Reflections from Japan IT Week 2025

In April, we attended Japan IT Week as exhibitors—one of the largest tech trade shows in the country. Walking through the halls of Tokyo Big Sight (Tokyo International Exhibition Center), one trend was impossible to miss: AI was everywhere.

From industry giants to new startups, nearly every booth featured a solution branded as “AI-powered.” AI for customer support. AI for automation. AI for analytics. And of course—AI for software development. The message many seemed to suggest was clear: if you want to stay competitive, let AI handle as much as possible.

But that kind of overreliance comes with hidden costs—especially in software engineering, where quality, context, and judgment matter deeply.

The Reality Behind the AI Buzz

Artificial intelligence has become one of the most talked-about technologies of our time. It’s often portrayed as a miracle solution to all business challenges—or as a disruptive force that threatens to replace humans entirely. But as is often the case, the truth lies somewhere in between.

AI has indeed come a long way. It can automate routine tasks, accelerate content generation, and enhance decision-making with better data analysis. But it still operates within narrow boundaries. It lacks human judgment, emotional understanding, and adaptability in unfamiliar or ambiguous situations.

To truly benefit from AI, we need to move past the hype and develop a clear-eyed understanding of what it can—and can’t—do.

A Developer’s Perspective

As Scriptide’s CEO, Daniel Fazekas, puts it:

“Many leaders in corporations in Japan and around the world believe AI will soon replace human software engineers. As someone who has been actively developing software for the last 10 years and contributing to successful digital projects, I humbly disagree. AI is a powerful tool, but it is not a substitute for the human thinking that underlies effective software design.”

Most of today’s “AI” in development refers to large language models (LLMs) like ChatGPT, DeepSeek, or Claude. These tools are undeniably impressive. They can generate fluent responses and even write functioning code. But they don’t reason. They don’t understand business goals or system architecture. They generate code by predicting patterns—not by designing systems.

When LLMs generate code, it's often almost correct. But in software development, almost working is the same as not working. An off-by-one error or an insecure pattern might be invisible at first—but in production, it matters. And perhaps most importantly: software development is about understanding user needs, weighing trade-offs, and building sustainable systems with long-term value rather than just writing code.

What AI Is Good At

This doesn’t mean we’re skeptical of AI tools. On the contrary—we use them every day at Scriptide.

AI helps automate repetitive tasks, draft documentation, and even bridge language gaps between our teams in Central Europe and Japan. Translating technical specifications, summarizing meeting notes, or generating first drafts of reports has never been faster.

AI also enhances the productivity of junior and mid-level developers, helping them explore solutions or learn by example. It’s an excellent companion for brainstorming, reviewing, and speeding up internal workflows.

But the key word here is companion. Not a replacement.

The Hidden Risks of Overreliance

Relying too heavily on AI in software development may look efficient on the surface—but it introduces several critical risks that can quietly undermine long-term quality and team growth.

Shallow understanding of code Developers might start using AI-generated code without truly understanding how it works. This makes future updates or debugging harder. As Addy Osmani writes in his article “AI Won't Kill Junior Devs – But Your Hiring Strategy Might”:

"Unchecked reliance on AI can lead to skill rot. It's like relying on a calculator without ever learning arithmetic – fine for quick answers, but you'll be in trouble if the calculator is wrong or if you need to understand the equation. The key is balance: juniors (and all devs) should use AI as a tool – a powerful one – but also continuously challenge themselves to work through problems manually, verify AI's answers, and seek understanding. Many teams are now explicitly warning: don't let "vibe coding" with AI replace solid engineering practices”

Technical debt – AI can generate code that “works for now” but doesn’t follow best practices or consider long-term maintainability. These shortcuts often create problems down the line, requiring significant rework or even a complete rewrite.

Weaker collaboration and knowledge sharing – In healthy teams, developers check each other’s code and learn from one another. This process—often called code review—helps everyone improve. But if AI starts replacing this step, teamwork and learning can suffer. Osmani warns that "Mentorship in the AI era also means doubling down on teaching the fundamentals and filling gaps that AI might hide."

Overconfidence in AI’s output – AI-generated code often looks correct, but can include hidden errors or even security issues. Without human review, these mistakes might go unnoticed until they cause serious problems.

AI is a powerful tool, but not a replacement for human judgment. The companies that succeed will be the ones that give their developers access to AI—but also keep people involved in the decision-making process. With the right balance, teams can use AI to speed things up without sacrificing quality, responsibility, or growth.