For decades, writing computer code was an incredibly expensive and difficult task. It was similar to building a house by hand, brick by brick, which is why companies had to hire many engineers at high salaries. However, we are witnessing a massive shift today. Artificial Intelligence has turned code from a scarce resource into something abundant and cheap. But be careful; while generating code is easy, building useful software remains a complex challenge that requires a new set of skills.
The history of computer science has always been about moving away from complexity. In the early days, humans had to manage memory manually and write difficult syntax. Over time, we created compilers and languages like C that made it easier for humans to understand instructions. Today, we are seeing a similar leap. We are moving toward a world where the code underneath matters less, and the software experience matters more. This phenomenon is often called “disposable software.” In the past, you would only write a program if you planned to use it for years. Now, because tools like Claude Code or ChatGPT can generate scripts in seconds, it is worth writing a program to solve a problem you have right now, using it once, and then deleting it. This concept changes how we view development. We are moving from building massive, permanent platforms to creating personal “scratchpads” that solve immediate, specific tasks.
One of the most mind-bending concepts in this new era is the changing nature of an API endpoint. Traditionally, when a user interacts with a piece of software, they are hitting a specific “endpoint” that triggers a pre-written, static piece of code. That code is a binary file that sits on a server, waiting to run the exact same way every time. However, with the rise of AI, we might see a future where the code is generated dynamically based on the request. Imagine a scenario where a user asks for a specific data visualization. Instead of running a pre-existing function, the server might generate new code on the fly to process that specific data shape and return the result. This means the average line of code might be run only once before being discarded. It fundamentally changes the economics of engineering because we no longer need to maintain every single line of code for years.
Despite these advancements, simply having access to these tools does not make everyone a master engineer. Think of it like flying an airplane. If a pilot suddenly fainted, could you land a commercial jet just because the radio is easier to use today than it was in the 1970s? The answer is likely no. You still need to understand how the systems interact, how to manage velocity, and how to communicate with air traffic control. Similarly, in software, AI removes the barrier of typing syntax, but it does not remove the difficulty of system architecture. You still need to understand how databases connect, how to handle offline support, and how to structure data so that it doesn’t break when a website updates its format. The “hard part” of engineering has shifted from knowing how to type code to knowing what to build and how to verify that it works.
Furthermore, because creating apps is now so easy, the world is flooded with new software. This creates a new problem: distribution. In the past, if you could build an app, you were rare and valuable. Now, anyone can build an app in a weekend. The real skill is no longer just construction; it is communication and “taste.” You must understand your users deeply and be able to explain why your solution matters. We are seeing engineers who are successful not because they write the fastest algorithms, but because they have the motivation to push through the friction of the real world. They use AI to handle the boring parts, allowing them to focus on orchestration—managing the different parts of the system to ensure they work together harmoniously.
Below is an example of a simple “disposable” script in Python that an AI might generate to solve a one-time problem, such as renaming thousands of files based on their creation date. In the past, writing this might have taken too much effort to justify, but now it is trivial.
import os
import time
from datetime import datetime
def rename_files_with_date(directory):
# This script is meant to be run once to clean up a folder
for filename in os.listdir(directory):
if filename.startswith("."):
continue # Skip hidden files
filepath = os.path.join(directory, filename)
if os.path.isfile(filepath):
# Get the creation time
creation_time = os.path.getctime(filepath)
date_str = datetime.fromtimestamp(creation_time).strftime('%Y-%m-%d')
# Construct new name
new_name = f"{date_str}_{filename}"
new_path = os.path.join(directory, new_name)
# Rename the file
os.rename(filepath, new_path)
print(f"Renamed: {filename} -> {new_name}")
# Usage: Point to your target directory
# rename_files_with_date("./my_messy_downloads")
We are entering an era of personal software where the gap between an idea and a working product is narrower than ever. However, do not mistake this for the end of engineering. The relevance of a human engineer is not fading; it is evolving. True expertise is now required to steer these powerful systems and provide the technical oversight that AI lacks. While AI is undeniably good at writing code, it often struggles with architecting maintainable and scalable systems. Your job is to develop the judgment, taste, and responsibility to guide these tools. You must become the architect who understands the “why” and “what,” leaving the “how” to the machine. Stay motivated, keep learning the fundamentals, and use these new powers to build things that truly matter.