Zoho founder Sridhar Vembu recently responded to a post by Meta engineer Arnav Gupta, who argued that job cuts will continue until professionals learn to effectively use artificial intelligence (AI) tools. Vembu shared Gupta's post on his timeline, describing it as an "Important post from Meta engineer Arnav Gupta on all the AI-led layoffs."
Vembu's Perspective on AI Costs
Vembu elaborated on the financial pressures driving these layoffs: "As he explains well, AI has increased costs massively for all tech companies. Our own AI bill is skyrocketing and to add insult to injury, server prices have gone up 200-300% in a year because the AI infrastructure boom is consuming all the advanced memory chips." He noted that these layoffs are an economic response by tech companies to control their primary controllable cost—people—in order to pay for AI and servers.
"Of course most are spinning it as the result of the 'AI productivity miracle' but reality is more cost control than a productivity miracle, at least not yet," Vembu stated. He concluded with a touch of irony: "Now let me go back to using AI to generate even more code, so that we don't fall behind all the other guys generating massive amounts of code."
Arnav Gupta's Detailed Analysis on AI Layoffs
Meta engineer Arnav Gupta had earlier shared a comprehensive post explaining the dynamics behind AI-driven layoffs. He began by revealing his personal stakes: "Somewhere in the upper echelons of my company is a list of 8000 names. There is a 10% chance I am on it. I will get to know in a few days on 20 May." He reflected on Coinbase's recent "AI layoffs" announcement, emphasizing that his thoughts are based on widespread feedback from friends across various tech companies.
The 'AI Productivity' Debate
Gupta questioned whether AI truly boosts productivity. He noted that even skeptics acknowledge AI's impact, especially in tech firms where usage skyrockets. However, he pointed out the lack of corresponding revenue growth: "Why have these companies not 2-5xed their revenue then? Why are their apps still almost exactly the same as it was 6 months back?"
He introduced a business framework: "Input, Output, Outcome." Code is an input, features are an output, and users spending money is an outcome. AI pricing, unlike outcome-based SaaS, charges per token regardless of results. This creates a cost imbalance when AI generates massive code without proportional revenue gains.
Challenges in the AI Era
Gupta highlighted that AI's speed eliminates the friction that once forced teams to prioritize and align on ideas. With cheap code generation, bad ideas are not filtered out, leading to alignment issues. Teams often build conflicting MVPs, and instead of collaborating, they rely on AI to reimplement each other's work, wasting resources.
What Layoffs Achieve
Gupta argued that layoffs address two short-term problems: offsetting AI spending and cutting the "alignment tax." AI spending per engineer can be substantial—$100 per day on Claude Code tokens, equivalent to a salary in some regions. To maintain the wage bill, companies may need to reduce headcount. Additionally, large organizations have redundancies; removing 10-20% of staff can temporarily speed up processes by reducing coordination overhead.
He concluded: "These are AI layoffs, even if AI is not replacing you." They stem from AI's cost structure and organizational inefficiencies, not direct replacement. "These layoffs will continue till we learn to use AI," Gupta emphasized, meaning until companies convert AI tokens into measurable outcomes.
Gupta's analysis resonated with Vembu, highlighting a shared concern about the economic realities of AI adoption in the tech industry.
