Action Bias

AUG 5, 2024

In the rapidly evolving landscape of artificial intelligence, I've been pondering a critical mindset that bridges the way we hire people and how we design AI assistants. As we increasingly integrate AI into our daily workflows, the expectations we have from our human colleagues are beginning to mirror those we have from our AI counterparts. At the core of this is a simple yet profound principle: a bias towards action.

When we bring someone into our team, we don't just want someone who can talk eloquently about problems or endlessly debate solutions. While effective communication is valuable, it's action that drives progress. Similarly, an AI assistant that merely generates verbose explanations without tangible outcomes isn't particularly helpful. What we need—from both humans and AI—is the ability to listen, reason, act, and then iterate based on feedback.

Consider this scenario: You're managing a project with ambitious goals set over the next two years. The sheer scale can be overwhelming. But by breaking it down into actionable steps—daily, weekly, monthly goals—you create a roadmap that is both manageable and adaptable. Each small action contributes to the larger objective, and importantly, you remain agile to change course based on new information.

This approach isn't just theoretical. In software development, for example, Agile methodologies emphasize iterative progress, customer feedback, and flexibility. Teams that adopt Agile practices can see productivity improvements of up to 25%, according to a study by McKinsey & Company. They focus on delivering small increments of value, learning from each release, and adjusting their plans accordingly.

Now, let's translate this to AI assistants. An effective AI should not only provide insights or suggestions but also take actions that move us closer to our goals. It should help schedule that critical meeting, fetch the latest market data, draft the first version of a proposal, or even automate routine tasks that free us to focus on more strategic initiatives.

The real smartness of an AI system isn't measured by its ability to generate endless paragraphs of text or engage in abstract debates. It's about delivering results and constantly evaluating the next best action based on those results. This iterative loop of action and reflection is reminiscent of how humans naturally operate and how reinforcement learning agents are designed.

Take, for instance, how we play chess. A player doesn't just think several moves ahead in isolation. They consider the opponent's possible responses, make a move, observe the new state of the board, and then re-evaluate their strategy. Even the most advanced chess engines, like AlphaZero, use this principle. AlphaZero evaluates around 80,000 positions per second, which is significantly fewer than traditional engines that might evaluate 70 million positions per second. Yet, it outperforms them by focusing on the most promising lines of play and learning from each move.

This concept of acting, learning, and adapting is crucial. In the business world, companies that are slow to act or adapt often fall behind. A study by Deloitte found that organizations with agile practices were 1.5 times more likely to achieve top-quartile financial performance. The ability to quickly implement decisions, measure outcomes, and pivot as necessary is a significant competitive advantage.

When designing AI assistants or any intelligent system, embedding this bias towards action can significantly enhance their effectiveness. Instead of getting caught in analysis paralysis or generating superficial outputs, the AI focuses on tangible steps that drive progress. It becomes a collaborator that doesn't just inform but actively contributes to achieving goals.

In the end, the synergy between human intuition and AI efficiency holds incredible potential. By aligning our expectations and creating a shared bias towards action, we can unlock new levels of productivity and innovation. Whether it's an employee making a strategic decision or an AI assistant automating a process, the goal is the same: take meaningful action, learn from the results, and continuously strive towards our objectives.

It's not just about doing tasks; it's about moving the needle. In a world where information is abundant but attention is scarce, focusing on actionable steps that lead to tangible outcomes is more important than ever. By embracing this mindset, we can ensure that both our teams and our AI systems are not just busy, but effective.