How Grab is rebuilding its engineering culture around AI speed

This article summarizes an episode of Amplify’s video series featuring Suthen Thomas Paradatheth, CTO at Grab.

Suthen Thomas Paradatheth, CTO at Grab/ Photo credit: Grab

Suthen Thomas Paradatheth, CTO at Grab, argues that AI makes writing code cheap while making checking code rare.

This change forces leaders to rebuild management, hiring, and physical operations in Southeast Asia before speed causes problems.

The hidden costs of building software fast

As Grab to releases new software tools at a record pace, leaders must now figure out which numbers show a better business instead of just a busy staff.

Old ways of measuring work can push companies toward the wrong goals, meaning executives can no longer rely on legacy metrics to track actual output.

“90% of our engineers are using some form of AI coding assistance daily,” Paradatheth notes. “We did not mandate anything. We made the tools available, taught people the skills on how to use them effectively, and then cut them loose.”

When output grows but work gets harder to measure
“Engineering productivity always has a bunch of caveats and asterisks,” he reports.

Still, the numbers tell a story. Using merge requests as a proxy, Grab has seen around a 40% increase in output per person, with turnaround time for similar-sized tasks dropping by 20 to 30%.

He adds that engineering output represents only a small piece of the puzzle, noting that true AI effectiveness requires company-wide institutional change alongside individual improvements.

Changing team roles and the risk of easy coding

Giving more people the ability to build software speeds up work, but it raises new questions about hiring and oversight. “Software engineering fundamentals still matter,” Paradatheth says.

On hiring, he draws a line.”You cannot come in and say, ‘I could ask any agent to do this, but I do not know what it has done.’ We also look for AI fluency and a sense of ownership, acting like an owner rather than waiting for instructions.”

Lines between departments fade when non-technical staff can automate their own work
The legal team built an automated tool to slash first-pass NDA reviews from hours to minutes, while the design team created a similar tool called Mosaic to generate brand-specific illustrations in a fraction of the usual time.

“If a tool is going to go out in production, if our end customers are going to be exposed, then it needs a review by production engineers,” he explains. “For internal use, we do not want engineering to be gatekeepers.”

Keeping humans accountable for independent systems

Building rules directly into the software

Finding hidden software problems faster

Fixing tiny delays in physical operations

Adapting autonomous systems to varied city streets