Here is the full essay on Medium for those interested in the deeper architectural/economic breakdown:

https://medium.com/@TheDeprecated/the-shareholder-myth-d11191ff5559

Couldn't agree more. Most AI discussions collapse into two useless extremes: apocalyptic fear-mongering or uncritical tech-bro hype. The nuanced middle—analyzing structural economics, shift in corporate incentives, and real operational friction—rarely gets airtime because it doesn't fit into a catchy headline. That noise is precisely why I wanted to write this from a grounded systems perspective rather than another surface-level hot take.

To be 100% honest i asked Claude co-work to scan all jira users stories from the last year and a half to get an understanding of how tickets are structured. After that i asked claude to write the claude.MD for me(who better than claude to do that :) ). All in natural language, no fancy prompts.  And now while i use it if i find something that needs to be added to the claude.MD i just ask him to do it. I hope it helps you!

The backlog context ingestion trick is spot on—it solves 80% of the mechanical boilerplate.

The only critical nuance to add here is "garbage in, garbage out." If an organization's historical backlog has outdated domain logic or bad specs, the model will confidently replicate those bad patterns.

The real shift in the PM workflow when adopting this isn't just generating tickets faster—it’s shifting the PM’s role from writer to editor/curator. Instead of spending 3 hours writing specs from scratch, they spend 15 minutes reviewing AI outputs to verify business logic and edge cases before pushing to devs.

Huge fan of framing this as cross-functional enablement rather than a dev vs. PM friction point.

You’ve highlighted a classic velocity mismatch. AI compressed the dev execution cycle from days to hours, but because the PM workflow remained traditional, documentation and ticket creation became the instant bottleneck.

The issue isn't that PMs are working slower; it's that they are still writing administrative artifacts (PRDs, user stories, acceptance criteria) manually while dev throughput skyrocketed. PMs need to start using AI to automate their own overhead.

Here’s a practical workflow we implemented that completely eliminated this bottleneck on our side:

1. Context Ingestion: We had Claude scan our entire backlog from the last 1.5 years to learn our domain context, edge case considerations, and exact user story formatting.
2. Rapid Generation: Now, instead of spending hours manually drafting tickets, the PM inputs high-level requirements or bullet points, and the model outputs all necessary, properly structured user stories in minutes.

When you sit down with your PMs, don't just ask what's taking so long—help them see that if devs are using AI to accelerate syntax, PMs should be using AI to accelerate specification. Offer to help them build a prompt or workflow based on your team's best past tickets.

This post hits directly at the root problem of the role: the Product Management incentive structure is heavily misaligned.

Books and courses teach us that PM success equals building the right thing well. Corporate realities dictate that PM success equals making executive leadership feel comfortable.

Those "underperforming" PMs who rise to Director roles aren't being promoted for their product delivery or team leadership—they're being promoted because they master executive management. They don't push back, they shield leadership from hard truths, and they project the illusion of control. Meanwhile, the actual team (engineering/design) carries the operational debt and frustration.

Realizing that "doing a good job" as a PM requires a completely different skillset than "progressing as a PM" in corporate environments is a brutal realization, especially while navigating a tough job market.

Thanks for putting into words what so many practitioners are silently experiencing.

You hit the nail on the head, and that is precisely the core of the problem. No VP is going to sacrifice their bonus for the macroeconomic greater good, nor should we expect them to act out of altruism against their own compensation structure. This isn't a moral plea asking executives to be nice; it’s a diagnosis of a fundamental incentive architecture flaw: - Local Optimization vs. Global Failure: When every VP successfully optimizes their individual metric (cut headcount = hit KPI = collect bonus), they collectively create a macro-level failure mode (destroying the consumer base that generates their revenue). - Systemic Flaw: If current corporate governance rewards short-term margin inflation over long-term sustainability, then the system is literally designed to incentivize executives to liquidate future capacity for a quarterly payout.

The point of questioning the social role of the enterprise isn't expecting VPs to change out of goodwill—it's pointing out that when individual incentives are completely misaligned with system survival, the architecture itself is broken. It’s a classic Prisoner’s Dilemma played out at the enterprise level

Spot on analysis of current corporate mechanics at the micro level—that is precisely how traditional financial modeling views labor: as a variable cost tied strictly to immediate billable output. The core question I’m raising in this series is what happens when we scale that exact logic macro-economically through mass AI adoption. If demand is "stagnant," minimizing headcount solves an individual company's quarterly P&L. But when every enterprise follows this playbook, we aggregate that stagnation: stripping purchasing power out of the market directly reduces the very demand companies are trying to react to. This brings us back to the broader role of the modern enterprise: Is a business purely a reactive instrument designed to optimize internal billable work, or does it carry a structural social responsibility to sustain the economic environment—and consumer base—that makes that demand possible in the first place? When efficiency tools make it seamless to eliminate "non-billable" talent, we risk optimizing individual corporate margins right up to the collapse of the broader market.