In the whirlwind of technological evolution, generative AI emerged as a beacon of transformative potential. Less than two years ago, the advent of ChatGPT set the tech world ablaze with predictions of a seismic shift that promised to redefine industries, economies, and the fabric of our daily lives. Goldman Sachs, with the precision of seasoned soothsayers, foretold the displacement of 300 million jobs.1 Yet, as we stand eighteen months into this future, the landscape is markedly different from what was prophesised.

This divergence from expectation to reality is not an anomaly but a pattern. The trajectory of generative AI closely mirrors the Gartner hype cycle, a model that outlines the journey of technological adoption from inflated expectations to disillusionment, enlightenment, and finally, productivity. Currently, generative AI likely sits at the peak of this cycle, with a journey towards real productivity still ahead.

img Gartner Research’s Hype Cycle diagram - Attribution-ShareAlike 3.0

The practical application of generative AI has been fraught with challenges. High-profile projects have stumbled, and a report from RAND highlights an 80% failure rate in AI projects—double that of their non-AI counterparts.2 Disillusionment follows failed projects.

Despite these setbacks, the allure of generative AI’s emergent abilities—unexpected competencies that surface as these models grow in complexity—continues to fuel investment and development. Tech giants like Microsoft, Apple and Google continue to drive AI forward, with the potential for generative AI to generate huge revenues.

As we navigate through the trough of disillusionment, a more nuanced approach to AI adoption is emerging. Companies are learning to use AI as an augmentative tool rather than a replacement for human labor. There’s a growing appreciation for smaller, specialized models. 3 AI is a tool, just like all technology, but it is not a panacea. It does not solve all issues. Please forgive the analogy but if AI is a hammer you need to use it on nails, not to saw wood.

The journey of generative AI is emblematic of technological evolution at large. It’s a path marked by grand visions, hard lessons, and gradual progress. The revolution may not unfold with the abruptness once imagined but evolve steadily, reshaping our world in ways both subtle and profound.

The tech industry has experienced a rollercoaster ride of success and failure over the past 30 years. By examining past cycles and analyzing the factors contributing to success or failure, we can gain valuable insights to help navigate this complex landscape. From the dawn of the Internet Age to the modern era of AI and data revolution, each period has left a lasting impact on the industry and the world as a whole.

The most impactful thing we can do as leaders in the space today is to make AI real through Applied Artificial Intelligence. Delivering business value through a combination of AI’s strengths, FinOps and domain knowledge. By being mature in our communications, clearly explaining risk and driving iterative improvements, we might be in a position reap the rewards AI promises and ignore the derailing potential of hype and over promise.

As we continue to explore the uncharted territories of Artificial Intelligence, let’s strive to separate enduring substance from fleeting hype. The future of AI is incredibly promising, but it’s up to us to guide it towards genuine, sustainable progress. As product leaders, let’s push forward with optimism while remembering not to repeat the sins of the past.

  1. https://www.forbes.com/sites/jackkelly/2023/03/31/goldman-sachs-predicts-300-million-jobs-will-be-lost-or-degraded-by-artificial-intelligence/ ↩︎

  2. https://www.rand.org/pubs/research_reports/RRA2680-1.html ↩︎

  3. https://www.bloomberg.com/news/articles/2024-08-08/move-over-llms-small-ai-models-are-the-next-big-thing ↩︎