The age of AI brings with it a plethora of buzzwords and varying interpretations depending on different people and use cases. In this blog, we will explore the implications of generative AI, such as Copilot and ChatGPT, on the productivity of regular software developers.
Interestingly, when I fed this topic into ChatGPT, it provided me with some irrelevant points that I consider less essential for surviving this regime change. ChatGPT can only provide information based on what it already knows, and it lacks the ability to predict the future. Therefore, let me articulate, in a human way, how we can navigate this landscape.
Of course chatGPT can only tell what it knows already but does not know what might the future. Let me articulate in human way how to navigate it
Relax its not the end, rather beginning
First and foremost, it’s crucial to realize that this is not the end but the beginning. The excessive verbosity of modern digital media, combined with the amplification provided by social platforms, often creates a sense that the world is on the brink of collapse. However, this perception couldn’t be further from the truth.
I hope in colleges, they still teach some basic computer science history. If we look back at the early days of programming when the first compilers were invented, there was a belief that developers would no longer be needed in the future. Fast forward 40 years, and we actually need more programmers than ever before, albeit with a significantly different skill set.
Throughout history, we have witnessed dramatic changes in technology that have consistently led to a greater demand for technology workers. This phenomenon extends beyond the tech industry and even applies to general labor. Many people feared that the advent of electricity and the industrial revolution would reduce the need for labor, causing concern among labor unions.
However, it turns out that these advancements in technology have resulted in massive gains in productivity, leading to progress in human society. Whether these changes are for the better or worse is up for debate, but it’s clear that overall, society has benefited.
In conclusion, while the noise and negativity from the media and social platforms may make it seem like everything is falling apart, it’s important to recognize that this has been a recurring pattern throughout history. Adaptation and embracing the opportunities presented by technological advancements are key to thriving in our ever-changing world.
Not everyone needs to learn ML/Data Engineering
According to ChatGPT, there is a notion that everyone needs to learn machine learning (ML) and data engineering. However, in reality, unless you aspire to be an ML engineer or data scientist, there is no real need to learn these skills extensively. It is sufficient to have a general awareness about them.
If I understand correctly, the future trend suggests that more and more people will need to learn how to operate and manage ML models rather than focusing on developing and writing them. This type of AI aims to reduce the dependency on data science professionals, allowing business users to directly interact with data through AI tools based on large language models (LLM) algorithms. This doesn’t imply that data scientists will become obsolete, but rather their skill sets will evolve and adapt to this changing landscape
Fundamentals will always matter
While the AI gold rush may be a temporary phase, there will be a tremendous need for computer scientists who understand working on computer systems, especially as the demand for operating high-performance computing (HPC) infrastructure, like Denvr Dataworks — High Performance Cloud & Cloud Infrastructure, grows. Computer science fundamentals play a critical role in enabling developers to effectively work with AI, optimizing machine learning models, solving complex AI problems, and adapting to new technologies. Mastery of these fundamentals empowers developers to navigate the evolving AI landscape and harness its full potential for innovation.
Computer science fundamentals are crucial in the age of AI as they provide the necessary foundation for working effectively with artificial intelligence. Understanding concepts such as algorithms, data structures, and computational thinking enables developers to optimize machine learning models, solve complex AI problems, and adapt to new technologies. Mastery of these fundamentals empowers developers to navigate the evolving AI landscape and leverage its full potential for innovation.
What to code rather than how to code
In a similar vein to when compilers were introduced, programmers no longer needed to concern themselves with the low-level memory layout requirements. Instead, they could focus on the logic of programming. Likewise, with applications like Co-pilot, programmers are no longer required to memorize the precise syntax of a program. They simply need to understand the desired outcome of the program. This shift allows developers to concentrate more on the logic and intent of their code, streamlining the development process and enhancing productivity.
Generalist vs Specialist
When it comes to software programming, I consider myself a generalist at heart. Given a program, I can devise a solution using available tools and programming languages. While it may not be the most optimal solution, it will certainly get the job done and buy you some time until specialists can take it further and refine it. In essence, this aligns with the role of solution architects who often follow a similar approach.
Nowadays, with the relatively low learning curve to become an average programmer, I believe there is a growing need for more generalists who can quickly get things up and running. While the solution may not be the most efficient, we can rely on the immense computational power of upcoming generations to compensate for any inefficiencies. In the end, it’s about finding a balance between achieving functional results and leveraging the ever-increasing computing capabilities at our disposal.
In conclusion, surviving the age of AI as a developer requires embracing opportunities and challenges, maintaining a balance between specialization and generalization. While awareness of machine learning and data engineering is beneficial, computer science fundamentals remain essential. Developers can focus on the logic and intent of their code, leveraging AI tools for productivity. Adaptability, continuous learning, and embracing the changing landscape are key to thriving in this transformative era.