Endless Loop? How to Survive the Micro-Hype Cycles in Generative AI

I used to think the Gartner Hype Cycle was spot-on for understanding the generative AI rollercoaster ride. For those unfamiliar, the Hype Cycle in the image above describes how we tend to create over-expectations about new technologies, reach the peak of the hype, and then fall into disillusionment until we find a more realistic view and move to a stable application.

This seemed to describe generative AI perfectly. After all, this technology exploded into our collective consciousness with incredible force, sparking extreme narratives—from it saving humanity to fears of it destroying or enslaving us (hard to compete with this level of hype).

The only problem is that we're not even two years into the conversation about generative AI and are already discussing a second round of disillusionment.

If you don't follow the area closely, you may have missed that we already talked about generative AI not delivering on its promises at the end of 2023, before the companies leading the market launched new versions of their large language models, like ChatGPT-4.

Suddenly, these new models, voice solutions, and applications cut short the 2023 disillusionment period and launched a new hype cycle. This 2024 hype cycle is now ending with more stories about projects failing and generative AI's limitations.

The reality is that the old hype cycle isn't keeping up with the breakneck speed of AI development. We now seem to be experiencing "micro-hype cycles." It feels less like a rollercoaster and more like an infinite loop.

This can generate confusion, anxiety, and heightened emotions. It can also lead companies and professionals to miss opportunities or waste resources.

How should we deal with so much uncertainty and noise? First, the answer depends on your interest and focus in the area.

If you're neck-deep in generative AI like me, you probably feel the relentless push to keep learning, adapting, and riding out these micro-cycles like the world will be dominated by machines tomorrow (well, at least that's how I feel sometimes).

The best way to deal with this information bombardment is to focus on the basics, work consistently, and maintain a strong level of scepticism and flexibility.

Don't jump on every new AI tool or feature and try to become an expert on it. Don't believe stories about incredible results and transformation that don't detail what was done and lack precise results measurement.

Understand the core principles, get deeper into the most proven tools, and find actual uses that solve your needs. Also, try your best to measure the results. Does using AI truly reduce the time you spend on a task? By how much, in minutes, hours, or days? Does it improve the quality of your work? How can you measure the improvement?

But while staying grounded is essential, don't be fooled by excessive pessimism. It's usually as exaggerated as the hype. AI, in general, has gone through decades of winter and summer cycles. However, these cycles have been getting shorter, and generative AI has reduced them even further. This is a foundational technology, not a trend that will disappear.

You don't need to believe me; use the tools and see for yourself the ways they can already transform many tasks and how we work, even if they are not as easy to use as some say or haven't achieved the level of science fiction abilities we sometimes think they have.

If you're not working with generative AI and are just wondering what is going on, there's no mad rush. This doesn't mean you should ignore this technology. Basic knowledge of generative AI tools will increasingly become a competitive advantage to get your next job or perform better in your current one. Many companies are already asking about it when interviewing or checking CVs.

If you're in that category, try to learn as much as possible and seek hands-on experience. Apply an AI tool to solve real problems, whether at work or in your personal life. If you don't know how to start, there are an increasing number of courses and support that can help.

AI can be intimidating initially, but the more you play with it, the more you'll see where it can make a difference. It's not about mastering everything overnight. It's about being open to learning and figuring out how it can help you tackle the challenges you're facing today.

Irrespective of having experience or just being interested in gen AI, the key is to stay flexible, open-minded and learning. Also, don't waste too much time paying attention to the micro-cycles of boom and doom.??