The AI Paradox: Developers Use It Widely, But Trust It Sparingly

SINGAPORE – February 02, 2026 – A fundamental paradox is unfolding in the world of software development. While artificial intelligence tools have become a near-ubiquitous presence in the daily workflows of engineers across Southeast Asia and India, a deep and persistent confidence gap threatens to limit their ultimate potential. A new study reveals that while developers have embraced AI for its speed, they remain deeply skeptical of its reliability, treating it more as a talented but erratic assistant than a dependable colleague.

According to the Agoda AI Developer Report 2025, nearly nine in ten developers in the region now use AI on a weekly basis, with many reporting significant productivity gains. However, this widespread adoption belies a stark reality: only 43% of these same developers believe AI can currently perform at the quality level of a mid-level engineer. This chasm between usage and trust highlights a critical new phase in AI integration, where the primary challenge is no longer adoption, but earning genuine confidence.

A Tool, Not a Teammate

The allure of AI for developers is undeniable. The report, which surveyed engineers across India, Indonesia, Malaysia, Singapore, Thailand, the Philippines, and Vietnam, found that the primary drivers for AI use are speed and automation. Many developers report saving between four and six hours per week by offloading tasks like writing boilerplate code, generating tests, and debugging simple errors. These tangible gains have cemented AI's place in the modern developer's toolkit.

Yet, this enthusiasm is heavily tempered by practical experience. The consensus among developers is that AI serves as an accelerator, not a replacement for human expertise. The tools are adept at handling routine and repetitive tasks, but confidence plummets when complexity and nuance are required. This sentiment is not unique to the region; global data, such as the 2025 Stack Overflow Developer Survey, shows a similar trend where initial excitement for AI tools has cooled into a more pragmatic, and sometimes critical, view as developers grapple with their real-world limitations.

The Trust Deficit: Inconsistency as the Core Challenge

The single greatest barrier to deeper AI integration is not cost, access, or tooling, but a fundamental lack of reliability. A staggering 79% of developers surveyed by Agoda cited inconsistent and unreliable outputs as their primary concern. This issue is particularly acute in markets like the Philippines (88%) and Thailand (84%), but even in mature tech hubs like Singapore (77%), the problem remains a significant hurdle.

This inconsistency forces developers into a state of constant vigilance. The report reveals that two-thirds of engineers (67%) state they always review AI-generated code before merging it, and a similar number routinely rework outputs to meet production standards. Instead of simply accepting AI suggestions, developers are spending a significant portion of their time verifying, correcting, and refining them. This reality paints a picture of AI as a starting point for a solution, rather than the solution itself.

This necessary but time-consuming validation process creates a hidden tax on the productivity gains AI promises. While the technology can accelerate the first draft of code, the subsequent effort required to ensure its quality, security, and correctness can erode those initial time savings, leading to frustration and reinforcing the trust deficit.

Human Oversight: The Amplified Role of the Developer

Contrary to early predictions that AI would diminish the role of the human developer, the report suggests the opposite is happening. The proliferation of AI has amplified the importance of human judgment, review, and ownership. Accountability, instead of being offloaded to a machine, is now more critical than ever. The developer's role is evolving from a pure creator to that of a discerning editor, a quality assurance expert, and a final arbiter of what is safe and effective to deploy.

This shift underscores the need for new skills and organizational frameworks. As Agoda's Chief Technology Officer, Idan Zalzberg, noted in the report, “The next differentiator will not be who adopts AI first, but who builds a clear framework around it for consistent and productive usage.”

This sentiment points to a growing recognition that successful AI integration requires more than just providing access to tools. It demands a cultural shift that prioritizes verification and a structured approach to human-AI collaboration. Teams that have established strong review habits and clear lines of ownership report higher confidence in their use of AI, while those without such structures remain more cautious.

Bridging the Gap: From Widespread Use to Trusted Maturity

Moving from today's state of cautious adoption to a future of trusted maturity requires addressing the reliability problem head-on. This involves a multi-faceted effort from both the creators of AI tools and the organizations that implement them. On the technical side, the industry is racing to develop more robust models, improve explainability, and build better MLOps practices that ensure consistency from training to deployment.

For organizations, the challenge is both technical and cultural. The report highlights that many companies lack formal AI policies, leaving developers to navigate the complexities of these new tools on their own. Establishing clear guidelines for AI use, providing structured training, and investing in human-in-the-loop systems are becoming essential for harnessing AI's full potential safely and effectively.

The confidence gap also has significant implications for talent. A new skills divide is emerging, separating developers who can simply use AI from those who can critically evaluate its output and integrate it into complex systems responsibly. As AI becomes more embedded in the development lifecycle, the ability to work effectively alongside—and often, to correct—these powerful systems will become one of the most valuable skills for any engineer.

Ultimately, the journey of AI in software development is still in its early stages. While productivity gains are real and immediate, the path to building true, lasting confidence is a marathon, not a sprint. It will be paved with better technology, smarter processes, and the enduring, indispensable oversight of human expertise.