AI Tools To Build AI: How AI Professionals Streamline Their Work

Artificial intelligence has reached a fascinating turning point: AI specialists now use AI to build AI. From data scientists to AI engineers, professionals are increasingly adopting AI coding assistants like Cursor and GitHub Copilot to enhance speed, quality, and collaboration across development pipelines.

In this article, you’ll learn about:

• Why our AI experts use AI in their work
• Which AI tools are used most frequently, and
• Why they are essential.

“Cursor has proven most helpful in my work for introducing project-wide changes (such as new parameters or functions), applying quick fixes, and maintaining coding standards and documentation. It significantly accelerates both the prototyping and final development stages, though it still requires careful oversight when implementing complex logic. One of my favorite features is the review mode, which clearly highlights AI-generated modifications and helps ensure no unintended changes make their way into the codebase. “

Igor Jakubiak, PhD, Head of AI & ML

“In my opinion, the benefit of using Github Copilot and Cursor is the reduction in time spent writing code, debugging, and improving existing code, meaning we gain what we are always lacking — time.”

Cezary Głowiński, PhD,  Chief Data Scientist

According to a recent Google DORA study, 90% of tech professionals now use AI at work — a 14% increase from the previous year.

These tools are no longer experimental; they’re becoming as fundamental to modern development as version control or cloud deployment.

A CNN-covered study highlights a crucial shift: AI has moved from novelty to necessity. AI now supports everything from documentation writing to code review, embedding automation into nearly every layer of the software process.

However, the data also reveals cautious optimism among developers. Only 46% of surveyed professionals said they “somewhat” trust AI-generated code. 31% said it “slightly improved” quality, while 30% saw “no impact.” In other words, AI-assisted coding still requires strong human oversight — a finding that resonates with how tools like Cursor and Copilot are used in practice.

At the same time, AI adoption is reshaping the job market. Job listings for software engineering roles fell by 71% between 2022 and 2025, and unemployment among new computer science graduates rose above that of art history majors.

Yet most experts believe that while AI will automate the mundane, it won’t replace the creative and strategic side of development.

Cursor, developed by Anysphere, is an advanced AI coding environment built atop Visual Studio Code. It brings project-wide reasoning, code search, and prompt caching for efficient reuse of queries.

For data scientists and AI engineers managing large projects, Cursor acts like a smart co-pilot that understands interdependencies across files and modules — something traditional autocomplete tools cannot do.

Why AI Specialists Use It

Cursor empowers developers with AI-powered coding assistance that enhances productivity across every stage of the development cycle. It offers smart autocomplete and intelligent code generation, enabling engineers to write, optimize, and document code faster. With codebase-level understanding, Cursor can interpret large projects contextually—making refactoring and debugging with AI not only more precise but also consistent with the overall architecture.

Cursor doesn’t work in isolation. Instead, it can connect and cooperate with other AI-driven tools that developers use—especially those used in machine learning, data engineering, and automation.

This includes tools for:

• ML model deployment — systems that help move machine learning models from development into production.
• Data orchestration — tools that manage data pipelines, workflows, and dependencies.
• Automating repetitive coding tasks — systems that handle frequent, routine operations like formatting, boilerplate generation, test creation, etc.

In other words, Cursor can plug into and enhance the AI ecosystem you already use. It works alongside those tools so that your development process—from writing code to managing data workflows to deploying ML models—can be more automated, consistent, and streamlined.

Team and Solo Impact

While Cursor can be a genuinely helpful companion for developers—especially when it comes to brainstorming, navigating complex code, or handling repetitive tasks—it’s important to keep expectations realistic when it comes to overall project speed. A study conducted by Model Evaluation & Threat Research (METR) found that when 16 experienced developers worked on familiar open-source repositories using Cursor and similar AI models, their actual time to complete tasks increased by roughly 19%. Interestingly, the same developers expected a 24% speed-up going in and even felt afterward as though they had worked about 20% faster, despite the measured slowdown. This suggests that while tools like Cursor may feel smooth and supportive, they don’t necessarily translate into faster delivery—and in some cases, they may even add time due to extra validation, context-switching, or over-reliance on AI suggestions.

In teams, Cursor accelerates onboarding and keeps multi-repository projects synchronized. Yet, as the Google study implies, AI assistance doesn’t equal autonomy — teams still need review processes and validation before merging AI-suggested changes.

GitHub Copilot, created by GitHub and OpenAI, remains one of the most recognizable AI programming assistants on the market. It’s integrated into popular editors and IDEs, providing real-time code generation, debugging help, and documentation suggestions.

Why AI Specialists Use It

Copilot acts as an open-source chatbot inside the development environment, helping to:

• Translate natural language prompts into executable code.
• Write boilerplate code for data processing or visualization.
• Generate docstrings, tests, or SQL queries.

Still, many professionals value speed but remain cautious about reliability—using Copilot to assist, not replace, their coding logic. A systematic study titled “Practices and Challenges of Using GitHub Copilot: An Empirical Study” found that while Copilot is widely used and brings benefits (such as useful code generation), practitioners also highlight limitations and the need for oversight.

Another empirical study, “Evaluating the Code Quality of AI-Assisted Code Generation Tools”, found that tools like Copilot generate correct code less than half the time (46.3% in one dataset) and carry technical debt or code smells that require developer review.

Using Cursor and GitHub Copilot side by side creates a complementary workflow that many developers find both fast and reliable.

Copilot is typically used for micro-level tasks: writing small functions, generating boilerplate, suggesting code snippets, creating tests, or translating natural-language prompts into quick prototypes. It shines when you need something immediately and locally, right where your cursor is.

Cursor, on the other hand, operates at the macro level. Developers rely on it for understanding and navigating the entire codebase: tracking dependencies, keeping architectural rules consistent, updating documentation automatically, refactoring across multiple files, and reasoning about large-scale changes. Because Cursor maintains project-wide context, it can evaluate how new code fits into the overall structure, not just whether it works in isolation.

When used together, the workflow often looks like this:

• Developers ask Copilot to generate a function, test, or SQL query quickly.
• They then use Cursor to integrate that snippet into the broader codebase, refactor related files, update dependencies, or ensure consistency with the existing architecture.

Copilot speeds up local coding, while Cursor ensures global coherence. Developers bounce between both tools: Copilot for speed, Cursor for reliability and context.

In practice, this combination lets engineers prototype rapidly while maintaining control over the structure and quality of the entire project — blending fast code generation with deeper project-wide reasoning.

Claude Code is an agentic coding tool that lets engineers use the capabilities of the underlying large language model, Claude (developed by Anthropic), more directly in a development workflow. It provides command-line and IDE integrations so that the AI can:

• Read and analyze large codebases, understanding context across many files.
• Edit files, run commands, or interact with the terminal/IDE environment (create files, edit code, update tests).
• Act more like a coding “agent” (rather than just autocomplete) — capable of making changes, reviewing pull requests, generating tests or documentation, and suggesting refactors.

Why AI Specialists Use It

AI specialists use Claude Code because it significantly improves both the speed and quality of development tasks. It provides a deep understanding of the entire codebase, allowing it to automate complex tasks across multiple files and modules. Developers use it for:

• Map dependencies and project architecture: It helps to save time when navigating large or complex systems.
• Automation of routine tasks: Claude Code handles tasks such as generating boilerplate code, refactoring, and writing documentation, allowing developers to focus on higher-level work.
• Task management and review automation: It integrates into the workflow to automate code reviews, suggest optimizations, and ensure consistency across the codebase, reducing the need for manual review and testing.
• Seamless integration with existing tools: Claude Code supports IDE and CLI integrations, automating pull request workflows and enabling smooth interaction with version control systems.

Team And Solo Impact

For solo developers, Claude Code enhances productivity by streamlining repetitive tasks and maintaining quality. Key benefits include:

• Faster onboarding and development: Claude Code helps developers understand and navigate new codebases quickly, reducing the time needed for familiarization.
• Automating mundane tasks: It handles repetitive coding tasks, such as generating boilerplate code, running tests, and writing documentation, which frees up time for more important tasks.
• Ensuring consistency and quality: By ensuring that changes are consistent with the overall architecture, it helps maintain a high standard of code quality even when working alone.
• Increased speed and autonomy: With Claude Code automating many aspects of the development process, solo developers can work faster and independently, while still ensuring accuracy.

For teams, Claude Code improves collaboration and streamlines workflows, allowing for more efficient development cycles. Its impact includes:

• Consistency across the codebase: Claude Code ensures all changes are aligned with the project’s architectural standards, maintaining consistency even with multiple contributors.
• Automated pull requests and code reviews: It automates the review process by running tests, checking for bugs, and suggesting changes before a human reviewer even sees the code. This reduces the manual effort involved in code reviews and speeds up the development cycle.
• Faster integration and scaling: By automating refactoring and identifying dependencies across modules, Claude Code helps teams integrate new features and scale projects more efficiently.
• Empowering less experienced developers: It helps guide best practices and enables non-specialists to contribute more effectively, improving team productivity and reducing errors.

Real-Life Example

Claude Code works as an AI-powered code reviewer integrated into the development pipeline. Every time a developer commits a new version of the code to the repository, the AI performs a two-step review process. First, it runs tests to check for issues or bugs in the new code. If the tests pass, the AI then reviews the code for improvements, such as best practices, performance optimizations, or potential errors.

If the AI doesn’t find any problems, it approves the changes and allows them to be merged into the repository. If issues are detected, it recommends fixes. This system effectively replaces a human code reviewer for routine checks, making the process faster and more automated. By doing so, the team can move more quickly, as every change undergoes a validation process, reducing the need for manual review and ensuring that the code adheres to the project’s quality standards.

The Gartner Generative AI analysis supports the same trend: AI-driven coding is here to stay, but it’s far from replacing engineers. Instead, it amplifies productivity, creativity, and speed.

The recent Google research found that AI in software development sits at “stage three to four” of maturity — meaning it can now troubleshoot across systems but still requires “lots of safety nets.”

The broader takeaway: AI coding assistants like Cursor and GitHub Copilot don’t replace talent — they extend it. While entry-level coding roles are becoming rarer, new opportunities are emerging in AI orchestration, model governance, and prompt engineering, where understanding both human logic and machine reasoning is essential.

AI-assisted coding isn’t just about speed — it’s about redefining how humans collaborate with machines. For AI specialists, adopting Cursor and GitHub Copilot is less about outsourcing work and more about evolving how work gets done.

The industry is still learning where the balance lies between automation and oversight. Some even compare software development to the fashion industry, where — in this new “style” — AI has become the thread that weaves the entire digital fabric together.