I'm excited to announce @SnorkelAI's $350M Series E at $3.5B, led by @insightpartners and @S32_VC. We've grown 18x+ in the last 12 months since launching our Data-as-a-Service offering, passing $375M ARRR this week. As AI advances to superhuman capabilities, AI data & environment development must advance with it - and basic staffing and crowdsourcing approaches are not enough. AI progress now requires deep research and technology work that combines human expertise with specialized AI in compounding ways. @SnorkelAI is building the RSI data engine and frontier data lab for this next phase. We're honored to have the support of existing investors Addition, @lightspeedvp, @GreylockVC, @GVteam, P7, Factory, @WellsFargo, Walden Catalyst Ventures, and new investors @ThirdPointLLC, @MarchCPs, @BlumbergCapital, @AllegisCapital, @Frontlinevc, and @standard_vc. – @SnorkelAI started as a research project a decade ago at @StanfordAILab. Our thesis was simple: AI progress would become increasingly data-centric – and therefore data development should be studied as a true research and technology problem, not just a staffing and crowdsourcing one. Today, as AI capabilities verge on superhuman, building the data and environments to safely measure and train AI is becoming too hard for even the smartest human experts to do alone. Only humans and AI agents, collaborating together in compounding ways, can meet the accelerating needs of the frontier, and keep humans in the driver’s seat of AI progress for decades to come. At @SnorkelAI, we are building the data lab to define the shape of this new “Data 2.0” frontier, and the new paradigms of human-computer interaction needed to advance it. Our key focus is building the RSI engine for data, where specialized AI models accelerate and improve human expert output, and in turn, scaled human supervision is used to continuously evaluate and improve these models – creating a powerful compounding loop to keep pace with an accelerating RSI frontier. With this round of funding, we are also doubling down on our commitments to support data development for open benchmarking and evaluation (more news here soon!); an increasingly diverse ecosystem of general and specialized intelligence; and a path to safe, well-aligned AI built on robust training and evaluation data. Data development will guide and drive the next stages of AI – and must do so in a human-centric, AI accelerated, open, diverse, and safe way. We are excited to support this mission in the next decade of research ahead at @SnorkelAI. More thoughts here:

Underrated interpretability result from our work with @EngramLab on @harvey law firm knowledge that makes a difference for the UX of long-horizon agents. The reasoning traces you get from parametric memory + study notes are much more intuitive than the interleaved bash commands you get from coding agents. When you read them (esp as a non-technical user), you can actually understand what’s going on. For example: "I remember two antitrust matters from earlier work, one that was terminated (client matter 1001-00004) and one that cleared after an HSR Second Request (client matter 1003-00003)" Compare this to the following from a coding agent (1 of 19 such bash commands btw): { "command": "cd /tmp/cache/1003-00003 && echo \"=== engagement letter head ===\" && sed -n '1,25p' Engagement/engagement-letter-hpe-fund-iv.docx.txt | cut -c1-500 && echo && echo \"=== practice group / partner mentions ===\" && grep -rhoi \"[^.]*\\(practice group\\|M&A partner\\|antitrust partner\\|responsible partner\\|lead partner\\)[^.]*\\.\" . | sort -u | head -12 | cut -c1-300 && echo && echo \"=== HSR outcome ===\" && grep -rhoi \"[^.]*\\(waiting period expired\\|expiration of the \\(extended \\)\\?waiting period\\|early termination\\|closed the investigation\\|closing letter\\|consent decree\\|no further action\\|without taking\\)[^.]*\\.\" . | sort -u | head -15 | cut -c1-400" } Interpretability like this becoming increasingly important for enterprise agent deployments. s/o to @dan_biderman @realJessyLin & team for innovating on multiple dimensions here.

Rox has been powering Global 2000 enterprises with $5T+ in combined market cap. Today, we’re putting it in everyone’s hands. Over the last 2 years, companies like @MongoDB, @togethercompute, and @Xbow shifted investment from legacy CRM and SaaS to Revenue Agents. But most teams have been locked out. They lacked in-house technical talent or FDEs required to set up the revenue-specific context, harnesses, and agent systems needed to run Revenue Agents in production. Today, we launched Rox Teams to remove that barrier. With Rox Teams, businesses of every shape, size, and vertical can activate Revenue Agents on their own. Set up in minutes. No FDEs required. & Nolaned this hilarious explainer video 👇

i'm quite excited about this! • it's cool in general that models can generate their own training data and learn from it. wasn't fully clear to me even a year ago that this would work reliably. • the thing that we're trying to build seems really important and no one has built it before • some traces from our model genuinely surprise me (e.g. the attached example, where it perfectly simulates the output of a complicated bash query, despite not being trained to do this) my main feeling overall is that there is so much to do. we see signs things are starting to work: models use their memories to produce better answers, knowing things saves lots of tokens, and all of this emerges with more compute but solving this memory calibration problem, on top of learning how to generate data in ways that scale nicely with compute, is going to take some time this is just a first step 🫡

Like real people, Engram's models use both neural memories and written notes to tackle hard tasks. Many legal tasks require reingesting the same persistent context: the firm filesystem. Scaling compute preemptively on that filesystem can obviate many challenges at inference time and unlock new types of behaviors.

working with @EngramLab to train models on law firm knowledge, we built a synthetic law firm (46 clients, 266 matters, 100M+ tokens) to study how well agents can search and reason across a firm's entire body of work.  more results to share soon!

Frontier models know law. But there are many ways to practice law. Everything we know about them can only be found in the huge filesystems of law firms. To me, how models understand and navigate those mega filesystems is the big open question in AI.

We're hosting our first @AmplifyPartners AI research conference on Aug 14 at Gallery 308! Featuring off-the-beaten-path work from the amazing @pgasawa @realJessyLin @spencerpoff @BenShi34 @IdanShenfeld @_chris_lu_ @FeinbergVlad

From Eric Kandel's Nobel Lecture (2000): "Learning is the process by which we acquire new knowledge about the world, and memory is the process by which we retain that knowledge over time. For me, learning and memory have proven to be endlessly fascinating mental processes because they address one of the most remarkable aspects of human behavior: our ability to acquire new ideas from experience. Most of the ideas we have about the world and our civilizations we have learned. So that in good measure, we are who we are because of what we have learned and what we remember"