AI is exceptionally effective for automating text-based work like deep research, data synthesis, and writing first drafts. However, fully automating creative asset generation, especially AI video, is currently ill-advised. The output quality is often poor and can negatively reflect on a brand, making human oversight essential.

AI video tools like Sora optimize for high production value, but popular internet content often succeeds due to its message and authenticity, not its polish. The assumption that better visuals create better engagement is a risky product bet, as it iterates on an axis that users may not value.

When you use AI to generate complex outputs like a website or video, you receive a static, single-layer product. If you don't understand the underlying components (e.g., code, video layers), you can't edit, debug, or evolve the asset, effectively trapping your organization with a 'snapshot in time.'

While many competitors focus on prompt-based "agentic editing," Tela's founder believes this is a temporary step. The ultimate goal is for AI to analyze a raw recording and automatically produce a high-quality final video without any user prompts or editing commands, leaving only the 'fun part of telling your story'.

YouTube's nascent AI video tools are best used to fill specific B-roll or visual gaps. Relying on them for full content creation is inefficient, as the effort to refine prompts and stitch clips together often outweighs the benefits. Treat them as a supplement, not a primary production method.

To truly evaluate a video AI's capabilities, developers should test its performance on complex temporal tasks. This includes analyzing rapid scene changes for context-switching ability and tracking the precise order of events for temporal accuracy.

Despite user requests, Supercut is holding back on building a traditional video editor. They believe it would become an "excuse" for their AI-powered "auto edit" to be mediocre. This strategic constraint forces them to perfect their core differentiator before adding table-stakes features.

As AI makes creating complex visuals trivial, audiences will become skeptical of content like surrealist photos or polished B-roll. They will increasingly assume it is AI-generated rather than the result of human skill, leading to lower trust and engagement.

The primary challenge in creating stable, real-time autoregressive video is error accumulation. Like early LLMs getting stuck in loops, video models degrade frame-by-frame until the output is useless. Overcoming this compounding error, not just processing speed, is the core research breakthrough required for long-form generation.