Akarsh Hegde

I learned that as software gets easier to build, coordination becomes the bottleneck.

At my previous work, I repeatedly watched our projects miss deadlines, cut corners in the SDLC, and eventually get rebuilt - not because engineers couldn t build them, but because teams discovered too late that reality had diverged from the plan.

I also noticed smaller teams consistently moved faster. Not necessarily because they had better engineers, but because context travelled faster: everyone knew what changed, what was blocked, what had been tried, and why.

After years building AI-native systems for enterprises and leading engineers, I ve seen the same pattern repeatedly: engineering slows down when context gets fragmented and feedback arrives late.

Product Hunt now flags comments that are AI-generated. Honestly, I think this is a good thing.

I am attaching a screenshot below that shows how AI comments are marked.

Most engineering processes were designed around a simple loop: a developer picks up a ticket, writes code, opens a pull request, and moves on. That assumption is breaking. An engineer can now orchestrate multiple AI agents, generate several implementations, discard most of them, review hundreds of lines of code, run experiments, and spend their time deciding what should exist rather than typing it.

I saw this shift while building AI-native systems for enterprises at 4good AI and leading a team of developers. The bottleneck is moving from producing code to directing, validating and integrating it.