reference/failure-modes.json

[
  {
    "id": "casado",
    "label": "Casado & Lauten — The Empty Promise of Data Moats",
    "applies_to_quadrants": ["one-shot-win"],
    "summary": "Most claimed data moats are real for a quarter and gone in a year. The marginal value of training data plateaus quickly, and competitors with less data can usually reach the plateau by buying a vendor product. The four conditions are designed specifically to filter for what survives this critique — a system that satisfies proprietary data origin + self-labeling workflow + decreasing marginal cost + defensible asymmetry is exactly the configuration Casado's argument does not refute. Cite this failure mode when the AI is at the bottleneck but the data is bought, scraped, or shared, or when the labeling pipeline is weak.",
    "url": "https://a16z.com/the-empty-promise-of-data-moats/"
  },
  {
    "id": "sernau",
    "label": "Luke Sernau — We Have No Moat, And Neither Does OpenAI",
    "applies_to_quadrants": ["one-shot-win", "wrong-thing"],
    "summary": "Commodity LLM capability moves faster than any incumbent's product roadmap. Capabilities that look like a competitive lead this quarter become free-tier defaults in the next vendor release. Sernau's argument applies most sharply to the capability layer (model weights, instruction-tuning); the four conditions specifically anchor the moat in the operational layer (proprietary data, self-labeling work, embedded process knowledge) that Sernau's argument leaves alone. Cite this failure mode when the AI investment is built on commodity vendor capability rather than operational substrate the firm uniquely controls.",
    "url": "https://www.semianalysis.com/p/google-we-have-no-moat-and-neither"
  },
  {
    "id": "shumailov",
    "label": "Shumailov et al — Model Collapse",
    "applies_to_quadrants": ["one-shot-win", "roman-candle"],
    "summary": "Models trained on the output of earlier models progressively lose tail behavior and degrade across generations. The four conditions protect against this only when the self-labeling workflow stays anchored to real-world outcomes rather than to other models' predictions. A self-labeling loop where the labels are actually a second model's outputs is a Shumailov failure mode in slow motion. Cite this failure mode when the AI investment's training signal is downstream of another AI's output rather than downstream of a real-world event.",
    "url": "https://www.nature.com/articles/s41586-024-07566-y"
  },
  {
    "id": "buffett-2007",
    "label": "Buffett — Berkshire 2007 letter (Roman Candle passage)",
    "applies_to_quadrants": ["roman-candle"],
    "summary": "A moat that must be continuously rebuilt against competition will eventually be no moat at all. Buffett's 2007 framing applied to AI: a business that requires constant new AI investment to maintain its position, without the four compounding conditions doing the work between investments, is in the Roman Candle quadrant. Cite this failure mode when the AI is at the wrong step in the value chain AND none of the four compounding conditions hold.",
    "url": "https://www.berkshirehathaway.com/letters/2007ltr.pdf"
  }
]