Due Diligence

“The talk about AI bubbles seemed very divorced from what was happening in frontier labs and what we were seeing. We are not seeing any slowdown of progress.” – Julian Schrittwieser – Anthropic

Those closest to technical breakthroughs are witnessing a pattern of sustained, compounding advancement that is often underestimated by commentators and investors. This perspective underscores both the power and limitations of conventional intuitions regarding exponential technological progress.

Context of the Quote

Schrittwieser delivered these remarks in a 2025 interview on the MAD Podcast, prompted by widespread discourse on the so-called ‘AI bubble’. His key contention is that debate around an AI investment or hype “bubble” feels disconnected from the lived reality inside the world’s top research labs, where the practical pace of innovation remains brisk and outwardly undiminished. He outlines that, according to direct observation and internal benchmarks at labs such as Anthropic, progress remains on a highly consistent exponential curve: “every three to four months, the model is able to do a task that is twice as long as before completely on its own”.

He draws an analogy to the early days of COVID-19, where exponential growth was invisible until it became overwhelming; the same mathematical processes, Schrittwieser contends, apply to AI system capabilities. While public narratives about bubbles often reference the dot-com era, he highlights a bifurcation: frontier labs sustain robust, revenue-generating trajectories, while the wider AI ecosystem might experience bubble-like effects in valuation. But at the core—the technology itself continues to improve at a predictably exponential rate well supported by both qualitative experience and benchmark data.

Schrittwieser’s view, rooted in immediate, operational knowledge, is that the default expectation of a linear future is mistaken: advances in autonomy, reasoning, and productivity are compounding. This means genuinely transformative impacts—such as AI agents that function at expert level or beyond for extended, unsupervised tasks—are poised to arrive sooner than many anticipate.

Profile: Julian Schrittwieser

Julian Schrittwieser is one of the world’s leading artificial intelligence researchers, currently based at Anthropic, following a decade as a core scientist at Google DeepMind. Raised in rural Austria, Schrittwieser’s journey from an adolescent fascination with game programming to the vanguard of AI research exemplifies the discipline’s blend of curiosity, mathematical rigour, and engineering prowess. He studied computer science at the Vienna University of Technology, before interning at Google.

Schrittwieser was a central contributor to several historic machine learning milestones, most notably:

• AlphaGo, the first program to defeat a world champion at Go, combining deep neural networks with Monte Carlo Tree Search.
• AlphaGo Zero and AlphaZero, which generalised the approach to achieve superhuman performance without human examples, through self-play—demonstrating true generality in reinforcement learning.
• MuZero (as lead author), solving the challenge of mastering environments without even knowing the rules in advance, by enabling the system to learn its own internal, predictive world models—an innovation bringing RL closer to complex, real-world domains.
• Later work includes AlphaCode (code synthesis), AlphaTensor (algorithmic discovery), and applied advances in Gemini and AlphaProof.

At Anthropic, Schrittwieser is at the frontier of research into scaling laws, reinforcement learning, autonomous agents, and novel techniques for alignment and safety in next-generation AI. True to his pragmatic ethos, he prioritises what directly raises capability and reliability, and advocates for careful, data-led extrapolation rather than speculation.

Theoretical Backstory: Exponential AI Progress and Key Thinkers

Schrittwieser’s remarks situate him within a tradition of AI theorists and builders focused on scaling laws, reinforcement learning (RL), and emergent capabilities:

Leading Theorists and Historical Perspective

These thinkers converge on several key observations directly reflected in Schrittwieser’s view:

• Exponential Capability Curves: Consistent advances in performance often surprise those outside the labs due to our poor intuitive grasp of exponentiality—what Schrittwieser terms a repeated “failure to understand the exponential”.
• Scaling Laws and Reinforcement Learning: Improvements are not just about larger models, but ever-better training, more reliable reinforcement learning, agentic architecture, and robust reward systems—developments Schrittwieser’s work epitomises.
• Novelty and Emergence: Historically, theorists doubted whether neural models could go beyond sophisticated mimicry; the “Move 37” moment (AlphaGo’s unprecedented move in Go) was a touchstone for true machine creativity, a theme Schrittwieser stresses remains highly relevant today.
• Bubbles, Productivity, and Market Cycles: Mainstream financial and social narratives may oscillate dramatically, but real capability growth—observable in benchmarks and direct use—has historically marched on undeterred by speculative excesses.

Synthesis: Why the Perspective Matters

The quote foregrounds a gap between external perceptions and insider realities. Pioneers like Schrittwieser and his cohort stress that transformative change will not follow a smooth, linear or hype-driven curve, but an exponential, data-backed progression—one that may defy conventional intuition, but is already reshaping productivity and the structure of work.

This moment is not about “irrational exuberance”, but rather the compounding product of theoretical insight, algorithmic audacity, and relentless engineering: the engine behind the next wave of economic and social transformation.