The impact of Genai and its implications for data scientists

Genai systems affect the way we work. This general notion is well known. However, we still do not know Genai's exact impact. For example, how much do these tools affect our work? Do they have a greater impact on certain tasks? What does this mean for us in our daily work?

To answer these questions, Antopic launched a study Based on millions of conversations anonymized in Claude.ai. The study provides data on how Genai is incorporated into real world's tasks and reveals real Genai use patterns.

In this article, I will review the four main findings of the study. According to the findings, I will get how Genai changes our work and what skills we need in the future.

Main findings

Genai is mainly used for software development and technical writing tasks.reaching almost 50 % of all tasks. This is probably due to the fact that the LLMs are mainly based on text and, therefore, are less useful for certain tasks.

Genai has a stronger impact on some groups of occupations than in others.More than a third of occupations use Genai in at least a quarter of their tasks. In contrast, only 4 % of occupations use it for more than three quarters of their tasks. We can see that only very few occupations use Genai in most of their tasks. This suggests that no work is completely automated.

Genai is used for increase instead of automationthat is, 57 % compared to 43 % of the tasks. But most occupations use so much, increase and automation in all tasks. Here, the increase means that the user collaborates with the Genai to improve their capabilities. Automation, in contrast, refers to tasks in which Genai directly performs the task. However, the authors guess that the proportion of the increase is even greater, since users can adjust Genai responses outside the chat window. Therefore, what seems to be automation is actually the increase. The results suggest that Genai serves as an efficiency tool and a collaborative partner, resulting in better productivity. These results align very well with my own experience. Mainly I use Genai tools to increase my work instead of automating tasks. In the following article you can see how Genai tools have increased my productivity and why I use them daily.

Genai is mainly used for tasks associated with medium to high salaries occupations.as data scientists. In contrast, the lowest and highest roles show a much lower use of Genai. The authors conclude that this is due to the current limits of Genai's abilities and practical barriers when it comes to using Genai.

In general, The study suggests that occupations will evolve that they will disappear. This is due to two reasons. First, Genai's integration remains selective instead of integral into most occupations. Although many works use Genai, tools are only selectively used for certain tasks. Second, the study saw a clear preference for the increase in automation. Therefore, Genai serves as an efficiency tool and a collaborative partner.

Limitations

Before we can obtain Genai's implications, we must analyze the limitations of the study:

• It is unknown how users used the answers. Are they copying fragments of passenger without criticism or editing them in their IDE? Therefore, some conversations that seem automation could have been the increase.
• The authors only used Claude.ai chat conversations but not API or Enterprise users. Therefore, the data set used in the analysis shows only a fraction of the real use of Genai.
• Classification automation could have led to the incorrect classification of conversations. However, due to the large amount of conversation used, the impact must be quite small.
• Claude is only based on text restricts the tasks and, therefore, could exclude certain works.
• Claude is announced as a latest generation coding model, which mainly attracts users for coding tasks.

In general, the authors conclude that their data set is not a representative sample of use of Genai in general. Therefore, we must handle and interpret the results carefully. Despite the limitations of the study, we can see some implications of Genai's impact on our work, particularly as data scientists.

Transcendence

The study shows that Genai has the potential to remodel the work and we can see its impact on our work. In addition, Genai is evolving rapidly and even in the early stages of the integration of the workplace.

Therefore, we must be open to these changes and adapt to them.

The most important thing, we must remain curious, adaptive and willing to learn. In the field of data, science changes occur regularly. With the change of Genai tools it will occur even more frequently. Therefore, we must keep up and use the tools to rely on this trip.

Currently, Genai has the potential to improve our abilities instead of automating them.

Therefore, we must focus on developing skills that complement Genai. We need skills to increase workflows effectively in our work and analytical tasks. These skills are found in areas with low penetration of Genai. This includes human interaction, strategic thinking and nuanced decision making. This is where we can highlight.

In addition, skills such as critical thinking, complex problem resolution and trial will continue to be very valuable. We must be able to ask the right questions, interpret LLM's exit and take measures based on the answers.

In addition, Genai will not replace our collaboration with colleagues in projects. Therefore, improving our emotional intelligence will help us work together effectively.

Conclusion

Genai is evolving rapidly and even in the early stages of the integration of the workplace. However, we can see some implications of Genai's impact on our work.

In this article, I showed him the main findings of a recent study by Anthrope on the use of his LLM. According to the results, I showed the implications for data scientists and what skills could become more important.

I hope that this article is useful and help it become a better data scientist.

See you in my next article.