The rise of sophisticated AI models has sparked widespread debate, particularly within the tech community. Two dominant viewpoints have emerged on social media, creating both excitement and anxiety. One perspective paints a bleak picture, suggesting that programming as a career is nearing its end, with AI poised to make human coders obsolete within years. Conversely, another view dismisses Large Language Models (LLMs) like ChatGPT and LLaMA as mere novelties, lacking substantial value for real-world software development.
The truth, however, is far more nuanced. These extreme viewpoints can be misleading and unsettling, especially for those new to software engineering or considering it as a profession. This article aims to explore the evolving landscape of programming in the age of AI, offering a balanced perspective on the changes ahead.
For a quick overview, here are the key takeaways:
- AI advancements will continue, likely following an S-curve pattern. However, the current position on this curve remains uncertain.
- LLMs are not expected to replace software engineers. Instead, they will become powerful tools, enhancing efficiency for both teams and individual developers, enabling greater output with fewer resources.
- Effective use of AI tools like ChatGPT requires detailed, logical instructions – essentially mirroring the core skills of a programmer, but expressed in a different “language”.
- If AI reaches a level where human involvement in software creation becomes entirely unnecessary, we will have likely achieved Artificial General Intelligence (AGI). At this point, society will face significantly broader challenges than just the future of programming.
Intrigued? Let’s delve deeper into the future of programming as a career.
The S-Curve of AI Advancement and its Implications for Programming Careers
The past year has witnessed remarkable progress in Artificial Intelligence. From rudimentary image generation to sophisticated AI-driven movie creation based on text, images, and speech (think Midjourney), the advancements are undeniable. OpenAI’s GPT model improvements, in particular, have demonstrated impressive capabilities in text and code generation. Experiments like AutoGPT showcase the potential of LLMs to autonomously execute complex, multi-step tasks.
These rapid developments might suggest exponential progress. However, technological advancement often follows a pattern of successive S-curves. New technologies typically experience a period of slow initial growth, followed by rapid acceleration, and eventually, a plateau as the technology matures. Subsequent iterations restart this cycle at a higher performance level, creating an overall linear-looking progression when viewed broadly, but actually composed of multiple S-curves.
Image depicting successive S-curves in technological development, illustrating periods of slow growth, rapid acceleration, and plateauing.
It’s reasonable to assume that AI’s development, at least for now, will follow this S-curve model. The critical question then becomes: where are we currently positioned on this curve? Are we on the verge of exponential acceleration, or approaching a plateau with limited immediate progress?
If pressed to speculate, it seems likely we are nearing a plateau in the current S-curve for LLMs and related AI technologies. While further incremental improvements are expected, a significant breakthrough – the “missing piece” – might be needed to trigger the next phase of exponential growth.
The increasing volume of AI-generated content (video, audio, code, images) also reveals its limitations. Many observe an “uncanny valley” effect, where AI-generated outputs, while impressive, still lack a certain human quality. As with many complex projects, perfecting the final 10% of AI capabilities may require 90% of the effort.
Predicting the timeline for the next S-curve and renewed exponential growth is challenging. It’s a complex question with no definitive answer.
LLMs: The Next Evolution in Programming IDEs, Not Career Replacements
Focusing back on programming and the question “Is Programming A Career with a future?”, the integration of models like OpenAI’s GPT into coding is undeniable. Tools such as ChatGPT, GitHub Copilot, and Copilot X are already enhancing developer productivity globally.
However, it’s crucial to understand that these AI tools are not replacing programmers; they are evolving the programmer’s toolkit. They are analogous to advanced Integrated Development Environments (IDEs).
The shift from basic text editors like Sublime Text to feature-rich IDEs like JetBrains products marked a significant leap in developer efficiency. IDEs, with features like auto-completion, automated refactoring, integrated testing, and dependency management, dramatically improved code quality and development speed.
AI-powered coding tools represent the next logical step in this evolution, enhancing IDE capabilities further.
Personal experience using Copilot and ChatGPT in projects since early this year confirms a similar boost in efficiency. Many routine tasks in new projects, while not intellectually demanding, are time-consuming. These include writing unit tests, creating frontend templates, generating boilerplate code for new classes, or formatting data outputs. AI tools expedite these tasks significantly, often by a factor of ten. They effectively “understand” the developer’s intent and generate the necessary code scaffolding.
For more complex tasks, like implementing intricate function bodies or working with unfamiliar libraries, AI assistance is also invaluable. A simple comment outlining the desired functionality often prompts the AI to generate relevant code blocks, usually closely aligned with the requirements.
Consider this PHP example:
<?php
class VideoProcessor {
public function formatVideo($video) {
// use ffmpeg to convert the video to a gif
}
}
?>Upon entering this comment, Copilot can generate functional code leveraging the FFMpeg library, requiring only minor adjustments.
This capability translates to substantial time savings. While deep familiarity with the library might offer marginal time gains, for tasks involving semi-familiar libraries, AI significantly reduces the need for extensive documentation research, Stack Overflow searches, or trial-and-error coding.
This highlights the core value proposition of AI in programming: it empowers developers to work more effectively and intelligently.
However, it’s important to acknowledge current limitations:
- Overconfidence and Inaccuracy: AI code generation can sometimes be overly assertive, suggesting non-existent arguments or functions within a project or library. While often correctable with minor adjustments, occasionally, the AI veers off-track, producing code that doesn’t meet the intended purpose. This, however, is becoming less frequent.
- Lack of Contextual Awareness: Current LLMs, including ChatGPT and Copilot, excel at generating code snippets for specific functions based on given inputs. However, building complete applications remains a challenge. Token limits (even GPT-4’s 8K token limit, roughly 6,000 words) restrict the context window. Furthermore, despite advancements with vector databases and AutoGPT, maintaining consistent development style and achieving overarching program objectives for projects beyond moderate complexity is still difficult. Simple applications like to-do lists are within reach, but complex systems like CRM for a barbershop are prone to incomplete functionality or ineffective code generation.
These limitations underscore a crucial point: programmers are not just “code writers.”
Programmers: Translators of Logic in the Age of AI
Asking various people for their definition of a programmer often yields responses centered around “someone who writes code.” While technically accurate, this definition overlooks a crucial aspect of the profession. A programmer, software engineer, or developer is fundamentally a translator – a translator of logical language.
The core role of a programmer is to translate concepts, ideas, or workflows from human-understandable language into a language that computers can interpret. Programming languages are meticulously designed to eliminate ambiguity and operate on pure logic.
Consider the simple instruction: “When the button is pressed, change the background to red.”
In a team meeting, human colleagues will likely grasp the intended meaning intuitively.
However, a computer lacks this intuitive understanding and requires significantly more detail. Which button? Which background? What shade of red? What happens if the button is pressed again?
To eliminate ambiguity, the instruction must be redefined with precision: “When the button with the ID ‘clicky’ is pressed, change the background of that same button to the color with the hexadecimal value #FF0000.”
Expressed in JavaScript, this becomes:
document.getElementById('clicky').addEventListener('click', function() {
this.style.backgroundColor = "#FF0000";
});JavaScript code demonstrating how to change the background color of a button to red when clicked, highlighting the logical precision required in programming.
A programmer familiar with JavaScript, given this code, could translate it back into a human-readable sentence similar to the refined instruction.
This translation process lies at the heart of programming. It is a primary reason why programming is likely to remain a relevant career, even with advanced AI tools.
The common question “Why isn’t ChatGPT producing the code I want?” frequently arises in online forums. The consistent answer: “You need to know how to talk to it.”
This highlights a crucial insight: effectively instructing AI to generate code is programming itself. It’s programming using natural language as the interface. The lower barrier to entry provided by LLMs, with their broad understanding and complexity, simply changes the form of programming.
Even if application development evolves into primarily prompt-based interactions with tools like ChatGPT, the need for precise, logical instructions to achieve reliable and functional outputs means programming remains essential.
Current AI limitations necessitate human intervention to assemble and refine AI-generated code fragments. This process still constitutes programming, requiring fundamental knowledge of the generated code’s language and its integration within the broader system.
Hypothetically, if AI advances to the point where these steps become trivial, and tasks like “compile these assets and deploy to example.com” become fully automated, the question arises: would programming languages become obsolete? In such a scenario, AI would effectively become the intermediary layer between data and results, eliminating the need for explicit programming.
However, this level of AI capability essentially equates to Artificial General Intelligence (AGI).
AGI: A Paradigm Shift Beyond Just Programming Careers
If AI reaches a level where it can operate autonomously in software development, requiring minimal human oversight, its capabilities would extend far beyond just programming. Such advanced AI would likely be capable of automating a vast range of creative and knowledge-worker professions.
This scenario could trigger a global economic upheaval, potentially rendering over 60% of the workforce redundant.
At a minimum, Universal Basic Income (UBI) would likely become a necessity. In more extreme scenarios, concepts like Fully Automated Luxury Communism (FALC) might emerge as societal models.
However, the widespread and immediate adoption of such disruptive technology across all industries is not guaranteed. While businesses constantly seek efficiency and profit maximization, the enterprise world often adapts to technological change at a slower pace than the tech sector anticipates.
Many organizations are still in the early stages of adopting technologies like Docker or frameworks like React. The enterprise landscape moves deliberately. Even with AI tools capable of automating a significant portion of development work, maintaining legacy systems and complex, interconnected software will require human expertise for the foreseeable future, even amidst AI adoption.
The software engineering field currently faces a substantial skills gap, with approximately 100,000 open positions in the US alone. Even if complete automation became available today, widespread integration and adaptation would take considerable time.
In Conclusion: Programming is Still a Strong Career Path
This exploration of AI’s impact on programming stems from personal reflections on the evolving tech landscape. It’s natural to feel some anxiety about the future of software engineering in the face of rapid AI advancements.
However, the emerging AI tools are more accurately viewed as opportunities to enhance productivity and elevate the role of the programmer. For individuals considering a career in technology, or those already in the field, continuous learning and embracing these new tools are crucial.
The coming years promise exciting developments in both AI and software development. It remains a dynamic and engaging field to be in. Based on the current trajectory, programming is far from a dead-end career.
While increased developer efficiency through AI may lead to smaller team sizes and potentially fewer traditional roles within established companies, the same technology empowers startups and smaller businesses to innovate and launch new products and services more rapidly and with fewer resources.
If the predictions outlined here prove incorrect and programming becomes obsolete within a few years, then it will have been a rewarding journey. In that hypothetical scenario, a fulfilling alternative in gardening and cabinet making awaits!
