Challenges Hindering the Widescale Deployment of Generative AI: Legal, Financial, and Environmental Considerations

The rise of generative AI technology has opened up a world of possibilities in terms of creativity and innovation. From creating art and music to generating text and even developing new products, the potential applications of generative AI are vast. However, despite its promise, there are several hurdles that must be overcome before generative AI can be more widely deployed.

One major obstacle is the issue of data usage. Generative AI applications are trained using publicly available data, which may include copyrighted work. This raises concerns about potential copyright infringement in the output generated by generative AI. Additionally, data found online may contain biases that could be perpetuated by generative AI, leading to discriminatory outcomes. Strict regulations, such as those outlined in the European AI Act, may also pose challenges for companies looking to invest in generative AI technology.

Another important consideration is the cost associated with implementing generative AI. Companies may need to invest in reskilling their workforce to work with this new technology, as well as purchasing expensive enterprise software packages. Furthermore, the occasional errors or “hallucinations” produced by generative AI can pose reputational and organizational risks, making companies hesitant to fully embrace this technology.

One of the biggest practical challenges of generative AI is its high demand for computing power. Data centers, which house the servers necessary to run generative AI models, require vast amounts of electricity and water to function. With resources already scarce in many countries, there are concerns about whether data center capacity can keep up with the growing demand for generative AI.

Despite these challenges, the potential economic impact of generative AI is undeniable. As companies continue to invest in this technology and overcome the hurdles associated with its deployment, we can expect to see a wave of new innovations and creative outputs. However, it is clear that there is still much work to be done before generative AI can reach its full potential on a wider scale.