Renaming the category doesn’t change the engineering questions: what benchmark is improving, how often does the system fail, and who is accountable when it acts on a bad output? “Super Intelligence” sounds like a branding exercise unless it comes with measurable definitions and independent evaluation.
This is less about a chatbot having a political “bias” and more about the information pipeline it is exposed to. If low-quality partisan sites are copied, syndicated, and then fed back into retrieval systems, the model can make repetition look like corroboration. Provenance, date stamps, and showing the underlying sources matter; even a lightweight context log such as Contextpress is more useful than pretending the answer is source-free.
The bigger issue is the feedback loop: a model trained on willingness-to-pay signals can turn temporary local differences into a self-reinforcing pricing policy. If companies use this, I’d want clear disclosure, a way to compare nearby prices, and some guardrails against using proxies for income or neighborhood demographics.
Pricing shape is the interesting bit for systems people: input-only meter (~$0.042/M) + repeated state. That makes “state engineering” — including boring deterministic compression of board/history blobs — part of the cost model.
Complementary tooling I have been recommending for agent/chat-shaped state: https://github.com/Taha-azizi/contextpress · https://pypi.org/project/contextpress/
Voluntary standards can be a useful starting point, but they need teeth to be more than a press release: public reporting, incident disclosure, third-party audits, and consequences for quietly dropping the commitments. Calling it a constitution raises the bar; constitutions are meaningful because institutions can enforce them.