In a world of AI agents, where do we fit in?
The New Stack Manu Narayan ● Covered by 4 sources
AI agents are taking over daily work tasks, and that's changing what your job is actually for. The pitch: stop grinding on tasks, start directing agents and owning the purpose behind the work.
Based on reporting by The New Stack, Manu Narayan — read the original for the full story.
Summary, retelling and take written by AI under human oversight; images are AI-generated illustrations. How we work · Report an error
There's a line getting repeated a lot lately in enterprise tech circles, credited to NVIDIA's Jensen Huang: tasks and purpose aren't the same thing. Coding is a task. Solving problems through technology is the purpose. And as AI agents chew through more and more of the task side, the argument goes, purpose is what's left for humans to hold onto.
The piece makes a case that this isn't just another SaaS-style upgrade cycle. AI's ability to handle complex software development tasks is reportedly doubling roughly every seven months, which is a pace that doesn't leave much room for the usual gradual adjustment. The comparison drawn is Edison and the lightbulb: he didn't make a better candle, he changed the entire curve of what light could do. The claim here is that AI-driven productivity works the same way — not a 5% or 10% bump, but a shift in the output curve itself.
The concrete example is a software engineer shipping a feature. Normally that means bouncing between a project tracker, a wiki, a code repo, and messaging threads just to get oriented, and doing it again every sprint. Swap in a team of agents pulling requirements, flagging architecture conflicts, summarizing recent changes, scanning for vulnerabilities, and drafting an implementation plan in parallel, and the engineer's job becomes reviewing and building rather than assembling context by hand. The article extends the same logic to support teams cutting time-to-resolution in half because agents draft responses before a human ever touches the ticket, and to legal teams reviewing contracts in minutes instead of days once research, compliance, and drafting agents do the groundwork.
EY's survey work gets cited too: nearly all the enterprise organizations it polled reported AI productivity gains, with about half calling them significant, and — notably — those gains were reinvested into growth rather than used to cut headcount. But the article's real warning is about speed, not adoption. AI-native companies are already shipping faster because they built with AI at the core from day one rather than bolting it on later, and larger organizations retrofitting AI onto existing structures are said to be falling further behind, not catching up.
The piece closes by arguing the fix isn't just moving faster, it's moving differently — building clear ownership of AI decisions, shared context, and guardrails that scale, so agents can act with speed without adoption splintering into disconnected experiments. The underlying message is that everyone effectively becomes a manager of agents: setting intent, delegating execution, reviewing output, and applying judgment no model can replicate.
My take — AI-written commentary, not fact-checked reporting
The Edison comparison is doing a lot of heavy lifting here, and it's the kind of framing companies love to repeat right before asking staff to do more with less oversight. Reinvesting productivity gains into growth instead of headcount cuts sounds great in an EY survey, but that's one data point from one report, not a guarantee about how this plays out everywhere. The real tell will be whether the
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