The tech hype cycle, introduced by Gartner is like a rollercoaster ride, with the potential to reach the heights of success but experience a lot of ups and downs on the way.  Widely used to understand and predict the maturity and adoption of new technologies, the cycle consists of five phases: the technology trigger, the peak of inflated expectations, the trough of disillusionment, the slope of enlightenment, and the plateau of productivity.

AI is currently on this ride, with the second wave of AI being driven by impressive developments in machine learning algorithms and data availability. But is this just the beginning of AI's journey, or have we already reached the peak of inflated expectations, with a downturn of disillusionment to come?

The first wave of AI took place in the 1950s and 60s when computer scientists started exploring machine intelligence's potential. However, the technology was limited, and the hype around AI soon fizzled out, leading to disillusionment. Fast forward to the 2010s, and we saw a resurgence of AI driven by advances in computing power, data availability, and machine learning algorithms. This resurgence was further popularized by models like OpenAI's GPT-3, including ChatGPT, which have become household names in the AI world. According to Statista, global total corporate AI investment from 2015 to 2020 rose from $12 billion to $67 billion, showing signs of more robust adoption and investor interest.

While the hype around AI is high, it's worth considering whether we have reached the peak of inflated expectations. One example would be the recent hype around natural language processing (NLP) and language models like OpenAI's GPT-3 and ChatGPT. While these models have shown impressive capabilities in tasks like language translation and text generation, they are not without limitations and ethical concerns. For example, the "text completion" feature of GPT-3 has raised concerns over the potential for the model to generate harmful or misleading content and the potential for bias in the training data used to develop the model.

Another example is the hype around autonomous vehicles, which has yet to become widespread despite years of investment and development. While there have been notable advancements in the technology, such as Tesla's autopilot feature, the full potential of autonomous vehicles has not yet been realized due to regulatory and safety concerns.

Overall, these examples illustrate how the current hype around AI may overlook the limitations and challenges that still need to be addressed before these technologies can become widely adopted and reliable. However, as with any technology, it's essential to consider the long-term potential and not just the current state of the technology.

Suppose we are in the trough of disillusionment as AI continues to evolve and mature. What could the Slope of Enlightenment and the Plateau of Productivity look like for AI in the future?

During the Slope of Enlightenment, we could see a significant shift in how AI is used across industries. As AI algorithms become more sophisticated and effective, we can see widespread healthcare, finance, and manufacturing adoption. For example, AI-powered robots could be used in manufacturing to improve production efficiency and reduce costs. AI could help doctors make more accurate diagnoses and personalize treatments based on a patient's unique genetic makeup in healthcare.

Moving towards the Plateau of Productivity, we could see AI becoming fully integrated into many aspects of our lives. This phase is marked by stability, refinement, and optimization of the technology, as well as increased standardization and regulation. We may see more personalized experiences in areas like retail and entertainment, where AI can be used to provide targeted recommendations and improve customer experiences, which would not be hard to imagine when voice assistants like Siri or Alexa have become commonplace in homes worldwide. In transportation, self-driving cars could become the norm, reducing traffic accidents and improving the efficiency of our roadways. In manufacturing, AI could help optimize supply chain management and logistics, reducing costs and increasing productivity.

As with any technology, ethical concerns could exist surrounding AI's widespread adoption. It will be essential to ensure that algorithms are unbiased and transparent and that the benefits of AI are distributed equitably. However, with proper oversight and regulation, the future of AI looks bright, and it could lead to significant improvements in many aspects of our lives.

But AI is not the only technology going through the hype cycle. The Metaverse, Blockchain, Crypto, and NFT are all examples of technologies that have seen a surge in hype and investment in recent years. The Metaverse, in particular, has gained significant attention with Facebook's rebranding as Meta and invested $10 billion in the space (more than five times the amount Meta paid to purchase the Oculus VR company in 2014). Blockchain, the technology behind cryptocurrencies like Bitcoin, has been hailed as a game-changer for industries like finance and logistics. Meanwhile, NFTs, unique digital assets that can be bought and sold like physical assets, have become popular among artists and collectors.

Despite the hype, it's worth remembering that all new technologies face challenges and limitations, and keeping tabs on their development could be worthwhile. Social media is a prime example of a technology that has matured and gone through the entire tech hype cycle. It started with MySpace and Friendster, then rose to mainstream popularity with Facebook before facing a period of disillusionment and criticism over privacy concerns. Social media has become the norm and significantly influenced our culture and way of living. We may take away that the maturation and adoption of technology are often a matter of time, and it could apply to cutting-edge technologies like the Metaverse or Blockchain.

As investors, it's crucial to navigate the tech hype cycle and not get caught up in the peak of inflated expectations. Instead, taking a long-term approach and understanding the limitations and challenges of technology is vital. For example, investing early in companies like Amazon, Apple, and Google proved to be a wise decision for those who saw the long-term potential of these companies. However, investing in technologies like 3D printing and virtual reality during the peak of inflated expectations proved to be less successful.

It could be helpful to look at the history and development of mature technologies to determine where we are in the tech hype cycle. As time will only tell, as technologists and investors, patience is a virtue.