An Overwhelmingly Negative and Demoralizing Force

AI in Game Creativity and “Slop” Content

• Many see AI-heavy game projects as “asset flips 2.0”: faster, cheaper, but shallow, buggy, derivative, and “soulless.”
• Several argue great games come from messy human iteration—trying ideas, discovering mechanics, and evolving art—rather than from prompting toward a pre‑baked “vision.”
• There’s concern that some studios now treat art, worldbuilding, and even game design itself as a mere “content problem” for AI to fill, rather than the core of what makes a game worth playing.
• Others counter that some genres already thrive on shallow appeal (e.g., “waifu + gambling” gacha games), so the market may tolerate or even reward AI‑assisted output if it hits certain aesthetic or addictive notes.

LLMs as Coding Tools: Useful but Dangerous

• Many developers report real productivity wins: boilerplate, simple refactors, config transforms, docstrings, and unit-test stubs are faster with LLMs.
• Others say this just front‑loads sloppiness: AI code tends to be verbose, poorly structured, and harder to reason about, so debugging and maintenance get worse.
• A recurring theme: orgs are shifting metrics to “speed-to-production” and volume, not deletion, simplification, or deep architectural work. This marginalizes devs who specialize in performance, correctness, and long‑term maintainability.
• Some warn of skill atrophy: if you rely on AI to write or even to explain code, you slowly lose the intuition needed to spot bugs or design good systems.

Management, Mandates, and Misaligned Incentives

• Many anecdotes describe AI being pushed top‑down by executives or VCs who view it as a cost‑cutting “power tool,” often without understanding its limits or the domain.
• Workers report AI-usage OKRs, pressure to “find a use for AI,” and performance reviews tied to tool adoption rather than outcomes.
• Several note that short management tenures and churn mean no one is around when AI-driven tech debt and quality problems finally explode.

Training, Juniors, and Long-Term Capability

• Educators and seniors see LLMs as catastrophic for beginners: they can produce plausible but wrong code that compiles, short‑circuiting real understanding.
• There’s anxiety about where “experienced developers” will come from if new devs grow up pasting and tweaking AI output instead of learning fundamentals.

Broader Economic and Cultural Concerns

• Comparisons abound: AI games as “fast fashion,” “McDonalds,” or clickbait—cheap, ubiquitous, environmentally costly, and crowding out higher‑quality work.
• Some predict a bifurcation: mass AI‑slop for most players and a smaller, premium market for “handcrafted” games and art.
• Others accept AI as inevitable and argue that resisting it is like resisting industrialization—suggesting adaptation, and perhaps policies like UBI, will be needed to manage the fallout.