Hard Fork
Will ChatGPT Ads Change OpenAI? + Amanda Askell Explains Claude's New Constitution
with Kevin Roose and Casey Newton
23 Jan 2026
24 min read
1h 28m
TL;DR
OpenAI is testing ads in ChatGPT for free and low-cost users, marking a shift toward ad-supported models despite Sam Altman's past claims that ads would be a "last resort." Kevin and Casey predict this will eventually degrade the free user experience similar to YouTube and Facebook. Anthropic's Amanda Askell reveals the new "constitution" guiding Claude's behavior—a philosophical framework designed to help the AI generalize better to unforeseen situations rather than just follow rigid rules.
Hard Fork is the New York Times podcast where tech columnist Kevin Roose and Platformer's Casey Newton break down the week's biggest developments in AI, tech, and the internet. This episode covers OpenAI's new ad strategy for ChatGPT and features an in-depth conversation with Anthropic philosopher Amanda Askell about Claude's constitutional AI framework.
Takeaways
1
Ad-supported AI follows predictable degradation arc OpenAI's ads follow the same pattern as Google Search and Facebook: starting clearly labeled but gradually blending into organic content as commercial pressures intensify. History suggests free users will face significantly worse experiences within 1-2 years, creating a two-tier system where only paid subscribers get clean, ad-free access to AI tools.
2
Scale economics make subscriptions insufficient OpenAI needs hundreds of billions of dollars to build the infrastructure for AGI-scale ambitions. At $20/month per subscriber, the math doesn't work—ad revenue is mathematically necessary to fund the company's stated goals, making this shift inevitable regardless of past rhetoric about ads being a "last resort."
3
Constitutional AI prioritizes principles over rigid rules Anthropic's new Claude constitution frames AI behavior as values-based rather than rule-based, teaching the model to understand *why* it should behave certain ways. This approach is designed to generalize better to unforeseen situations where rigid rules don't apply, treating AI alignment as a philosophical problem rather than a technical compliance issue.