I'm a developer for a major food delivery app

Credibility of the Confession Post

• Many commenters find the story highly plausible given known behavior of gig-economy platforms (tip-offset lawsuits, intense A/B testing, lobbying against labor rules), but still treat it as “unverified anecdote.”
• Skeptical points:
  • Burner laptop + library Wi-Fi but then revealing “put in my two weeks yesterday” is seen as poor opsec and a credibility red flag.
  • Doubts that a single backend engineer would have clear insight into money flows and political-spend cost centers.
  • Naming like “Desperation Score” sounds too on-the-nose; people expect euphemisms (“acceptance elasticity,” “payrate sensitivity factor”).
  • Writing style and structure trigger “this might be LLM-assisted fanfic” reactions.
• Counterpoints:
  • Many details match publicly documented industry behavior (tip-offset settlements, benefits-fee surcharges after regulation, large-scale experimentation).
  • Some argue an engineer can infer business tactics from code, logs, product meetings, and PM boasting, even without formal accounting access.
  • A few suggest using AI to intentionally rewrite whistleblower text to obfuscate writing style is rational, so “LLM-like writing” isn’t disproof.

Priority Delivery and Algorithmic Tricks

• Users report mixed experiences: some see clear speed gains with “priority”; others see drivers still doing multiple stops or restaurants blaming apps for delays.
• Debate on the claim that regular orders are artificially delayed:
  • Some say this is exactly what they’d implement if optimizing for upsell.
  • Others argue that widely delaying orders would hurt throughput; replies note you can add a small initial delay and then keep drivers fully utilized (queuing theory).
• Official descriptions from at least one service say priority mainly determines batch order (you’re delivered first if batched), not faster dispatch overall.

Tips, Pay, and “Desperation” Scoring

• Strong emotional reaction to the idea that:
  • High-tipping customers cause lower base pay for drivers, so tips mostly subsidize the platform.
  • “Desperate” drivers (who accept low-paying jobs quickly) are systematically shown worse offers.
• Commenters link to prior AG actions against delivery platforms for using tips to offset guaranteed pay and to academic work on algorithmic wage discrimination.
• Some note that “100% of tip goes to driver” can still coexist with lowering wages or base pay around that tip.

Ethics, Incentives, and Systemic Critique

• Many frame the described behavior as a rational outcome of current incentives: semi-monopolistic platforms, oversupply of low-skill labor, and shareholder-primacy.
• Others push back that “the system” isn’t an excuse; individual executives, PMs, and engineers choose to implement exploitative mechanisms.
• Comparisons are drawn to:
  • Past Uber scandals and aggressive lobbying.
  • Dark patterns, “enshittification,” and “dark gamification” in tech.
  • The “banality of evil” in bureaucratic, KPI-driven environments that abstract away human impact.

Broader Views on Food Delivery Apps

• Some users say they now avoid in-app tipping or the apps entirely, favoring cash tips or direct restaurant orders.
• Others note that restaurants sometimes prioritize app orders due to dependence on those platforms, making it hard for consumers to “opt out.”
• Multiple anecdotes describe misleading status messages (blaming restaurants for delays) and unexplained small credit-card charges, feeding a general sense that the sector is “shady” even if specific Reddit claims remain unproven.

Meta and Verification

• Several commenters caution against turning one anonymous Reddit post into canon; suggest:
  • Journalistic investigation.
  • Driver-led experiments (tracking base pay vs tips, acceptance patterns).
• Regardless of the post’s factual status, many see it as a crystallization of long-running suspicions about gig platforms’ treatment of workers and customers.