PhysicsWallah says AI now writes about 90 per cent of its code as headcount and faculty dip
PhysicsWallah told analysts that AI now writes around 90 per cent of its code, and credited AI efficiencies for slightly lower headcount and faculty numbers in the second quarter of FY27. Profit doubled to Rs 142 crore on revenue of Rs 1,242 crore, while the offline business remains loss-making.
Edtech company PhysicsWallah reported its Q2 FY27 results with a strong AI message. Consolidated revenue was Rs 1,242 crore, up 18 per cent year on year, and net profit doubled to Rs 142 crore from Rs 70 crore, according to Entrackr's report of the earnings call.
Management said total employees stood at about 19,100 and faculty at about 7,100, both lower than the previous quarter, partly because of AI efficiencies in content creation, quality checks, test-paper preparation and evaluation. It also said AI now writes around 90 per cent of the company's code.
Employee expenses fell 2.3 per cent year on year excluding ESOP costs, while technology spending rose to Rs 104 crore in the first half from Rs 70 crore a year earlier.
The offline business narrowed its pre-Ind AS EBITDA loss to Rs 108 crore in H1 from Rs 120 crore. Quarterly offline revenue rose 9 per cent to Rs 541 crore, which management linked to lower NEET enrolments and delayed exam notifications. The company runs 370 centres and plans 25 to 30 more a year.
It has also earmarked less than $20 million for acquisitions and is in advanced talks with a couple of fully online education businesses; management expects M&A to add under 5 per cent to FY27 revenue growth. Other outlets' reports differ slightly on the headcount framing, so the figures here follow the Entrackr account.
Why it matters
This is one of the clearer listed-company examples in India of AI being tied to headcount and cost discipline, rather than only to product features. It also shows the shift of spend from people to technology.
Indicops view
Indian enterprises should expect investors to ask for AI-linked efficiency metrics. Leaders should track code quality, review burden and learner outcomes alongside cost, and plan reskilling openly rather than letting AI-driven workforce changes surprise teams.
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