We’re in the midst of a new Gilded Age, where a handful of tech giants are locked in a race to own the future. In this winner-take-all battle, artificial intelligence (AI) is the new gold. You can’t open a newspaper or attend a conference without hearing about the transformative power of AI.
The hype is understandable. AI has the potential to be a true game changer, transforming industries and upending business models. But the hype has also gotten ahead of the reality. We are still in the early days of AI, and the technology is not yet ready for prime time. The industry is plagued by overinflated expectations, unrealistic promises, and a failure to deliver on the hype.
As someone who has been working in AI for over 20 years, I’ve seen these hype cycles firsthand. I’ve seen companies overhype the capabilities of AI, oversell the business value, and underdeliver on the promises. This is not to say that AI is not a valuable technology. It is. But we need to be realistic about what AI can and cannot do today.
In this article, I want to take a step back and look at the history of AI hype cycles. I want to explore the patterns and dynamics of these cycles and what we can learn from them. I also want to offer some advice on how to navigate the current cycle of AI hype.
The History of AI Hype Cycles
AI hype cycles are nothing new. In fact, they’ve been around since the dawn of the AI industry. The first recorded instance of AI hype was in the 1950s, when a group of researchers at Dartmouth College convened a conference on the topic. They released a report that predicted that “within a generation… the problem of creating ‘intelligent’ machines will substantially be solved.”
This optimistic prediction spurred a wave of investment and interest in AI. But the reality failed to meet the hype. The early AI systems were brittle and limited in their capabilities. They couldn’t live up to the grandiose claims made by the Dartmouth researchers.
This pattern would repeat itself in the following decades. There would be a new breakthrough or a new application of AI, followed by a period of hype and investment. But the technology would always fall short of the hype, leading to a backlash and a loss of interest. This happened in the 1970s with the rise of expert systems, in the 1980s with neural networks, and in the 1990s with machine learning.
Each of these cycles followed a similar pattern. There would be a breakthrough in AI research, followed by a flood of investment and hype. But the technology would eventually stall, leading to a loss of interest and a retreat from the field.
The current cycle of AI hype began in 2012, with the release of the paper “Deep Learning” by a team of researchers at Google. This paper showed how a new type of neural network, called a deep neural network, could be used to automatically learn to recognize objects in images.
This breakthrough spurred a wave of investment and excitement in AI. Venture capital poured into the field, and companies began to open AI research labs. The media declared that we were on the verge of a new era of AI.
But, as with previous cycles, the reality has failed to meet the hype. The current crop of AI systems is good at narrow tasks, but they are still far from being able to match the cognitive abilities of humans. We are still in the early days of AI, and the technology is not yet ready for prime time.
Despite the hype, AI is not yet ready to transform the world. But that doesn’t mean it’s not a valuable technology. We just need to be realistic about its current capabilities.
How to Navigate the Current Cycle of AI Hype
If you’re working in AI, it’s important to be aware of the hype cycles and the patterns of overinflated expectations and unrealistic promises. Here are a few tips on how to navigate the current cycle of AI hype:
Don’t get caught up in the hype. It’s important to be aware of the hype, but don’t get caught up in it. The media loves to hype up new technologies, and AI is no exception. Be critical of the claims being made and don’t believe everything you read.
Be wary of overinflated promises. When you’re evaluating an AI technology, be wary of promises that seem too good to be true. If a company claims that their technology can do everything, it’s probably not true. AI is still a young technology, and it’s important to be realistic about its capabilities.
Don’t believe the hype about job loss. One of the most common claims made about AI is that it will lead to mass unemployment. This claim is exaggerated and it’s not supported by the evidence. Yes, AI will change the workforce, but it will also create new job opportunities. Don’t believe the hype about job loss, and don’t be afraid to invest in AI.
Be patient. We are still in the early days of AI, and the technology is not yet ready for prime time. The current crop of AI systems is good at narrow tasks, but they are still far from being able to match the cognitive abilities of humans. Don’t expect AI to transform your business overnight. Be patient and invest in the long term.
Conclusion
AI is a valuable technology, but we need to be realistic about its capabilities. The industry is plagued by overinflated expectations, unrealistic promises, and a failure to deliver on the hype. We are still in the early days of AI, and the technology is not yet ready for prime time.
If you’re working in AI, it’s important to be aware of the hype cycles and the patterns of overinflated expectations and unrealistic promises. Be critical of the claims being made and don’t believe everything you read. Be patient and invest in the long term.