Announcing our investment in Recursive Super Intelligence

Today’s frontier reasoning models have settled into a familiar and predictable pattern of improvement. 

To crack the next coding benchmark or computer use task, we create an environment for an agent to operate in, define a set of verifiable or non-verifiable rewards and let the agent hill-climb on these metrics. While this has yielded tremendous gains in a subset of white collar work, these agents have severe limitations. They are incapable of discovering new knowledge since they can only answer questions in the distribution of what humans have previously specified. This is a hindrance when trying to unearth new insights or find an intelligent starting point on the road to new breakthroughs. 

Solving long horizon tasks and building systems capable of lifelong learning requires something fundamentally different. To solve a problem no human has solved, models must be able to:

• pose their own questions by learning their own shortcomings and how to overcome them with new lines of inquiry – not merely understanding how to respond to what humans may desire
• reward progress for interesting and novel results, not just for accuracy and getting full marks on a benchmark
• collaborate with teams of agents to harness their insights and improve as a collective when stuck, rather than as an individual (inspired by processes from biological and cultural evolution)

Achieving these goals requires significant investment in research that has not been systematically prioritized by mainstream labs. This work spans techniques and infrastructure for unsupervised environment design for general-sum games, multi-agent evolution, deployment time continual improvement, and much more. 

It also necessitates a phase shift in what problems and domains we think about solving. Of all the scientific verticals where this agent could operate from biotechnology to nuclear fusion, it makes most sense to start automating AI research for two reasons. Firstly, since AI can code and AI is code, it is the most immediately approachable problem. Secondly, there are significant compounding benefits to building a system that can solve tough problems in AI from data efficiency to credit assignment, so that if a scientific problem requires a new algorithm or system design, the agent will innovate at lightning speed to get there. 

A mission this bold commands a special team with a history of diverse, groundbreaking research spanning all of the above topics and more, assembled across oceans from San Francisco to London, to concentrate time and effort on this singular endeavor. A superhuman team, if you will.

We believe Recursive Super Intelligence is that rare assembly of talent, ready to kickstart a new paradigm and make AI models synonymous with originality and knowledge discovery. The founders share some of the most impressive, star-studded CVs of any Amplify portfolio company, and not just because there’s eight of them. They hail from almost all major AI labs and have written some of the most iconic papers of the last decade. And on a personal note, we’ve been fortunate to know several on this team for many years. 

Richard Socher, the CEO of RSI, was one of the first visionaries to see the importance of neural networks in solving natural language processing. His work on GloVE embedding models is now one of the most highly cited papers in the history of NLP. His subsequent startup MetaMind, which enabled enterprise access to computer vision and NLP solutions, was acquired by Salesforce, where he not only spent several years as Chief Scientist but became widely credited for inventing prompt engineering itself. 

Josh Tobin, now CTO of RSI, is no stranger to this problem as he was previously CEO of Amplify portfolio company Gantry. Having started his career as one of the first 25 people at OpenAI, he left to build a continuous machine learning improvement platform that helped machine learning engineers understand how their deployed models are performing, ways to improve it from data curation to experimentation, and then actually operationalize those improvements. After a second stint at OpenAI where he led Deep Research, we are excited to see him back on the frontlines of startups and self improving systems. 

Jeff Clune’s litany of work in evolutionary algorithms and open-endedness needs no introduction. Before LLMs even existed, he produced seminal research such as POET, which highlighted why populations of agents making mutated improvements outperform maximizing an objective. Since then, his team - of whom Jenny Zhang, Shenghran Hu and Cong Lu have excitingly joined RSI - have pioneered work in pairing evolutionary algorithms with LLMs from Darwin Godel Machines to HyperAgents (one of the first public demonstrations of an AI improving its own code), and inspired similar research across the ecosystem such as DeepMind’s AlphaEvolve. If the future of AI is evolutionary, I can’t think of any better than Jeff to guide us there. 

Tim Rocktaeschel's reputation similarly precedes him, having authored fundamental papers in model collaboration like Rainbow Teaming and Debate with LLMs, generative world models with his mind-boggling work on Genie, and even the invention of RAG itself. In particular, his work on autocurricula such as PLR and ACCEL laid the groundwork for agents that ask their own questions during training in a previous era of RL. His students now lead research teams globally, and we’re ecstatic former members of his UCL lab like Dominik Schmidt have joined the founding team. 

Yuandong Tian spent a decade at Meta after completing his PhD at CMU, where he improved the training and inference of LLMs with work on StreamingLM and GaLORE, made LLMs reason continuously with Coconut and also did architecture work on attention sinks and positional encodings. He even published an open source version of AlphaGo called OpenGo, where he wrote over 90% of the code for a new infra platform called ELF which made the system train on only 2000 GPUs, despite himself being a senior researcher and manager at the time! 

Tim Shi has spent close to a decade in the trenches of AI and startups. After dropping out of his PhD at Stanford where he worked on reinforcement learning, he became an early member of OpenAI where he led work on using AI to control a computer - highly prescient research! Subsequently, he left to be the founder and CTO of Cresta, a text and voice based company that both analyzes customer calls and automates customer experience end to end. 

Alexei Dosovitskiy is a legendary figure in computer vision having invented the Vision Transformer, one of the most influential and highly cited papers in the history of the field. More broadly, his work has covered neural radiance fields, simulators for robotics, a host of architectures for vision learning and even applications in modeling RNA. 

Caiming Xiong has a long history straddling both product and research, having joined MetaMind as a senior researcher in 2014 and subsequently spent a decade at Salesforce where he led Applied Research. Over this period, he built a practical pipeline to convert NLP, time series analysis and CV research into real products across Commerce, Marketing, Availability and Sales Clouds across the CRM, and also authored a paper that invented prompt engineering back in 2018. 

It’s rare to see so many seemingly disparate threads and remarkable researchers come together, but at the same time, nothing could make more sense. This team is uniquely qualified to attack the most pressing problem in artificial intelligence today and have already started hiring a fantastic crop of infra and research talent. They’re on the cusp of unlocking a universe of insights for scientists everywhere, and we couldn’t be more chuffed to partner with these folks, and get a front row seat to the development of recursive super intelligence.