May 22, 2025
Transformer-based large language models (LLMs) like GPT-4 have captivated many with their versatility, tackling everything from essays to vibe coding. But beneath this generalist flair lies a critical weakness: they struggle with problems that require multi-step reasoning, like Einstein’s riddle. These puzzles that are solved by building on previous steps expose the limits of current AI, particularly in logic, planning, and understanding.
Why? Because LLMs are trained to predict the next word or phrase and not to think step by step. Recent studies show they fail at complex logic puzzles, basic arithmetic, and tasks requiring compositional reasoning.
1. Shallow Generalism
LLMs seem generally capable, but their strength is confined to language generation, not reasoning. They don’t truly “understand” the content—they remix patterns seen during training. This makes them unreliable in domains requiring precision or real-world knowledge.
Solution? Multimodal models and structured benchmarks may help, but current LLMs remain pattern imitators, not thinkers.
2. No Internal World Model
Humans anticipate the future using internal mental models. LLMs don’t have anything like this. They generate output in one pass with no feedback loop or self-correction. They are unable to plan, to revise, or to simulate complex outcomes.
Solution? Early research is being done to explore using reflective loops and biologically inspired architectures, but practical implementations are still limited.
3. Poor Multi-Step Reasoning
LLMs falter with chained logic or arithmetic. For instance, GPT-4 solves simple puzzles but fails classic five-variable riddles. Its arithmetic accuracy drops drastically as complexity increases.
Solution? Techniques like chain-of-thought prompting show promise, but these are workarounds, not fundamental fixes to the problem.
4. Pattern Matching, Not Understanding
Transformers don’t grasp meaning; they pattern-match based on training data. This can lead to convincing but incorrect answers—so-called hallucinations.
Solution? Hybrid models combining neural networks with symbolic logic or external tools may offer a path forward.
5. Diminishing Returns from Scaling
Simply making models bigger yields less value. Experts warn we’re nearing data and cost ceilings. Training models on synthetic data introduces risks of error amplification.
Solution? Focus is shifting toward small domain-specific models and alternative paradigms like probabilistic reasoning or neurosymbolic systems.
Are we witnessing a bubble about to burst, or is this a plateau before the next leap?
The current AI hype is facing significant technical and conceptual limits. One, LLMs are not the one-size-fits-all solution that many tout. They are best at pattern-based language tasks where high-quality training data exists. Think text generation, summarization, translation, and conversational interaction. Outside of these domains, using an LLM is like trying to pound a square nail into a round hole; you should not do this.
Second, while it seems a new LLM is being created every other month, some of these companies may falter soon. Simply adding more computing or training data is no longer improving outcomes, as suggested by Sam Altman in a recent podcast. This could be a healthy correction vs a pending collapse. What’s clear is that breakthroughs— not just more compute — will be needed to reach true general intelligence.
