When prompted to build an MVP, Fable 5 interpreted "minimal" too literally, delivering a version that was overly narrow and not genuinely useful. This conservative execution makes it less suitable for agile development cycles where an ambitious, "good enough" V1 is required to get customer feedback.

When working at the frontier of AI, designers must resist the urge to polish every detail. Since underlying models and product shapes change rapidly, time is better spent on future-looking conceptual problems that AI cannot yet solve, rather than on features with a short lifespan.

Modern AI can rapidly build complex products ("zero to n"), but it lacks the human intuition to simplify by removing features. This critical skill, honed through real-world usage and experience, is what prevents products from becoming bloated and unfocused.

The "bitter lesson" of AI applies to product development: complex scaffolding built around model limitations (like early vector stores or agent frameworks) will inevitably become obsolete as the models themselves get smarter and absorb those functions. Don't over-engineer solutions that a future model will solve natively.

The extreme intelligence of models like GPT-5.5 is not beneficial for simple, everyday tasks. The long "thinking" times and complexity are drawbacks, suggesting the average user struggles to find problems that warrant such powerful capabilities in consumer applications like ChatGPT.

Many product builders overestimate current AI capabilities. Understanding AI's limitations, like the non-deterministic nature of LLMs, is more critical than knowing its strengths. Overstating AI's capacity is a direct path to product failure and bad investments.

AI can accelerate development, marketing, and sales tasks. However, it currently lacks the strategic judgment, customer empathy, and "taste" required for strong product management—deciding what to build and why.

Fable 5 demonstrates a surprising weakness in UI/UX design, creating outputs described as worse than "AI slop." This highlights that even models with strong general vision capabilities may lack the specific training or aesthetic sense required for effective front-end design, forcing users to use other models.

A key advancement in Sonnet 4.5 is its work style. Unlike past models with "grand ambitions" that would meander, this AI pragmatically breaks down large projects into small, manageable chunks. This methodical approach feels more like working with a human colleague, making it more reliable for complex tasks.