27-05-2025
AI Prioritization: Why AI Is Automating Creative Tasks
Lorick Jain, Chief of Staff & AI Lead, DocNexus.
We often hear folks talk philosophically about how they are frustrated that AI is being used in art, music, video generation and coding. But do we know why? All the above are forms of art for people within those fields. But the burning question still remains: Why? I believe the answer can be found in looking at the world through the lens of a prioritization matrix.
For example, what do shareholders want? Reduce costs. Where are the costs, and what is the largest count of individual atomic units leading to that cost? Expensive functions, such as ops, talent and manual processes within R&D. Let's take the talent cost bucket. You might argue, "Well, a CEO gets paid cumulatively more than the entire workforce (in some cases)," but that atomic unit is harder to automate given the number of functions or variables required to be taken into account while automating that function.
AI today is good at specific tasks, so it is easier to chase responsibilities that do not have many variables to deal with. For example, a software engineer writes good code and design systems. To me, this is more nuclear than a CEO's job, handling product, PR, legal, management, operations and other functions that require a ton of soft skills AI is not good at currently.
In my opinion, the prioritization of AI in automating expensive, cognitive roles like coding stems from several factors:
• Type Of Work: AI does well at pattern recognition, data processing and structured tasks, making coding—a logic-based field—an easy target.
• Data Availability: Coding generates vast datasets (e.g., code repositories, bug fixes and documentation) for training AI models. This large availability of data is what feeds the juggernaut that is AI to become better at these tasks, and thus, there is a natural progression of products in a capitalistic society.
• Economic Incentives: Automating expensive roles (e.g., software engineers) offers cost savings. Tech companies that develop AI might naturally target their own high-value sectors first. Think of it as dogfooding (using your own product) for your customer zero (a.k.a. employees). Automating coding can also accelerate product development, boosting revenue.
• Technological Feasibility: Software automation (e.g., GitHub Copilot) can be easier to deploy at scale. Widespread adoption is far easier since this is a repeatable copy-paste model. One complex task is built, replicated and scaled to millions of nodes in the distribution, greatly reducing the cost and complexity of rollout.
• Industry Focus And Innovation Culture: Tech firms drive AI development, prioritizing tools for their workflows. They have large R&D budgets and might tend to run after shiny objects. Why? Because of the people. You are in the people business if you are in the software industry.
• Societal And Labor Dynamics: The tech industry embraces automation as innovation. Builders within the tech industry are ambitious believers in bringing the storybooks they read in their childhood to life. They want to see the world they read in comics come to life. Look at people wanting to industrialize the moon and have reusable rockets take us to Mars or WALL-E around you in every sphere of life. Stories coming to life.
1. Conduct an audit check. Understand your role and your altitude within the company/firm, and tap into lateral positions within other companies to understand AI requirements on the job. That is the first place to understand where you stand and what gap needs to be bridged.
2. Stay updated. Subscribe to top voices in any industry, as they are pivoting their audience to leverage AI in their respective fields.
3. Understand that you need not feel that AI is "daunting" to understand and use. As long as you understand what problem to solve and what application layer needs to be served for end users, you are going to be successful in adopting and leveraging AI.
4. Stay up to date through newsletters. Consider learning the various use cases of AI tools through popular newsletters. Learn the how-tos of AI use cases by searching for the tool on YouTube and looking at how it is being used.
5. Start making inroads into improving AI products. Once you master using AI products, begin providing feedback to tool creators and influence the roadmap of those AI products. The important idea here is for you to envision the future and be in the position to start creating your own luck. Finally, dive deeper into the algorithmic understanding of AI based on your interest levels.
I believe AI may go after creative roles first due to technological readiness, economic incentives and data advantages. Advances in robotics and AI generalization will continue snowballing innovation from more creative to less creative job role automation.
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