Issue 59 |💡AI Has Made Thinking Cheap. Here Are 5 Ways To Make It Valuable, Too
There is a surprising upside to applying AI to everything. The new “Cognitive Economics” of AI enable us to get smarter, not just work faster.
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A startling thing happened when I started power-using AI.
I began applying it to a bit of everything and anything. I took a picture of my shelf of unread books and asked it to tell me how many I had (256! Yes, I tsundoku) and suggest my next read. I uploaded my calendar for February and prompted it to look for patterns, including what it deduced were my priorities based on how I spent my time. I started a voice mode chat with it to explore the seeds of an IoNTELLIGENCE post that was sparked (this essay, in fact) while reading a book on AI.
This promiscuous usage can be attributed to curiosity and the urge to experiment with technology. However, I also apply AI in unexpected ways because it's so easy. These are not instances in which I would generally turn to a friend or thought partner for advice, either. These are new use cases. Moreover, they all added value to me in small yet significant ways.
We’re experiencing a profound shift in how we deploy intelligence. The fundamental change is that AI dramatically reduces the "activation energy" required to think deeply about something, leading to novel cognitive and advisory applications.
AI is making thinking “cheap.”
That’s a double-edged sword. While we can apply AI to do more and work faster, I’m equally excited about its potential to help me think more strategically about everything.
🚨 The Big Idea: The New Cognitive Economics of AI
The “dismal science” can help us make sense of this moment. Full disclosure: I’m not an economist, but I took enough Econ classes to understand that it often comes down to costs versus benefits.
We can analyze AI’s effect on the “price” of thinking in terms of three forces:
The Cost-Reduction Effect
AI reduces the time, effort, and expense required to analyze, predict, and synthesize information. This leads to predictable use cases (e.g., summarizing documents, market research). More interestingly, it makes intelligence so cheap that we start applying it to problems we never would have before.
The "Why Not?" Effect
When intelligence is expensive (in time, expertise, or money), we are selective about where we apply it. AI makes it so easy to test hypotheses, explore patterns, and analyze everyday things (such as books and calendars) that we ask questions just because we can. This unlocks casual inquiry as a new mode of intelligence usage. We don't need a "good reason” to analyze something anymore.
The Latent Curiosity Effect
There might have been areas where we could have benefited from structured thinking in the past, but the effort wasn't worth it. AI reveals dormant questions—things you wouldn’t have asked before, but now that it’s easy, you do. Having on-call intelligence empowers you to expand on any idea that crosses the transom of your mind. The Internet enables horizontal hyperlinking (breadth), but AI-like-WiFi facilitates vertical exploration (depth).
There’s another economic principle at play here: elasticity. A good or service is considered "elastic” when people consume more as it becomes cheaper. We’re learning that the “demand” for intelligence is highly elastic: when the price drops, usage skyrockets - and not just in the expected areas.
AI isn't just automating or accelerating current tasks; it's creating new categories of intelligence applications that never existed before.
The New “Cognitive Economy"
The “Sharing Economy” can help us understand the new algebra of intelligence. Pre-Airbnb, most homeowners would not consider renting their spare room to strangers; the benefit wasn’t worth the effort. Likewise, providing on-demand rides for someone would have been unfeasible as a side hustle before Uber.
AI has a similar effect by reducing costs and enhancing the efficiency of utilizing newly abundant knowledge. A whole new calculus emerges. Just like Uber turned every car owner into a potential driver, AI makes every person a more prolific analyst, strategist, and thinker.
Previously inessential cognitive exercises might now be performed because AI makes it easy. At a more macro level, AI enables micro-experiments that wouldn’t have justified hiring a professional, reading a book on the topic, or even asking a friend. Why? Because AI is an always-available “cognitive Task Rabbit,” it deftly handles thinking tasks that were once below the threshold of human collaboration.
In effect, the threshold for strategic thinking has dropped. People can now analyze things that were previously too "small" to justify examination.
What Does This Mean For You?
Under the new “Cognitive Economics of AI,” AI can augment our everyday thinking. Even minor decisions and casual inquiries should now be AI-enhanced.
However, a new problem emerges: How do we avoid paralysis by analysis, let alone losing the signal amid this new potential “noise?” In other words, how do we direct this newfound cognitive abundance toward the highest-value pursuits?
This new ability becomes valuable only if the added AI research yields better results. In my experience so far, asking AI always pays off.
For example, getting AI to proofread a critical email and suggest ways to make it more persuasive has proven to be a valuable last step. AI consistently offers worthwhile improvements, takes very little time, and has zero marginal cost regarding my subscription to Chat GPT. Crucially, this quick, effortless step yields a better outcome: a more convincing message.
This is essential because the ultimate goal is to move from more thinking to better thinking. AI should be judged not by how often we use it but by how effectively it improves real-world outcomes.
With that objective in mind, here are five concrete suggestions for strategically using this newly-abundant intelligence.
📈 5 Ways To Harness AI’s New Cognitive Economics
1. Always ask AI - Especially Questions You’ve Never Asked Before.
The cost of intelligence is now nearly zero. You have permission to go crazy (at first!) and explore anything that might benefit from investigation. Thinking is so cheap now that any insight it generates is net-valuable. What would you ask if the price of the answer was nearly nothing?
2. Use AI for Pattern Recognition, Not Just Answers.
One of AI’s superpowers is spotting patterns you wouldn’t see alone. Ask AI to review past decisions, emails, or work habits to uncover trends you can use to improve how you operate. Its analysis of my schedule revealed that I was overindexing on some things and neglecting others. What would it discover about you?
3. Use AI to Iterate Rather Than Generate.
The best AI use cases often involve refinement, not creation. AI is particularly powerful at making surprising but critical improvements. Think of AI as a new form of SaaS: Second Opinion as a Service. Before making a big decision or sending out important work, run it through AI for one last layer of optimization. Upload that email to a potential client into Chat GPT for a final refinement, and watch your closing likelihood increase.
4. Prioritize High-Leverage AI Inquiries.
Just because AI makes it easy to analyze everything doesn’t mean you should. After experimenting, aim AI-powered thinking on higher-impact areas. Before running an AI analysis, ask yourself: If this gives me a new insight, will it change my behavior?
5. Measure AI’s Impact on Real-World Outcomes.
AI should improve performance, not just thinking speed. If AI insights don’t yield better results, they’re just noise. Track one or two key areas where you use AI—writing, scheduling, hiring, investment decisions—and measure whether AI-assisted thinking tangibly improves efficiency, persuasion, or effectiveness.
🏆 Key Takeaways
🏁 If You Only Do One Thing
What small tasks in your work and life could benefit from an AI second opinion?
Let me know how it goes in the comments!
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IoNTELLIGENCE by Ion Valis. I'm a strategic advisor and performance coach to entrepreneurs and executives. To learn more about my work, visit my website or connect with me on LinkedIn.