Artificial intelligence (AI) is transforming the way we create software. From automating repetitive tasks to predictive code generation, AI-powered tools are speeding up processes and reducing human error. 

According to data from Digital Ocean, by 2023, 47% of companies will have integrated artificial intelligence or machine learning into their development processes. Today, the figure is likely to be higher.

However, this revolution is not without its challenges. Over-reliance on AI, biases in models and potential job losses are issues that generate debate. To harness its potential, it is key to understand how to integrate it in a balanced way, combining its power with human expertise.  

The impact of artificial intelligence on development

Yes, we all know that AI is changing the way we develop software. The important thing is to know what is changing to really understand why it has been so critical from a few years ago.

Task automation  

AI is eliminating manual work on tasks like code testing, deployments, or bug fixing. Tools like GitHub Copilot suggest code snippets automatically, while bots review pull requests in minutes. This reduces hours of tedious work and speeds up development cycles.  

In addition, automation makes it possible to scale projects without increasing the team. For example, in testing, AI algorithms simulate thousands of scenarios in seconds, something that used to take days. Developers can focus on innovation, while AI handles repetitive tasks.  

But be careful that automation does not replace human supervision. Sometimes, AI can miss contextual errors or business logic. The key is to use it as an assistant, not a permanent replacement.  

Improved development efficiency  

With AI, processes such as debugging or API integration are faster. Platforms such as ChatGPT help solve technical doubts in seconds, avoiding searching forums or documentation for hours.

It also improves collaboration. Tools such as Tabnine analyze patterns in the team’s code to offer consistent suggestions. Thus, a uniform style is maintained, even if there are new or remote developers.  

Improved estimation accuracy  

AI analyzes historical project data to predict timelines and resource requirements. For example, tools such as Jira with AI estimate delivery times based on previous sprints, considering variables such as complexity or team performance.  

This reduces classic human miscalculations, such as underestimating tasks or forgetting unforeseen events. Managers can plan with realistic metrics, avoiding overloading the team or missing deadlines.  

Of course, models need quality data to be accurate. If they are trained on poorly documented or inconsistent projects, predictions will be unreliable. AI is good, but it doesn’t guess: it depends on what we teach it.

Predictive coding  

Tools like AWS CodeWhisperer or DeepCode use AI to predict what code to write next, like a super-powerful autocomplete. They learn from millions of public repositories to suggest functions, variables or even detect vulnerabilities in real time.  

This is ideal for avoiding common errors, such as security bugs or incorrect syntax. It also speeds up the onboarding of new developers, as the AI guides according to industry standards.  

AI-powered development tools

To take full advantage of AI, specialized tools have emerged that every developer should know about.

They simplify work, and also open up possibilities that once seemed like science fiction. Here are some of the most relevant ones.

Natural language tools  

ChatGPT and similar tools allow you to generate pseudocode, documentation or even simple scripts just by describing what you need. For example: “Create a Python function that calculates the average of a list”. The AI understands the request and provides a code base to adjust.  

This is useful for rapid prototyping or for those learning to program. It also helps translate business requirements into technical specifications, bridging the gap between non-technical teams and developers.  

Of course, the generated code may require adjustments. AI does not always understand complex requirements or edge cases, so every line needs to be validated.  

Documentation tools  

Plugins like Swimm or Scribe automate the creation of technical documentation. They analyze code, extract explanations and even generate step-by-step tutorials. This saves hours of manual writing and keeps the doc updated with every change in the repository.

In addition, some tools convert meetings into structured text. For example, Otter.ai transcribes technical discussions and links them to relevant tickets or documents.  

Increased estimation accuracy  

Platforms such as Forecast or ClickUp use AI to analyze team progress and adjust estimates in real time. If there are delays, they recalculate delivery dates automatically.  

They also identify bottlenecks, such as tasks that always take longer than planned. Teams can then adjust processes or redistribute workloads.  

Predictive coding  

Tools like Kite or IntelliCode (from Microsoft) integrate AI models directly into IDEs like VS Code. They learn from your coding style and prioritize personalized suggestions, speeding up code writing.  

In addition, some detect “code smells” or possible performance improvements. For example, they suggest replacing a loop with a more efficient function or point out unused variables.

Potential risks of AI in development

It’s important to remember that artificial intelligence is not a magic wand.

Its adoption without caution can lead to technical, ethical and even labor problems. It is crucial to understand these risks to avoid falling into common traps. These are the most relevant ones.

Bias in AI models  

If AI training data includes code with bad practices or cultural biases, the tools will replicate them.

For example, a model trained with mostly English repositories may have trouble understanding comments in other languages.  

There is also a risk of bias in technical decisions. Imagine an AI that prioritizes solutions in a specific language (such as Python) because it is more common in its data, ignoring more suitable options for the project.

Over-reliance on artificial intelligence  

Teams that rely too much on AI could lose critical skills, such as debugging code without help or understanding algorithm fundamentals. This is risky if the tool fails or is unavailable.  

In addition, AI can create a false sense of security. If a developer does not review the generated code, he or she could inadvertently implement bugs or vulnerabilities.  

The solution is clear: treat AI as a teammate, not a boss. Use it to leverage skills, not replace them.  

Lack of transparency  

Many AI models are “black boxes”: we don’t know exactly how they make decisions. If a tool suggests strange code, how do we understand why? This complicates debugging and security auditing.  

For example, if an AI recommends a specific library, is it because of performance, popularity, or because it has a commercial agreement? Without transparency, it is difficult to trust.  

To mitigate this, prioritize tools with clear explanations of their processes and encourage ethical AI standards in the industry.  

Job displacement  

While AI creates new roles (such as model trainers), it could reduce demand for junior or repetitive positions. Companies may prefer to hire fewer developers and rely more on automation.  

This also affects freelancers: if an AI makes basic prototypes in minutes, clients might pay less for simple jobs.  

The key is to adapt: focus on skills that AI cannot replicate, such as critical thinking, creativity or complex project management. AI is a tool, not a rival.

There is no doubt that artificial intelligence is a watershed in the way developers work on their projects, optimizing times and allowing professionals to deliver projects and components in less and less time.

However, not all is good with AI. If it is adopted irresponsibly and freed from human supervision, the quality of the work may be poor, and the developer may lose competencies critical to his job.