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This is one piece of a longer conversation with David Von Dollen, our Head of AI, on where adoption is actually heading. He breaks down the gap between the two in the clip. ▶️

The more interesting question is what becomes possible that wasn't before: new problems worth solving, new skills, new roles that didn't exist a year ago. Most organizations are still on the first version. The second is where it gets interesting.

Most teams are using AI to do the same work faster. That's the smallest version of what it's for. The bigger shift is treating it as an enabler. An accelerant gets the same work done faster. An enabler changes what's worth working on.

When delivery slows on your team, is the first move to add engineers, or to look at where ownership and review queues are actually stuck? Full analysis here: baires.dev/zdoe

His piece lays out engineering team scaling strategies that run in sequence: diagnose the real bottleneck first, design small pods with clear ownership, and bring in nearshore engineers as true owners rather than a task queue.

Past a certain size, adding engineers slows delivery. The math is unforgiving: a 5-person team has 10 communication channels, a 9-person team has 36, a 15-person team has 100+. Each new channel is another place for delay and misalignment to pile up.

When you're deciding where delivery capacity sits, is cost still the deciding factor, or has time-zone overlap moved up the list? Full analysis here: bairesdev.com/blog/software-…

His read on where the work goes next: distributed-first delivery by default, with the regional choice increasingly turning on time-zone overlap and collaboration speed, not only the lowest labor cost.

Software outsourcing is growing straight through the tech hiring slowdown. Worldwide IT spending is set to rise 10.8% in 2026, while CFOs' headcount growth expectations have fallen from 6% to 2% (Gartner).

Learn more about E³: joinecubed.com

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