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.

The perception of a 'critically thinking' AI doesn't come from a single, powerful model. It's the result of using multiple levels of LLMs, each with a very specific, targeted task—one for orchestrating, one for actioning, and another for responding. This specificity yields far better results than a generalist approach.

The future of AI is not a single all-knowing model, but a "router" model that triages requests to a suite of specialized expert AIs (e.g., doctor, programmer). The primary technical and business challenge will shift to building the most efficient and accurate routing system, which will determine market leadership.

Issues like 'saturation' and 'maxing' reveal a fundamental flaw: benchmarks test narrow, siloed abilities ('Task AGI'). They fail to measure an AI's capacity to combine skills to solve multi-step problems, which is the true bottleneck preventing real-world agentic performance and the next frontier of AI.

Advanced AI architectures use a 'harness' to orchestrate complex tasks. This 'brain' is separated from the agent's direct execution loop, allowing it to coordinate multiple agents and tools. If one agent fails or goes down a wrong path, the harness ensures the overall, long-running process remains intact, making the entire system more resilient and manageable.

Early AI metaphors centered on a single omnipotent entity like Ultron. Practical limitations like token windows and processing threads mean the more effective model is a 'swarm' or 'colony' of specialized agents, where orchestration becomes the key challenge.

Breakthroughs will emerge from 'systems' of AI—chaining together multiple specialized models to perform complex tasks. GPT-4 is rumored to be a 'mixture of experts,' and companies like Wonder Dynamics combine different models for tasks like character rigging and lighting to achieve superior results.

The next level of AI leverage isn't just using a single, powerful agent. It involves using a general-purpose AI to delegate complex jobs to specialized agents, each operating within its own purpose-built harness. This modular approach enables more sophisticated and reliable automation.

Human intelligence leaped forward when language enabled horizontal scaling (collaboration). Current AI development is focused on vertical scaling (creating bigger 'individual genius' models). The next frontier is distributed AI that can share intent, knowledge, and innovation, mimicking humanity's cognitive evolution.