Leonid Belkind, co founder and CTO at Torq, joins The Tech Trek to talk about what changes when an engineering organization does more than experiment with AI tools. Torq builds agentic security operations, and Leonid shares how his team is using AI across engineering, product, hiring, customer success, and go to market work.

This conversation gets past the shallow version of “AI makes coding faster.” Leonid makes a clear distinction between coding and software engineering, and explains why the best teams are using AI to shift cognitive load, not remove judgment.

Practical takeaways

• AI does not erase software engineering. It changes where engineering judgment shows up.

• Strong engineers still produce better AI generated work because they know what to ask, what to test, and what tradeoffs matter.

• Hiring processes need to reflect how engineers actually work now, including how they use AI to build, explain, and defend technical decisions.

• Productivity should not only be measured by speed. Leonid talks about throughput, maturity of delivery, and whether teams can produce more without lowering quality.

• AI adoption becomes more powerful when it moves beyond engineering into product, customer success, revenue operations, and talent.

Key moments

00:32

What Torq means by agentic security operations and why different tasks need different AI approaches.

01:49

Why building AI native products with AI native methods creates a useful feedback loop for engineering teams.

05:28

How AI shifts cognitive load so engineers can spend more attention on user experience, architecture, and product value.

10:34

The difference between software engineering and coding, and why that distinction matters more now.

15:13

How Torq has changed technical interviews to evaluate AI assisted engineering instead of pretending AI does not exist.

21:51

How one R&D group measured meaningful delivery gains after adopting AI more deeply.

24:25

Why AI adoption is moving into product, customer success, revenue operations, and talent teams.

One Line That Stuck

“Software engineering as a discipline is not going away. It just changes a phase a bit.”

Practical moves to steal

For hiring, Leonid suggests giving candidates more complex take home work because AI is now part of the real engineering workflow. The evaluation then shifts to the candidate’s ability to explain the architecture, defend decisions, describe how AI was used, and show how they tested and constrained the output.

That is a much better signal than asking someone to work as if the tools do not exist.

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