AI can generate code faster, but that does not make software delivery simple. It shifts the pressure to requirements, architecture, review, and technical judgment.

Goncalo Silva, CTO at Doist, explains how AI is changing the way teams behind Todoist and Twist build software. He shares why greater individual autonomy has led to more collaboration, why deep expertise still matters, and how faster execution is reshaping product delivery, project planning, and engineering hiring.

What Leaders Can Take From This

• Faster code generation makes strong planning and clear requirements more important, not less important.

• Designers, product leaders, and engineers can work from richer prototypes, but production systems still need experienced technical judgment.

• Engineering capacity does not have to move into other functions. Teams can use it to improve reliability, performance, quality, and the amount of valuable work they ship.

• Token counts are a weak measure of progress. Doist looks at team feedback and whether projects are staying on track.

• Engineering interviews need to test architecture, decision making, curiosity, and depth, not simply whether a candidate can produce working code.

Approximate Highlights

00:00 Meet GonCalo Silva and the products behind Doist

02:00 How broadly AI is being used across Doist

04:15 Why greater autonomy has brought teams closer together

09:45 Where nontechnical coding works, and where it creates risk

17:50 How AI compressed a major refactoring effort by 20 to 30 times

25:05 Measuring AI value without counting tokens

30:20 Why faster execution requires more up front planning

34:50 How Doist changed its engineering interview process

One Line That Stuck

“We are the bottleneck. Our attention span, our ability to memorize, our ability to understand, and deep expertise.”

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