“Our data centre costs have gone up 3x to 4x. Our AI spending has gone up. But revenues are not growing any faster.”
That was Sridhar Vembu, at Zoholics 2026.
On the industry’s favourite vanity metric, he was blunter: “70% of our code is generated by AI — to me, that metric is useless. What have been the outcomes? We have yet to see significant results.“
And on the American playbook of cutting headcount to protect margins, he rejected it outright. Zoho’s answer is not fewer people. It is more efficient, affordable AI: smaller models, leaner infrastructure, and a deliberate shift toward pushing more of the work into verifiable, deterministic code rather than expensive LLM calls.
My own reading of this, for technology leaders outside the Zoho context: continue investing in foundation models from the major labs for your core workflows. But quietly build a small, focused team to track the emerging, smaller, more efficient models — especially the open-weight models coming out of China. That is where the cost curve eventually breaks.
If you are working through the AI cost and capability question as a technology leader, I write about this regularly in the Founder Catalyst Digest newsletter. Practical perspectives, not theory.
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