What lessons from past Technology Hype Cycles can be applied to the hype around Artificial Intelligence (AI)?

What is a tech hype cycle, and why should Product and Business leaders understand it?

In the world of technology, trends, and buzzwords pop up at a dizzying pace. Everyone is talking about virtual reality one minute, and the next, all anyone can discuss is blockchain. But how do these trends evolve, and why do they seem to come and go so quickly? That's where the tech hype cycle comes into play. A concept developed by market research firm Gartner, the hype cycle tracks the journey of new technologies from their initial introduction to the peak of inflated expectations, through the trough of disillusionment, and ultimately, to their plateau of productivity. Understanding the hype cycle is critical for business leaders because it can help them make informed decisions about when and how to invest in emerging technologies. By anticipating where technology falls on the cycle, leaders can avoid getting caught up in the hype and wasting resources instead of focusing on those that have reached the plateau of productivity and can offer real benefits to their organization.

Exploring 30 years of technology and its rise and fall in the hype cycle 

Over the course of 30 years, the tech industry has experienced a rollercoaster ride of success and failure. While certain companies have managed to thrive, others have faced insurmountable obstacles and ultimately collapsed. As the industry evolves rapidly, we must remain vigilant to stay ahead of emerging trends and developments. By examining past cycles and analyzing the factors contributing to success or failure in tech, we can gain valuable insights to help us navigate this complex and unpredictable landscape. 

The 1990s: Dawn of the Internet Age: Computers, CD-ROMs, dial-up Internet, LAN technology, GUIs, cell phones, video conferencing, BBS, fax machines, and multimedia have all undergone significant transformations since their introduction. Dotcom businesses and web portals were popular trends in the late 1990s, but desktop publishing is now a standard feature in most software suites. These trends have left a lasting impact on the industry and continue to shape our interactions with technology today. 

The Early 2000s: Aftermath of the Dotcom Bubble: The advent of high-speed internet, social media, and smartphones has created a seismic shift in our society. Peer-to-peer (P2P) and Bluetooth technology have become ubiquitous, while virtual worlds and RSS feeds have yet to gain traction. Customer relationship management (CRM) software has become an essential tool for modern businesses. While WiMAX struggled to gain popularity, LTE technology has overtaken the world. 

The Early and late 2010s: In the early 2010s, the business industry experienced the rise of two significant phenomena: "Big Data" and "BYOD." Big Data refers to analyzing vast amounts of data to gain insights and make informed decisions. On the other hand, BYOD stands for "Bring Your Own Device" and refers to the trend of employees using their personal devices for work-related tasks. While "3D Printing" didn't revolutionize the manufacturing industry as some had predicted, "Blockchain" technology still holds immense potential for improving transparency, security, and efficiency in various sectors. Another emerging technology is "IoT," or the "Internet of Things." This refers to the growing network of interconnected devices that can communicate and exchange data with each other. Finally, "Chatbots" have found specific applications in areas such as customer service, where they can quickly and efficiently respond to common inquiries. 

Recent Years: The AI and Data Revolution: In the modern era, where speed and efficiency are paramount, cutting-edge technological advancements have taken the forefront. Among these, Artificial Intelligence, Machine Learning, the Internet of Things, Blockchain, and Augmented/Virtual Reality are leading the way in transforming industries. These technologies are pivotal in shaping the future by automating tasks, predicting consumer behavior, and delivering significant impact. Their importance increases as our society progresses, pushing us towards a more innovative, connected world. Furthermore, integrating AI and Machine Learning with other technologies, such as quantum computing, is revolutionizing how we analyze and optimize data, making the process faster and more efficient than ever before.

‍What can we learn from previous hype cycles when addressing today's AI hype cycle? 

Understanding past hype cycles can help us all make informed decisions today. Whether you're an executive leading a tech giant or a product leader driving strategic initiatives, these lessons are not just historical footnotes but guideposts for navigating the future.

When I reflect on my career, one hype cycle stands out the most to me as one we can learn from as we evaluate the potential of AI, and that's the Dotcom boom. In fact, the AI hype cycle, and the Dotcom bubble offer interesting parallels, especially as we think about navigating the terrain of emerging technologies. The Dotcom bubble serves as a cautionary tale for all technological advancements that follow, including the current enthusiasm surrounding Artificial Intelligence. At the turn of the millennium, the Dotcom era's exuberance led to inflated expectations, impractical business models, and a market crash that left even promising companies in ruins. Here are five lessons that I believe the AI sector could learn from the Dotcom bubble:

1. Sustainable Growth Over Quick Wins: The Dotcom bubble was driven by a rush to capitalize on emerging internet technologies without fully understanding their sustainable applications. In contrast, today's AI initiatives must prioritize long-term viability over short-term hype. This means investing in scalable and ethical AI solutions with a clear path to creating genuine value.
2. Explicit Business Models: One of the most significant failures of the Dotcom era was the absence of profitable business models. Similarly, AI projects must have a clear monetization strategy that justifies their long-term investment. This is where the expertise of a full-stack product manager, with the ability to scrutinize every aspect of the business, becomes invaluable. Just as the Dotcom bubble reshaped our approach to technology investment and innovation, the current AI hype cycle presents tremendous opportunities and significant risks. By heeding the lessons from the Dotcom era, we can navigate the complexities of AI with greater wisdom and caution, thereby enabling sustainable growth and long-lasting impact.
3. Regulatory Preparedness: Dotcom companies often needed to prepare for the regulatory landscape they faced. As AI technologies push boundaries, companies must anticipate and prepare for potential regulations around data privacy, ethical considerations, and more.
4. Balancing Innovation and Skepticism: The Dotcom bubble showed us that skepticism can be as important as enthusiasm regarding emerging technologies. Questioning AI applications' practicality, ethical implications, and financial sustainability can save us from the pitfalls of blind optimism.

‍ Making AI real through the use of applied AI. 

The most impactful thing we can do as product leaders today is to make AI real through Applied Artificial Intelligence. Applied AI is using AI technologies and techniques to solve specific, real-world problems across various domains and industries. Unlike general AI, which aims to create machines with the ability to perform any intellectual task a human can do, applied AI focuses on specialized tasks. These tasks can range from natural language processing in customer service chatbots to predictive analytics in healthcare and computer vision systems in autonomous vehicles. Here are five points to consider about applied AI:

1. Domain-Specific: Applied AI solutions are often tailored for particular industries or functions, such as finance, healthcare, or marketing.
2. Integrative: They often require integration with existing software, hardware, or human processes, making the role of a full-stack product manager quite pivotal in ensuring all elements work seamlessly together.
3. Ethical Considerations: While developing an applied AI system, considerations around data privacy, fairness, and transparency become crucial.
4. Feedback Loops: Many applied AI systems continuously use real-time data to improve algorithms' performance. This requires robust data pipelines and monitoring systems.
5. Human-in-the-Loop: Applied AI solutions often involve a human element, whether a doctor interpreting AI-generated medical images or a financial analyst using AI tools for market prediction.

As we continue to explore the uncharted territories of Artificial Intelligence, let's strive to separate the enduring substance from the fleeting hype. The future of AI is incredibly promising, but it's up to us to guide it in a direction that avoids past mistakes and forges a pathway to genuine, sustainable progress. As product leaders, let's push forward with optimism while trying not to repeat the sins of the past.

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