Veo 2: Our video generation model

Model quality and comparisons

• Many find Veo 2 visually impressive, with some examples “stunning” and often preferred over Sora Turbo on shared prompts (e.g., pelican on a bicycle).
• Others note clear artifacts: morphing/skating legs, non-physical object motion, uncanny faces, weird slow-motion feel, inconsistent adherence to prompts.
• Benchmarks show Sora not clearly leading; Kling and Tencent’s Hunyuan are cited as competitive or better on some prompts.
• Some argue this is “the worst it will ever be”; others doubt linear/exponential improvement will automatically lead to full movies or “holodecks.”

Access, openness, and cherry-picking

• Frustration that Veo 2 / VideoFX are geo-restricted or behind waitlists; some say we should ignore closed, demo-only releases.
• Several recall earlier Google models where internal access revealed heavy cherry-picking compared to glossy demos.
• Others argue demos still meaningfully indicate progress, akin to early JWST images.

Compute and open-source ecosystem

• Hunyuan, LTX, and other open(-ish) models already run on high-end consumer GPUs (e.g., 24 GB), though often with constraints and tricky setups.
• Debate over whether open models (like Stable Diffusion/Flux in images) will dominate video versus closed players (Midjourney/ChatGPT-style).

Use cases and practical value

• Near-term uses: b-roll, backgrounds, ads, meme/dank content, auto-generated music videos, stock-like footage, filler in games and websites.
• Some are already using it in TV stations and for public advertising spots.
• Skeptics question whether current limitations on continuity, character consistency, and control make it unsuitable for coherent narratives or serious production.

Creators, labor, and value of human-made work

• Strong concern that video gen tools will displace videographers, VFX artists, animators, and YouTubers, shifting value and control to platforms like Google.
• Disagreement over whether audiences will keep valuing “human-made” content once AI becomes indistinguishable, or whether non-synthetic will become a premium/artisanal niche.

Training data, platforms, and legality

• Google’s access to YouTube is seen as a huge advantage; others note everyone can scrape it, legally or not.
• Debate over whether human training on YouTube versus corporate model training are morally or legally different, especially around copyright and consent.

Misinformation, trust, and safety

• Many worry hyperrealistic video will supercharge propaganda, election interference, and cults of personality, further eroding trust in media.
• Suggestions include cryptographic signing of camera output and public education campaigns that “you can’t trust images/videos/audio anymore,” though others see this as technically or socially fragile.
• Some argue similar fears existed for earlier technologies (print, photography, TV, Photoshop); others counter that ease, scale, and speed of modern generation are qualitatively new.

Societal and philosophical reactions

• Threads explore accelerationism, capitalism-as-AI, and whether life is getting “worse” despite tech progress.
• Split between those excited by democratized creativity and those who see “zero-effort slop,” porn, and ad content as the main outcome.
• Persistent disagreement over whether these systems “understand” anything versus being sophisticated pattern predictors—and what that means for future AGI claims.