Evolving OpenAI's Structure

New Structure & Stated Changes

• For‑profit arm is converting from a capped‑profit LLC to a standard equity structure inside a Public Benefit Corporation (PBC).
• The original nonprofit will retain formal control and become a large shareholder, using proceeds for “mission‑aligned” philanthropy.
• The cap on investor returns disappears; existing profit‑participation units are expected to convert into uncapped equity, greatly enriching current holders.

PBCs, Profit, and Suspected Motives

• Several commenters explain PBCs as normal for‑profits with a public‑benefit charter that mainly offers legal cover for decisions that don’t maximize shareholder value.
• Many doubt that will constrain behavior in practice; they see this as a branding move to sound altruistic while enabling unlimited upside.
• The structural rewrite is widely read as a way to unwind the original nonprofit / capped‑profit promise without saying “we want full profits now.”

Control, Governance, and Legal Pressure

• The mention of California and Delaware attorneys general is interpreted as a sign regulators forced some concessions (e.g. nonprofit control).
• People question who actually controls the nonprofit board and point to the failed CEO ouster as evidence that formal governance doesn’t constrain top leadership.
• Some see this as classic self‑dealing: value is shifted from the nonprofit’s public mission into private equity.

Competition, Market Structure, and Moats

• A key line about “many great AGI companies” is taken as implicit admission the space isn’t winner‑take‑all—or that OpenAI no longer expects to win outright.
• Others argue it can’t admit winner‑take‑all without drawing antitrust fire.
• Debate over whether OpenAI still leads: some say ChatGPT is the default with huge mindshare; others report switching to Google/Anthropic/Chinese models and see frontier LLMs as increasingly commoditized.
• Several note that tech giants’ distribution (OS, browsers, Office, phones) may eventually eclipse a standalone provider, as happened with IE vs Netscape or Teams vs Slack.

AGI, Hype, and Limits of LLMs

• Long subthread argues whether current models are “emerging AGI” or still just powerful pattern‑matching autocomplete.
• Skeptics emphasize hallucinations, lack of agency, no real self‑improvement, and likely diminishing returns from scaling.
• Optimists point to rapid benchmark gains, multimodality, and broad task coverage, arguing AGI is “when, not if,” though timelines vary from years to many decades.
• Some see this structural shift itself as tacit admission that near‑term, self‑improving AGI is unlikely; others think it’s just investors cashing out regardless of timelines.

Risk, Regulation, and Power Concentration

• Several compare AGI efforts to nuclear weapons or “digital gods” and criticize the lack of stringent, global oversight.
• There’s concern that US regulation may end up being protectionist (e.g., bans on foreign models) rather than safety‑driven.
• Commenters question how “democratic AI” can coexist with closed, centralized control and opaque lobbying against stricter rules.

Mission Drift and Future Enshittification

• Many feel this marks the final abandonment of the original “open, nonprofit” ethos in favor of a conventional Silicon Valley wealth‑maximization play.
• Fears that chat products will inevitably move toward ads, subtle commercial bias, and behavioral manipulation once profitability pressure mounts.
• Some hope open‑source and local models will remain a non‑enshittified alternative, but doubt most users will choose them over integrated, proprietary defaults.