Why a Slow Thinking AI is the Step Forward

The AlphaGo Story

In 2016, the world watched as an AI program called AlphaGo faced off against one of the greatest Go players of all time, Lee Sedol. The match was electrifying, filled with moments of brilliance and unexpected twists, including Lee Sedol's famous "divine move" in Game 4, which forced AlphaGo into a mode of reflection, challenging its initial strategies.

Unlike previous AIs that relied purely on brute force, AlphaGo demonstrated something new: a mix of intuition and deliberate analysis. It wasn’t just a victory for artificial intelligence—it was a glimpse into a future where AI could do more than just react quickly; it could think deeply.

AlphaGo's success was more than a story about winning a game. It was a testament to the power of slow, deliberate thinking—a capability that is becoming increasingly essential for AI to truly fulfil its potential in complex, real-world scenarios.

Most of the time, the narrative you'd get about AI is how it "speeds" things up. How it's the productivity booster of the 21st century.

But today, we're looking at the other end of the spectrum, where an AI that thinks slow can actually be good for all of us (and no, it has nothing to do with AI taking over the world ??).

In Buddhism, the concept of taking the middle path is often talked about. By the end of this piece—complete with a book recommendation, real-life stories of slow thinking AI, and some actionable insights—I hope we'll understand when to use each type of AI thinking and how to combine both effectively, i.e. taking the middle path of AI adoption.

As a matter of fact, as I'm rewatching The Lord of The Rings trilogy, I'm also reminded of Gandalf's quote: "A wizard is never late, nor is he early. He arrives precisely when he means to."

Overview of System 1 and System 2 Thinking

Daniel Kahneman's book Thinking, Fast and Slow introduced two modes of human thought: System 1 and System 2.

System 1 is our intuitive, fast-thinking side—it operates automatically and effortlessly. As Kahneman explains,

System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control.

It helps us make snap judgments, such as recognizing a friend's face in a crowd or braking quickly when a car stops in front of us. It’s efficient for everyday decisions, but it also comes with biases and errors, especially in complex or uncertain situations.

On the other hand, System 2 is the deliberate, effortful part of our cognition. "System 2 allocates attention to slow, effortful, infrequent, logical, calculating, conscious mental activities that demand it," writes Kahneman.

System 2 is activated when we need to focus—solving a challenging math problem or weighing the pros and cons of a major life decision. While System 1 is instinctive, System 2 is reflective, requiring focus and careful thought.

Kahneman describes these two systems as "agents within the mind, with their individual personalities, abilities, and limitations." This metaphor gives us a powerful framework to understand how AI has been evolving from a "fast thinker" like System 1—adept at recognizing patterns and automating tasks—to a "slow thinker" like System 2, capable of analyzing situations deeply and methodically.

Stories of Slow Thinking AI

AlphaGo wasn’t the only example of AI's evolution toward slow thinking. Here are a few other stories where AI demonstrated the ability to think deliberately, much like Kahneman's System 2:

1. IBM Watson in Jeopardy! — Watson faced complex questions filled with cultural references and wordplay when it competed in Jeopardy! in 2011. To perform successfully, it needed to analyze context and weigh various possible answers, much like System 2. Watson's victory showed that AI could handle more than simple, factual lookups; it could engage in nuanced, reflective reasoning.
2. AlphaFold and Protein Folding — Google DeepMind 's AlphaFold solved one of the biggest challenges in biology: accurately predicting the 3D structures of proteins. Protein folding involves nearly limitless possibilities, requiring careful evaluation of each configuration. AlphaFold didn’t just calculate quickly; it engaged in systematic analysis to find the right structure, demonstrating a "slow thinking" capability essential for breakthroughs in science.
3. Waymo Self-Driving Cars — Autonomous vehicles must make rapid decisions, like braking to avoid a pedestrian, but they also require longer-term deliberation—anticipating other drivers' behavior, planning routes, and optimizing passenger safety. This combination of quick responses and thoughtful, predictive decision-making embodies the dual thinking styles.
4. BenevolentAI in Drug Discovery — During the COVID-19 pandemic, BenevolentAI worked to discover existing drugs that could treat the virus. Drug discovery is inherently slow and intricate, involving complex biological interactions that must be analyzed systematically. AI's role in this process was to engage in careful evaluation, mirroring System 2's reflective thinking.
5. Libratus in Poker — Poker requires more than calculating odds; it involves bluffing, strategizing, and anticipating opponents' moves. Libratus, an AI developed to play Texas Hold'Em, used a strategic approach, improving each day by reflecting on its previous games. It wasn’t just reacting—it was thinking strategically and slowly, much like a human poker player.

What This Means for People Development

The evolution of AI into a more deliberate, slow thinking tool has significant implications for people development. As AI takes over more of the "fast thinking" tasks, humans need to shift their focus to areas where deliberation, creativity, and judgment are paramount. Understanding when to trust quick intuition versus when to slow down and analyze deeply will be an increasingly critical skill.

1. Emotional Intelligence and Adaptability: As AI takes over routine tasks, emotional intelligence becomes a crucial differentiator. Leaders and employees need to develop empathy, self-awareness, and adaptability to complement AI’s logical capabilities. This balance will be key in roles that involve human interactions and complex decision-making.
2. Collaboration Between Humans and AI: Building the ability to collaborate effectively with AI is paramount. This includes knowing when to leverage AI insights and when to apply human judgment. AI can offer powerful data-driven recommendations, but humans must interpret these insights and add contextual, ethical considerations to them.
3. Continuous Learning and Creativity: With AI handling predictable, repetitive processes, human focus should shift toward continuous learning and creativity. Developing skills that AI cannot easily replicate, like creative problem-solving, strategic thinking, and innovative brainstorming, will be essential to stay relevant in the workforce.
4. Decision-Making Frameworks: Employees should be trained in structured decision-making frameworks to understand when to engage in slow, deliberate analysis versus trusting instinct. This helps in recognizing situations that demand deeper reflection, much like System 2, ensuring the human-AI partnership is effective and balanced.
5. Coaching and Mentoring Skills: With AI taking on more routine and analytical tasks, managers will have the opportunity to focus on their teams' personal and professional growth. Equipping team members and future leaders with skills like coaching and mentoring becomes essential. These skills help foster growth, motivation, and stronger relationships—areas where human insight, empathy, and understanding are irreplaceable. As AI manages more of the operational workload, leaders can use their freed-up time to invest in building people-focused, empathetic workplace cultures.

What This Means for Business Processes

AI's move towards slow thinking isn’t just about better decision-making; it changes how businesses should design their processes. Traditionally, businesses have sought efficiency through automation—the domain of System 1.

But to tackle complex challenges, AI must become part of strategic workflows, helping businesses to reflect on options, evaluate risks, and innovate.

1. Product Development: AI can assist in simulating different scenarios, weighing potential features, and testing concepts, allowing teams to iterate with deep insights. This kind of slow, deliberate analysis ensures that products are not only feasible but also optimized for user needs.
2. Risk Management: Slow thinking AI can help companies anticipate market shifts, evaluate compliance risks, and develop long-term strategies rather than just responding to problems as they arise. This proactive approach is essential for building resilience in an uncertain environment.
3. Customer Experience Personalization: AI can analyze customer behaviour patterns over time to provide personalized experiences. Instead of just reacting to customer needs instantly, slow thinking AI can predict future needs and build a long-term strategy for deeper customer engagement and loyalty.
4. Strategic Decision-Making: AI should be integrated into strategic decision-making processes. By analyzing vast datasets and considering different factors over an extended period, AI can help leaders make more informed, deliberate choices that align with long-term business goals.
5. Supply Chain Optimization: In supply chain management, slow thinking AI can be used to anticipate disruptions, optimize inventory levels, and improve logistics planning by reflecting on historical data and predicting future trends. This ensures a more resilient and adaptive supply chain, even under volatile conditions.

In Closing... Slow Down to Go Far

In May 2024, I went for a 10-day silent retreat, where between the 6+ hours of daily meditation, boredom and lack of mental stimulation (which got me familiarized with the mating sounds of monkeys and squirrels), I appreciated the practice of living in the present. In fact, it was the slow-down-to-move-fast break that I needed, because that was where the idea of AI-infused training solutions came to me.

As AI evolves, it becomes more than just a tool for automating mundane tasks; it becomes a partner in thinking. Much like the lessons from a 10-day silent meditation retreat, where the key to insight is to slow down and observe without judgment, AI's future lies in its ability to slow down and think deeply. This shift to slow thinking is what will allow AI to support humanity in facing its most complex and challenging problems—not just with speed, but with wisdom.

The next step is to ask ourselves: How can we leverage this kind of slow thinking AI to improve not just our businesses, but our personal decision-making and growth as well? In a world that often pushes us to go faster, perhaps the real innovation lies in teaching both AI and ourselves to slow down.

Resources:

1. Analysis of Watson's Strategies for Playing Jeopardy!
2. AlphaFold: Using AI for Scientific Discovery
3. DeepMind's Official Publications on AlphaFold
4. Waymo's Approach to Self-Driving Technology
5. BenevolentAI: Using AI to Combat COVID-19
6. Libratus: The AI that Beat Humans at Poker
7. AlphaGo versus Lee Sedol