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Over the past two years, leading industry reports have consistently shown that adopting artificial intelligence (AI) for software development can significantly improve the efficiency of engineering teams. A striking example is Amazon, which announced in 2024 that its in-house AI assistant helped save $260 million and reduced development time by 4,500 person-years.
After similar headlines, many CEOs, CTOs, and engineering leaders are no longer asking whether to adopt AI, but how to do it effectively and safely. And if you’re leading a team of 10 to 100 developers at a product-focused startup or a scaling company in fintech, healthtech, or edtech, chances are you’ve already experimented with tools like GitHub Copilot, Cursor, or Amazon CodeWhisperer.
Still, while many company leaders focus on finding AI tools with clear ROI and defining the right metrics for effectiveness, many developers and the broader community remain ambivalent about how deeply AI should be integrated into the development process and how safe that integration is.
In this article, we bring together recent industry research, developer feedback from Reddit, and insights from Bamboo Agile’s tech leaders in an effort to offer a clear-eyed look at the real advantages and risks of AI-driven development and to help you make more informed decisions on adoption.
Given the scope and complexity of the topic, we’ve divided our analysis into four focused parts:
Part 1 – AI benefits in software development and the most popular tools (current article)
What existing research says about AI-driven development
Recent reports indicate that interest in adopting AI for software development remains high, particularly in North America, Western Europe, and Asia. According to GitHub’s 2024 survey, 97% of 2,000 developers from the United States, Germany, India, and Brazil have used AI tools at least once for either work or personal purposes, regardless of their company’s official policy.
In turn, a larger survey of 65,000 developers across 185 countries conducted by Stack Overflow showed a lower percentage of actual usage but reflected an accelerating pace of interest in AI tools – from 44% of regular users in 2023 to 62% in 2024.
With several caveats, the majority of respondents agreed on the following key benefits:
improved development productivity (83%) – considered the primary advantage;
faster onboarding of junior developers(71%);
increased operational efficiency in software development (58.5%).
When it comes to C-level attitudes toward AI adoption, it’s not rhetoric that speaks loudest – it’s investment. According to the SoftBank Vision Fund, 63% of surveyed tech leaders in 2024 reported a noticeable increase in their companies’ GenAI budgets, while 24% said those budgets had doubled. Moreover, in organizations with budgets under $500 million, CEOs often sought to oversee AI initiatives personally. Meanwhile, in larger organizations, more CIOs – and, in some cases, CTOs – began reporting directly to the CEO rather than to the CFO, as was previously the case. In other words, technology leaders have increasingly shifted from the role of “infrastructure optimizers” to that of strategic leaders, taking ownership of AI initiatives and the responsibility that comes with them.
To some extent, this could be seen as a continuation of the 2023 trend, when many companies, in Deloitte’s words, were “working feverishly to build generative AI features into everything from productivity suites to help desk solutions to software development tools.” However, 2024 marked a turning point. As the role of technical leaders grew stronger, companies began to act more cautiously: chaotic experimentation slowed, and questions around ROI and security started to take center stage.
At the same time, tech leaders found themselves in a difficult position. On the one hand, they faced mounting pressure from C-level executives, middle managers, and clients who expected fast returns and demonstrable business value – expectations that are often difficult to meet within a short timeframe. On the other hand, they had to uphold critical safety standards, maintain development quality, and ensure long-term sustainability.
Our research: what developers say AI is actually good at
AI-driven development is a hot topic on Reddit, one of the few places where engineers openly share their day-to-day experiences with generative tools, away from management narratives.
To better understand how these technologies are actually used in practice and how developers truly feel about them, the Bamboo Agile team analyzed over 2,000 Reddit comments posted in early 2025. Our goal was to capture unfiltered, hands-on insights from practitioners reflecting on both the strengths and limitations of the tools they’ve worked with.
In this section, we explore the areas where there are the greatest practical benefits from using AI tools for developers.
Most useful AI use cases
AI-driven programming
Programmers were among the first to show interest in AI tools, mainly because their impact on coding was immediately apparent. For instance, a 2023 experiment by GitHub and MIT, cited by McKinsey, showed that developers writing an HTTP server in JavaScript with Copilot completed the task 56% faster than those who didn’t use it.
Still, Reddit discussions reveal that the real value of AI tools is often more situational than the headline numbers suggest.
Helpful when switching between languages
User marmot1101, responding to sharp criticism of AI in programming, wrote about its usefulness when working with multiple languages and interpreting.
At the same time, the user admitted that they didn’t like it when AI auto-completed “all but the most trivial things” and provided “1000 suggestions” that were probably not needed.
The less you know, the more it helps
Another developer added that, in their view, AI was most helpful when working outside one’s area of experience, for example, when learning unfamiliar APIs or adapting to new platform idioms. In such cases, it helps accelerate debugging and makes it easier to navigate unfamiliar environments. However, in familiar territory, FearsomeHippo relies on AI tools much less, typically using them only for minor tasks like generating a quick function or a simple interface.
Strategy, context, and limited freedom
A user named Fzpzbelieves that results vary greatly depending on how exactly and for what purpose the AI tool is used and that developing an effective “strategy” takes practice. They also note that models perform best when given clear context – the more specific, the better. Though Fzpz recommends limiting creative freedom, they find AI helpful for generating meaningful drafts and ideas, which ultimately saves time and improves code quality, even if the output isn’t always directly usable.
According to GitHub, a behavioral analysis of approximately 900,000 Copilot subscribers confirmed that developers became more likely to accept AI-generated code as they grew familiar with the tool. Referring to a related internal technical study with MIT, GitHub also reaffirmed that Copilot tended to be more effective for less experienced developers. The company views this trend as a step toward democratizing software development for a broader audience.
IDE automation, refactoring, and on-the-fly documentation
User hitanthrope writes that while AI “requires adult supervision,” it has “certainly become a useful tool” in their arsenal. After plugging GitHub Copilot into their IDE, they found that the assistant often “types ahead” exactly what they were planning to write. They also use it for refactoring and generating “on-the-fly documentation”: by simply explaining their intent, the AI provides annotated examples. According to hitanthrope, this is especially helpful when dealing with outdated and poorly documented technologies.
Rapid prototyping with Claude
Some developers mention “vibe coding” – a rapid prototyping or task automation approach using AI, bypassing deeper engagement with the underlying technology. Though Claude isn’t typically positioned as a vibe coding tool, user Sevii describes using it that way. In one case, they used Claude to build a reasonably complicated iOS game without learning Swift, saving several hours of work. Sevii notes, “AI can just write you a bash script from a plain‑language prompt. Doing it yourself might take a couple of hours.” They find AI highly effective for such quick‑and‑dirty tasks, but less trustworthy for precise modifications within existing services.
“I’d highlight two proven areas where AI tools bring real value to software development. The first is catching small bugs in code – something AI handles quite well at this stage. The second is generating routine, boilerplate code, which helps free up developers for more important tasks. On the flip side, the biggest unmet expectation so far has been AI’s ability to work with internal and external system integrations.”
“In my view, the most practical value of AI tools right now lies in explaining someone else’s code and generating code based on a well-crafted prompt. As for commonly mentioned benefits on Reddit, like automating routine tasks, generating on-the-fly documentation, low-code solutions, refactoring, and so on, I’d say only the first one really holds up. The rest require so much double-checking that it often feels easier to do it yourself.”
AI in DevOps
A 2024 survey of 500 DevOps professionals conducted by Tricentis and Techstrong Research highlighted AI’s potential to bridge developer skill gaps (54%), reduce costs (47%), and improve overall software quality (42%). Nearly 60% identified testing as the most valuable area for AI investment within the DevOps lifecycle.
Still, AI use in DevOps gets less attention on Reddit than its use in programming, even though there is evident interest in the topic. For example, one user is exploring the use of GitHub Copilot as an AI plugin for DevOps tasks but remains unsure whether it’s the most suitable option and seeks input from the community.
Another developer, luckydev, shared that they were building an AI agent within a LocalOps setup and asked the community which day-to-day SRE tasks would be most worthwhile to automate using it.
User Recent-Technology-83 enthusiastically supported the project, calling automation of repetitive tasks a significant way to reduce engineer burnout. They endorsed the idea of generating Infrastructure as Code (IaC) and self-serve provisioning, noting that infrastructure management can be tedious. Additionally, they suggested using AI for predictive issue analytics, faster incident response, vulnerability prioritization, and automated security patching. Recent-Technology-83 also expressed interest in the specific SRE pain points luckydev aimed to address.
In some posts on Reddit’s r/devops, users share reviews of automated testing tools, offering insight into how different solutions perform under real DevOps workloads.
Thus, according to Quodo’s review of top DevOps testing tools for 2024, the key benefits of AI-powered solutions include:
automatic test case generation;
intelligent test selection;
predictive analytics and anomaly detection;
reduced test maintenance costs;
integration with CI/CD pipelines.
For example, the article notes that Functionize offers intelligent test automation with self-healing capabilities, QA Wolf provides test autogeneration and CI/CD integration for continuous testing, and Tricentis Tosca uses AI to optimize test suite composition.
AI-enabled DevOps tools developed by leading tech companies, such as Scryer and Chaos Monkey at Netflix, TestGen-LLM at Meta, and various SRE frameworks at Google, are frequently discussed in engineering blogs and conference talks. These tools offer features such as automated incident response, failure injection for resilience testing, infrastructure governance, and error budgeting. While often promoted as examples of AI-driven operations, most of the available information comes from the vendors themselves, making it hard to independently assess their real-world effectiveness.
Maxim Leykin, CTO at Bamboo Agile
“My wife, a junior QA tester, used ChatGPT and Google Gemini to completely migrate her entire workflow from Postman to Insomnia in just two working days. According to her, without these tools at hand, it would have taken her two weeks.”
Sergey Botyan, Lead DevOps Engineer at Bamboo Agile
“At Bamboo, we occasionally rely on ChatGPT to write scripts, assist with technical problem-solving, and analyze software products available on the market. So far, ChatGPT hasn’t let us down in these tasks. Still, while AI is a tool that genuinely speeds up and improves our work, it shouldn’t be doing everything for you as an engineer.”
Most popular AI tools in the dev community
In 2024, around 70% of the 65,000 respondents to the previously mentioned Stack Overflow survey indicated the AI tools for developers they considered the most popular among their peers.
Here’s a summary of the top 8, with brief descriptions.
1. ChatGPT (82.1% of respondents)
The most well-known and widely used AI tool to date, valued for its accessibility and versatility. Developers rely on it for code generation and explanation, QA, documentation, learning new technologies, and more. It’s also appreciated for its cross-language support, contextual awareness, speed, and adaptability.
2. GitHub Copilot (41.2%)
One of the most frequently mentioned tools in surveys and forums, mainly due to its seamless IDE integration. Reddit users report that it’s helpful for generating missing tests, performing code reviews, and refactoring messy files. Multiple studies and developer accounts confirm that Copilot significantly reduces time spent on routine coding tasks, particularly in environments with strong IDE support.
3. Google Gemini (23.9%)
A free alternative to ChatGPT, integrated with Google Workspace and Android Studio. The tool can generate and explain code, SQL queries, regular expressions, and configuration files. Thanks to these features, developers use Gemini in frontend, mobile, machine learning, and data science projects – both as a coding assistant and a reference source. According to some Reddit users, it’s also useful for creating tutorials, JSDoc comments, prototypes, and automating workflows based on data from Google Sheets or Docs.
4. Bing AI (15.8%)
A browser-based assistant built on GPT-4 and Microsoft’s Prometheus model. It’s known for delivering fast, up-to-date answers with citations. Useful for debugging, researching edge-case errors, exploring APIs, and working with outdated libraries or obscure CLI tools. Often used as a “second brain” to navigate Stack Overflow and documentation.
5. Visual Studio IntelliCode (13.6%)
An IntelliSense extension that applies machine learning to recommend context-aware code patterns. Microsoft positions it as especially useful for teams with shared style guides, particularly in .NET, C#, ASP.NET Core, and Azure workflows. However, independent validation of this use case remains limited.
6. Claude (8.1%)
Known for its large context window and strong reasoning abilities. Effective for refactoring, code review, and working with legacy systems thanks to its ability to handle pull requests and multi-file codebases without losing track of context. Particularly suitable for tasks requiring in-depth analysis and consistent behaviour in secure environments.]
7. Codeium (6.1%)
A free alternative to GitHub Copilot. Doesn’t require registration for basic setup and supports offline use. Integrates easily with VS Code, JetBrains IDEs, Neovim, and others. Also offers Codeium Enterprise, which enables on-prem or VPC deployment – a key feature for teams with strict security or privacy requirements.
8. WolframAlpha (5.6%)
A specialized AI tool for symbolic math, analytics, and computational tasks. Widely used in engineering, R&D, fintech, education, and science. Offers a web interface and API, and integrates with enterprise analytics pipelines and BI tools.
Cursor
Cursor deserves a separate mention. Despite not making the top 8, it’s one of the fastest-growing AI tools for developers at the moment ($200 million in revenue and over 1 million users as of April 2025 – Financial Times).
Cursor is a specialized AI IDE built on GPT-4 and Claude, with suggestions, editing, and refactoring integrated directly into the development workflow. Some developers view it as a good fit for teams adopting an AI-first approach, appreciating its intelligent handling of complex projects and scalable codebases – particularly in ML, data engineering, and frontend/TypeScript-heavy environments. It’s also valued for navigating and documenting legacy code, where context and structure are critical.
At least one Reddit post mentioned a manager recommending Cursor over traditional editors like Nvim, although this approach may be seen as controversial, especially when working with outdated libraries.
Our experts’ take
Maxim Leykin, CTO at Bamboo Agile
“Of the tools we’re currently using, GitHub Copilot has proven the most effective in practice. In fact, we started using it a bit earlier, in the form of a specialized AI tool called Korbit. These days, GitHub Copilot partially replaces that tool, but we still use both, including for writing routine boilerplate code. The value of these tools is clear to me.
We’ve also done a lot of experimenting with ChatGPT at the API level – it performed decently, but Gemini turned out to be better, in both quality and security. Personally, I’m also drawn to the trend of embedding AI tools into development environments, especially ones like JetBrains and Android Studio. I believe that’s where the future lies.”
Alexey Shinkarev, Engineering Manager at Bamboo Agile
“In my view, Copilot is currently one of the main utilities used inside IDEs. It’s convenient for handling various relatively small tasks, and I think that applies to all areas of development. Claude and ChatGPT perform better when reasoning is required – but first, you need to feed them documents or tables, and only then start ‘talking’ to them. That said, I personally find myself using Gemini more and more lately.”
Conclusion
As we’ve seen, AI tools can deliver real value in specific scenarios – a point many developers acknowledge. But behind the headlines about growing adoption and success stories lies a range of hidden risks, from security vulnerabilities to unpredictable behavior, that can seriously complicate development processes, especially when these tools are introduced without proper oversight.
In part 2, we’ll explore these risks in more detail, drawing on industry research and real-world insights.
Partner with a team that knows where AI helps – and where it hurts
19. “Harden and Catch for Just-in-Time Assured LLM-Based Software Testing: Open Research Challenges”, Mark Harman et al., 2024, https://arxiv.org/html/2504.16472v2
Aliaksei Patapau is a writer with over 12 years of experience in digital communications and content production. For the past two years, he has been covering software-related topics such as product development, automotive and connected car technologies, and engineering team management.
He works closely with the company’s tech experts, grounding their insights in credible research and real-world case studies. This approach enables him to produce materials that are both analytical and practical – yet engaging to read.
Aliaksei’s work is published across the blogs and social media channels of Bamboo Group, Bamboo Agile, and Bamboo Apps.
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