We are delighted to announce the third issue of the University of Dallas Business Review, connecting the scholar-practitioner faculty at the Gupta College of Business to managers across the globe.
In 1996, I was an optimistic undergraduate student at the University of Missouri-Columbia when Palm Computing released the first PalmPilot. The next few years saw the device rise in popularity as prognosticators and consulting firms predicted how quickly mobile computing would change society and disrupt businesses everywhere. Fully believing that great changes were imminent, I started a company to develop applications that would change how businesses operated and consumers spent leisure time. My timing was off by a little bit. Over ten years passed between the PalmPilot’s launch and Apple revolutionized mobile computing by introducing the iPhone.
Like today’s discussions about artificial intelligence, mobile computing advocates were confident that massive productivity gains were on the horizon, and detractors were equally as convinced that the future was bleak. Both groups were right about some predictions, but few prognosticators in 1996 anticipated how much time would pass between the visions and when they would become a reality.
Bill Gates is quoted as saying: “We always overestimate the changes that will occur in the next two years and underestimate the change that will occur in the next ten.” The practice of overestimating the changes that will occur in the near future could hold for generative artificial intelligence the way it did for mobile computing. Some reasons for this phenomenon include the inability to accurately estimate the time required to build infrastructure, change laws, create and implement regulations, and for enough people to gain the skills required to use the new technology. Only with hindsight can we know if today we are in the PalmPilot era of generative AI when the infrastructure needs time to mature, or if we are entering the iPhone era.
Asking if we are in the PalmPilot or iPhone era of generative AI might not be the most helpful way to think about generative AI. For some industries, the infrastructure in 1996 was sufficient for them to create great value, while others needed to wait for the batteries, screen and network speeds to improve. Leaders who continue to learn about the advancements in generative AI and other types of AI will recognize when their industry is about to leave its PalmPilot era and enter its iPhone era. Continuing education is the best antidote to the optimistic predictions of CEOs raising money to fund money-losing businesses. Below are some sources I use to keep current with trends in AI:
The Algorithm by MIT Technology Review
Wall Street Journal Technology Newsletters
Here is a note about predictions and the pace of change in AI.
On January 9, 2025, Goldman Sachs published a report titled: What to expect from AI in 2025: hybrid workers, robotics, expert models. One of the predictions was that we would see only a handful of AI providers because of the high cost of developing advanced AI models. Only 18 days after that report was published, DeepSeek, a new AI model reportedly developed inexpensively using older chips was the top iPhone app download in the U.S. Early indications were that the model performed comparably to the most advanced model published by Google, OpenAI and others. Even more interesting was the developers made the model available for others to modify and deploy for free, challenging the notion that only a few companies would have access to the most advanced models.
