Predicting the future is a business unto itself with pundits, futurists, scientists, engineers, and more constantly sharing their musings of the future. And this business has incredible implications on society - whether the predictions manifest or not.
For example, take a look at just one survey of the future: the 1970 McGraw-Hill Survey of Technological Breakthroughs. As expected, the experts had hits and misses.
They got it right regarding technologies that were understandable and gaining adoption at the time of the survey, such as the expansion of plastics in various applications, widespread use of electronics in patient monitoring, electric vehicles and the use of computers in business. The more complicated the technology under consideration, the less accurate the predictions were. For example, there was no wide scale adoption of 3D TVs by 1980, a cure for cancer by 1990, automated piloted vehicles by 2000, nor a cashless society by 2010.
What’s the takeaway? Predicting the future of technology is often an extrapolation of the past and current technical trends; however, forecasters can’t know everything and will miss game-changing technologies and their implications. For example, while those in the survey anticipated the widespread deployment of computers in business and manufacturing, they underestimated Gordon Moore’s 1965 prediction, and how Moore’s Law put computers into the hands of billions of people around the world.
Predicting the future of technology is far more difficult today than a half century ago. With so much change and convergence happening rapidly, how could anyone asked to predict the future of technology today anticipate when a game-changer would emerge and disrupt industries and our way of life?
Just consider the impact of the release and rapid scaling of ChatGPT, which transformed the worldview of nearly everyone except the small number of workers who had previously experienced generative AI. As Bill Gates reminded us in the early days of personal computing, the software industry was tiny. Today, it is a dynamic global giant turning its attention to AI so innovations are going to come much faster.[i] Therefore, it’s reasonable to expect watershed technologies like ChatGPT to come more quickly. So, what can we do?
The United States must get ready for waves of change, disruption, and opportunity.
Rapid technological change is not only happening with generative AI, of course. The bioeconomy is growing almost as rapidly, with thousands of laboratories using CRISPR gene editing tech to amplify biotech’s potential, and synthetic biology is developing fast. Digital technologies continue to scale across numerous physical, virtual, social and personal domains. Automation is accelerating, and automated vehicles are hitting the streets. We are on the precipice of several new ages: a clean nuclear energy age; the quantum age; and a new space age; etc. - all converging and creating unprecedented feedback loops between one and another.
How do we garner competitive advantage without knowing the tech disruption that is around the corner?
First, we must enhance our predictive capacity through greater collaboration and communication, and wider use of technology and modern and agile business models. Second, we must build a more adaptive and flexible innovation ecosystem that is responsive to the waves of technology change by preparing our best tech, talent, and infrastructure to surf the waves rather than be drowned by them. The following recommendations — consider these a sketch of an innovation blueprint — would be a good start:
- Establish channels from the research community for rapid transfer of research and development (R&D) results and new technology across industry.
- Revisit national R&D and technology plans, programs and policies more frequently. Establish on-ramps to pivot quickly to emerging opportunities and off-ramps when technical pathways shift. But we must also persist in basic research to push the frontiers of science and technology to allow us to create new futures.
- The regulatory apparatus must speed up. For example, multiple versions of ChatGPT and the Runway generative video model were released by mid 2023.[ii] The Biden Administration’s Executive Order on Artificial Intelligence came in October 2023, but it will take time to budget new efforts. Congress has not passed AI regulatory legislation. States are moving faster, but they’ll most likely create a patchwork of rules that are cumbersome and costly for businesses to comply. Meanwhile, the EU is setting its own rules.
- We need new kinds of infrastructure to enable many more people to participate in ideation, product design and development. Plus,we must address the escalating demand for the compute power AI eats and its hunger for data for training.
- Our tech hubs must be ready to quickly mobilize diverse assets —talent, research and technology, capital, business capabilities, entrepreneurs and start-ups — to leverage new technologies faster.
- Universities and training providers must produce skills at scale as new technologies scale, and ease transitions for those displaced by disruption and change.
- We need to remember that markets are often faster and more efficient than government plans in reallocating resources to rapidly opening opportunities. We must lower the cost of market entry, new business formation and the creative-destruction needed to take full advantage of technological disruptions.
More than ever, it is in the timeliness and efficiency with which we allocate and reallocate our productive resources to leverage a torrent of new technology opportunities that our future competitiveness and prosperity rests. Optimizing the innovation ecosystem for speed and scale is where we need to focus. The recommendations above would do exactly that by reducing friction and spurring innovation. What ideas would you add to the mix?
[i] The Age of AI has begun, GatesNotes, The Blog of Bill Gates, March 21, 2023.
[ii] 2024 Generative AI Predictions, CB Insights, 2024.
