LLMs Raise the Bar in Software Development: Adapting to a New Era of Democratized 10x Productivity
Large Language Models (LLMs) like GPT-4 are transforming how we work and learn in the software development industry. With cutting-edge knowledge now available to everyone, how will developers thrive in a post-GPT world?
This is a personal opinion piece with my current view on what’s coming. I might be plain wrong or change opinions tomorrow, so I invite you to respond to this post and share your thoughts and perspectives on this topic too.
Introduction
Large Language Models (LLMs) such as OpenAI’s GPT-4 are revolutionizing the software development industry by democratizing access to advanced knowledge and programming expertise. GitHub Copilot, for example, brings LLM capabilities directly into developers’ Integrated Development Environments (IDEs), and the upcoming release of GitHub Copilot X promises even more advanced features.
These tools will make us faster and produce higher-quality code, thus producing below-average code will soon become unacceptable, and developers must adapt to stay competitive.
The Evolving Role of the AI-enhanced Software Developers
In this new landscape, the day-to-day work of a software developer could shift towards a role more similar to that of a lead developer, with less emphasis on writing code and more focus on ensuring quality, reviewing code, deeply understanding business requirements, and agreeing with their team on code style, architecture, and boundaries. One of the most critical skills that software developers might need to acquire soon is their ability to build prompts and evaluate, fix, accept, or reject code and documentation proposed by LLMs.
Additionally, LLMs can significantly accelerate the learning process for developers. Previously, learning a new tool or language could take weeks or months of practice, with developers often becoming stuck in the process. The learning experience typically involved long periods of confusion followed by small moments of realization. LLMs can absorb the documentation of any tool and, with browsing capabilities, inspect the entire internet to find solutions to blockages in seconds. This drives developers through a series of realization moments, one after another, without getting stuck in obstacles. As a result, there’s no reason not to produce not only good but also idiomatic code for any technology you can imagine.
The post-GPT software engineers will barely write code themselves but still hold responsibility for any code pushed.
As I see it, the software engineer of the post-GPT world will need more knowledge of quality standards, design patterns and architecture, security, understanding of business processes, and soft skills. All of them already desirable qualities for excellent developers, but now made essential, because knowing how to write code for a specific stack won’t be enough anymore. They will need to master transferable programming skills that make them effectively polyglot, being able to use any programming language or framework in no time, and there will be no excuses for undocumented or poorly tested code.
Thriving in a Post-GPT Software Development World
Many developers are afraid of the upcoming changes, and there are good reasons to be concerned. We come from a world with a massive demand for anyone who could contribute to creating software. Knowing how to code could easily get you a great job offer with great perks. Now we are headed to a world where one developer with the proper set of skills could produce as much work as an entire team does today.
To remain competitive in such a landscape, developers must rethink their strategies and adapt to the new situation. Embracing continuous learning, specialization into hard-to-master topics, focusing on creative problem-solving, honing soft skills, and staying up-to-date with industry trends and emerging technologies will be essential. By cultivating their ability to evaluate others’ code and effectively guide its improvement critically, developers can navigate the evolving demands of the job market and thrive in a post-GPT software development world.
I think the world is gonna find out that if you can have ten times as much code at the same price, you can just use even more — Sam Altman in the Lex Fridman podcast (1:36:52)
But is it worth the effort? Many are worried that having AI-enhanced developers who do 10x the work will result in 10x fewer developers needed. I believe that at least during the next few years, even if productivity is increased by 10x, the amount of work required and what we expect from software might easily outgrow the productivity increase. AI is not here to compete against competent software developers but to allow us to work on all those ideas that never left the backlog because we had to focus on delivering the ones from the top.
As a personal opinion piece, I welcome your thoughts, experiences, and perspectives on this topic. Please feel free to share them and contribute to the conversation!
