Google DeepMind CEO Demis Hassabis says today’s agentic AI is a practice run for AGI, making self-improving AI the next industry milestone to watch.
The AI industry is moving from model releases to a deeper question: can AI systems help improve the systems that come after them? Google DeepMind CEO Demis Hassabis has described today’s agentic era as a practice run for AGI, with recursive self-improvement becoming one of the next milestones to watch.
That does not mean fully autonomous self-improving AI has arrived. Hassabis has been careful to distinguish between hard recursive self-improvement and the softer form already emerging today: coding agents, research assistants and tool-using systems that make human AI teams faster.
For AI tool users, founders and developers, this distinction matters. The most important agent tools may not be the ones that simply answer questions. They may be the systems that help teams write code, run experiments, analyze results, coordinate workflows and compress the time between idea and deployment.
Why self-improving AI is now the industry’s next milestone
The phrase self-improving AI can sound abstract, but the core idea is straightforward. If AI systems materially accelerate the work of AI researchers, software engineers and infrastructure teams, then model progress can compound faster. The system does not need to rewrite itself end to end to change the speed of the industry.
This is why agentic AI is strategically important. Agents can plan tasks, write code, inspect repositories, run tools, search documentation, summarize experiment results and help coordinate technical work. Each of those capabilities feeds back into how future AI systems are built.
Agents are the stress test before AGI
Hassabis’ framing of agents as a practice run matters because agents expose the operational problems that chatbots hide. When a model uses tools, browses, writes files, edits code, coordinates tasks and takes multi-step actions, its reliability, safety and governance become much more important.
This makes the agentic era a stress test for companies, governments and users. Before AGI arrives, organizations will need to learn how to evaluate agents, assign permissions, review outputs, monitor costs, handle failures and decide which tasks should remain under human control.
What this means for AI tool selection
NexusAI users should evaluate agent tools by their contribution to feedback loops. A useful AI agent should not only produce text; it should help a team move from research to implementation, from bug to fix, from idea to test, and from experiment to decision.
Important selection criteria include tool integration, coding ability, memory, context handling, permission controls, auditability, cost visibility, benchmark-independent reliability and whether the agent improves the team’s actual operating cadence.
The risk is moving faster than governance
Self-improvement creates obvious upside: faster research, stronger tools, better scientific discovery and more capable software systems. But it also creates governance pressure. If the speed of AI development increases, safety testing, economic preparation and institutional decision-making need to accelerate as well.
The practical risk is not only a distant singularity scenario. It is that businesses and governments may remain stuck in chatbot-era assumptions while the industry quietly moves into tool-using agents that can affect real workflows, real software and real organizational decisions.
