The overwhelming hype surrounding artificial intelligence in recent years has led to inflated expectations. Hype Correction is presented as an antidote.
The current sentiment surrounding AI might be shifting. While new models from companies like OpenAI or Google once generated significant excitement, there may be a growing sense that the initial dazzle has faded.
Despite any evolving perceptions, AI remains a groundbreaking technology, recognized as one of the most significant advancements in decades, even with its inherent drawbacks and challenges.
However, a lingering question persists: Is this the extent of its immediate impact?
This sentiment is understandable, given the overwhelming hype that has surrounded AI for several years. Promises included AI solving climate change, achieving human-level intelligence, and eliminating the need for human labor.
The reality has often presented “AI slop,” instances of chatbot errors, and tools primarily focused on tasks like improving email newsletters. This suggests a need to reevaluate the true purpose and capabilities of AI.
This reevaluation is central to a new series of stories, titled Hype Correction. While AI continues to be a prominent technology, a recalibration of expectations is necessary.
An introductory essay in the series questions what remains once the initial “wow factor” of AI dissipates. It prompts consideration of how this technology will be perceived in the coming years and if its significant financial and environmental costs will be deemed justified.
Other articles in the series explore various facets of AI. James O’Donnell examines Sam Altman’s role in AI hype through his statements, while Alex Heath analyzes the AI bubble, detailing its implications and key indicators.
Michelle Kim investigates a major claim from the AI hype cycle: the complete elimination of certain job categories by AI. The ability of ChatGPT to pass the bar exam raised questions about replacing lawyers, but current analysis suggests this is not imminent, and perhaps not a future reality.
Edd Gent explores the efficacy of AI coding, concluding that its full potential is still under evaluation. David Rotman also highlights the practical efforts required for AI materials discovery to achieve a significant breakthrough comparable to ChatGPT.
Garrison Lovely investigates the perspectives of prominent figures in AI safety, questioning if concerns about an impending demise from superintelligent AI persist as public fear subsides. Margaret Mitchell also points out that the focus on generative AI hype can overshadow more significant AI breakthroughs that warrant recognition.
It is important to acknowledge that AI existed before ChatGPT and will continue to evolve. While the recent hype cycle has been intense and its long-term effects are uncertain, AI’s presence is enduring. The non-fulfillment of some early promises should not come as a complete surprise.
A more probable scenario suggests that truly transformative applications are yet to emerge, attracting significant investment. The intense hype was unsustainable in the short term, and the current period may mark the beginning of a post-hype phase. Ideally, this “hype correction” will lead to more realistic expectations.
