Poolside is a foundation model company bringing intelligence to everywhere work gets done. Our mission is to drive abundance for humanity by creating artificial general intelligence.
Flower: Advance Collaborative Superintelligence
Flower is a full-stack AI neolab with a mission to advance collaborative superintelligence by building open-source frontier models, agents, and infrastructure.
– Next Frontier AI
Next Frontier AI is run by SPRIND – the German Federal Agency for Breakthrough Innovation. Think of SPRIND as a state-backed deep-tech fund and company builder: we deploy public money with a venture mindset, take real technical risk and are explicitly mandated to try things that can fail.
Home – Next Frontier AI
Next Frontier AI is run by SPRIND – the German Federal Agency for Breakthrough Innovation. Think of SPRIND as a state-backed deep-tech fund and company builder: we deploy public money with a venture mindset, take real technical risk and are explicitly mandated to try things that can fail.
Pacing the Frontier
A statement from over 1000 employees of frontier AI companies
Developing Enterprise Frontier Safeguards with our customers
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Debates On Frontier Artificial Intelligence Governance: The AI Triad
Analytical Paper Optional: All enrolled students have the option of completing a research paper of at least 20-25 pages, with faculty and peer review of a substantially complete draft. This paper can be used to satisfy the analytical paper requirement for J.D. students. Prerequisite: This course is intended for students intending to work in the […]
Datacurve | The data engine for frontier AI
Custom data for long-horizon reasoning, software engineering, and data science.
Worldbuilding with Spatial Intelligence
The next stage in the development of AIs is to give them spatial intelligence.
Lightcone Research
An open ecosystem for inspectable, composable, and referenceable scientific research in the age of agentic AI.
Why A Neurotech VC Bet On Anthropic: Intelligence, Interpretability, and the NeuroAI Stack — Kaleida Capital
Why would a neurotech VC bet on Anthropic? This article examines how frontier AI, neuroscience, model behavior, and intelligent systems intersect within Kaleida Capital’s NeuroAI thesis and the infrastructure shaping next-generation intelligence.
From AGI to ASI
Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations. Achieving this goal would have profound and far-reaching impacts on human society, which raises many complex questions for the decade ahead. This report investigates how AI itself might continue to develop in a post-AGI world along the continuum of machine intelligence. The endpoint of this continuum, Universal AI, is theoretically well understood, which provides some formal grounding for the main focus of this report: the transition from human-level AGI to artificial general superintelligence, which, intuitively, can be understood as a system that is more intelligent and cognitively capable than large organisations of humans. After characterizing ASI, the report discusses four potential pathways from AGI to ASI: scaling AGI, AI paradigm shifts, recursive improvement, and ASI emerging from large-scale multi-agent collectives. The report then discusses possible frictions and bottlenecks along these pathways. Determining whether the impact of these frictions will be negligible or substantial raises a number of concrete open research questions. Due to large uncertainties for predicting ASI progress, it cannot be ruled out that AI progress might continue to accelerate over the next years. This could imply that the image of a single transformative step change, caused by the introduction of human-level AGI into our society, could be inaccurate. More apt might be the prospect of a series of transformative societal changes caused by AI-enabled progress and breakthroughs across many areas of science and technology. Preparing for this prospect requires a massively interdisciplinary endeavour of global scope and interest.
From AGI to ASI
Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations. Achieving this goal would have profound and far-reaching impacts on human society, which raises many complex questions for the decade ahead. This report investigates how AI itself might continue to develop in a post-AGI world along the continuum of machine intelligence. The endpoint of this continuum, Universal AI, is theoretically well understood, which provides some formal grounding for the main focus of this report: the transition from human-level AGI to artificial general superintelligence, which, intuitively, can be understood as a system that is more intelligent and cognitively capable than large organisations of humans. After characterizing ASI, the report discusses four potential pathways from AGI to ASI: scaling AGI, AI paradigm shifts, recursive improvement, and ASI emerging from large-scale multi-agent collectives. The report then discusses possible frictions and bottlenecks along these pathways. Determining whether the impact of these frictions will be negligible or substantial raises a number of concrete open research questions. Due to large uncertainties for predicting ASI progress, it cannot be ruled out that AI progress might continue to accelerate over the next years. This could imply that the image of a single transformative step change, caused by the introduction of human-level AGI into our society, could be inaccurate. More apt might be the prospect of a series of transformative societal changes caused by AI-enabled progress and breakthroughs across many areas of science and technology. Preparing for this prospect requires a massively interdisciplinary endeavour of global scope and interest.
From AGI to ASI
Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations. Achieving this goal would have profound and far-reaching impacts on human society, which raises many complex questions for the decade ahead. This report investigates how AI itself might continue to develop in a post-AGI world along the continuum of machine intelligence. The endpoint of this continuum, Universal AI, is theoretically well understood, which provides some formal grounding for the main focus of this report: the transition from human-level AGI to artificial general superintelligence, which, intuitively, can be understood as a system that is more intelligent and cognitively capable than large organisations of humans. After characterizing ASI, the report discusses four potential pathways from AGI to ASI: scaling AGI, AI paradigm shifts, recursive improvement, and ASI emerging from large-scale multi-agent collectives. The report then discusses possible frictions and bottlenecks along these pathways. Determining whether the impact of these frictions will be negligible or substantial raises a number of concrete open research questions. Due to large uncertainties for predicting ASI progress, it cannot be ruled out that AI progress might continue to accelerate over the next years. This could imply that the image of a single transformative step change, caused by the introduction of human-level AGI into our society, could be inaccurate. More apt might be the prospect of a series of transformative societal changes caused by AI-enabled progress and breakthroughs across many areas of science and technology. Preparing for this prospect requires a massively interdisciplinary endeavour of global scope and interest.
A snapshot of research into answering if frontier AI agents can run R&D into AI (which not surprisingly failed apart from "minor findings" and "engineering steps"). The paper lists its TBDs strengths and limits, worth a read before people who don't know theirs jump in arxiv.org/pdf/2607.27191
