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Bill Gurley critiques AI companies claiming AGI/ASI capabilities for failing to solve simpler problems like detecting espionage distillation in real time.
Article questions why frontier AI labs like OpenAI and Anthropic do not disclose the size of their training data, suggesting that improvements may come from data volume rather than genuine intelligence.
A tweet criticizes how AI models waste tokens on HTML <div> tags, suggesting that tokenizers inefficiently allocate 40% of tokens to structural elements.
This paper introduces the SAPS (Synthetic Algorithmic Predictive Systems) framework, arguing that modern AI systems do not think but tokenize and compute statistical patterns, and clarifies the critical distinction between artificial and synthetic systems.
The author reflects on their decade-long journey as an Android developer, emphasizing the value of human connections over the pressure to adopt AI, and argues for prioritizing personal growth and community.
The author criticizes AI coding tools as being like hyperactive junior developers that produce flashy but inefficient code that requires extensive babysitting and repair, questioning why these tools are marketed as senior-level assistants.
The author expresses disappointment in AI progress, arguing that despite years of development and massive spending, large language models still struggle with basic reasoning, referencing an Apple paper that exposes fundamental flaws. They question whether the hype around superintelligence is misguided.
The author argues that AI models like GPT and Claude over-optimize human creations, missing the value of imperfection, messiness, and emotional depth in art and life.