Why Is AI Washing Harmful to the AI Industry?

Today, Artificial Intelligence (AI) is at the height of its media coverage. From finance to medicine, AI is making its presence felt throughout every sphere of our lives. Nevertheless, I am afraid that many companies create more marketing hype around AI than actually concentrate on developing the substance of the technology. The SEC chair is also unsure whether AI’s buzz corresponds to the real picture and the real dissemination of technology.

Everyone writes about the AI boom, but are we really living it? Businesses “parasitize” AI in marketing companies using the AI-powered prefix, while, in fact, they use a basic chatbot in customer support. It seems that everyone is already beginning to understand that AI-washing is a new reality of the market, but how does it affect it from the inside?

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Media hype and the actual technology are not aligned

The concept of AI-washing and the hype surrounding artificial intelligence have created a disconnect between the media narrative and the actual development of the technology. We’re not at the peak of AI development right now. While everyone talks about AI, many solutions are just in name only, with truly groundbreaking advancements still limited in their application.

The matter is that technologies like AGI (Artificial General Intelligence) have great complexity to be created. That’s why they are still in their infancy, and the main progress is yet to come. And this media hype only hampers achieving AGI and also creates commercial obstacles. To be precise, there is a strong market demand for solutions that can be deployed immediately and deliver instant results. However, the algorithms are still far from perfect, and hardware is only beginning to evolve to meet the needs of AI-driven companies.

Furthermore, regulators are struggling to keep pace with AI’s rapid advancement. The technology develops so fast that regulators must rush to guarantee the safety of AI adoption. We have yet to see future progress in ethical alignment and compliance, though it is lagging technology’s creation and development.

Expectation vs. Reality

AI washing and companies’ lies about introducing AI create a massive gap between society’s expectations of the technology’s capabilities and the actual state of affairs. In finance, we often encounter examples of companies exaggerating the AI-driven technologies that they already use.

For instance, in traditional monitoring or fraud detection, financial organizations might claim to use AI tools. In reality, they rely on standard, predefined rules with minimal or no machine learning involved — this is automation, not AI.

Similarly, companies may claim that their onboarding process uses AI, but only a small element, like document recognition, might be powered by AI. In reality, most of the process relies on traditional methods with minimal AI integration. Also, AI’s potential in credit scoring seems excellent — it can help with more detailed and sophisticated big data analysis. However, it is again often a conventional data-based automation with minimal ML usage.

Sometimes adapting real AI technology can be too expensive for a company, sometimes the things they’re talking about are still in development. Either way, using false information misleads both customers and stakeholders.

Problems AI washing entails

AI developers today face significant challenges, including the increasing need to prove that AI is more than just hype and can deliver real value. This is compounded by difficulty keeping up with marketing teams, which often make overly optimistic promises about AI’s capabilities. A similar problem is that companies have stopped setting strategic long-term goals and replaced them with temporary ones based on AI’s hype.

AI washing often leads to funds being diverted from other areas to those simply claiming to use AI. We should prioritize strategic, long-term projects over short-term, hype-driven ones. However, there’s a growing trend of being swayed by the mere mention of “AI”, resulting in investments in projects that lack real, tangible AI or clear growth potential.

Additionally, finding the right talent for AI development is a significant hurdle. According to the research, only about 300,000 AI researchers and practitioners are currently worldwide. This number becomes alarming, given the growing need for professionals in the AI sphere. Moreover, Big Tech companies often snap up top experts for exorbitant salaries, making it nearly impossible for smaller teams to compete in the labor market.

Conclusion

Summing up, what initially attracted money and attention to the industry is now killing consumer and investor confidence and generating skepticism about AI technology. While it continues to attract funding and talent, it also promotes unrealistic expectations and skepticism. The emphasis on hype-based narratives over genuine innovation erodes consumer and investor confidence.

As a result, this dynamic is paradoxically slowing AI’s growth. Instead of driving the industry forward, focusing on immediate visibility prevents AI from developing in ways that could make it truly profitable and valuable in the long term. The industry risks delaying its progress because of prioritizing short-term gains over sustainable innovation.

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