Agree with you on some of that as well.
Some days I am amazed at how quickly ai fixes something and other days, it creates more problems than solutions.
For now, it’s a bit of both but it’ll be interesting to see how things pan out.
People always say "the best software engineers will survive" the AI transition.
I used to believe that. Now, I’m not so sure.
I set up Claude on a multi-node cluster to see how it handled a complex, low-level system issue today.
It managed to extract the telemetry, debug a deep distributed systems bug, and figure out the exact fix, entirely autonomously.
If we are already at the point where AI can handle low-level debugging across multiple nodes, the "senior engineers are safe" narrative might have a much shorter shelf life than we think.
@peytoncasper The really interesting metric would be output per dollar, not who spends the most on agents.
I suspect the best engineers end up using less compute than you'd expect because they're better at knowing what to delegate
@yorambac really interesting direction. curious how much capability you found you could preserve through distillation while getting it small enough to run locally
@vaibhavbetter Robotics probably makes this tradeoff even harsher because the startup needs more capital and more time before the equity gets a chance to become liquid
@thatguybg The primitives are converging fast. feels like the real differentiation moves to context, permissions, memory and how reliably the agent can actually finish things
@MartinGTobias also explains why fundraising can look bizarrely easy from the outside for some founders.
You're only seeing the last 10% of the process
@MartinGTobias probably an underrated advantage of building something small first - you learn which problems actually require capital and which ones just require work.
@zuess05 maybe the bigger change is that the downside of trying has collapsed.
You can build something meaningful with 2 people and very little capital, while the upside hasn't really changed