Ethan Mollick’s concept of “wait calculation,” inspired by the rapid advancement of AI, challenges the traditional notion of linear progress. Mollick posits that in many fields, particularly those involving cognitive tasks, the relentless pace of technological development renders past efforts almost obsolete. He illustrates this with a thought experiment: if faced with a 12,000-year journey through space with current technology, wouldn’t it be more prudent to wait a few centuries for advancements that could drastically reduce travel time? This analogy applies to numerous fields where years of research and development might now be replicated, or even surpassed, by AI in a fraction of the time. This raises a profound question: could we have achieved more by simply waiting for technology to mature?
This concept is not merely about job displacement by robots, but a broader reassessment of the value of human effort in a rapidly changing technological landscape. Mollick uses his own experience in developing educational games as a case in point. Years of painstaking work, while producing valuable results, could potentially have been bypassed by leveraging the current capabilities of AI like ChatGPT. The advancements in natural language processing and other AI domains essentially compress and accelerate the process of innovation, making past efforts appear less efficient in retrospect. This raises a paradoxical situation: while past efforts were essential for achieving the current level of technological advancement, their value is diminished by the very progress they helped to create.
This paradox is further complicated by the evolving nature of AI capabilities. A common misconception is that AI excels at cognitive tasks while lacking in physical dexterity. However, Mollick’s example, echoing Claude’s stated capabilities, highlights the evolving reality of AI robotics: tasks like changing diapers or pitching manure, traditionally considered the domain of manual labor, are now within the realm of AI-powered robots. The advancements in robotics are occurring concurrently with progress in AI cognition, though public perception lags behind due to the less visible nature of robotic development compared to the readily accessible interfaces of AI chatbots. This disparity between perception and reality is likely to diminish rapidly as AI-powered robots become more prevalent in everyday life.
The implications of “wait calculation” extend beyond individual projects and encompass entire fields of endeavor. Imagine the potential impact on scientific research, where years of painstaking experiments could be expedited by AI-driven simulations and analysis. Or consider the field of medicine, where AI could accelerate drug discovery and personalize treatments based on individual patient data. The “wait calculation” compels us to re-evaluate the traditional timelines of progress and consider the potential for exponential leaps forward enabled by AI. It’s not just about doing things faster, but about achieving outcomes that were previously unimaginable.
However, the “wait calculation” is not without its limitations. Mollick acknowledges that the development of AI itself required the very cognitive work that it now seemingly renders obsolete. This creates a kind of technological bootstrapping problem: progress requires effort, but that effort may be devalued by the very progress it creates. The question then becomes how to strategically balance present efforts with the anticipation of future technological advancements. This requires a nuanced understanding of the trajectory of technological development and the ability to discern which tasks are best tackled now and which are better left for the future. It’s a delicate balancing act between action and anticipation.
Ultimately, Mollick’s “wait calculation” is not a call for inaction but a provocation to rethink our approach to innovation and progress. It urges us to consider the potential of exponential technological growth and to strategically position ourselves to leverage its transformative power. It’s a call to embrace a more dynamic and adaptable approach to problem-solving, recognizing that the tools of tomorrow may render the efforts of today less efficient, even obsolete. This requires a shift in mindset, from linear progression to exponential possibility, and a willingness to constantly re-evaluate the value of our efforts in light of the ever-evolving technological landscape. The future, powered by AI, is not just coming – it’s accelerating. The question is, are we ready to wait for it, or will we continue to strive forward, knowing that our efforts may be eclipsed by the very progress we help create?
