Researchers seek better use of this data, but as any scientist knows: though re-reading a textbook may give new insights, they eventually need to try their idea to see if it holds. Autonomous labs are central to our strategy. They provide huge amounts of high-quality data (each experiment can produce GBs) that exists nowhere else. They generate valuable negative results seldom published. But most importantly, they give our AI scientists the tools to act. We’re starting in the physical sciences. Progress is limited by our ability to design the physical world. We’re starting here because experiments have high signal-to-noise and are fast, physical simulations effectively model many systems, but more broadly, physics is a verifiable environment. AI has progressed fastest in domains with data and verifiable results — for example, in math and code. Here, nature is the RL environment. One of our goals is to discover superconductors that work at higher temperatures than today's materials. Significant advances could help us create next-generation transportation and build power grids with minimal losses. But this is just one example — if we can automate materials design, we have the potential to accelerate Moore’s Law, space travel, and nuclear fusion. We’re also working to deploy our solutions with industry. As an example, we're helping a semiconductor manufacturer that is facing issues with heat dissipation on their chips. We’re training custom agents for their engineers and researchers to make sense of their experimental data in order to iterate faster. Our founding team co-created ChatGPT, DeepMind’s GNoME, OpenAI’s Operator (now Agent), the neural attention mechanism, MatterGen; have scaled autonomous physics labs; and have contributed to some of the most important materials discoveries of the last decade. We’ve come together to scale up and reimagine how science is done. We’re backed by investors who share our vision, including Andreessen Horowitz who led our $300M round, as well as Felicis, DST Global, NVentures (NVIDIA’s VC arm), Accel and individuals including Jeff Bezos, Elad Gil, Eric Schmidt, and Jeff Dean. Their support will help us grow our team, scale our labs, and develop the first generation of AI scientists.

50% of the workforce will be AI agents by 2030 while every SaaS company is still stuck charging "per seat" for AI agents that aim to reduce seats. Those two things can't be true at the same time. Today we announce $21M in seed funding to fix this! Lightspeed knows this is a trillion $ problem, and so does Sequoia Capital EQT Ventures and FUSE We're building the infrastructure that lets SaaS companies break free from the seat-based trap and return to growth. We get Saas companies back to growth by: 1- create a pricing model that aligns on how your customers get value (outcome, task, or FTE savings) 2- instrument your AI agent code so that you can iterate your pricing and show customers the value your agents delivered 3- enable your sales team to sell AI agents - the way customers want to buy. Hint: it's different than selling seats 4- Show your AI Agent ROI to your customers with concrete data The seat-based model is already dead. Most companies are just waking up to it. Build agents. Get Paid.