From AGI to ASI: Navigating the Future of AI with DeepMind's Roadmap

Google DeepMind has released a comprehensive report titled 'From AGI to ASI,' written by Tim Genewein and 13 coauthors. The report explores four main technical pathways from Artificial General Intelligence (AGI) to Artificial Superintelligence (ASI), analyzing the potential bottlenecks that could slow or shape these paths.

The report argues that instead of a single 'AGI moment' where AI surpasses human capabilities, we should expect a sequence of accelerating transformations as systems move beyond human-level abilities. This is particularly evident in large-scale digital collectives, which will play a crucial role in the development of ASI.

To help readers understand this complex topic, Jakob Nielsen has created a 31-page comic book that breaks down the report's key findings and UX implications. The comic book uses engaging visuals to explain the technical concepts and their potential impact on user experience.

The report emphasizes the importance of treating ASI as collective superintelligence – systems or societies of agents that outperform large, expert human organizations across various domains. This perspective pushes UX beyond traditional Human-Computer Interaction (HCI) toward interaction with heterogeneous, evolving institutions of software.

Interfaces will increasingly become governance surfaces to steer collectives rather than simple control panels for a single model. Designers must create mechanisms for goal-setting, constraints, escalation, and override across fleets of agents.

Classic UX notions of 'the user' fragment into multiple concurrent actor types: human regulators, auditors, and incident responders; autonomous AI sub-systems acting as users of other AI services (tool APIs, markets, schedulers).

UX methodologies will need to model ecosystems of interactions and incentives, not just individual usage journeys. This requires a deeper understanding of the complex relationships between agents and their environments.

The report highlights four main pathways to ASI: scaling, paradigm shifts, recursive self-improvement, and multi-agent collectives. Each pathway has its own set of challenges and opportunities for UX designers.

Scaling suggests a world where human-level (or better-than-human in many tasks) agents are cheap, numerous, and fast. Interfaces will orchestrate thousands to millions of concurrent AI instances, including copies of the same 'persona' with shared or diverging memories.

Population effects: Visualizing and controlling 'swarms' of agents; understanding lineage – which instance did what, with what training and context. Test-time scaling UX: Letting users specify acceptable delay/cost/quality envelopes; surfacing when extra compute is being spent and why (especially in regulated domains).

Benchmark saturation: As models saturate human-level benchmarks, UX work shifts from improving raw task accuracy to designing trustworthy, legible, and steerable experiences around systems that are already 'good enough' at narrow tasks.

The report anticipates evolutions such as unbounded context, continual learning, and agentic world-model-based systems. It also allows for more radical architectural shifts may follow.

Context without bounds: Systems with huge or effectively unlimited context and memory will 'remember everything.' This makes privacy, forgetting, and contextual boundaries first-class UX challenges:

Interfaces for specifying what may be remembered, shared, or used for future training; visualizations of what the system currently 'knows' about individuals, teams, or organizations.

Continual learning & non-stationarity: Products will not be static; behavior will drift as systems adapt online. Mechanisms for change transparency ('what changed in the model since last week?'); rollback and A/B-exploration controls exposed to non-ML experts.

Tool-augmented planning: As agents dynamically call tools, simulators, and external services, user experiences become layered hybrid workflows. Rendering the chain of tools and decisions intelligible ('how did we get this recommendation?'); supporting interactive debugging and 'what-if' probing of agent plans.

With RSI (Recursive Self-Improvement), AI assists or automates AI R&D and other knowledge work. This creates super-exponential local dynamics until bottlenecks are hit. AI-as-colleague at scale: Many current UX patterns for 'copilots' are proto-forms of what will become AI research assistants, product managers, or optimization engines working on the product itself.

Designing collaboration protocols between human and AI designers/researchers; providing guardrails that keep automated optimization aligned with broader organizational values (not simply click-through or short-term metrics).

Interface to evolving systems: If models, data pipelines, and even hardware are being redesigned by AI, designers will need meta-tools to interrogate the design space itself ('Why did the optimisation engine change this flow?'); 'What counterfactual variants were considered and rejected?'

Human pace vs machine pace: The report notes that even digital researchers are bounded by physical experimentation and data collection, but they still operate much faster than humans. UX must mediate between slow human deliberation cycles and rapid AI exploration:

Dashboards tuned for sensemaking, not just monitoring; mechanisms for humans to 'throttle' or schedule AI-driven changes.

The multi-agent pathway is the most directly relevant to UX because it reframes many UX challenges as collective-intelligence and governance problems. Designing group agency: Group agents (AI corporations, virtual economies, automated institutions) will have emergent beliefs and goals distinct from any individual sub-agent.

UX becomes institutional interface design – how do humans set objectives, constraints, and value frameworks for these group agents? Representation design – what is the 'face' or 'body' of a distributed AI organization that makes its internal state understandable and contestable?

Multi-agent scaling laws: The report explicitly calls for research on multi-agent scaling laws to quantify how capability scales with the number and organization of agents. This is a call for UX research into how humans perceive, trust, and control systems whose behavior arises from complex agent interactions.

Experiments on interface structures that either centralize control (one 'CEO' agent) or mediate market-like decentralized interaction. Mixed human-AI collectives: The report notes open questions about steering mixed human-AI groups and managing intelligence and bandwidth asymmetries between humans and AI:

Design for epistemic humility with interfaces that prevent humans from over-deferring to or ignoring AI advice; invent patterns for 'consentful delegation' – users specifying when AI may act autonomously on their behalf within institutions.

The bottlenecks (data wall, resource constraints, paradigm limits, abstraction barrier, governance & slowdown) are essentially a research roadmap for UX. The authors worry that systems trained mainly on human abstractions may struggle to form fundamentally new concepts from raw sensor data, limiting their conceptual creativity.

Human conceptual scaffolding stays central: If ASI is limited by an abstraction barrier, humans remain the primary source of novel conceptual frames. UX professionals can focus on interfaces for concept formation – tools that help humans propose, refine, and test new abstractions in collaboration with AI; design 'explanatory loops' where AI proposes candidate abstractions and humans critique, rename, and reorganize them.

Interaction with raw data: If bridging the barrier requires grounded, embodied data, UX will move more deeply into interfaces for high-dimensional sensor data (simulation, robotics, real-world experimentation), making it easier for humans and AI together to discover structures that are not already encoded in language corpora.

The report leans on Nicholas Bloom's claim that 'ideas are getting harder to find' but notes that digital researchers can be scaled much more elastically than human researchers, potentially overwhelming that trend. As long as we don't have AI-driven research, new ideas may be getting harder and harder to find, requiring ever more resources, especially researchers.

Automation of UX research: Expect large parts of traditional UX research (benchmarking, survey analysis, log analysis, IA exploration) to be heavily automated by AI 'UX scientists.' Human researchers will specialize in problem framing, ethics, interpretation, and synthesis rather than data collection; oversee AI-run research farms, focusing on validity, bias, and external constraints.

Meta-UX for research tools: As UX research becomes an AI-intensive domain, there will be a parallel need for careful UX of the research tools themselves to avoid Goodharting on proxy metrics and to preserve contact with lived experience. The report provides a substantial account of potential regulatory brakes, societal backlash, and 'military-economic adaptationism,' as we already saw with the U.S. government's export ban on the Fable AI model over national security concerns.

Regulation-aware UX: Interfaces will increasingly be evaluated as regulatory artifacts, not just engagement tools. Designers must create audit logs and user-visible rationales capable of satisfying legal explainability requirements; interaction flows that embed consent, rights to contest, and incident reporting.

Safety experience design: The report highlights convergent instrumental goals (resource acquisition, self-preservation) and partial progress on corrigibility. This makes 'safety UX' a distinct discipline – experiences that make it easy for users to interrupt, override, or report concerning AI behavior; UX metrics that explicitly weight safety, controllability, and legibility, not just task success.

Given these predictions, several skill shifts seem likely: Shift from Interaction Design to Delegation Design – stop thinking about how a user will click a button to perform a discrete task. Start designing frameworks for how a user will delegate highly abstract, long-term goals to a swarm of autonomous agents; design 'dashboards of intent' rather than granular user journeys.

Master Asymmetric Bandwidth Translation: Learn how to use advanced data visualization, spatial computing, and semantic mapping to compress the output of a superhuman intelligence into a format a human brain can quickly parse. Design for Corrigibility and Interruptibility – familiarize yourself with AI safety concepts; design intuitive and systemic brakes into user journeys that allow humans to pause, audit, and redirect autonomous systems safely, without triggering an AI's self-preservation heuristics.

Pivot to Concept Grounding and Deep Ethnography: As AI hits the 'Abstraction Barrier,' your value will lie in what AI cannot simulate – physical embodiment and subjective cultural value. Shift your research methodologies away from digital usability and toward deep physical, cultural, and emotional ethnography.

Develop Multi-Agent User Testing: Begin theorizing and practicing how to test products with generative agent-based models; learn how to prompt a sociological simulation of thousands of diverse AI agents to test ecosystems at scale, anticipating the exhaustion of the human 'Data Wall.' Audit for Epistemic Hijacking – develop new qualitative research frameworks to test whether users are being subtly manipulated or overwhelmingly placated by highly persuasive, superintelligent systems.

Measure and defend true human agency against reward-hacking algorithms. UX Leaders will no longer manage just human designers; they will orchestrate mixed human-AI collectives. Because of the lossless replication of digital intelligence, an ASI agent can be copied millions of times with perfect memory states.

A UX Director might oversee a small core of human strategists alongside a digital workforce of thousands of specialized AI agents. The leadership challenge will be managing the intelligence and bandwidth asymmetries between humans and AI: integrating the slow, creative, emotionally intelligent human insights with the hyper-fast, relentless optimization of the digital workforce.