OpenAI acknowledged that user "time spent" declined after implementing content restrictions. The subsequent decision to loosen these rules is likely not a sign of strength but a strategic move to re-stimulate growth and engagement as the platform shows signs of hitting market saturation.
Top AI labs face a difficult talent problem: if they restrict employee equity liquidity, top talent leaves for higher salaries. If they provide too much liquidity, newly-wealthy researchers leave to found their own competing startups, creating a constant churn that seeds the ecosystem with new rivals.
The ultimate outcome of AI might not be a singular superintelligence ("Digital God") but an infinite supply of competent, 120-IQ digital workers ("Digital Guys"). While less dramatic than AGI, creating an infinite, reliable workforce would still be profoundly transformative for the global economy.
The massive capital required for AI infrastructure is pushing tech to adopt debt financing models historically seen in capital-intensive sectors like oil and gas. This marks a major shift from tech's traditional equity-focused, capex-light approach, where value was derived from software, not physical assets.
The AI boom's sustainability is questionable due to the disparity between capital spent on computing and actual AI-generated revenue. OpenAI's plan to spend $1.4 trillion while earning ~$20 billion annually highlights a model dependent on future payoffs, making it vulnerable to shifts in investor sentiment.
The huge capital needs for AI are creating a battleground between banks and private credit firms. Blue Owl's $27B financing for Meta's data center, which paid Meta a $3B upfront fee, shows how alternative asset managers are using aggressive debt structures to win deals and challenge incumbents like JP Morgan.