DeepMind maps four paths for reaching AI superintelligence

Google’s latest research drops a fairly complete picture of where it thinks AI is heading.
A 60-page DeepMind paper, “From AGI to ASI,” lays out four ways systems could move from human-level intelligence to superintelligence. The straightforward path is scaling: bigger models, more compute, more data—basically the trajectory behind systems like Gemini. The second is discontinuous change via new algorithms that don’t look like today’s transformers. The third is recursive self-improvement, where models begin iterating on their own architectures and training loops. The fourth is collective intelligence: many AGI-level agents coordinating into something larger than any single system. 

If that’s the roadmap, the next problem is reliability. Google researchers propose “faithful uncertainty,” a way for models to align what they say about confidence with what they actually “believe” internally. Instead of either answering confidently or refusing, a model can explicitly hedge when it’s uncertain. That uncertainty signal can drive decisions like when to call tools, when to search, or when to rely on memory. It’s also framed as a fix for what the paper calls a “utility tax,” where strict anti-hallucination rules make models too cautious to be useful. 

Then there’s knowledge handling. Google Cloud’s Open Knowledge Format (OKF) tries to standardize how enterprise information is packaged for AI systems. It turns scattered internal data into structured Markdown folders with lightweight metadata. Links between documents form a simple knowledge graph. The goal is portability: agents can ingest it across clouds and frameworks without custom pipelines for every system. 

On top of that sits execution. Gemini-SQL2, built on Gemini 3.1 Pro, translates natural language into SQL queries and runs them against real databases. On the BIRD benchmark, it reaches 80.04% execution accuracy, ahead of OpenAI’s GPT-5.5-xhigh at 72.8% and Anthropic’s Claude Opus 4.6 at 70.9%.