AI is evolving fast, and two of its most respected thinkers say we’re entering a bold new phase—one where machines stop waiting for our input and start learning on their own. David Silver and Richard Sutton call it the “Era of Experience,” and if their predictions hold true, it will reshape everything from enterprise apps to how we build intelligent systems.
Silver, a senior researcher at DeepMind, and Sutton, a pioneer of reinforcement learning, believe we’re moving beyond AI that simply trains on massive datasets curated by humans. Instead, the future lies with agents that learn actively by exploring the world, making decisions, and adjusting based on feedback—just like we do.
Their recent paper outlines what this shift means and how businesses can prepare for a landscape filled with autonomous AI agents that are constantly evolving, adapting, and growing smarter through real-world interactions.
From Passive Learning to Active Intelligence
Silver and Sutton aren’t just theorists—they’ve helped build some of the most influential AI systems to date, including DeepMind’s AlphaGo and AlphaZero. Back in 2019, Sutton famously wrote The Bitter Lesson, arguing that breakthroughs in AI come not from hand-coded rules or expert knowledge, but from scaling general-purpose algorithms powered by massive compute.
That philosophy has shaped how the most powerful AI systems today are built. Models like GPT-4 and DeepSeek-R1 reflect this shift, relying on huge amounts of training data and reinforcement learning to develop complex reasoning skills. But even these cutting-edge tools are still largely trained on static, human-generated data.
That’s what’s changing.
In the Era of Experience, AI systems will learn not just from us, but with us—through direct interaction, experimentation, and continuous adaptation. Instead of feeding models millions of pre-labeled examples, developers will design agents that learn by trial, error, and feedback from their environment.
What Makes This Era Different?
According to Sutton and Silver, self-learning AI will operate across four key dimensions:
- Streams of Experience: Rather than acting in isolated bursts, future AI agents will learn over time, building memory and context just like humans. With longer context windows and evolving memory structures, these systems will be capable of setting long-term goals and adapting their behavior over months or even years.
- Autonomous Actions and Observations: Instead of waiting for prompts, agents will independently interact with software, APIs, and digital tools. Early signs of this are already here—agents that can browse the web, use computers, or interface with APIs like OpenAI’s MCP are laying the groundwork for autonomous decision-making.
- Self-Generated Rewards: Today, most reinforcement learning relies on hand-crafted rewards. But in the next phase, agents will define their own reward systems, adjusting goals as they learn and aligning them with real-world feedback. Nvidia’s DrEureka hints at what this might look like—AI systems that shape their own incentives as they grow.
- Non-Human Reasoning: Current reasoning models mimic human thinking, but future agents might invent entirely new ways of processing information. Whether through symbolic computation or continuous reasoning, these agents won’t just think like us—they may discover smarter ways to think altogether.
What It Means for Enterprises
The implications for businesses are huge. If AI agents start navigating the web, apps, and APIs like humans do—only faster and smarter—companies will need to design for a new kind of user: the autonomous agent.
That means rethinking how we build digital tools:
- Expose Actions and Observations: APIs and apps should provide clear, structured ways for agents to take action and observe outcomes. Interfaces like Google’s Agent2Agent protocol show what this future might look like, where agents can discover each other and collaborate seamlessly.
- Design for Discovery and Autonomy: Apps that are accessible to agents—through secure, machine-friendly endpoints—will be better positioned to integrate with next-gen AI systems. This also improves resilience, ensuring agents behave safely and predictably when interacting with your software.
- Optimize for Agent Collaboration: The best apps won’t just tolerate AI agents—they’ll actively enable them. That means building interfaces that allow for flexible interactions, dynamic feedback loops, and continuous learning on both sides.
As Silver and Sutton note, AI systems will still use human-style actions when it helps—such as using UIs to collaborate with people. But increasingly, they’ll shift toward code-level and protocol-driven actions that bypass traditional inputs altogether.
If these predictions play out, there could soon be billions of AI agents operating online and in the real world—handling logistics, managing data, or supporting users without human intervention. And companies that build with this in mind will have a major edge.
The “Era of Experience” won’t be driven by rules—it will be shaped by interaction, learning, and adaptation at scale. For developers, designers, and enterprise leaders, the challenge is clear: build systems that don’t just serve humans, but teach and evolve with machines.
