Generate videos in Gemini and Whisk with Veo 2

Original Article ↗ Hacker News Discussion ↗

Creative potential and “one‑person movie” debate

  • Many see Veo 2 as a big leap: 8‑second, high‑quality clips open the door to solo or tiny‑team films.
  • Some predict a single‑creator, AI‑assisted movie grossing $100M soon; others argue distribution, marketing, and IP barriers make that unlikely.
  • Existing near‑examples (like small‑team animated films and AI shorts such as “Kitsune”) are cited as proof of the trajectory, not full realizations.

Economics, distribution, and discovery

  • Even if production costs approach zero, attention remains scarce: users expect a YouTube/TikTok‑like world with vast slop and a few breakout hits.
  • Success is expected to remain Pareto‑distributed: story, branding, and marketing still dominate, not pure technical capability.
  • Platforms that best surface gems from massive AI output are seen as the real power centers.

IP, copyright, and style cloning

  • Discussion of US law: purely AI output isn’t copyrightable, but human editing/selection can create a protectable work.
  • Many expect laws to change as industry adopts AI.
  • Ghibli‑style marketing examples raise ethical concerns about training data and derivative “soul‑less” mimicry.

Art, taste, and authenticity

  • Some find early AI films exciting, rough, and more “human” than overly polished studio output; others dismiss them as amateurish, cliché, and depressing for real artists.
  • Debate over whether people care more about authenticity/authorial intent versus entertainment value.

Technical limitations and workflow pain

  • Major complaints: 8‑second cap, character inconsistency, low resolution, cost per minute (one user burned $48 on a dozen clips), and high rejection rate from content moderation.
  • Text‑to‑video is described as emotionally draining: endless prompt tweaks, slow feedback, results far from intent, little sense of authorship.
  • Users want more controllable pipelines (sketches, paths, keyframes; “…‑to‑3D‑scene”; integration with tools like Blender/DAWs).

Whisk, Imagen 3, and access issues

  • Whisk uses “prompt transmutation” (image → text description) rather than true latent image encoding; some speculate legal/safety, not technical, reasons.
  • Access is patchy: regional blocks (GDPR concerns), paid tiers, broken UI/rollouts, and confusing product overlap (Veo vs Google Vids).

Google’s role in the AI race

  • Some frame Google as an “embrace, extend, extinguish” giant; others note it pioneered the core transformer tech and now benefits from in‑house hardware (TPUs).
  • There’s praise for recent Gemini 2.5/Veo progress but frustration that product UX lags far behind the underlying models.