The End of Easy Progress

This interactive report explores the central thesis: core AI intelligence is hitting a plateau of diminishing returns. The era of exponential gains from simply scaling up models is ending, forcing a fundamental shift in what "progress" means for artificial intelligence.

The Crowded Plateau

This section visualizes the end of exponential growth in core intelligence. As progress slows, top models are converging in capability, and companies are shifting strategy from building one "God model" to a fragmented portfolio of specialized AIs.

Benchmark Convergence

Top models now cluster together on general knowledge benchmarks like MMLU, with only marginal differences separating them.

Strategic Fragmentation

As a single model can no longer be best at everything, labs now offer specialized variants. Filter by company to see their strategy.

Hitting the Wall

The brute-force scaling paradigm is constrained by three fundamental barriers. These "walls" are not future problems; they are the present-day realities forcing the industry to seek new paths to progress.

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The Data Wall

The supply of high-quality public data is exhausted. Labs now rely on lower-quality sources and synthetic data, which risks "model collapse."

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The Compute Wall

The astronomical energy and financial cost of training and running frontier models is becoming physically and economically unsustainable.

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The Architectural Wall

The Transformer architecture excels at pattern matching but fundamentally struggles with true reasoning and planning, a gap scaling alone cannot bridge.

The Ghost in the Machine

Scaling has failed to solve deep, persistent flaws. These are not minor bugs but foundational deficits of the current paradigm. Click each card to learn more.

LLMs generate text that *appears* reasoned but they lack true causal understanding. They are stateless and cannot adapt plans dynamically. In a classical planning benchmark, GPT-4's autonomous success rate was a mere 12%, revealing its reliance on word association, not logic.

Factual inaccuracy, or "hallucination," is a feature, not a bug. Models are designed to be plausible, not truthful. This leads to non-deterministic and often incorrect outputs, with severe real-world consequences, such as a mayor being falsely implicated in a bribery scandal by ChatGPT.

AI pioneers Geoffrey Hinton and Yann LeCun warn that the current path is futile. Hinton fears we are creating uncontrollable systems we can't understand. LeCun argues the architecture is a dead end, lacking the "world model" a child learns through sensory input. The path may lead to systems that are either unreliable or uncontrollable.

The Shifting Frontier

In response to the walls and flaws, innovation is accelerating on new, more pragmatic frontiers. Progress is now being redefined beyond raw intelligence.

Paradigm Shift: New Architectures

Labs are developing more efficient architectures to get more performance for less computational cost.

The Action Paradigm: Agentic AI

Instead of just generating content, agentic systems use LLMs as "orchestrators" to coordinate external tools (APIs, web search) and take action in the real world. This outsources difficult reasoning to reliable, deterministic software, sidestepping the model's own flaws.

The Bandwidth Paradigm: Multimodality

Native multimodality (processing text, images, audio in a unified way) increases the "bandwidth" of human-computer interaction. This unlocks vast new sources of non-text data, helping to breach the Data Wall and ground models in a richer understanding of the world.