The Zoho founder explicitly states that while his company has avoided layoffs, it has essentially stopped creating new jobs. This direct substitution effect represents a fundamental shift in resource allocation priorities. Vembu questions whether the saturated global software market actually needs more software, even as AI dramatically accelerates development speed. His warning about AI potentially "wiping" jobs reflects deep concern about India's massive youth population facing an uncertain employment landscape in an era where automation makes goods cheaper but raises questions about income distribution.
The transformation in workforce management at major Indian IT firms is nothing short of dramatic. Over the past two years, bench strength across TCS, Infosys, HCLTech, Wipro, and Tech Mahindra has dropped by 25%, eliminating approximately 75,000 positions from nearly 3 lakh to about 2.25 lakh. The industry is now targeting 8-10% bench strength by FY27, down from the traditional 20-30% buffer that companies maintained for decades. This shift isn't merely cyclical—it reflects a deeper structural change. Industry experts note that the concept of bench strength becomes obsolete only when firms can predict skill-based demand with 90% accuracy three months in advance, a capability that AI-driven forecasting is making increasingly achievable.
The justification for reducing bench strength comes from concrete productivity improvements. TCS reports 10-15% overall productivity gains when leveraging AI for client engagements, with specific cases showing 30% faster remediation and 80% fewer incidents through AI-led monitoring. Infosys sees productivity benefits in the 5-15% range through AI programs, while HCLTech demonstrates 25-30% improvement in software development and 40-50% in business process operations. These companies measure productivity through sophisticated frameworks—TCS uses autonomy progression from Level 2 to Level 3, Infosys employs business outcome-based metrics, and HCLTech utilizes cohort-based validation starting with 100-200 developers before scaling. The productivity gains are substantial enough that revenue per employee is rising even as headcount growth slows, with HCLTech posting a 3.3% year-over-year increase in revenue per employee for five consecutive quarters. Transcripts +6
The timing of the 8-10% bench strength target for FY27 aligns with multiple strategic imperatives. Cost structure pressures are significant—TCS aims to reduce employee costs from 47% to 45% of revenue, while all three companies face margin pressures from wage hikes and AI investments. Client expectations have fundamentally changed, with demands for AI-led productivity commitments leading to new pricing structures. The industry is witnessing revenue-employment decoupling, with NASSCOM projecting $315 billion in IT revenue for FY26 while workforce growth remains limited to 2.3%. Perhaps most importantly, skill requirements have shifted dramatically: traditional mid-level delivery roles have declined 20-30%, while demand for AI, generative AI, data, and cloud skills has surged 30-40%. Transcripts +3
The decision to invest in AI capability building rather than maintain bench strength rests on superior economics. Traditional bench strength represents significant fixed costs with utilization losses and skill obsolescence risks. AI capability building, by contrast, delivers 10-50% productivity improvements across different service types, creates new revenue streams (AI revenues already represent 8.2% of Infosys's total revenue), enables better pricing on AI-driven services, and provides differentiated competitive advantages. TCS's Talent Market Place now handles nearly half of internal resource allocations through AI-driven recommendations. Infosys has 90% of its employees AI-aware, with 10% as AI builders. HCLTech has trained 135,000 employees in GenAI technologies. These capabilities create dynamic response mechanisms that traditional static bench capacity cannot match. Transcripts +4
The reduction in bench strength represents a paradigm shift from static buffer capacity to dynamic capability optimization. In the pre-AI model, companies maintained 20-30% bench strength with 45-60 day deployment timelines, providing immediate availability but with limited skill flexibility and high carrying costs. The post-AI model targets 8-10% bench strength with 30-45 day timelines, using AI-driven resource allocation and rapid skill deployment that can respond 30-40% faster than traditional training cycles. While initial response time may be slower, the skill match accuracy is superior, cost efficiency improves 30-40%, and flexibility increases significantly. TCS demonstrated this agility by handling six mega deals in five quarters, bringing AI into execution from day one. Infosys uses variable hiring models with subcontractors for immediate demand. HCLTech made strategic investments of INR 3,500 crores in AI datacenter capacity to handle demand surges. Transcripts +4
Vembu's approach creates distinct competitive positioning for Zoho. The company focuses on quality, reliability, and brand value rather than rapid expansion in what he views as a saturated software market. This strategy offers advantages in cost structure efficiency through stable workforce and lower fixed costs, quality differentiation enabling premium pricing, and strategic agility as a smaller private company. However, it faces significant risks: scale limitations in revenue growth and market share, talent attraction challenges in a stable workforce environment, and technology infrastructure costs from steep rises in server and memory prices. While major IT firms pursue growth through AI transformation and massive reskilling, Zoho bets on quality-over-quantity in specialized segments where deep expertise creates competitive advantage.
Client demands for AI-delivered services have fundamentally transformed workforce requirements across the industry. Companies now face concrete productivity commitment demands—TCS clients expect 10-15% improvements, Infosys clients ask for AI-led productivity commitments with new pricing structures, and HCLTech clients seek creative ways to reduce token costs. Commercial models are evolving from time-and-material to outcome-based engagements. Service level expectations now include AI-specific performance metrics around availability, accuracy, and explainability. These expectations are driving massive reskilling initiatives—TCS logged 14.6 million learning hours in Q1 FY27 alone, Infosys has 90% of employees skilled on AI platforms, and HCLTech trained 135,000 employees in GenAI technologies. The industry is developing a "barbell" workforce structure with fewer mid-level roles, elite top talent, and continued junior hiring for scalable pipelines. Transcripts +6
The trajectories of Zoho and major Indian IT firms are increasingly divergent. Zoho continues its quality-focused strategy with stable workforce, infrastructure optimization, and niche market leadership. Major IT firms pursue massive workforce transformation through AI reskilling, platform-based service models, and enterprise-scale AI transformation. Both strategies can succeed in their respective market segments—Zoho in quality-conscious mid-market segments and major IT firms in scale-intensive enterprise transformations. The competitive dynamics will increasingly favor specialization over generalization, with success depending on executing distinct capabilities rather than following industry norms. As AI continues to reshape the technology landscape, the fundamental question remains whether efficiency gains will translate into broad-based prosperity or concentrated benefits, a concern that Vembu has repeatedly highlighted in his warnings about AI's impact on employment.
