As a 20-year-old code author and CEO of an organization that serves software developers, I used to be reflexively skeptical of early predictions that generative AI would eventually render most software development skills obsolete.
Even though I'm still a bit skeptical, the experience I've had in my day by day development work with genetic AI has made me open my mind to what I imagine is feasible. AI will change software development in pretty fundamental ways, each for higher and for worse. Let's start with the positive points.
No more grunt work
Developers spend an inordinate period of time on details like syntax and punctuation. Much of this will (and will) disappear. Instead of poring over manuals or piecing together snippets of code swaps, they describe a desired result and receive perfectly formatted code in response. Large Language Models (LLMs) can even inspect existing code to catch typos, punctuation errors, and other details that drive developers crazy.
Reinvent frameworks
Software frameworks like Spring, Express.js, and Django have led to large increases in productivity by abstracting the day-to-day points of software development, establishing consistent guidelines, and providing pre-built code for common functions. Gen AI will increase its value by creating standard code, automating repetitive tasks, and suggesting code optimizations. AI can even help tailor framework components to a selected project.
The rise of the generalist
For many developers, their expertise in a specific language is the be-all and end-all. Knowledge of Python or Ruby doesn't matter as much when machines can spit code in any language. Likewise, specialized backend capabilities equivalent to testing and code optimization are quickly transferred to Gen AI models. The Most worthy skills will probably be those who machines don't do well, equivalent to creating compelling user interfaces, translating user needs into specifications, and inventing recent ways to support customers. Software “poets,” or individuals who think up big ideas about what may be achieved with code, will probably be within the highlight.
A revolution in testing
Gen AI was designed for software testing. The developer writes the code and the bot creates as many test scripts as you would like. A recent one IDC survey found that the 2 most anticipated advantages of genetic AI by a large margin are software quality assurance and security testing. This will disrupt the DevOps practice of continuous integration/deployment and send many testing specialists in search of recent areas of labor.
Citizen development on steroids
The current number of low-code/no-code development tools is already good, and next-generation AI will take it to the following level. For all its automated elegance, low/no-code still requires people to place together a workflow on a whiteboard before handing it off to the software. In the long run, they can provide the model a hand-drawn sketch of the specified workflow and receive the crucial code back in a matter of seconds.
However, AI will not be a panacea
Despite all its promise, genetic AI mustn’t be viewed as a panacea. Consider these potential disadvantages.
There is a risk of over-testing
Because models can perform tests quickly, we could find yourself with many more tests than we’d like. Excessive testing is a typical problem in software development, especially in organizations that measure performance by the variety of tests generated by a team. Running too many duplicate or unnecessary tests slows down projects and creates bottlenecks further up the pipeline. If AI can recommend when to remove tests, we are going to see an enormous unlocking of developers – this vision of Gen AI excites me for the long run.
Deterioration of skills
“I’ll all the time pick a lazy one who does a tough job because he finds a straightforward technique to get it done,” is a quote often incorrectly attributed to Bill Gates. Even if the origin of the quote is unclear, the opinion is justified. Lazy people find shortcuts that make exertions unnecessary. Gen AI is a drug for lazy developers. This can result in the creation of bloated, inefficient, and underperforming code. It can stifle the innovation that makes great developers so beneficial. Remember that Gen-AI writes code based on existing patterns and data. This can limit the innovation potential of developers who may not consider out-of-the-box solutions.
Trust deficit
Gen AI is barely nearly as good as the information used to coach the model. Poor data quality, training shortcuts, and poor prompt engineering may end up in AI-generated code that doesn’t meet quality standards, is flawed, or doesn’t do its job. This may cause an organization to lose confidence in the standard of genetic AI and miss out on its many advantages.
Now the cash query: Will AI make software developers obsolete?
Although some headline-grabbing pundits have suggested this, there is no such thing as a historical precedent for such a conclusion. Technological advances – from high-level languages to object orientation to frameworks – have steadily increased developer productivity, but demand has only increased. Gen AI could weaken the marketplace for basic low-end programming skills, but the larger impact will probably be to maneuver your complete career up the worth chain to do what LLMs don't currently do thoroughly: innovate. Remember that generational AI models are trained on what’s already known, not what could possibly be. I don't expect a machine to develop a revolutionary user interface or dream up an Uber any time soon.
However, developers won’t experience such a change again of their careers. Instead of raging against the machine like I did to start with, they need to ride the wave. The prospect of eliminating much of the boredom of constructing software should excite anyone. The risk that some features might disappear needs to be an incentive to take motion. Quality developers who translate business requirements into elegant and powerful software will all the time be in high demand. Make it your mission to level up your skills.
