False binaries.
Three popular binaries. Each inherits from a school whose maintained hypothesis is breaking.
- 01
Automation vs augmentation, hard vs soft skills, generalist vs specialist - the three binaries that dominate the AI-and-jobs conversation.
- 02
Each inherits from a specific school of thought - and each school's maintained hypothesis is now breaking.
- 03
The bundle frame replaces all three. Each critique below names where the binary inherits from, the seven blind spots it forces, and the frame that replaces it.
A binary frames the question. A wrong frame produces wrong answers.
Career advice, executive briefings, and policy papers all run on these binaries. When the binary is wrong, the inferences it licenses are wrong. The three pages below take each binary, name where it inherits from, list the seven blind spots it forces, and offer the bundle frame that replaces it.
- 01
Automation vs augmentation.
Inherits from The task model on one pole, the centaur framework on the other.The binary forces you to argue which side a task lands on - and misses that AI rewrites the bundle the task was part of. Seven blind spots and the bundle frame that replaces it.
Open the critique - 02
Hard vs soft skills.
Inherits from Deming's social-skill complementarity hypothesis - which is breaking.The claim that 'soft skills will protect you' assumes a clean human-AI partition. Foundation models substitute tacit cognitive work and increasingly handle conversational surfaces. Seven blind spots and the bundle frame that replaces it.
Open the critique - 03
Generalist vs specialist.
Inherits from The centaur and co-intelligence framework (human leads, AI assists).'Generalists with AI will win' inherits from a framework where the human is the orchestrator. Foundation models are themselves the best generalists. Seven blind spots and the bundle frame that replaces it.
Open the critique