In today’s tech landscape, artificial intelligence (AI) seems to be taking the world by storm on social media. However, in many corporate settings, the integration of AI is being approached with considerable caution.
A recent post on X by user Shikhar (@shekhu04) has highlighted the stark difference between the excitement surrounding AI online and the limitations imposed in professional environments. The post details a conversation with a software development engineer (SDE) at a major multinational corporation, showcasing how certain workplaces remain hesitant to embrace AI tools.
today I was talking to a friend who works as an SDE at a big MNC
I asked him about all the new AI models launching every other week. tbh he looked genuinely surprised he didn’t even know most of them
then I asked him, “Don’t you use AI to code?”
he said he is not allowed to.…
— Shikhar (@shekhu04) February 26, 2026
According to the post, the developer was unaware of numerous AI models frequently mentioned in tech discussions, despite the rapid developments in the field. When inquired about the use of AI for coding, he indicated that such practices are not permitted in his workplace.
“All AI tools are blocked while working on client projects,” the post states.
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Prioritizing Security
The primary reason for these restrictions is data protection. Clients, especially those who outsource development tasks, are concerned about the potential exposure of proprietary code or sensitive information that could inadvertently be used in AI training systems.
In heavily regulated sectors, stringent compliance standards dictate how data is managed. Even if AI coding tools promise significant productivity boosts, companies may refrain from implementing them until legal, contractual, and security protocols are firmly established.
This cautious approach sharply contrasts with the vibrant online narratives where developers share AI-generated code snippets, automation experiments, and innovative productivity techniques.
Two Distinct Realities
On social media platforms like X, it may seem like AI-assisted development is the norm. Startups and independent developers often swiftly adopt new tools, highlighting enhancements and improvements in their workflows.
In contrast, large corporations generally adopt innovations at a more measured pace. The process involves navigating through multiple layers of compliance reviews, cybersecurity assessments, and client permissions. For many engineers working on external projects, the risks associated with data exposure outweigh the advantages of expedited coding.
The viral discussion has resonated with professionals who note that the perception of widespread AI use doesn’t always align with the day-to-day realities in established organizations.
As AI tools continue to advance, the gap between public experimentation and corporate regulation may start to close. However, the situation highlighted in this post serves as a reminder that while technological innovations can spread rapidly online, institutional change often unfolds at a considerably slower pace.
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