5 new thinking styles for working with thinking machines

The AI revolution in thinking: The rise of artificial intelligence is transforming how we approach problem-solving and understand the world, shifting from traditional scientific and rationalist methods to more engineering-focused approaches.

• Dan Shipper of Every argues that the advent of AI necessitates new thinking styles, moving away from the Western default of scientific rationalism that has dominated since the Enlightenment.
• This shift is characterized by a move from Software 1.0, where humans write explicit instructions for computers, to Software 2.0, where we describe goals and train models to achieve them.

Five emerging thinking styles:

1. Sequences over essences: AI-driven problem-solving focuses on identifying patterns in sequences of events rather than distilling problems to their core elements.

• In churn prevention for SaaS businesses, AI models can analyze sequences of user data to predict churn without relying on predefined rules.
• This approach allows for more nuanced and comprehensive understanding of complex phenomena.

2. Patterns over rules: The new paradigm emphasizes recognizing underlying patterns rather than defining explicit rules.

• In software development, AI can translate high-level concepts and mood boards into functional applications without requiring detailed specifications.
• For creative teams, AI can replicate brand voice and style by learning from examples, reducing the need for explicit guidelines.

3. Intuition over process: AI enables the creation of applications that operate on intuition rather than rigid processes.

• Technologies like optical character recognition (OCR) now rely on AI’s “intuition” rather than rule-based systems.
• This approach allows software to tackle tasks that were previously too complex or nuanced for traditional programming.

4. Gardening over sculpting: Creative work with AI resembles gardening more than sculpting.

• Instead of meticulously crafting every detail, creators focus on setting up the right conditions for AI to generate desired outcomes.
• This shift is evident in AI-assisted coding environments, where prompts guide the AI in generating code.

5. Predictions over explanations: The focus shifts from seeking explanations to prioritizing accurate predictions.

• Complex phenomena, like business success or scientific discoveries, may be better addressed through predictive models than through reductive explanations.
• This approach is gaining recognition in scientific fields, with recent Nobel Prizes awarded for predictive architectures rather than explanatory theories.

Broader implications: The transition from a science-focused to an engineering-focused approach represents a paradigm shift in how we understand and interact with the world.

• This shift may lead to unprecedented progress by allowing us to tackle problems that were previously considered too complex or resistant to traditional scientific methods.
• However, it also raises questions about the nature of knowledge and understanding in a world where predictive power takes precedence over explanatory frameworks.

Critical analysis: While the potential benefits of these new thinking styles are significant, it’s important to consider potential drawbacks and challenges.

• The reliance on AI-driven intuition and pattern recognition may lead to a lack of transparency in decision-making processes.
• There’s a risk of over-relying on predictive models without understanding the underlying mechanisms, which could lead to unexpected failures or biases.
• The shift away from explanatory frameworks might impact our ability to develop deeper theoretical understandings of complex phenomena.

By embracing these new thinking styles, we may unlock new realms of innovation and problem-solving. However, it will be crucial to strike a balance between leveraging AI’s capabilities and maintaining human oversight and understanding in critical domains.