The current state of AI development shows striking parallels to earlier phases of technological revolutions. We’re in what Perez calls the “installation period” – a time of creative destruction marked by rapid innovation, speculative investment, and institutional disruption. This period typically culminates in a financial bubble and subsequent crash, followed by a more mature “deployment period” where technology’s real benefits begin to manifest across society.

Why do our traditional metrics of intelligence so often miss the mark? The answer lies partly in how we experience our own thinking. Tasks that require conscious effort – like solving complex math problems – feel more intellectually demanding than activities we perform automatically, like riding a bike or understanding a joke. This subjective experience leads us to overvalue certain cognitive performance types while underestimating everyday intelligence’s complexity.

The enthusiasm surrounding AI’s economic potential bears a striking resemblance to previous technological revolutions. Just as the Industrial Revolution’s steam engines and mechanical looms promised to revolutionize manufacturing, today’s AI advocates paint pictures of unprecedented productivity gains. Goldman Sachs, for instance, projects that generative AI could boost annual global GDP by a remarkable 7% over time, while McKinsey suggests potential economic gains of $17.1 to $25.6 trillion over the next decade.

The ancient philosophers who first studied logic could never have imagined its application in modern AI systems. Yet their fundamental insights about valid reasoning continue to shape how we approach complex business problems. As we stand on the cusp of a new era in AI-enhanced decision-making, understanding these logical foundations becomes ever more crucial.

For decades, we’ve approached artificial intelligence development through the lens of DNA-like digital code and machine-like precision. But recent revelations in molecular biology suggest we’ve been working from an outdated playbook. The emerging understanding of how life actually works – messier, more collaborative, and far more sophisticated than previously thought – offers crucial lessons for the future of AI development.