AI's Code-Breaking Hopes Dashed by Common Sense Deficiencies, Says Critic

Boise, Idaho – Artificial intelligence, often touted as a panacea for complex analytical challenges, faces significant hurdles in areas humans find trivial, according to public comments made by David Freddoso. The former Washington Examiner columnist and author expressed disappointment with AI's current capabilities, particularly its failure to grasp fundamental pattern recognition without explicit human guidance, even in tasks seemingly suited for its strengths, such as code-breaking.

Freddoso, known for his critical views on AI, recently highlighted this frustration via a social media post on August 14, 2025. He recounted an experience where AI struggled to decode a message "hidden in plain sight" in downtown Boise, stating, "Code-breaking should be low-hanging fruit for AI. The first disappointment is its failure to recognize, without significant prompting, that these characters represent letters and that each time you see a specific character, it's another instance of that letter." He further asserted, "Just more proof that AI is garbage."

Experts in artificial intelligence largely concur with Freddoso's underlying critique regarding AI's lack of common sense reasoning and contextual understanding. Despite rapid advancements in large language models (LLMs) like GPT-3 and more recent iterations, AI systems frequently misinterpret nuanced human language and fail to apply learned knowledge to novel situations. Researchers at USC Viterbi and other institutions emphasize that while LLMs excel at pattern matching and generating grammatically correct text, they often lack genuine comprehension, leading to "hallucinations" or logical inconsistencies when faced with real-world ambiguities.

The challenge stems from AI's reliance on vast datasets to learn patterns, rather than possessing an innate, human-like understanding of cause and effect or social cues. For instance, an AI might struggle to discern sarcasm or infer implicit assumptions in a conversation, tasks a two-year-old human can manage. This limitation is particularly evident in complex problem-solving where intuitive judgment and adaptability are crucial, such as in self-driving cars or medical diagnostics, where human oversight remains indispensable.

While AI has demonstrated remarkable capabilities in highly specialized analytical tasks, including complex calculations and data analysis, its performance in symbolic reasoning and mathematical word problems often falls short. Researchers note that AI struggles with tasks requiring flexible causal reasoning or the ability to generate new information beyond its training data. The "black box" nature of deep learning models further exacerbates this, making it difficult to understand how AI arrives at its conclusions, hindering debugging and trust.

The field of AI is actively exploring "neuro-symbolic AI," a hybrid approach that aims to combine the pattern recognition strengths of machine learning with the logical, rule-based reasoning of traditional symbolic AI. This emerging area seeks to address the current limitations by imbuing AI with a more robust form of common sense. However, as Freddoso's recent experience highlights, achieving truly human-like reasoning and contextual understanding in AI remains a significant and unresolved challenge.