The Generative AI Question: Is Technological Innovation Approaching A Bubble, Or A True Disruption?

The time is right to unpack if GenAI is merely riding a wave of inflated expectations, or if it is approaching a place where practical applications are beginning to outstrip the hype.

By Manjula Mahajan

In November of 2022, OpenAI announced the launch of ChatGPT, its Large Language Model-based Generative AI chatbot. What happened next was beyond the most optimistic of predictions. In just two months, ChatGPT crossed 100 million users to become the fastest-growing consumer app of all time; it remains one of the top 20 most-visited websites. The chatbot’s launch and the subsequent mass response drove OpenAI’s valuation through the roof, not only securing it massive funding from some of the biggest names in the global tech arena but also high-impact, future-forward partnerships. 

Most important, however, is the fact that it hypercharged the ongoing AI growth. Artificial intelligence and related technologies, already established as must-haves for businesses, suddenly became buzzwords on everyone’s lips. Its success brought more attention to the burgeoning GenAI space and led to a mushrooming of competitors such as Claude, Gemini, Llama, and Grok, amongst others, as well as expanding generative AI use cases to other areas such as image generation.

But, nearly two years in, some of the hype around generative AI, or GenAI as it is commonly known, has cooled down. Even as businesses scramble to explore its potential, the loftier promises made during the boom period are now being questioned and re-evaluated to more clearly identify what is achievable, what is feasible, and by when. And so, the time is right to unpack if the technology is merely riding a wave of inflated expectations, or if it is approaching a place where practical applications are beginning to outstrip the hype.

The Present and Future of Generative AI: Strategic Shifts Defining the Industry

Let’s be clear: generative AI is undeniably impressive. Its capability to produce text, images, music, and code has revolutionised industries ranging from healthcare to entertainment. But with each stunning breakthrough comes the concern of overinflation. The fear stems from a familiar cycle in tech: high initial enthusiasm followed by disillusionment when the technology fails to deliver on exaggerated promises. NFTs and cryptocurrencies are the most recent examples of such failed hype cycles.

However, comparing generative AI to previous bubbles overlooks one critical factor: its versatility. Unlike the more niche innovations, generative AI has demonstrated its ability to impact a wide array of industries and sectors. There are already GenAI apps catering to user requirements across applications.  

As a matter of fact, as a technology, GenAI appears to be settling into a more stable growth phase where its practical applications are beginning to outshine the initial hype. CIOs are rethinking their approach to GenAI and moving away from broad, one-size-fits-all models to specialised, purpose-built solutions. It’s less about what GenAI can do in general, and more about what it can do specifically for their business and users.

This shift in business mindset has CIOs defining realistic targets aligned with specific business outcomes for any GenAI investment. Such strategic pivots highlight the growing recognition that, as impactful as it can be, the technology requires deep evaluation of its utility and careful integration with existing systems and infrastructures. More importantly, it is acknowledged that GenAI isn’t a magic cure-all but demands continuous training and optimisation to meet the unique needs of each organisation.

Many CIOs are also reprioritising exploring the broader AI ecosystem to evaluate other AI-related technologies that may provide a better fit for certain tasks over GenAI, or which may unlock greater value from it. For instance, machine learning, natural language processing, and predictive analytics offer complementary capabilities that, when combined with GenAI, create a more holistic approach to innovation.

Continued Investment, Adoption, and Adaptation: A Focal Point for Innovation

That said, investments in generative AI show little signs of slowing down. Microsoft invested a massive $13 billion into OpenAI, while competing Anthropic – the developer of Claude – has so far received around $5 billion in funding from the likes of Google and Amazon, with a potential $1.5 billion more yet to come. Venture capital is still pouring into GenAI startups, and large corporations are continuing to expand their AI capabilities for one simple reason: the technology represents an innovation frontier that companies are reluctant to abandon.

According to a recent BCG report, investments into GenAI by large- and medium-size enterprises will grow by up to 30 per cent, year-on-year, by the end of 2024. What’s more, these investments are likely to deliver around 3x the returns over companies that make little to no GenAI investments.

It helps wider adoption that, while its commercial applications are only just beginning to be explored, GenAI has already transformed everything from content creation and customer service automation to drug discovery and supply chain management.

The technology is also becoming more accessible, with both start-ups and established enterprises building custom applications on top of existing GenAI models to cater to specific business needs. These applications will not only reduce the barrier to entry for enterprises which might be cautious about experimenting with or investing in the technology but will also make more innovation possible in the field by expanding use cases.

The Future of GenAI: Balancing Optimism with Caution 

As a technology, GenAI is at a pivotal moment in history. It is no longer in its infancy, but it hasn’t reached full maturity, either. This is why it is essential to adopt a balanced approach built on the understanding that, while GenAI can offer tremendous value, it can only do so when applied with clear objectives, well-defined metrics for success, and a realistic grasp of its limitations.

While concerns around over-hyping are real, the underlying technology has enough depth and versatility to avoid becoming a bubble. The road ahead will not be without its challenges, but we are on the brink of unlocking even greater potential as businesses learn to navigate the complexities of this powerful technology. As with any transformative innovation, success will come to those who approach it with a blend of optimism, caution, and strategic foresight.

(The author is the VP and Head of IT, Business Technology, and Data Analytics at Model N)

Disclaimer: The opinions, beliefs, and views expressed by the various authors and forum participants on this website are personal and do not reflect the opinions, beliefs, and views of ABP Network Pvt. Ltd.