The Real Story Behind Sam Altman’s Firing From OpenAI

Interest in OpenAI drama and perceived AI “edge”

• Several commenters express fatigue with the Altman/OpenAI saga, arguing OpenAI no longer has a durable technical edge and cannot monopolize AI.
• Others counter that revenue, user numbers, and ChatGPT’s mindshare suggest OpenAI still matters greatly, comparing its position to early Google or Facebook.

Reliability of the WSJ account

• One commenter suggested the story was fictional but refused to provide evidence, drawing strong pushback as unserious and against community norms.
• Multiple replies defend the article’s reporting standards, arguing mainstream investigative work with “dozens of interviews” is likely broadly accurate.

Chinese vs US AI and industrial strategy

• One view: Chinese AI companies have clearer, profit-focused alignment, unlike US “one company for all people” cultures riven by ideology and “safety” factions.
• Others argue US tech firms remain dominant and that diverse, value-driven teams can outperform monocultures.
• Claims appear that China is moving beyond manufacturing, using AI both for economic advantage and under a national policy to rely only on domestic services by 2028; some mention hacking and corporate espionage.

Altman’s leadership, firing, and board confusion

• Commenters highlight a pattern in the article: Altman bypassing safety reviews, secrecy about control of the fund, and allegedly misleading internal and external stakeholders.
• The most confusing element for many: the sequence where executives helped build the case to fire him, then rapidly flipped to lead a revolt to reinstate him.
• Explanations offered: board incompetence, lack of prepared narrative, fear of exposing their sources, and executives prioritizing organizational stability once they saw staff overwhelmingly back Altman.

Copyright, training data, and personhood analogies

• One faction sees training on unlicensed copyrighted works as theft and a strategic mistake that invites lawsuits and undermines long-term defensible business models.
• Others question whether copyright law even clearly covers “statistical” uses like model training, comparing it to indexes or consultants reusing learned knowledge.
• A long sub-thread debates analogies to human learning, when (if ever) machine systems might deserve rights, and whether big tech is exploiting a double standard: treating LLMs as “like humans” to justify training, but not when it comes to rights or working conditions.
• There’s concern that if some jurisdictions sharply restrict training data via copyright, others will gain a competitive advantage by allowing it.

Commoditization, open source, and business models

• Many argue LLM tech is rapidly commoditizing, with open-source and local inference improving fast and undermining “API as a service” models, especially given privacy concerns about query logs.
• Others stress that user scale and brand matter more than raw tech; ChatGPT’s hundreds of millions of users and cultural presence are seen as a powerful moat.
• A counterpoint: unlike Facebook, inference costs are high, and monetization will be harder; skeptics question whether a sufficiently profitable consumer AI business is even possible under current cost curves, though others cite batching, model distillation, and hardware trends as partial mitigations.

Ethics, safety, and trust

• Some see Altman’s alleged behavior as disqualifying, saying they wouldn’t trust products from a leader portrayed as manipulative and consequence-resistant.
• There is frustration that AI “safety” discourse seems to have faded; commenters ask whether past fears were exaggerated or if better fine-tuning truly reduced risks.
• A recurring economic and political theme is that wealthy actors in tech and finance appear “immune to consequences,” consistent with broader plutocratic dynamics rather than something unique to this episode.