A viral post about X by software engineer Arnav Gupta, claiming that artificial intelligence is contributing to layoffs even if it is not directly replacing employees, sparked widespread debate in the tech industry.
Mr. Gupta reflected on the broader changes occurring across midsize and large technology companies, saying the company could be affected by layoffs scheduled for later this month. He said that while companies are rapidly increasing adoption of AI tools, they are struggling to turn that investment into meaningful business growth.
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“Will AI make us more productive? Oh, that’s a great question! What if we flipped that around a little bit and simply said, “AI didn’t change anything.” I don’t think anyone would agree with that, even the most skeptical about the impact of AI. The explosion in AI usage is impossible to ignore, especially in high-tech companies. Even the most conservative companies, such as capping AI spending and not giving employees AI tools, are definitely doing some work there. Even if it’s as sad as editing a document using Gemini or Copilot within Google or the Microsoft Office suite, that’s what AI writes. ”
The main focus of Mr. Gupta’s post was the distinction between “inputs, outputs, and outcomes.” He argued that code generated by AI should only be viewed as input, functionality is output, and customer spending represents the actual business outcomes that companies seek.
According to Gupta, many companies are significantly increasing their spending on AI infrastructure and enterprise subscriptions, but aren’t seeing comparable results. He suggested that this imbalance is one reason why companies are cutting jobs.
This post also highlighted the increasing operational costs associated with AI deployment. Mr. Gupta argued that if enterprises frequently use large language models for coding tasks, it can cost thousands of dollars per employee per year. He said some organizations are using headcount reductions to offset AI-related spending as companies now try to manage these additional costs while maintaining profitability.
Mr. Gupta also claimed that AI-assisted coding has exposed what he calls a “coordination” problem within large organizations. He said many projects slow down not because the coding itself takes time, but because teams, managers, and stakeholders struggle to align on priorities, execution, and decision-making.
According to him, the speed of software development through AI has made organizational inefficiencies more visible. He argued that some companies are now experimenting with flatter structures, smaller teams, and AI-driven workflows to reduce friction and speed up execution.
Despite the concerns, Mr. Gupta argued that AI still cannot completely replace the majority of workers. Rather, they argued that the current wave of layoffs reflects that companies are still trying to figure out how to turn AI-driven productivity into measurable business results.
He also noted similarities in language used in recent layoff announcements in technology, including references to “AI-native teams,” “manager coding,” and “agent-based workflows.” Mr. Gupta suggested that these trends point to a broader restructuring of how work is organized across industries.
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