With AI models and workflows becoming obsolete in as little as a year, mastering a single tool is a failing strategy. The most valuable skill is becoming comfortable with constant change and the process of repeatedly being a beginner, as this adaptability is the only sustainable advantage.
The public's perception of AI is largely based on free, less powerful versions. This creates a significant misunderstanding of the true capabilities available in top-tier paid models, leading to a dangerous underestimation of the technology's current state and imminent impact.
AI labs deliberately targeted coding first not just to aid developers, but because AI that can write code can help build the next, smarter version of itself. This creates a rapid, self-reinforcing cycle of improvement that accelerates the entire field's progress.
The debate around AI's impact presents an asymmetric risk. Underestimating AI's capabilities could lead to obsolescence for individuals and companies. Conversely, overestimating its short-term impact results in some wasted preparation, a far less severe and more recoverable outcome.
A key critique suggests AI is a "fake tool" because it automates tasks that produce little real value, like pointless memos. This criticism inadvertently highlights that much of current corporate knowledge work is itself performative and lacks substance, a problem that predates AI.
Drawing on Frédéric Bastiat's "seen and unseen" principle, AI doomerism is a classic economic fallacy. It focuses on tangible job displacement ("the seen") while completely missing the new industries, roles, and creative potential that technology inevitably unlocks ("the unseen"), a pattern repeated throughout history.