This article explores whether the future of AI-assisted art will be determined by prompting skills or by the ability to afford more tokens, questioning the democratizing promise of AI as compute costs become a barrier.
There's a lot of prompt engineering happening nowadays in AI assisted art/video making, app design and other fields and this is valuable skill that separates good AI from mediocre AI. But I keep wondering if that's a temporary phase rather than a long lasting advantage. As generation engines get more expensive to run at higher adherence to prompts, longer context, more iterations, higher resolution, the real differentiator might stop being who can write better prompts and understands the model better and start being who can simply afford to burn more tokens. For an AI artist with a modest budget using the perfect prompt on the first few tries might not be enough if there is someone with deep pockets who can force hundreds of variations, run every idea through multiple engines, upscale everything, and iterate until they land on something better, regardless of whether their prompting was any good. If that's where this is heading prompting skill becomes a nice-to-have rather than the actual moat, and the gap between professional studios and independent artists could widen based purely on compute spend rather than creative or technical ability. If AI art goes this way, we might see a distinction where independent artists become good at working within constrained tools and resources while studios and well funded creators can just throw money at the problem until quality differences show up. There's a moral tension in all this. AI tools were supposed to lower the barrier to entry, letting people without formal training or big budgets make things they couldn't before. And in a lot of ways they have. But if the ceiling on quality ends up being dependent on who can afford more tokens and compute, then one barrier of technical skill and training is being replaced with another barrier of raw spending power. That feels like a strange outcome for a technology that markets itself as equalising creativity. So is skill going to matter less over time or will the tools get cheap enough that this concern won't really matter.
The article argues that the real challenge in AI isn't just building smarter models but making them cost-efficient at scale, highlighting the importance of reducing token usage, improving speed, and optimizing infrastructure.
The author reflects on how AI-generated art challenges traditional notions of artistic value, questioning whether beauty alone suffices and whether the human intent behind AI-assisted art matters.
Pippa, an AI video startup, tries to differentiate itself by paying artists royalties when their styles are used, but it faces skepticism from artists while its models remain similar to other text-to-video services.
This podcast episode discusses AI tokenomics and the emerging issue of 'tokenflation', focusing on measuring the value of AI tokens, the limitations of benchmarks, and future innovations in AI efficiency.
A video from simonjgreen discussing how to scale intent, quality, and artistry when working with AI tools, likely covering workflows and prompting strategies for AI-assisted creative work.