Behind OpenAI's plan to make A.I. flow like electricity

Perceptions of OpenAI’s CEO and Leadership

• Many commenters view the CEO as evasive in interviews, speaking in vague generalities and dodging hard questions.
• Comparisons are made to other high-profile founders, with some seeing “say anything to keep money flowing” vibes and weak alignment with stated AGI ideals.
• Several point to shifting positions on equity and nonprofit principles as trust-damaging.
• Serious personal abuse allegations from a relative are raised; some see this as disqualifying, others note the facts are not independently verified in the thread.

$7T Vision, Data Centers, Jobs, and Energy

• The initial multi-trillion-dollar AI chip plan is widely mocked as absurd; later “hundreds of billions” still seen as extreme.
• Claim of “half a million jobs” from AI data centers is questioned: modern facilities are capital- and energy-intensive but light on labor.
• Some suspect the real play is subsidies, tax credits, and regulatory capture around data centers and power infrastructure.
• Large AI energy use is compared to (and expected to exceed) Bitcoin; some see this as wasteful, others argue high energy use is justified by AI’s utility.

“AI as Electricity” / Utility Analogy

• The analogy that AI will “flow like electricity” is debated.
• Supporters see it as a useful framing: general-purpose “digital smartness” available on demand.
• Critics say it’s hubristic: electricity is physically universal and scalable; current LLMs are closed, costly, and require huge centralized infrastructure.
• Some argue the analogy only works if small, open models are widely available, which is not OpenAI’s direction.

Economics, Hype, and Real-World Value

• Several call current AI dynamics a bubble, “pump and dump,” or patent-medicine-style hype.
• Noted that flagship services (ChatGPT, Copilot) reportedly lose significant money per user; Nvidia and energy providers may be the main winners so far.
• Skeptics say concrete enterprise “money-printing” use cases are scarce beyond spam and low-value automation.
• Others counter that LLMs already aid coding, translation, creative work, chip design, and could transform domains like tax/accounting guidance.

Ethics, Creativity, and Climate

• Strong disagreement over generative models: some see them as revolutionary creative tools; others as derivative, low-quality slop.
• Many artists reportedly resent training on their work without consent or compensation; one survey is cited indicating overwhelming desire for control.
• Debate over whether this is fair use or copyright violation is noted as legally unresolved.
• Climate concerns about massive compute are raised; proponents respond that compute costs and energy per capability tend to fall over time.

Government, Regulation, and Grift

• Multiple comments draw parallels between AI mega-projects and long-running government IT boondoggles: huge budgets, little delivery, entrenched contractors.
• Worries that AI will justify new bureaucracies, subsidies, and opaque contracts, with taxpayers underwriting speculative private bets.
• Some point out the irony of self-styled market libertarians seeking large state support.

Big-Tech Power and Strategic Positioning

• Observers see large incumbents (especially a key cloud partner) as using equity and profit-sharing structures to box OpenAI in and eventually dominate it.
• Theory: the cloud partner lets OpenAI burn investor money and, if/when the model proves unprofitable, can cheaply tip into majority control while keeping most upside.