Will Chat-GPT Replace Software Engineers?

Introduction

When I first heard about Generative AI, I have to admit, I was a little scared. As a software engineer, I've spent years developing my skills and expertise in a constantly-evolving field, and the thought of being replaced by a machine was unsettling, to say the least.

My team at Sych and I use it to generate code, write documentation, and test software, all with remarkable accuracy and efficiency. However, as we delved deeper into it, we can confidently say that Generative AI won't replace us, at least not in the forseeable future. Instead, it has the potential to turn us into something even more powerful and capable: Super Engineers. In this article, I'll share my personal journey of discovery with generative AI and how we use it at Sych to enhance our work. I'll explain why I believe generative AI won't make us obsolete, but rather amplify our abilities and make us even more valuable to our clients. So, let's explore the world of Super Engineers and see how generative AI is leading the way!

What Chat GPT Believes

I asked ChatGPT whether Generative AI will replace Software Engineers and here is what it had to say:

No, I don't believe that Generative AI will replace Software Engineers. While Generative AI has the potential to automate certain aspects of software development, it cannot replace the creativity, problem-solving skills, and expertise that software engineers bring to the table.

Generative AI models are designed to learn patterns from large amounts of data and generate outputs based on that learning. However, they still require human guidance and oversight to ensure that the outputs are accurate, reliable, and aligned with the intended goals.

Moreover, software engineering involves a lot more than just writing code. It requires a deep understanding of software architecture, design principles, algorithms, data structures, debugging, testing, and deployment. Software engineers need to be proficient in multiple programming languages, frameworks, and tools, and they need to be able to collaborate effectively with other team members.

While Generative AI can help automate certain routine tasks and accelerate the development process, it cannot replace the human intuition, creativity, and problem-solving skills that are essential to software engineering. In fact, Generative AI can be seen as a tool that software engineers can use to enhance their productivity and effectiveness, rather than a replacement for their skills and expertise.

Chat GPT Does Not Understand The Code That It Generates

Suppose you ask ChatGPT to generate code that calculates the factorial of a given integer. ChatGPT might generate the following code:

public static int factorial(int n) {
    if (n == 0) {
        return 1;
    } else {
        return n * factorial(n - 1);
    }
}

This code uses recursion to calculate the factorial, and while it is correct, it may not be the most efficient way to do it. In fact, for large values of n, this code could result in a StackOverflowError due to the large number of recursive calls.

As a programmer with knowledge of a more efficient way to calculate the factorial, you could ask ChatGPT to improve its code. For example, you might suggest using a for loop instead:

public static int factorial(int n) {
    int result = 1;
    for (int i = 2; i <= n; i++) {
        result *= i;
    }
    return result;
}

This code calculates the factorial using a loop and is more efficient than the recursive implementation, especially for larger values of n.

Here's a simplified probability table that ChatGPT might have used to decide between the two implementations:

Since the recursive implementation is more common in online code examples (data it has been trained with), ChatGPT might have initially suggested that implementation. However, as a programmer with knowledge of the more efficient implementation using a for loop, you can suggest ChatGPT to use that instead.

Why System Design needs Human Engineers

System design is a complex process that involves analyzing requirements, identifying constraints, and selecting the right components to meet those requirements. It requires an experienced human touch to understand the bigger picture, ask the right questions, and know what the right piece of the puzzle is.

Generative AI can certainly help by providing pieces of the puzzle, but only human engineers have the knowledge and expertise to see the whole picture, connect the dots, and ensure that all the pieces fit together seamlessly. It's like building a jigsaw puzzle - the AI can provide individual pieces, but only the human engineer has the vision to see the completed picture and understand how all the pieces fit together. That's why, even with the help of generative AI, human engineers remain essential for effective system design.

System Design for a Ride Sharing Service like Uber

Consider Uber, which provides a platform that allows customers to request rides using their mobile app, which connects them with nearby drivers who use their personal vehicles to provide transportation services.

How effective would Generative AI be in designing a system like Uber's when approached by anyone vs an experienced software engineer?

I asked Chat GPT how I can design a system like Uber and note how generic the response was:

To design a system like Uber, you would need to consider various factors such as user experience, scalability, security, reliability, and performance. This would involve selecting the appropriate programming languages, frameworks, and tools, as well as designing the architecture and infrastructure to support the system's requirements. You would also need to consider the regulatory and legal requirements that apply to the transportation industry in your target market.

Using Chat-GPT for System Design as an Experienced Engineer

On the contrary, as an experienced engineer, I would design the system myself but rather than spending hours researching and experimenting on specific topics I am not experienced with, I can simply ask Chat-GPT to fill in those specific gaps. This can streamline my system design process.

Here are some examples of specific questions I might ask Chat-GPT while designing a system like Uber:

Question: "What is the optimal data structure for storing and querying the massive amounts of geospatial data required for Uber's ride-matching algorithm?"

Response: "When dealing with massive amounts of geospatial data, you might consider using specialized data structures like R-trees or quad-trees to enable efficient spatial indexing and querying. Additionally, you might consider using geohashing to encode geospatial information into a single string representation that can be easily queried and indexed. Finally, you might consider using a distributed database like Apache Cassandra or Apache HBase to enable scalable and fault-tolerant storage and querying of geospatial data."

Question: "What is the most optimal load balancing algorithm to use for the high-volume requests generated by Uber's ride-sharing service?"

Response: "When dealing with high-volume requests like those generated by Uber, you might consider using a distributed load balancing algorithm like consistent hashing, which ensures that requests are evenly distributed among multiple servers while minimizing the need for frequent rebalancing. Additionally, you might consider using a global load balancer to ensure that requests are directed to the closest available server, minimizing latency and improving user experience."

Question: "What is the most efficient way to process and analyze large-scale data sets generated by Uber's ride-matching algorithm?"

Response: "To process and analyze large-scale data sets generated by Uber's ride-matching algorithm, you might consider using distributed computing frameworks like Apache Hadoop or Apache Spark, which enable efficient parallel processing of data across multiple machines. Additionally, you might consider using machine learning models to predict and optimize ride matching, reducing the need for manual intervention and improving user experience."

Question: "What is the most effective way to implement a real-time messaging system for Uber's driver and passenger apps, while ensuring reliability and scalability?"

Response: "To implement a real-time messaging system for Uber's driver and passenger apps, you might consider using a publish-subscribe messaging architecture like Apache Kafka, which allows for efficient and scalable communication between multiple clients. Additionally, you might consider implementing data replication and partitioning to ensure high availability and reliability, as well as using message brokers like RabbitMQ or Amazon MQ to ensure message delivery and ordering."

Ofcourse, I wouldn't blindly rely on its responses because it can make mistakes and is only as good as the data it's been trained with. Ultimately, I would treat it more like an advisor rather than an authoritative source of knowledge to increase productivity.

How to Become a Super Engineer with Generative AI

Now that we have an idea about why generative AI won't replace but rather assist software engineers, let's explore some specific ways that you can incorporate it into your workflow and start unlocking your own superpowers.

Use it as Your Knowledge Sidekick

Think of generative AI as your knowledgeable friend who's always got your back. Stuck on a tricky problem? Need to pick up a new programming language quick? It's got you covered.

Level Up Your Code Generation

Generative AI can take the grind out of coding. It's like having a smart assistant who knows just what code you need and serves it up for you. It keeps the coding process fresh and exciting.

Unleash it for Testing and Debugging

Testing and debugging often feels like searching for a needle in a haystack. But generative AI excels at this - it can spot bugs faster and even suggest fixes. It's like having an extra pair of eyes that never miss a thing.

Embrace Collaboration

Working in a team has its challenges. Here's where generative AI shines - it offers unbiased, objective suggestions, and ideas. It's like having a great facilitator who ensures everyone's on the same page.

Customize Your Superpowers: Augment Chat-GPT With Your Own Private Data

While this is a topic worth discussing in a separate article, you can use tools like to LlamaIndex to expand the knowledge of your LLMs. In fact, this is something we have started doing quite commonly for our clients to build generative AI tools based on their own knowledge sources to streamline processes not only in development but other areas such as automated internal information directories or customer service.

Conclusion

I hope this article has shown you that generative AI like Chat-GPT doesn't have to be something to fear as a software engineer. Instead, it can be a tool to help you become a Super Engineer, capable of achieving more than you ever thought possible. Embracing generative AI doesn't mean replacing humans with machines; it means enhancing our abilities and creating a new level of collaboration between man and machine.