Moderate Gap
Former ChatGPT co-inventor releases new AI model trained with RLCD method
"Inventing Chatgpt" is generating significant organic discussion across ai-tech communities, with 2 posts and 15 comments tracked. Key terms: inventing chatgpt kept, chatgpt kept asking, kept asking myself, asking myself superhuman, myself superhuman chat.
What people are saying
**Summary**
A person claiming to be a ChatGPT co-inventor announced a new AI model called "Jev" trained using a novel method called RLCD, positioning it as significantly faster (20-200x) and cheaper (40-400x) than existing frontier models. The announcement framed the work as a response to the question of why superhuman chat models haven't yet produced AGI, suggesting the new approach addresses that gap.
**Dominant Framing**
Reddit discussion centers on the technical claims: users are examining the stated performance improvements and the novelty of the RLCD training method. The framing emphasizes efficiency gains and cost reduction as the key differentiators from current models. The co-inventor credential is noted but appears secondary to the technical specifications being discussed.
**Notable Disagreement**
Engagement is sparse (9 and 6 comments across two posts), making it difficult to identify substantive disagreement. The low comment volume suggests either skepticism about the claims, limited visibility, or users waiting for independent verification before engaging. No mainstream media coverage has picked up the story, leaving the announcement largely confined to Reddit discussion without external corroboration or critical analysis from established tech outlets.
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2
Social mentions
0%
Mainstream coverage
+0.60
Sentiment delta· Mostly positive
Where this story began · r/accelerate
"After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output..."
Coverage Timeline
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Sources2 social
Mainstream Coverage
No relevant mainstream coverage detected.
Social Mentions
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