There has been a lot of news about AI over the last two years, and AI companies are valued at astronomical amounts. But will AI deliver on all its promises? Or is it hyped?
New technologies often follow a predictable cycle. I have personally observed the hype cycle of new technologies before.
Some 25 years ago, I attended the Borland/Inprise conference in Denver, Colorado. One of the speakers, Scott McNealy from Sun Microsystems, envisioned a future where Java would be ubiquitous.
Fast-forward to today, and while Java (and even older languages like COBOL) have been helpful and remain in use, it is no longer at the forefront of programming languages. It has never delivered as much as promised.
This phenomenon is what is known as the Gartner Hype Cycle, a model that describes the adoption, maturity, and social application of new technologies. The cycle consists of five phases:
- Technology Trigger
- Peak of Inflated Expectations
- Trough of Disillusionment
- Slope of Enlightenment
- Plateau of Productivity
What is Generative AI?
Generative AI is a kind of artificial intelligence that, based on a prompt, can perform specific tasks, such as generating new material statistically derived from the input data provided by the user. Both input and output can consist of text, images, or even video. Generative AI models are based on what is called Large Language Models. These generative models can be based on input, creating realistic answers.
These modern AI models are trained on large amounts of data, often text and images, from the web and other sources. One source of training data is the Common Crawl, a repository of 250 billion web pages. Generative Artificial Intelligence technology is groundbreaking because it can be controlled using natural language and used in virtual assistants to analyse and process new data.
Generative AI combines several AI systems, including Deep Learning (a Neural Network with many layers), Reinforcement learning and other Machine Learning models. One type of AI, known as Natural Language Processing, has been in existence for many years. But after the release of ChatGPT, it has advanced rapidly in the past years.
Generative AI Hype: Riding the Wave of the Hype Cycle
Already in August 2023, Gartner positioned Generative AI at the “Peak of Inflated Expectations” phase. This stage is characterised by high enthusiasm and inflated expectations about the technology’s potential (Pure AI). Despite significant investments and promising advancements, some experts predict that Generative AI may enter the “Trough of Disillusionment” within 2-5 years. This phase occurs when initial enthusiasm wanes as early implementations fail to deliver on overhyped promises.
The Reality Check: AI’s Current Capabilities
While groundbreaking, the technology is currently underdelivering in several areas. Enterprises are beginning to realise that AI cannot solve every problem. Here are a few sobering truths about AI’s capabilities, which indicate that we may be entering a Generative AI hype:
- Job Replacement: AI is unlikely to take over most jobs soon. It may automate specific tasks, but it will only partially replace humans.
- Code Writing: AI can help when writing code, but can only contribute to 10-30% of the process. Human intervention remains critical to meeting expectations or achieving high code quality.
- Marketing Copy: AI tools can generate content, but they require a more nuanced understanding to create compelling and contextually appropriate marketing materials consistently. If you don’t edit the content well, there will be errors, or users will recognise certain words, such as “delve”. Medical researchers suddenly started using the word “delve” four times more often after ChatGPT was introduced. There are many places where AI-generated text is not allowed. That does not mean someone cannot use AI for professional marketing copy; you can, but it has to be fact-checked and rewritten extensively. It can save time, but it hardly replaces humans.
- Artificial General Intelligence (AGI): The dream of AGI, an AI with human-like reasoning and understanding, remains distant. Current AI models, including large language models (LLMs), excel at pattern recognition but need more authentic learning or reasoning abilities.
Other examples
Generative Adversarial Networks (GANs) and foundation models, such as those used in AI-powered image generators and AI-generated content, are at the forefront of current generative AI technologies. These models enable the generation of text and images for content creation across industries. While the ability to generate text or produce highly realistic images is impressive, it’s essential to remember that these innovations are still in the process of maturing. As with other technologies, we must be cautious not to let inflated expectations overshadow the practical value they offer today.
The Funding Bubble: A Cautionary Tale
Since Sam Altman at OpenAI presented ChatGPT in November 2022, an enormous amount of money has been invested in new AI companies, sparking concerns about a potential bubble. Investment in AI is probably only one of the rows of bubbles in promising technologies. This is not the first time; it is typical for any new technology and part of what is expected based on the Technology hype cycle. Venture capitalists and investors are pouring billions into AI startups, hoping to capitalise on the next big technological breakthrough.
However, history has shown that such frenzied investment often leads to inflated valuations and unrealistic expectations. As with previous tech bubbles, there is a risk that many of these investments will not yield the anticipated returns, leading to significant financial losses and market corrections.
The Value of AI: Augmenting, Not Replacing Humans
Despite these limitations and acknowledging the hype surrounding Generative AI, AI is most valuable when it supports people and strengthens what they can do, rather than trying to replace them. Here are some practical applications of AI that demonstrate its potential:
- Complementary Tool: Companies are finding success using AI as a complementary tool. It can help with repetitive tasks, provide data-driven insights, and assist decision-making processes.
- Strategic Use: Artificial Intelligence should be deployed thoughtfully and strategically. Businesses can enhance productivity as well as innovation by focusing on areas where AI can provide clear benefits.
- Workflow Integration: AI is becoming integral to workflows, complementing other technologies. It can streamline processes, minimise manual workload, and improve efficiency in many business areas.
With this in mind, AI will deliver enormous productivity gains and financial value. But like all hyped technologies, being more humble about the predictions is wise.
Embracing the Future of AI
The journey of AI, particularly Generative AI, illustrates the cyclical nature of technological evolution. This hype cycle does not appear to be different from earlier hype cycles. So, while it may only fulfil some expectations, its potential to enhance and augment human efforts is undeniable. By keeping expectations realistic and using AI wisely in our work, we can use its power to move forward and create new ideas. Ultimately, Effective AI will emerge, but until then, the road may be bumpy.
Navigating the Generative AI hype cycle
As we navigate the AI hype cycle, it’s important to remain realistic about AI’s capabilities while being open to its transformative possibilities. By doing so, we can ensure that AI becomes a valuable ally in our journey toward technological advancement. As with other contemporary technologies, such as Blockchain, Virtual Reality, and 3D printing, which are also likely to underdeliver compared to the hype, AI will be helpful but will not deliver on the promises.
Since the general principles of new technology are nothing new, studying the Gartner Hype Cycle may be worthwhile. While we should follow the development of the hype cycle for AI, it is possible that it may not deliver as much as promised. There are plenty of opportunities to use the new technology for various applications. There are strong reasons to believe that AI is entering the “Peak of Inflated Expectations” phase. It doesn’t mean the technology is useless; it may not be as ubiquitous as promised.
Gislen Software and AI
While it is realistic to acknowledge that Artificial Intelligence is presently oversold and overhyped, based on the AI hype cycle, we believe it is still beneficial to utilise the technology as much as possible and recommend it to our clients. Just because there is hype doesn’t mean it is not valuable. Like all technologies, it can deliver good value but rarely solve all problems. Contact us to discuss how we can help you with AI or any software development.
What is the Gartner Hype Cycle and how does it apply to Generative AI?
The Gartner Hype Cycle is a graphical representation of the maturity and adoption of technologies through five distinct phases: the Technology Trigger, the Peak of Inflated Expectations, the Trough of Disillusionment, the Slope of Enlightenment, and the Plateau of Productivity.
Generative AI is currently positioned at the Peak of Inflated Expectations. This means that while the technology has shown immense potential, the public and investment discourse is dominated by “hype” that often exceeds what the software can actually deliver in a production environment today.
We are likely heading toward the “Trough of Disillusionment,” where the initial excitement wanes as organizations struggle with the complexities of real-world implementation.
Is Generative AI currently overhyped?
Yes, in several key areas. While GenAI is a transformative tool, it is frequently marketed as a “silver bullet” for all business problems.
The current hype is fueled by astronomical company valuations and the rapid viral success of tools like ChatGPT.
However, when you look at the actual output, such as code generation only assisting with 10–30% of the process or marketing copy requiring heavy human editing, it becomes clear that the “AI will do everything” narrative is ahead of the technical reality.
What exactly defines Generative AI technically?
Generative AI refers to a subset of artificial intelligence that creates new content—text, images, video, or audio—based on patterns learned from existing data.
It is primarily powered by Large Language Models (LLMs) and Deep Learning (neural networks with many layers).
Unlike traditional AI, which might classify data or predict a single value, Generative AI uses statistical probability to predict the next most likely token (like a word or pixel) in a sequence.
It relies on massive datasets, such as the Common Crawl, to understand natural language and generate realistic, human-like responses.
Will AI replace human jobs in the near future?
It is highly unlikely that AI will replace entire jobs in the short term. Rather, it will automate specific tasks within those jobs.
AI excels at repetitive, data-heavy tasks and initial drafting, but it lacks the nuanced understanding, emotional intelligence, and critical reasoning required for complex roles.
The future involves augmentation, where humans use AI to increase their efficiency, but the human “pilot” remains essential for quality control and strategic decision-making.
How effective is AI at writing software code?
Current AI models can contribute significantly to the coding process, but their contribution is typically limited to 10% to 30% of the total workload.
While AI is excellent at generating boilerplate code, identifying simple bugs, or suggesting syntax, it often fails at understanding high-level system architecture or the specific business logic of a unique project.
Human developers are still required to review, test, and integrate AI-generated snippets to ensure they meet security and performance standards.
Can AI generate professional marketing copy without human intervention?
While AI can produce text quickly, it cannot yet produce high-quality, contextually nuanced marketing materials without human oversight.
AI-generated copy often feels “flat” or uses predictable vocabulary (statistically likely words). Furthermore, search engines and users are becoming adept at identifying unedited AI content.
To be effective, AI-generated drafts must be fact-checked, rewritten for brand voice, and edited for cultural context.
What is Artificial General Intelligence (AGI) and is it close?
AGI is a theoretical form of AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a human-like level or beyond.
While current LLMs are impressive at pattern recognition, they do not possess true “reasoning” or “understanding.” They are essentially sophisticated statistical predictors.
Experts agree that we are still a significant distance away from achieving true AGI.
Why do AI models often use specific, repetitive words like “delve”?
This happens because LLMs are trained on massive datasets where certain academic or professional terms appear frequently.
When the model predicts the “most likely” word in a professional context, it often defaults to these favorites.
Since the release of ChatGPT, the frequency of the word “delve” in medical research papers has increased fourfold, acting as a “digital fingerprint” of AI-assisted writing.
It highlights the model’s reliance on statistical probability rather than creative variety.
Is there an investment bubble in the AI sector?
History suggests we may be in a funding bubble. Since late 2022, billions of dollars have poured into AI startups, leading to massive valuations that are often disconnected from current revenue or proven business models.
This pattern is typical for new technologies (similar to the dot-com era or the early days of Blockchain).
While many of these companies will fail when the hype cools, the underlying technology will remain and eventually mature.
How can businesses derive actual value from AI today?
The most value is found when AI is treated as a complementary tool rather than a human replacement. Successful strategies include:
- Automating repetitive workflows: Data entry, summarization, and basic customer support.
- Data-driven insights: Using AI to find patterns in large datasets that would take humans weeks to analyze.
- Initial Drafting: Using GenAI to overcome “blank page syndrome” in writing or design.
Strategic Augmentation: Integrating AI into existing software to enhance productivity without removing human oversight.
What are the risks of over-relying on Generative AI?
The primary risks include “hallucinations” (where the AI confidently states false information), legal issues regarding copyright of training data, and a decline in original thought or brand voice.
If a company relies too heavily on AI without human fact-checking, it risks spreading misinformation and damaging its professional reputation.
Should companies still invest in AI despite the hype?
Absolutely. Just because a technology is hyped doesn’t mean it is useless.
The goal for businesses should be to avoid “inflated expectations” while still experimenting with the technology.
AI will deliver significant productivity gains and financial value over time.
The key is to be wise about where you deploy it, focusing on practical applications that solve real problems rather than chasing every shiny new feature.