Summary
AI has changed the economics of engineering.
Every engineering leader is being asked to decide where the next dollar should go: more engineers, infrastructure, reliability, new product work — or tokens, AI models, agents, tooling, and experimentation.
The question isn’t whether to spend on AI. It’s what should get funded, what should get cut, and how do you know the investment is actually creating value?
Discussion:
\- How are execs reallocating engineering investment because of AI?
\- When should AI spending displace other engineering investment — and when should it be additive?
\- How do you decide an AI investment isn’t paying off and stop funding it?
Host
Madhu Vohra
Co-Founder/CTO @ Clario
Madhu Vohra is CTO at Clario and a seasoned engineering leader with deep experience in systems and infrastructure engineering. She has built and led high-performing software teams, with a focus on technical excellence, execution, innovation, and talent development.
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