GenAI, short for Generative Artificial Intelligence, is quite simply any type of AI that can create new content such as text, images, videos, and music. It studies existing data sets, identifies patterns in them, and then comes up with the best possible answer in the form of content or an idea.
GenAI has been steadily taking charge of the content world, enhancing and easing jobs in multiple sectors across industries. Most people find it an easy tool to utilize in their day-to-day jobs. However, the full extent of its impact, potency, and exploitation amount is yet to be seen. Meanwhile, it is already causing some disruptions in the professional life of employees, especially that of Data Scientists. Let’s look at it from an in-depth perspective, how GenAI is impacting the work of people and its implications on Data Science.
GenAI and its Exact Usage
While we are still unsure of the exact impact of GenAI, data sets accumulated from workplaces paint a pretty convincing picture of its influence. GenAI is mainly used for technical writing and software development, only because LLMs (Large Language Models) are text-based. It completes around 50% of all tasks but is less useful for certain other tasks. The limitation of LLMs, largely constricting them to only texts, creates this glass ceiling for GenAI.
To be more precise with data, GenAI can be used to do nearly one-fourth of the tasks of nearly one-third of all occupations on Earth. This limits its use to certain corners and pockets of the entire job market. Only 4% of all occupations make use of GenAI for almost the majority of their tasks i.e. three-fourths of it. Due to this odd ratio, GenAI is never really getting fully automated.
The Automation-Augmentation Ratio of GenAI
As per data derived from millions of Claude.ai chats, the ratio of GenAI usage for Augmentation and Automation is 57% to 43%. In other words, nearly 57% of GenAI usage is done for augmentation reasons, which means that people use GenAI to enhance their capabilities. They prompt questions, get the answers, and then either learn it, embellish it, or use the information from it to form their answers. This is where GenAI finds majority of its usage.
The remaining 43% of its use goes into automation. This means tasks that GenAI performs entirely from scratch for its users, and the users don’t have to do anything except prompt. While it is lower than the Augmentation numbers, it still forms a high percentage of GenAI usage.
However, there are speculations that the augmentation number might be higher, and the automation number be lower. This is because some users might adjust the GenAI answers for automation according to their will. They do that to avoid any form of plagiarism from AI. This automatically changes its use to augmentation. Hence, a lot of what appears to be automation is augmentation.
Hence, GenAI is, after all, a collaborative partner and an efficient enhancement tool. Since it reduces automation use, it further proves that GenAI will not be a threat to jobs and occupations around the world. Instead, it may enhance and embellish it. Since too few tasks in too few jobs entirely rely on GenAI for their completion with great efficiency, GenAI will not take over the professional world in the strictest sense of eating up jobs. These jobs are also too scattered and unrelated.
How Does GenAI Impact Data Science and Related Jobs?
GenAI is usually used for tasks that are in the mid-to-high range. The low and the highest range of jobs do not usually use GenAI. Experts attribute this pattern to the limitations of GenAI and the practical barriers that come along with it. Such LLMs only operate as text-based.
The perfect example of a mid-to-high range of jobs is Data Science. Data Science is mostly about studying large data sets, interpreting them, extracting valuable information, and transforming them into insightful models. During this procedure, GenAI finds repeated use, which informs a Data Scientist’s decision-making more precisely.
However, in either case, Data Scientists will always take the final call before presenting the output. For this reason, GenAI, once again, isn’t completely taking over a Data Scientist’s career. But Data Scientists will stand at a considerable advantage if they adapt to GenAI. Learning how to adapt GenAI and using it properly, will give them a distinctive benefit over others.
In the field of Data Science, changes happen regularly. With GenAI tools, change will happen even more frequently. Hence, we must stay up-to-date and use the tools to support us in this journey.
Conclusion
While it is conclusive that GenAI isn’t anywhere near to disrupting the job market entirely, it is worth noting that it is still in its nascent stage and that it will only grow from here. However, since the model will always be more of an augmentation tool rather than an automation one, GenAI will remain more of a collaborative partner and not a single autonomous entity.
GenAI has its limitations as well. It is still in the early stages of workplace integration. Since GenAI is going to augment a large part of our work, it becomes imperative for us and also gives us an advantage to stand out in areas where GenAI has a low penetration rate. Areas such as human interaction, strategic thinking, nuanced decision-making, etc., are where GenAI will forever find it hard to reach. Hence, improving our skills in these areas will make us stand out from the rest. Other skills such as critical thinking, complex problem solving, and judgment will also remain highly prized.
Finally, GenAI will not replace our collaboration with colleagues in projects. Hence, improving our emotional intelligence will help us to work together effectively.
