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.

Unlike a human expert, an LLM's probability estimates and conclusions can be drastically altered by simple rephrasing or irrelevant suggestions. This instability shows they are too easily "pushed around" and lack the coherent world model necessary for trustworthy, high-stakes decision support.

LLMs learn two things from pre-training: factual knowledge and intelligent algorithms (the "cognitive core"). Karpathy argues the vast memorized knowledge is a hindrance, making models rely on memory instead of reasoning. The goal should be to strip away this knowledge to create a pure, problem-solving cognitive entity.

Classifying a model as "reasoning" based on a chain-of-thought step is no longer useful. With massive differences in token efficiency, a so-called "reasoning" model can be faster and cheaper than a "non-reasoning" one for a given task. The focus is shifting to a continuous spectrum of capability versus overall cost.

Models like GPT Live prioritize low latency and natural interaction, making them feel more human. However, this is a specific optimization target that differs from deep, strategic reasoning. Users must understand they are interacting with a conversational layer, which may not have the same raw intelligence as the underlying frontier model it calls upon.

The perceived decline in conversational quality of some frontier models isn't necessarily a flaw. Labs may be optimizing them for internal, high-capability agentic tasks at the expense of polish and coherence for public-facing chatbots, prioritizing AGI over user experience.

Benchmarking reasoning models revealed no clear correlation between the level of reasoning and an LLM's performance. In fact, even when there is a slight accuracy gain (1-2%), it often comes with a significant cost increase, making it an inefficient trade-off.

Humans evolved to think and have experiences long before they developed language for output. In contrast, LLMs are trained solely on input-output tasks and don't 'sit around thinking.' This absence of non-communicative internal processing represents a core difference in their potential psychology.

As large language models are optimized for rationality and objective problem-solving, their ability to simulate the irrationality and subjective values inherent in human behavior has plateaued. This necessitates a new modeling paradigm focused on capturing human diversity, not just super-intelligence.