<ul><li class="ql-align-justify">The rise of AI is quietly rewriting the rulebook on human problem-solving.</li><li class="ql-align-justify">For centuries, Western thinking has idolized rationalism and science, the engines behind modern marvels like rockets, vaccines, and smartphones.</li><li class="ql-align-justify">Our mental playbook—hypotheses, theories, and frameworks—has mirrored this success, producing tools like <a href="https://en.wikipedia.org/wiki/Porter%27s_five_forces_analysis">Porter’s five forces</a> and <a href="https://jobs-to-be-done.com/jobs-to-be-done-a-framework-for-customer-needs-c883cbf61c90">Christensen’s jobs-to-be-done</a> framework.</li><li class="ql-align-justify">But AI demands a new mindset.</li><li class="ql-align-justify">As we transition from Software 1.0 (human-written instructions) to Software 2.0 (goal-driven training models), traditional rationalist approaches may no longer suffice. </li></ul>

Rethinking How We Think in the AI Era

<ul><li class="ql-align-justify">This transition is reshaping science: the 2024 Nobel Prizes in physics and chemistry went to computer scientists optimizing predictive architectures—not traditional theorists crafting better explanations.</li><li class="ql-align-justify">This shift transforms science into engineering, emphasizing creation over understanding.</li><li class="ql-align-justify">The question is no longer "What is this?" but "How can I predict this?"</li><li class="ql-align-justify">This pivot could redefine progress, transcending Enlightenment rationalism and opening new paradigms for navigating complexity and understanding ourselves.</li></ul>

<ul><li class="ql-align-justify">In a post-AI world, however, essence gives way to sequence. The focus shifts from identifying universal truths to analyzing patterns of behavior or chains of events.</li><li class="ql-align-justify">For example, a SaaS business once relied on rigid rules to predict churn—like tracking login frequency or payment data.</li><li class="ql-align-justify">Now, AI models examine vast sequences of user behavior, uncovering nuanced patterns no human could predict or explicitly define.</li></ul>

<ul><li class="ql-align-justify">Western thought fixates on explanations, seeking rules to control and demystify success, whether in individuals like Mark Zuckerberg or in scientific phenomena.</li><li class="ql-align-justify">Yet explanations often fail to capture reality's complexity. Zuckerberg’s success, for instance, stems less from explicit principles and more from intuitive decision-making he likely can’t fully articulate.</li><li class="ql-align-justify">The post-AI era flips this dynamic. Predictions, not explanations, are becoming paramount.</li><li class="ql-align-justify">AI models, trained on vast data, encapsulate intuition without needing human comprehension of underlying processes. </li></ul>

5. Explanations vs. predictions

<ul><li class="ql-align-justify">These tasks rely on nuanced, ineffable judgments that AI can now replicate and transfer.</li><li class="ql-align-justify">In a post-AI world, intuition becomes not just accessible but deployable, breaking free from human exclusivity and revolutionizing fields reliant on tacit expertise.</li><li class="ql-align-justify">This shift challenges the traditional belief that only processes can be mechanized, opening doors to new, intuitive possibilities for technology and human collaboration.</li></ul>

<ul><li class="ql-align-justify">Previously, creativity demanded precision and control, akin to sculpting—a process where every detail was manually crafted, with each step entirely reliant on the creator’s intent.</li><li class="ql-align-justify">Coding, for instance, required meticulous shaping of logic and structure, piece by piece.</li><li class="ql-align-justify">Now, AI tools like Cursor reframe the process as gardening. Instead of chiseling ideas into existence, creators establish conditions for ideas to flourish.</li><li class="ql-align-justify">Prompting AI models replaces manual labor, transforming the act of creation into one of guidance and curation. </li></ul>

4. Sculpting vs. gardening

<ul><li class="ql-align-justify">The focus shifts from direct intervention to fostering an environment where growth can happen organically.</li><li class="ql-align-justify">This evolution challenges traditional notions of authorship, emphasizing collaboration over singular ownership.</li><li class="ql-align-justify">As the creative process becomes more intuitive and adaptive, the role of the creator morphs into one of partnership with intelligent tools—an unpredictable and deeply collaborative journey.</li></ul>

<ul><li class="ql-align-justify">In a post-AI world, the focus shifts from crafting explicit rules to identifying patterns through examples—a transformation that challenges traditional problem-solving.</li><li class="ql-align-justify">Rather than painstakingly coding systems or defining principles, AI enables us to translate abstract preferences and complex behaviors into actionable frameworks.</li><li class="ql-align-justify">For instance, instead of dictating how software should function, you can provide a collection of UI inspirations, and AI will discern the underlying patterns to build it. </li></ul>

2. Rules vs. patterns

<ul><li class="ql-align-justify">Similarly, in creative work, AI can replicate brand voice and style by analyzing curated examples, bypassing the need for rigid systems. </li><li class="ql-align-justify">This approach turns "taste" into a teachable input, empowering teams to scale and adapt their output fluidly.</li><li class="ql-align-justify">The surprising twist is that clarity now hinges on high-quality examples, not logical frameworks—shifting the emphasis from explaining to showing.</li><li class="ql-align-justify">This pattern-first mindset reframes intellectual and creative work, pushing us to think less about rules and more about resonance.</li></ul>

<ul><li class="ql-align-justify">In a pre-AI world, success depended on isolating and mastering the essence of a problem—defining clear, static rules to address challenges.</li><li class="ql-align-justify">Software required pinpointing the core user and their problem; marketing plans relied on fixed theses and strategies.</li><li class="ql-align-justify">This reductionist approach assumed that breaking problems into their simplest elements would yield the best solutions.</li></ul>

1. Essences vs. sequences

<ul><li class="ql-align-justify">This shift is profound: AI doesn't "understand" the problem as humans would but detects intricate correlations across millions of variables.</li><li class="ql-align-justify">By feeding historical data into machine learning models, businesses can predict outcomes with staggering precision, bypassing traditional rule-setting altogether.</li><li class="ql-align-justify">The surprising implication? AI redefines problem-solving as probabilistic rather than deterministic.</li><li class="ql-align-justify">Instead of designing solutions around abstractions, we now let machines find actionable insights buried in complexity—a radically different approach to understanding and shaping the world.</li></ul>

<ul><li class="ql-align-justify">Traditionally, software required reducing tasks to explicit rules—a framework that worked well for structured systems like customer relationship management.</li><li class="ql-align-justify">However, many complex tasks, like optical character recognition (OCR), resist such reduction. Deep learning now enables software to develop "intuition," effectively handling tasks once deemed unprogrammable. </li><li class="ql-align-justify">This breakthrough extends beyond OCR into realms thought untouchable by software, like evaluating startup pitches or diagnosing medical conditions.</li></ul>

3. Process vs. intuition

<ul><li class="ql-align-justify">Instead of designing step-by-step solutions, engineers now train AI systems by exploring infinite possibilities until one works. This isn’t about finding universal truths; it’s about achieving specific outcomes.</li><li class="ql-align-justify">This paradigm shift—from theorizing to engineering—could fundamentally reshape how we approach problems, pushing us to embrace more flexible, results-oriented thinking styles.</li></ul>

If rationalism gave us the Enlightenment, could engineering-thinking fuel the AI age?

A world with thinking machines requires new thinking styles. 

AI is turning science into engineering—with unprecedented changes for how we see the world

DAN SHIPPER

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