AI models struggle to plan at different levels of abstraction simultaneously. They can't easily move from a high-level goal to a detailed task and then back up to adjust the high-level plan if the detail is blocked, a key aspect of human reasoning.

Reinforcement learning incentivizes AIs to find the right answer, not just mimic human text. This leads to them developing their own internal "dialect" for reasoning—a chain of thought that is effective but increasingly incomprehensible and alien to human observers.

Recursive self-improvement won't trigger a rapid intelligence explosion because AIs currently lack the ability for genuine strategic decision-making and open-ended research. These skills, crucial for major breakthroughs, are far more than just coding, which is what current AIs excel at.

A critical gap in AI is that systems cannot yet learn a model of their world from raw experience and then use that model for high-level planning. Successes like AlphaGo relied on a pre-defined model (the game rules), but true intelligence requires discovering these models independently.

Current AI models resemble a student who grinds 10,000 hours on a narrow task. They achieve superhuman performance on benchmarks but lack the broad, adaptable intelligence of someone with less specific training but better general reasoning. This explains the gap between eval scores and real-world utility.

A key bottleneck in predicting fast intelligence explosions is the difficulty of measuring an AI's "research taste"—its ability to design good experiments and set research direction. This skill, more than coding, may be the primary driver of a rapid takeoff. It is a critical parameter that is currently not well-studied or benchmarked.

Unlike humans who have an intuitive sense of when to stop searching, agents can get stuck in expensive, fruitless loops trying to find information that may not exist. Teaching models the judgment to abandon a task is a new and vital frontier for reliable agentic AI.

The current focus on pre-training AI with specific tool fluencies overlooks the crucial need for on-the-job, context-specific learning. Humans excel because they don't need pre-rehearsal for every task. This gap indicates AGI is further away than some believe, as true intelligence requires self-directed, continuous learning in novel environments.

Current AI "agents" are often just recursive LLM loops. To achieve genuine agency and proactive curiosity—to anticipate a user's real goal instead of just responding—AI will need a synthetic analogue to the human limbic system that provides intrinsic drives.