Excited to see this incredible progress and leadership, especially for the US/Western open frontier! Congrats @reflection_ai !! 🚀

Strongly agree here. And a few quick thoughts: - (1) Basic research will increasingly be about the empirical science of studying and understanding, not just improving model performance. As AI models are increasingly *grown* as much as they are engineered, and as the complexity scale increasingly leads to emergent properties over discretely engineered ones, a lot of basic research is going to look more like classical empirical science - i.e. doing experiments, exploring, understanding, modeling, etc. (One type of experiment: a benchmark, if done correctly; that's what we are attempting to fund and support through @SnorkelAI Open Benchmarks Grants!) - (2) Basic research can prioritize *global optimization* (e.g. trying new architectures / algorithms / paradigms), which will always involve sacrificing local optimality at first. These first, less optimal steps - which could take months, or years - are hard for *any* commercially-funded organization to do fully (especially once basic economic gravity returns in some areas of the space). This is the gap that basic research institutions have always- and will always- need to fill! Also: these global optimization explorations do not always need 10K+ GPUs!! In fact, many will be selected specifically to explore new paradigms/approaches that are more compute efficient. - (3) Basic research has many other beneficial externalities, e.g. supporting training/teaching, open dissemination of knowledge, etc.

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: snorkel.ai/blog/data-2-0-and…