Pundits, futurists, scientists, engineers, and others are continually giving their predictions for the future. And whether or not the predictions come true, this business has enormous societal repercussions.
Consider a single future survey, such as the 1970 McGraw-Hill Survey of Technological Breakthroughs. As expected, the experts had successes and failures.
They were correct about technologies that were understandable and gaining use at the time of the study, such as the use of plastics in a variety of applications, the broad use of electronics in patient monitoring, electric vehicles, and the use of computers in the workplace. The forecasts were less accurate as the complexity of the technology under consideration increased. For example, there was no widespread adoption of 3D televisions by 1980, a cure for cancer by 1990, self-driving cars by 2000, or a cashless society by 2010.
What is the takeaway? Predicting the future of technology is frequently an extrapolation of previous and present technical trends; nonetheless, forecasters cannot know everything and will overlook game-changing breakthroughs and their ramifications. For example, while poll respondents predicted the widespread use of computers in business and manufacturing, they overestimated Gordon Moore’s 1965 prediction and how Moore’s Law put computers in the hands of billions of people worldwide.
Predicting the future of technology is much more difficult now than it was fifty years ago. With so much change and convergence occurring so quickly, how could anyone asked to forecast the future of technology today foresee when a game changer would arise and destroy industries and our way of life?
Consider the impact of ChatGPT’s release and rapid scalability, which transformed practically everyone’s viewpoint save for a small number of workers who had previously encountered generative AI. As Bill Gates pointed out in the early days of personal computing, the software business was small. Today, it is a dynamic global company focussing on AI, so breakthroughs will happen much faster.[i] As a result, it’s realistic to expect watershed technologies like ChatGPT to arrive faster. So what can we do?
The United States must get ready for waves of change, disruption, and opportunity.
Rapid technological progress is not limited to generative AI, of course. The bioeconomy is expanding almost as quickly, with thousands of laboratories utilising CRISPR gene editing technology to maximise biotech’s potential, while synthetic biology is rapidly evolving. Digital technologies continue to expand into new physical, virtual, social, and personal worlds. Automation is gaining traction, and self-driving cars are becoming more common. We are on the verge of numerous new ages, including a clean nuclear energy age, a quantum age, and a new space age, among others, all of which are converging and producing unprecedented feedback loops between them.
How can we gain a competitive advantage if we are unaware of the imminent technological disruption?
First, we must improve our predictive capacity by increasing collaboration and communication, as well as expanding our use of technology and modern, flexible business structures. Second, we must create a more adaptable and flexible innovation ecosystem that responds to technological change by preparing our finest tech, talent, and infrastructure to ride the waves rather than drown in them. The recommendations below—consider these a sketch of an innovation blueprint—would be an excellent start.
1: Establish channels within the research community to facilitate the rapid transfer of research and development (R&D) results and new technology across industries.
2: More frequent reviews of national R&D and technology strategies, initiatives, and policies are recommended. Establish on-ramps to quickly respond to emergent opportunities, as well as off-ramps as technological approaches shift. But we must also continue basic research to push the boundaries of science and technology, allowing us to create new futures.
3: The regulatory apparatus must speed quickly. For example, by mid-2023, numerous versions of ChatGPT and the Runway generative video model have been launched.[ii] The Biden Administration’s Executive Order on Artificial Intelligence was issued in October 2023, but it will take time to pay for new initiatives. Congress has not passed any AI regulation legislation. States are going faster, but they are likely to produce a patchwork of rules that make it difficult and expensive for firms to comply. Meanwhile, the EU establishes its own rules.
4: We require new types of infrastructure to allow far more people to engage in ideation, product design, and development. Furthermore, we must handle the growing need for the processing power that AI requires, as well as its hunger for training data.
5: To accelerate the adoption of new technologies, our tech hubs must be able to quickly mobilise a wide range of assets, including people, research and technology, financing, business capabilities, entrepreneurs, and start-ups.