// ai limitations

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Oct 13

Why AI excels at code reconstruction but fails at customization

Current LLMs demonstrate a sharp competency gap: they can reverse-engineer and rebuild massive codebases from scratch, yet stumble on targeted modifications to existing libraries—tasks humans consider simpler. This inverts the intuitive hierarchy of difficulty. LLMs operate well when reconstructing from general principles but break when asked to navigate specific constraints and existing architecture. The risk is not overestimated AI intelligence, but systems whose strengths are orthogonal to production software work, where understanding what *not* to break matters more than what to build from nothing.

theme-ai ai capabilities ai limitations drug discovery

Aug 3

Why AI Won't Shortcut Drug Discovery

Source: a16z

The venture capital narrative around AI-powered drug discovery treats molecular biology as a pure information problem solvable by scaling compute and model sophistication. The actual bottleneck is experimental validation and unknown biological complexity. Andreessen Horowitz's argument cuts against its own industry's hype cycle: even with perfect computational predictions, the wet-lab work, regulatory pathways, and fundamental biological surprises remain time-intensive and irreducible. Venture funding that treats biology as software-complete is capital deployed away from the grinding infrastructure—biotech manufacturing, clinical trial design, disease modeling—where pharmaceutical velocity actually lives.

theme-ai ai capability claims ai model performance ai limitations

May 24

AI's Math Breakthrough Reveals Why Creative Tasks Stay Hard

Source: Azeem Azhar, Exponential View

DeepSeek's o1 model shows strong performance on mathematical reasoning, but this progress hasn't extended to creative or strategic work where correctness is ambiguous. AI systems excel when optimizing toward a clear ground truth—like math or code—but falter when tasks require judgment, taste, or tradeoffs learned through lived experience rather than training data. Near-term AI productivity gains will concentrate in engineering, science, and coding. Industries betting on AI for strategy, marketing, or novel problem-solving will see diminishing returns for years.