Top AI Coding Tools in 2025: What Works & What Doesn’t

AI coding tools are no longer a novelty. From startups to enterprises, developers are using them to accelerate development, auto-generate tests, and build products faster than ever. Some engineering teams claim they’ve become up to 10x more productive with the right AI coding tool in their workflow.

To get a real-world understanding of how these tools are being used, we hosted a community discussion with over 70 tech leaders—CEOs, CTOs, and engineering managers. Here's a breakdown of what’s working, what’s not, and where AI in software development is headed next.

AI Coding Tools Developers Are Using the Most

These generative AI coding tools came up repeatedly during the session:

GitHub Copilot – Completes code inside your IDE based on context.
Cursor – A modern coding environment with built-in AI coding generator features.
ChatGPT & Claude – General-purpose AI models that can also write, translate, and debug code.
Keploy – Generates unit and integration tests directly in GitHub PRs. A game-changer for test automation—no intern needed for test cases anymore.
Lovable – Quickly creates UI components from prompts.
v0 – Converts designs or text prompts into frontend code instantly.
Replit – A cloud-based IDE with integrated AI coding language support.
Codeium – A rising star among GenAI coding tools for everyday coding help.

These tools are far beyond autocomplete—they generate entire functions, test cases, UI layouts, architecture suggestions, and even convert code between programming languages.

How Developers Are Using AI Coding Tools in 2025

The session included plenty of real use cases showing how developers are integrating AI coding generators into their workflow:

Code completion & snippets – Faster coding in environments like VS Code.
SQL generation – Writing optimized queries with ChatGPT or Claude.
Rapid prototyping – Spinning up full apps (React, Laravel, etc.) in hours.
Test automation – Generating unit and end-to-end tests automatically.
Debugging – Suggestions for fixing broken code blocks.
Code translation – Converting legacy code or shifting between languages.

Most teams reported productivity boosts of 20–40%. Some even reported 10x improvements when AI was fully integrated into their stack.

Examples from Engineering Leaders

Code Suggestions in IDEs
Ron Laughton (ReviewInc) shared how Copilot in Visual Studio often finishes his thoughts with working code, saving significant time.

Complex SQL Queries
Dan Perez (Aquent) uses Copilot for daily tasks, but prefers ChatGPT for writing intricate database queries.

Auto-Generated Tests
Teams are using AI coding tools to automatically generate unit and integration tests by inputting a function or endpoint.

Language Conversion
Developers are leveraging generative AI coding tools to move codebases between languages—enabling flexibility without rewriting everything manually.

How AI Tools Help Build MVPs Faster

Some companies are building MVPs at unprecedented speed using GenAI coding tools.

One-Month MVP
Philippe Dallaire (Consuly) used Claude to build an MVP in under a month—a process that typically takes up to a year. A senior engineer still planned the structure, but the execution was handled with AI help.

Frontend Development Without Prior Experience
Nikhil Nathar (AvanSaber) used Lovable and Cursor to build a React app without deep React knowledge. Prompts like "create a dashboard with a sidebar" returned functional frontend code needing minimal cleanup.

Will AI Replace Developers?

One of the most debated questions was around hiring trends. Here's what the group concluded:

Fewer junior roles – Many basic tasks are now handled by AI coding generators.
Senior engineers are still essential – Architecture, system design, and smart use of AI still require expertise.
Team structures are changing – Some are hiring more because their team’s output has increased; others are scaling down.

Christian Frunze (GetKen.ai) expanded his team after AI helped them ship faster. Meanwhile, Jayakrishnan Melethil (CodeLynks) cut R&D costs by 30% with AI, yet maintained output. AI changes team needs—but not always in predictable ways.

Using AI for Testing and QA

AI-powered test automation was a hot topic.

End-to-end testing – Tools like Keploy AI, TestRigor, Functionize, and Mabl can auto-generate full test suites.
Review bots – Jayakrishnan’s team uses bots that check PRs for bugs before human reviewers step in.

These tools are especially helpful for standard test cases. For complex logic or permission workflows, human oversight is still required.

HD Vo noted that while half the tests are now generated by AI, the rest still need refining.

The Rise of Agentic AI

Some forward-thinking teams are trying agentic AI coding tools—models that:

Understand developer goals
Modify code
Commit to GitHub
Run and verify tests

Joseph Khorshed (Cequens) rebuilt their site using an agentic AI assistant. He rated it 8/10 for that task. Others placed general effectiveness around 3–6/10. But momentum is building fast.

Risks and Challenges of AI Coding Tools

Despite the benefits, leaders shared concerns:

Security – Avoid uploading full codebases to public models.
IP issues – Ensure your code isn’t being used to train public models.
Model limits – Large or complex codebases still confuse AI tools.
Code quality – Faster output doesn’t always mean better quality. Refactoring may still be needed.
Skill development – Junior developers may skip foundational learning if over-reliant on AI.

If your work involves sensitive data (fintech, healthcare, etc.), consider self-hosted GenAI coding tools or enterprise-grade solutions with privacy safeguards.

Best Practices for Using AI Coding Tools

Here’s what experienced teams recommend for successful AI adoption:

Start small – Try it on a side project or non-critical task.
Train your devs – Teaching prompt writing and tool selection is essential.
Always review – Don’t deploy AI-generated code without a sanity check.
Integrate into CI/CD – Use bots to generate and review tests automatically.
Track your speedup – Measure how much faster your team ships.
Secure your repos – Avoid uploading sensitive data to public tools.
Maintain test coverage – Regenerate tests when your code changes.
Debug manually – AI can help, but it’s not infallible.

What’s Next for AI in Coding (Next 24 Months)

The community shared strong predictions for what’s coming:

AI will handle more routine tasks – From bug fixes to boilerplate code.
Junior roles will shrink – As AI coding generators take over simple tasks.
Design and architecture matter more – Developers will shift focus to higher-level thinking.
More integration with design tools – Think: UI auto-updates as you change prompts.
QA will be AI-augmented – With more AI-generated documentation and testing, but human checks are still vital.

Final Thoughts

AI coding tools are transforming the way we build software. Used right, they can:

Accelerate MVPs
Lower dev costs
Improve test coverage
Let senior devs focus on high-impact work

But they also introduce new challenges—around quality, security, and developer growth.

The smartest teams are experimenting now. They’re picking safe features to test with AI coding tools, training their developers, and evolving fast. If you’re not already using a generative AI coding tool, now’s the time to try one.

The future of software development is being written—with AI right in the loop.

FAQs

1. Are AI coding tools replacing developers?

Not exactly. While AI can handle repetitive tasks like code generation and test writing, senior developers are still essential for architecture and decision-making.
Most teams are using AI to augment, not replace, human developers.
It reduces the need for junior roles but boosts overall team productivity.

2. What are some real-world use cases for AI coding tools?

Teams are using AI for autocomplete, SQL query generation, rapid prototyping, test automation, and debugging.
For example, developers are building full MVPs in weeks instead of months.
AI also helps convert code between languages and read legacy systems.
It’s all about speeding up the dev cycle without sacrificing quality.

3. What are the biggest risks with using AI coding tools?

Key risks include code security, IP leakage, and reduced code quality if outputs go unchecked.
Some models struggle with large or complex codebases.
Junior developers might skip learning core fundamentals.
Always review AI-generated code and avoid sharing sensitive data with public models.

4. How can teams use AI coding tools more effectively?

Start small with side projects or low-risk features to build confidence.
Train your devs on prompt writing and reviewing AI outputs.
Integrate tools like Keploy into your CI/CD for testing automation.Always pair AI tools with human oversight for best results.

AI Coding Tools: What’s Working, What’s Not, and Where It’s Headed

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