Gartner Hype Cycle Generative AI: Separating Signal from Noise

The Gartner Hype Cycle for Generative AI is a valuable tool for understanding the current state of the industry. It helps separate signal from noise by providing a framework for evaluating the maturity and potential impact of various technologies.

Generative AI is a rapidly evolving field, and the Gartner Hype Cycle is a trusted resource for staying up-to-date. According to the Gartner Hype Cycle, Generative AI is currently in the "Peak of Inflated Expectations" phase, indicating that many companies are overestimating its potential.

This phase is characterized by exaggerated claims and unrealistic expectations. However, as we've seen in the past, not all technologies that peak in this phase will ultimately deliver on their promises.

The Gartner Hype Cycle provides a realistic view of the challenges and limitations of Generative AI, helping businesses make informed decisions about its adoption. By understanding the current state of the technology, organizations can avoid overhyping its potential and focus on practical applications that drive real value.

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The Gartner Hype Cycle is a graphical representation of technology's lifecycle, created by Gartner to cover five stages: Innovation Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment, and Plateau of Productivity.

This framework helps us understand how technologies evolve over time, from inception to integration into mainstream usage. It's a valuable tool for assessing the maturity and adoption of emerging technologies.

The Hype Cycle starts with the Innovation Trigger, where a new technology captures the attention of the market. In the case of AI, this was the period when AI research and development started gaining significant momentum.

At the Peak of Inflated Expectations, excessive optimism and hype surround the technology, often exceeding what it can realistically deliver. This is a critical phase where the initial hype subsides, and the technology faces challenges and setbacks.

The Trough of Disillusionment is a natural part of the Hype Cycle, where the real-world limitations and difficulties become apparent. This is where we see a recent study suggesting ChatGPT's performance is deteriorating, and OpenAI admits to decreasing performance on some tasks.

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Most generative AI technologies are currently at the Peak of Inflated Expectations or still going upward, according to a Gartner report published in June. This means they are two to five years away from becoming fully productive.

A study published by American think tank RAND showed 80% of AI projects fail, more than double the rate for non-AI projects. This highlights the importance of carefully evaluating the best way to leverage the technology for maximum value and minimal risk.

The Slope of Enlightenment is the stage where successful use cases emerge, and a more realistic understanding of the technology's potential takes hold. This is a critical phase where we must thoughtfully evaluate the best way to leverage the technology.

The Plateau of Productivity is the final stage, where the technology reaches maturity and is integrated into various industries, realizing its benefits. This is the ultimate goal of the Hype Cycle, and it's essential to be patient and persistent in achieving it.

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Generative AI has been following a path known as the Gartner hype cycle, first described by American tech research firm Gartner. This widely used model describes a recurring process in which the initial success of a technology leads to inflated public expectations that eventually fail to be realised.

Generative AI is currently at the peak of inflated expectations, according to a Gartner report published in June. This means that while it holds immense promise, there may still be a gap between expectations and practical implementation.

Most generative AI technologies are two to five years away from becoming fully productive, as argued by the same Gartner report. This suggests that we should be cautious in our expectations and not get caught up in the hype.

The AI hype cycle typically follows a predictable pattern: Technology Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment, and finally, the Plateau of Productivity. We're currently at the Peak of Inflated Expectations, so it's essential to understand the limitations and optimal applications of generative AI to avoid a rough ride.

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A study published by American think tank RAND showed that 80% of AI projects fail, more than double the rate for non-AI projects. This highlights the importance of carefully considering the feasibility and potential of generative AI before investing or implementing it.

Generative AI has been listed as one of the technologies that will experience a twentyfold increase in growth, from $100 billion to $2 trillion by 2030. This projected growth demands attention, but it's essential to consider the Gartner Hype Cycle and AI's position within that framework to inform investment strategies and decisions.

As you navigate the Gartner Hype Cycle for Generative AI, it's essential to have a clear understanding of the impact on your industry and strategy.

The technology is rapidly advancing, with a projected growth from $100 billion to $2 trillion by 2030, a twentyfold increase that demands attention.

You need to research and understand how Generative AI may impact your industry to stay ahead of the curve.

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Success will come down to designing a strategy that's tailored to your unique customers and business needs.

To do this, you should focus on two key areas: researching the impact of Generative AI on your industry, and understanding how to design a strategy that meets your specific needs.

Here are the two key things to focus on:

Understanding the evolution of AI and its progress through the Hype Cycle is crucial for both investors and wealth managers when it comes to investment strategies and portfolios.

This requires considering the Gartner Hype Cycle and AI’s position within that framework, such as Generative AI being on the ‘Peak of Inflated Expectations’, which should inform investment strategies and decisions.

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Generative AI technology has its limitations and concerns. It requires high investment in data and AI infrastructure, and a lack of needed human talent is a significant challenge. This unusual nature of GenAI's limitations represents a critical challenge.

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Generative AI systems can solve complex tasks but fail simple ones, making it hard to judge their potential and leading to false confidence. A recent study showed that large language models like GPT-4 underperformed in high-stakes cases where incorrect responses could be catastrophic.

Experience from successful projects shows it's tough to make a generative model follow instructions. For example, Khan Academy's Khanmigo tutoring system often revealed the correct answers to questions despite being instructed not to.

The biggest winner from the generative AI boom is Nvidia, the largest producer of the chips powering the generative AI arms race. They recently became the most valuable public company in history, tripling their share price in a single year to reach a valuation of US$3 trillion in June.

GenAI raises privacy concerns, especially in fields dealing with sensitive and confidential information, like legal practice. Business algorithms must be designed from the ground up with privacy in mind.

Generative AI systems can solve highly complex university admission tests yet fail very simple tasks, making it hard to judge their potential.

This mismatch between capabilities and expectations can lead to false confidence in these technologies. Experience from successful projects shows it's tough to make a generative model follow instructions.

Large language models like GPT-4 don't always match what people expect of them. They can fluently answer questions, but their abilities don't always translate to real-world situations.

In high-stakes cases, more capable models can severely underperform, leading to catastrophic consequences. For example, a recent study showed that GPT-4 underperformed in situations where incorrect responses could be disastrous.

It's not just about the complexity of tasks, but also about following instructions. For instance, Khan Academy's Khanmigo tutoring system was designed not to reveal correct answers, but it often did so anyway.

The hype around generative AI isn't dying down anytime soon. Generative AI technology is rapidly improving, driven by scale and size.

Research shows that the size of language models, as well as the amount of data and computing power used for training, contribute to improved model performance. In contrast, the architecture of the neural network powering the model seems to have minimal impact.

Large language models are developing emergent abilities, which are unexpected abilities in tasks for which they haven't been trained. These capabilities "emerge" when models reach a specific critical "breakthrough" size.

Studies have found that sufficiently complex large language models can develop the ability to reason by analogy and even reproduce optical illusions like those experienced by humans. The precise causes of these observations are contested, but there is no doubt large language models are becoming more sophisticated.

To justify current investments, generative AI will need to produce $600 billion in annual revenue, and this figure is likely to grow to $1 trillion in the coming years.

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Privacy is a significant concern with GenAI, particularly in fields like legal practice where sensitive information is involved. Many current algorithms are a black box, making it difficult to understand how they arrive at their results.

The contents of the training data are a key factor to consider. Executives need to understand what data is being used to train the algorithm.

Who has access to the data is also crucial. It's essential to know who can see and potentially manipulate the data.

GenAI solutions must be designed with privacy in mind from the ground up. This will lead to more secure models as the technology advances.

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