AI tools have become a core part of modern software development. Developers rely on them throughout the life cycle, from writing and refactoring code to testing, documentation, and analysis.

Once experimental add-ons, these tools now function as everyday assistants and are firmly embedded in routine workflows. But why have AI tools become so essential – and how are developers actually using them?

The insights in this article draw on findings from the JetBrains State of Developer Ecosystem Report 2025, which tracks how developers use tools, languages, and technologies, including AI tools, in real-world environments. Shifting the focus from technical model performance, this article looks at usage patterns, developer preferences, and adoption trends across tools, regions, and workflows.

Before we work through which AI tools developers use most, why they choose them, and how these tools fit into everyday work, let’s first clarify what AI tools are and why they matter so much right now.

Disclaimer: Please note that the findings in the article reflect data collected during the specific research period set out in the report.

Table of Contents

·       What AI tools are and why they matter now

·       Most popular AI tools among developers

·       What makes developers choose one AI tool over another

·       How developers use AI tools in daily workflows

·       Global snapshot: How AI tool adoption differs across regions

·       Barriers to adopting AI tools

·       Future of AI tools: What developers want next

·       FAQ

·       Conclusion

What AI tools are and why they matter now

Today’s AI tools for developers span several categories. They include code assistants that suggest or generate code, as well as tools that review code autonomously. Many come as IDE integrations that understand project context.

There are also AI-powered search and navigation tools, refactoring helpers, and documentation generators. In addition, teams now use testing assistants and autonomous or semi-autonomous agents to support more complex workflows.

Understanding today’s AI tools list for developers matters because these tools directly address growing pressures in modern development. They shorten development cycles, reduce manual tasks, and help teams maintain quality, which is especially important as codebases grow.

This growing reliance makes it important to understand which tools developers actually use most. In the next section, we will see what these AI tools are.

Most popular AI tools among developers

Developers rarely rely on a single AI tool. Instead, they combine multiple tools depending on their IDE, workflow style, and project requirements. According to the AI usage insights in the the JetBrains State of Developer Ecosystem Report 2025, adoption clusters around three main categories: IDE-native assistants, standalone AI-powered development environments, and browser-based or cloud chat tools.

Across these categories, the most popular AI assistants are GitHub Copilot, JetBrains AI Assistant, Cursor, Windsurf, and Tabnine. Adoption of these top AI tools varies based on ecosystem, IDE choice, and workflow style.

IDE-native assistants, such as GitHub Copilot and JetBrains AI Assistant, remain among the most popular AI tools because they operate inside the editor and integrate directly into existing workflows, making them more context-aware.

Standalone AI-focused editors and assistants, such as Cursor and Windsurf, often emphasise more experimental or agent-style workflows. This is an area that is evolving across the ecosystem, with increasing convergence between IDE-native tools and more agent-driven capabilities.

Other tools focus on specific priorities. For example, Tabnine attracts teams that prioritize privacy and local inference. Region-specific tools also play an important role in areas with strong domestic AI ecosystems or regulatory constraints.

This diversity becomes clearer when comparing the best AI tools for developers side by side.

Comparison table: AI tools overview

AI tool	Typical use case	Underlying models	Distinct features	Integration type
GitHub Copilot	Code generation and completion	GPT family	Tight GitHub + VS Code workflows	IDE / Cloud
JetBrains AI Assistant	Context-aware help, refactoring	Claude / GPT / Gemini	Deep IDE context + privacy focus	In-IDE
Cursor	Inline edits, debugging, chat	Claude / Gemini	Fast UI, multi-step edits	IDE plugin
Windsurf	Autonomous task execution and code changes	Claude / GPT	Agent-like capabilities	Standalone
Tabnine	Privacy-oriented code suggestions	Proprietary / DeepSeek	Local inference options	IDE plugin

What makes developers choose one AI tool over another

Developers are not choosing AI tools solely on novelty. They evaluate how well a tool fits existing workflows, how reliable the output feels, and whether the tool aligns with team constraints. The JetBrains State of Developer Ecosystem Report 2025 identifies several of these practical considerations that shape decision-making.

Integration quality ranks among the most important factors. Developers prefer AI coding tools that work seamlessly inside their preferred IDE. A tool that interrupts flow or requires constant context switching often fails to gain long-term adoption.

Accuracy and code quality are equally crucial. Developers expect AI coding tools to produce reliable results that they can trust. When outputs require extensive correction, confidence drops quickly.

Privacy and data security also influence developer AI preferences. This is especially true in enterprise environments. Tools that offer local processing or clear privacy guarantees often see stronger uptake in regulated industries.

Finally, pricing, transparency, and vendor reputation affect adoption. Developers value clear pricing models, flexible access, and vendors with a track record of supporting developer tools. Trust builds over time through consistency and ongoing communication.

Let’s see how developers evaluate each of these factors in this AI assistant comparison.

Key factors influencing tool choice

Factor	Why it matters	How developers evaluate it
IDE integration	Supports smooth workflows	Works natively in their preferred IDE
Code accuracy and quality	Affects trust and usability	Produces correct, clear, and maintainable code
Privacy and security	Protects source code and IP	Provides clear data handling and local mode options
Pricing and access	Impacts adoption at scale	Offers flexible tiers and predictable costs
Transparency	Builds confidence	Discloses model provider and data policies
Vendor reputation	Signals long-term reliability	Demonstrates a history of dev tools and quality support

How developers use AI tools in daily workflows

Developers integrate AI tool usage throughout the development life cycle rather than limiting it to a single task. Most workflows combine several forms of AI access depending on the problem at hand.

When coding with AI tools, developers may use in-IDE assistants for context-aware code help and chat-based interfaces for problem-solving and prototyping. In addition, developer AI assistant usage may combine browser tools for quick inline answers, APIs for automation and CI/CD tasks, and local models for privacy-restricted environments.

Across these use cases, developers are clearly no longer relying on a single tool. AI workflows increasingly involve choosing the right tool for the task at hand, be it writing code, refactoring, debugging, generating documentation, testing, or understanding unfamiliar code.

The JetBrains State of Developer Ecosystem Report 2025 indicates that developers frequently switch between AI access points in this way. They choose the interface that best fits the task rather than expecting one tool to handle everything.

Workflow types and examples

Workflow type	Typical use case	Example tools	Integration context	Developer benefit
In-IDE assistance	Code suggestions, refactoring	JetBrains AI Assistant, GitHub Copilot	IDE	Immediate, context-aware help
Chat-based interaction	Explanations, brainstorming, regex, prototyping	ChatGPT, Claude	Browser / Cloud	Fast iteration and reasoning
API integration	Automation, CI tasks, documentation	OpenAI API, Anthropic API	Backend / DevOps	Scalable automation
Browser extensions	Quick inline code insights	Codeium, AIX	Web	Lightweight access
Local/private models	Secure, offline coding	Tabnine, DeepSeek (self-hosted models)	On-premises / Enterprise	High privacy and control

With AI firmly established in daily workflows, the next section looks at regional differences in AI tool adoption.

Global snapshot: How AI tool adoption differs across regions

Global AI adoption patterns do not look the same everywhere. Regional ecosystems, regulations, and developer communities shape which tools gain traction. The JetBrains State of Developer Ecosystem Report 2025 highlights clear regional AI trends.

In North America, developers commonly adopt mainstream tools such as GitHub Copilot, JetBrains AI Assistant, and Claude-based assistants. Strong cloud infrastructure and rapid LLM innovation encourage experimentation with multiple tools.

European developers balance adoption with privacy considerations. Data residency and compliance requirements influence tool selection, leading to broader interest in solutions that offer transparency and local processing options.

In the Asia-Pacific region, developers often combine global tools with regional offerings. Mobile-first development cultures and fast-growing ecosystems drive rapid experimentation, particularly with cloud-based assistants.

Mainland China stands out due to its strong domestic AI ecosystem. Developers there frequently rely on local tools and models such as DeepSeek, Qwen, and Hunyuan, which align better with infrastructure and regulatory realities.

Regional highlights and local leaders

Region	Most used tools	Local ecosystem drivers	Notable observations
North America	GitHub Copilot, JetBrains AI Assistant, Claude	Strong cloud and LLM innovation	High multi-tool adoption
Europe	JetBrains AI Assistant, GitHub Copilot	Privacy regulations, data residency	Balanced adoption across tools
Asia-Pacific	GitHub Copilot, Gemini	Mobile/cloud-first development cultures	Rapid experimentation and growth
Mainland China	DeepSeek, Qwen, Hunyuan	Strong domestic AI ecosystem	Preference for locally hosted models

While AI tool usage worldwide is undoubtedly gaining momentum, barriers to AI adoption also exist, which we explore in the next section.

Barriers to adopting AI tools

Despite growing interest, not all developers or teams adopt AI tools easily. The JetBrains State of Developer Ecosystem Report 2025 shows that such AI adoption challenges often stem from uncertainty rather than opposition.

Privacy and security concerns remain the most common AI coding tool barriers. Teams worry about exposing sensitive code or intellectual property, especially when tools rely on cloud processing. Without clear guarantees, organizations may restrict or ban usage.

Legal and ownership questions are other reasons why developers avoid AI tools. Developers and managers want clarity about who owns AI-generated code and how licensing applies. Uncertainty leads many teams to limit AI use to non-critical tasks.

Individual barriers matter as well. Some developers lack confidence in using AI tools effectively or struggle to evaluate output quality. Others distrust AI suggestions due to past inaccuracies.

Cost, licensing, and infrastructure constraints can also limit adoption, particularly for larger teams. Per-seat pricing and usage caps further complicate budgeting and rollout decisions.

Obstacles and evaluation criteria

Barrier	Why it matters	Typical impact
Privacy and security concerns	Increases the risk of exposing sensitive code	Usage blocked or restricted
IP and code ownership concerns	Creates legal uncertainty	Hesitation to rely on AI for core code
Lack of knowledge or training	Reduces confidence in using tools	Slower individual adoption
Accuracy and reliability issues	Impacts trust in outputs	More manual review required
Internal policies and processes	Requires compliance and complex approval workflows	Delayed tool rollout
Cost and licensing	Exceeds budget or per-seat limits	Partial or limited deployment

In the next section, we move from the barriers of today to developers’ hopes for the future.

Future of AI tools: What developers want next

Developers do not simply want more AI features. They want better ones. The JetBrains State of Developer Ecosystem Report 2025 not only indicates greater adoption but also shows that developers are hopeful about the future of AI. Their expectations focus on reliability, integration depth, and control rather than novelty.

Higher code quality tops developer AI expectations. Developers want fewer hallucinations, cleaner outputs, and suggestions that respect project conventions. Trust grows when AI behaves predictably.

Deeper IDE integration also ranks high. Developers expect future AI tools to understand entire projects, not just individual files. Context retention across sessions and multi-file awareness are increasingly important.

Privacy remains central. Many developers want local or on-device options that allow them to use AI without sharing code externally. Transparent data handling builds confidence.

Pricing clarity and explainability also influence future AI assistant trends. Developers want predictable costs and better insight into why tools suggest certain changes.

But most significantly, as AI tools evolve, developers want support for complex workflows and architecture reasoning. The goalpost is also shifting. Developers now expect future AI tools to move beyond basic autocomplete and act as collaborative partners.

Developer expectations and trends

Expectation	Why developers want it	Example improvements
Higher code quality	Trust and reliability	Fewer hallucinations, cleaner output
Deeper IDE integration	Seamless workflows	Context retention, multi-file awareness
Privacy and control	Secure code handling	On-device or local LLM options
Transparent pricing	Predictable team adoption	Usage-based models, clearer tiers
Explainability and reasoning	Trust in decisions	Clearer chain-of-thought summaries
Context awareness	Handling real projects	Larger context windows, project-wide understanding

The following FAQ addresses some of the most common questions developers ask when evaluating and using AI tools.

FAQ

What are the most popular AI tools among developers today?
According to the report’s findings, developers commonly use tools such as GitHub Copilot, JetBrains AI Assistant, Cursor, and Tabnine, often combining them rather than using a single tool.

Are AI tools safe for use with private or proprietary code?
Safety depends on the tool. Developers increasingly prefer tools that provide clear privacy policies or local processing options.

Which AI tools work best inside IDEs?
IDE-native tools tend to perform best for daily coding tasks because they understand project context and workflows.

Do developers prefer local AI models or cloud-based solutions?
Preferences vary. Some developers value cloud flexibility, while others prioritize local models for privacy and compliance.

How do AI tools help with debugging and documentation?
They explain code, identify errors, suggest fixes, and generate comments or documentation drafts.

Are AI tools suitable for enterprise teams with strict security requirements?
Many are, especially when they offer strong privacy guarantees, administrative controls, and predictable pricing.

Can AI tools speed up development without reducing code quality?

Yes, when developers use them intentionally. AI tools speed up repetitive tasks such as code generation, refactoring, testing, and documentation, while reviews, IDE checks, and automated tests help maintain quality.

Conclusion

AI tools have evolved from optional add-ons into essential components of modern software development. Developers now rely on them for coding, refactoring, documentation, testing, and learning, integrating AI assistance throughout daily workflows.

Current adoption trends show that developers value accuracy, deep integration, and privacy above experimental features. The JetBrains State of Developer Ecosystem Report 2025 reflects broad and growing use across regions, tools, and development styles.

As AI tools continue to evolve, they move toward deeper context awareness, stronger reasoning, and more secure deployment options.

For developers, AI no longer represents a future possibility. It has become a practical, everyday partner in building software.

Ryan sits down with Shireesh Thota, CVP of Azure Databases at Microsoft, to discuss the evolution of databases at Microsoft; Azure’s comprehensive portfolio that includes SQL Server, CosmosDB, and Postgres; and the challenges that come with database architecture, from the importance of cost governance and multi-cloud strategies to the future of databases when it comes to AI.

Learning Python often starts with the same building blocks: variables, functions, and loops. However, once young people have learnt these essential foundations, they may be eager to grow their skills and start using Python to explore data and create something meaningful to them. 

Our free ‘More Python’ project path helps learners move beyond the basics and use data to create impactful projects of their own.

Python as a tool for exploring the world

Python is the most widely used programming language in the world, not just because it’s accessible, but because it’s powerful. It is used to analyse data, build models, create data visualisations, and explore important questions.

For young learners, this means learning Python can become more than a coding exercise. It can be a way to investigate topics they care about, analyse and understand information, and tell powerful stories about real-world issues.

Working with data helps learners see how coding connects to the world around them — and builds confidence along the way.

Why learning with data matters

In our day-to-day lives, data is everywhere: in sports results, maps, and scientific research, to name only a few examples. Learning how to work with data helps young people develop skills that go far beyond programming, including:

• Thinking logically and solving problems
• Interpreting and questioning information
• Making decisions based on evidence

Data also underpins many of the AI systems people use today. For example, large language models, used to build tools such as ChatGPT, are trained on vast amounts of data. Therefore, understanding how data is collected, organised, and used is an important part of AI literacy.

In Python, structures like lists and dictionaries make it possible to organise, analyse, and explore data in creative ways. Using these tools to build projects can help abstract computing concepts start to feel more concrete and meaningful.

What learners create in the ‘More Python’ project path

The ‘More Python’ project path supports learners through three stages: Explore, Design, and Invent. Each stage builds skills while giving learners more ownership over what they create.

In the Explore stage, young people learn new concepts and build confidence in using data and core Python structures, such as lists and dictionaries. Projects include:

• Making an interactive chart of Olympic medals
• Building a model of the solar system
• Creating a frequency graph that learners can analyse to crack a code

These projects help learners develop new skills, while exploring how Python can be used to analyse and explain real-world information.

As learners progress to the Design stage, they start making creative choices about how their projects look and behave. In this stage, they:

• Create a project that produces encoded art based on a user’s name
• Build an interactive world map that helps users learn interesting facts

Here, Python becomes a creative medium. As well as putting their new skills into practice, learners think about audience, interaction, and presentation to make their projects their own.

In the Invent stage, learners bring everything together. Using the skills they have built, they design and create a data visualisation on a topic they are passionate about. This final project gives learners the freedom to choose their data, shape their idea, and tell a story that matters to them.

By this point, learners are planning and creating their own projects, growing in confidence and independence.

Take the next step with Python

If the young people you support have already learned the basics of Python, ‘More Python’ offers a clear and creative next step. The projects are designed to be accessible, and young people can work through them at their own pace, whether they are learning independently, at a Code Club, or in the classroom.

By working with data, getting creative, and making their own original projects, learners can build confidence and start to see what they can achieve with Python.

Alongside the ‘More Python’ project path, you can access hundreds of free coding projects on our Code Club Projects site. Find more projects to suit your learners’ interests, and support them to build their digital skills through creativity and making.

The post Levelling up with Python: Create with data appeared first on Raspberry Pi Foundation.

1160. This week, we look at why "mogul" means both a ski bump and a powerful person. Then, we tackle when to use "awhile" versus "a while," with a trick to help you remember.

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Subagents are becoming a core primitive for serious AI-assisted development. In this episode, Kaushik and Iury disambiguate "agent" terminology, unpack plan mode vs subagents, and explain how parallel, scoped workers improve research quality without polluting the main thread.

Full shownotes at fragmentedpodcast.com.

Show Notes

Resources & Documentation

Official Documentation

Agents, Modes, Subagents: official harness docs

• Claude Code Subagents
• Gemini CLI Subagents
• Opencode Subagents
• Cursor Subagents
• Antigravity Agent Modes
• AOE Scouting

Research Papers & Articles

• Introducing Claude Opus 4.5
• Deep-Research Agents Paper
• Post: GPT-5 System Card by Alex
  Xu
• Self-Driving Codebases Blog -
  multi-agent systems making 1,000 commits/hour

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[!fyi] We transitioned from Android development to AI starting with
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