The pace of commoditization in image generation is wild. Every 3-4 months the SOTA shifts, and last quarter's breakthrough becomes a commodity API.
What's interesting is that the bottleneck is no longer the model — it's the person directing it. Knowing what to ask for and recognizing when the output is good enough matters more than which model you use. Same pattern we're seeing in code generation.
There is a decent chance there will be no clear consensus... Maybe people going custom LoRas etc should publish for the 3x most common models. Or maybe the tooling will make it so that switching models in a workflow will be painfree, as has kind of happened with LLMs.
I'm happy the models are becoming commodity, but we still have a long way to go.
I want the ability to lean into any image and tweak it like clay.
I've been building open source software to orchestrate the frontier editing models (skip to halfway down), but it would be nice if the models were built around the software manipulation workflows:
We don't even need formal regulation to start — just honest internal conversation. I work in tech and most teams I've been part of never once discussed the ethical implications of what we were building. Not because people are evil, but because the incentive structure doesn't reward asking "should we?" — only "can we ship it?"
The gap isn't education, it's accountability. Engineers building engagement loops know exactly what they're doing. They just don't have a professional body that can revoke their license for it.
Fair point — I contradicted myself. What I meant is: the first step doesn't require waiting for regulation (just have the conversation). But long-term, some form of professional accountability would help. Those are two different timescales, not alternatives. I wrote it badly.
And no, not vibebait — just a poorly structured comment from a guy with a fever typing on his phone.
So much AI statementmaking seems to be structured around "It's not X, it's not Y, it's not Z [emdash] it's A" and "What's important is '[experiential first-person descriptive quote]'". Maybe they overfit on Linked In data.
This extends beyond AI agents. I'm seeing it in real time at work — we're rolling out AI tools across a biofuel brokerage and the first thing people ask is "what KPIs should we optimize with this?"
The uncomfortable answer is that the most valuable use cases resist single-metric optimization. The best results come from people who use AI as a thinking partner with judgment, not as an execution engine pointed at a number.
Goodhart's Law + AI agents is basically automating the failure mode at machine speed.
What's interesting is that the bottleneck is no longer the model — it's the person directing it. Knowing what to ask for and recognizing when the output is good enough matters more than which model you use. Same pattern we're seeing in code generation.