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The biggest development was the spread of malicious code injection using invisible characters.

• A new technique that hides bad code using non-visible characters spread rapidly in GitHub and public software ecosystems. It reportedly increased in March 2026, affecting more than 151 public locations on GitHub alone, with contaminated items reaching 433 or more [1].
• This approach is designed to mislead reviewers because the characters used are invisible on screen. There were also cases where a single line of code concealed a large amount of content that’s not apparent at a glance—shaking trust in the published software significantly [1].
• There are also concerns that AI can generate large volumes of “plausible code” and make the injection less noticeable. In other words, if a malicious actor uses AI, the number of “clean-looking but dangerous” artifacts can increase [1].

Behind the convenience of AI-generated code, safety verification gaps also became more visible.

• In AI-assisted coding, examples showed what happens when teams simply keep default settings from the start, creating an unsafe state. In particular, for systems that use login credentials, if access-permitting settings remain, the setup becomes easier to misuse [2].
• Another issue is that AI-written text looks well-structured, which can reduce the thoroughness of review. If something gets approved without understanding what’s inside, safety holes may be discovered later [2][6].
• Even in major development efforts like Linux, it was pointed out that while AI speeds up work, accepting output without understanding the content can lead to serious defects. The reality is confirmed: AI can assist, but you can’t delegate the final judgment [8].

Next, a common way of connecting AI to other AIs spread.

• Common rules for connecting AI with external tools and data are moving into the practical stage. Anthropic’s MCP is becoming popular as a kind of “universal plug” for connecting AI to a variety of services [3][4][7].
• This makes it easier to handle files, conversation tools, spreadsheets, data storage destinations, and more—without rebuilding everything separately for each AI. Reducing development effort and making it easier to move between AI applications is a major benefit [3][4][7].
• Around this ecosystem, tools that support the common rules and services that improve the quality of AI instructions are also emerging. The foundation for embedding AI into real work flows is starting to take shape [7][10].

In enterprises, AI is beginning to move into the center of development and maintenance.

• Fujitsu announced a system to automate software updates and refactoring aligned with legal/regulatory changes and institutional updates using AI. The idea is that AIs with different roles proceed in sequence—from requirement organization to design, implementation, and testing [5].
• In a proof-of-concept, it was shown that responding to a medical reimbursement revision reduced a task that previously took 3 person-months down to about 4 hours. The target includes major systems in healthcare and public administration, and the plan is to expand to finance, distribution, manufacturing, and the public sector going forward [5].
• This indicates that AI is moving beyond text generation toward taking on larger, highly repetitive workloads in bulk [5].

A smart way forward is to treat AI not as a fast-moving tool, but as a partner that requires verification.

• AI can speed up tasks significantly, but the better the output looks, the easier it is to skip checking the details. That’s why it’s important not only to use it because it’s convenient, but also to take a final look at whether it’s truly safe and whether you can explain it yourself [2][6][8].
• In particular, for people using AI at work, it’s reassuring to use a standard where you can restate the key points in your own words rather than simply rubber-stamping AI suggestions. If something isn’t understandable, it’s better not to use it as-is—even if it’s fast [6][8].
• For those who interact with AI regularly, it’s recommended to narrow down how you use the tools instead of adding more tools. You’ll likely see effects more clearly when you start with situations where the burden is heavy every time, such as drafting text, organizing research, or summarizing long content [9].
• When using AI in companies or teams, it’s important not to prioritize convenience too much—decide in advance who will be responsible for verification. The faster AI speeds things up, the more you need to clarify the person who will take the final responsibility [5][6].
• And as the trend of connecting AI to other AIs and external tools continues, it also becomes important not to connect to too many places at once. It may be best to start with a small number of connections, confirm that everything runs safely, and then expand gradually [3][4][7].