This month’s preliminary reading from the University of Michigan’s Surveys of Consumers is a gut punch, clocking in at 50.3, down 6.2% from October’s 53.6 and a whopping 29.9% below last November’s level.

It’s the second-lowest on record since the survey kicked off in 1960, edging just above the all-time low of 50.0 hit in June 2022 amid peak inflation chaos. The “current economic conditions” sub-index cratered to a record low of 52.3 down 10.8% from last month, driven by a 17% plunge in views on personal finances, while the “consumer expectations” index slipped to 49.0, its weakest in six months.

This isn’t just a blip; it’s widespread gloom cutting across ages, incomes, and even political lines—everyone’s feeling the squeeze except the stock-market heavyweights, whose sentiment actually rose 11% thanks to near-record S&P highs.

Year-ahead inflation expectations ticked up to 4.7% from 4.6%, but long-run ones eased slightly to 3.6%, hinting at some guarded optimism on prices stabilizing eventually. A federal government shutdown that’s dragged on for over a month, sparking fears of broader economic fallout—like delayed payments, furloughs, and a hit to growth.

Joanne Hsu, the survey director, nailed it: “Consumers are now expressing worries about potential negative consequences for the economy.” Layer on sticky inflation still biting at essentials, high borrowing costs, and job jitters unemployment ticked to 4.4% in October, and it’s no wonder folks are battening down the hatches.

Consumer spending drives ~70% of U.S. GDP, so this vibe check could throttle holiday retail and Q4 momentum if it lingers. While Main Street’s hunkered down paycheck-to-paycheck households are livid about inequality and “booming” markets that feel rigged.

Wall Street’s partying: S&P 500 near all-time highs, GDP chugging at 3.8% annualized in Q2, and private payrolls adding 42,000 jobs last month better than feared, but still a slowdown. It’s a classic disconnect—asset owners thrive on low cash holdings fund managers at 3.5-3.8%, lowest in 15 years and AI hype.

The average American sees grocery bills up 20% since 2021 and shutdown uncertainty as recession signals. If the “bubble” you’re eyeing is stocks or maybe housing/commercial real estate, this sentiment crater is a flashing yellow light.

Perceptions are already recessionary, worse than 2008 in spots, yet markets shrug it off. History says sentiment leads spending by 3-6 months, so watch December’s final read out Nov 21 and jobs data for cracks.

The Conference Board’s Consumer Confidence Index (CCI) is a key monthly gauge of U.S. consumer attitudes toward the economy, based on surveys of about 3,000 households. It breaks down into two main components.

Present Situation Index (PSI): Measures views on current business/labor conditions and personal finances. Gauges short-term outlooks for income, business, and jobs readings below 80 often signal recession risks.

Unlike the University of Michigan’s sentiment index which hit a near-record low of 50.3 in preliminary November data, as we discussed, the CCI tends to be more volatile and employment-focused. It’s released mid-month preliminary around the 10th-15th.

We’re still awaiting the official November 2025 release—expected on Tuesday, November 25 delayed slightly due to the ongoing federal government shutdown impacting data collection. The cutoff for the survey was likely around November 10-12, so it may capture some early-month shutdown effects.

The most recent data is from October 2025, released on October 29. 94.6 down 1.0 point from September’s upwardly revised 95.6—a six-month low, in the 41st percentile historically. Present Situation Index: 129.3 up 1.8 points—modest improvement, but still below 2025 averages amid sticky inflation.

Expectations Index: 71.5 down 2.9 points—below the recession-warning threshold of 80 for the ninth straight month since February 2025, reflecting pessimism on future jobs and growth. This sideways slide marks the third consecutive monthly decline, with consumers citing inflation especially at the pump and groceries, tariff uncertainties, and the government shutdown as top drags.

Recession fears eased slightly fewer expect one “very likely” in the next year, but more now believe we’re already in one—for the third month running. Holiday spending plans are muted: While some started early to dodge potential tariffs, most intend to shop October-December.

With November peaking—but overall budgets are tighter, especially for lower-income households <$75K and younger folks, where confidence dropped sharply. Both indices show deteriorating vibes, but Michigan’s plunge is steeper—highlighting the “bubble” disconnect you mentioned strong markets vs. Main Street pain.

With the shutdown now in its second month, November’s CCI could dip further if furloughs and delayed payments amplify gloom—potentially crimping holiday retail consumer spending = ~70% of GDP. Yet, like Michigan, it’s a perception gap: Unemployment at 4.4%, Q3 GDP at 2.8% annualized, and S&P highs mask the squeeze for non-asset owners.

Economists like Stephanie Guichard note: “Confidence is stuck in a narrow range since June,” but a shutdown resolution could spark a rebound. If not, expect Q4 growth to cool toward 1.5-2%.Watch for the November drop on the 25th—I’ll keep an eye out.

JPMorgan Chase’s recent analysis on AI infrastructure investments highlights a stark reality for the sector: to achieve even a modest 10% return on the trillions in projected spending through 2030, AI products and services would need to generate approximately $650 billion in annual revenue perpetually.

This figure underscores the immense scale required to justify the buildout, amid warnings of a potential “AI bubble” if demand doesn’t keep pace. Global AI-related capital expenditures like data centers, chips, and compute are forecasted to total $5–7 trillion over the next decade, driven by hyperscalers like Microsoft, Google, and Amazon.

In 2025 alone, these firms are on track for ~$350 billion in AI infra spend—a 60%+ year-over-year jump. At 10% ROI: $650 billion/year ~0.58% of global GDP. Lower hurdle 6% ROI, Drops to $360 billion/year.

Higher hurdle 12% ROI; rises to $810 billion/year. JPMorgan illustrates the challenge with everyday equivalents: Equivalent to ~$35/month from each of the world’s 1.5 billion iPhone users. Or ~$180/month from each of Netflix’s 300 million subscribers.

However, the bank stresses that corporations—benefiting from AI-driven productivity gains—would bear most costs, not consumers directly. Early adopters already report $35+/month in time savings per user.

Echoing past tech overbuilds like 2000s telecom fiber, JPMorgan notes a $1.4 trillion funding gap for data centers alone, power constraints limiting new capacity to 122GW through 2030, and the danger of idle infrastructure if AI adoption slows.

Even leaders like OpenAI’s Sam Altman have flagged excess capacity concerns. This projection contrasts with JPMorgan’s own aggressive AI push: The bank is investing $18 billion in tech for 2025 up $1 billion YoY, with over 175 AI use cases live, yielding $1.5 billion in savings from fraud prevention, personalization, and efficiency.

Globally, JPMorgan sees “astronomical” compute demand but cautions that end-user value must accelerate to avoid a bust. The report, shared widely on platforms like X, has sparked debate—some view it as a bubble signal, while others including JPMorgan analysts rebutting skeptics like Michael Burry argue AI’s productivity upside makes the math feasible.

Sam Altman, is one of the most influential voices in AI, blending optimism about its transformative potential with pragmatic concerns about risks, infrastructure, and governance. While stressing the need for responsible scaling, democratic oversight, and alignment to avoid misuse.

Altman often describes AI progress as a “gentle singularity,” a gradual but exponential shift toward superintelligence that empowers humanity rather than overwhelming it. He views AGI (artificial general intelligence) as achievable and imminent, but downplays its drama:

My guess is we will hit AGI sooner than most people think and it will matter much less. Superintelligence, he predicts, could arrive by 2030, enabling breakthroughs beyond human limits. Altman is bullish on 2025–2027 as a pivotal period of rapid advancement, outpacing recent years.

Altman sees AI development as an exponential curve, with 2025 marking the entry of AI agents into the workforce—autonomous systems handling cognitive tasks like coding or analysis, boosting company output.

He outlines ambitious internal goals: an automated AI research intern by September 2026 running on hundreds of thousands of GPUs and a full AI researcher by March 2028. By 2026, AI could generate “novel insights,” accelerating discoveries in fields like medicine and physics.

In a recent X post, he shared OpenAI’s latest report on progress, highlighting recommendations for scaling responsibly. He predicts that by 2035, individuals could access intellectual capacity equivalent to the entire 2025 global population.

AI agents join workforce; small discoveries possible. Transforms knowledge work (e.g., coding, analysis); economic output surges. Automated AI intern; novel insights from AI. Speeds scientific breakthroughs; recursive self-improvement begins. Abundance in intelligence/energy; “anything else” becomes possible.

Universal access to vast intellect. Democratizes genius reshapes society, work, and creativity. Altman is “determinedly optimistic,” arguing AI will elevate humanity through abundance: cheaper intelligence nearing the cost of electricity, turbocharged economies, and solutions to grand challenges like curing diseases.

He envisions a “Cambrian explosion” in creativity via tools like Sora, where AI democratizes art and entertainment. AI agents will act as “virtual coworkers,” enhancing productivity without fully replacing humans. In a July 2024 X post, he stressed AI’s national security value: “AI progress will be immense from here, and AI will be a critical national security issue.

He advocates for U.S.-led coalitions to ensure AI remains “democratic” and benefits all, preventing authoritarian monopolies. While hopeful, Altman acknowledges dangers: a potential “AI bubble” akin to the dot-com era, driven by surging investments (e.g., OpenAI’s $1.4 trillion compute commitments over eight years).

He warns of misuse by rogue actors (e.g., cyberattacks) and societal harms like job displacement or AI addiction. His “doom score” isn’t zero, but he focuses on mitigation: layered safety value/goal alignment, reliability, robustness.

In a November 2025 X thread, he clarified OpenAI’s stance against government bailouts, emphasizing market accountability: “If one company fails, other companies will do good work.” He calls for technical alignment and societal adaptations, like universal compute access as a “human right.”

On user impacts, he worries about over-reliance (e.g., AI as “therapist” reinforcing delusions) and advocates treating “adult users like adults” while measuring long-term well-being. OpenAI plans 30 gigawatts of compute, with ambitions for 1 gigawatt weekly by reducing costs potentially halving capital expenses.

Altman pushes for U.S.-built fabs, energy, and data centers to maintain competitiveness, viewing it as essential for economic edge. Revenue projections: $20B annualized run rate in 2025, scaling to hundreds of billions by 2030, funding via equity, debt, and AI cloud sales.

He critiques uneven distribution, favoring “techno-capitalism”: encourage wealth creation but widely share benefits to raise both floor and ceiling. OpenAI’s 2025 restructure—to a public benefit corporation governed by a nonprofit—aims to attract capital while prioritizing humanity’s benefit, with $25B committed to health and AI resilience.

In his “Gentle Singularity” essay, he envisions a future of “wildly abundant” ideas and energy, with AI enabling personalized lives and resilience through widespread distribution. Reflecting personally, he sees AGI as “the most important technology humanity has yet built,” worth the “painful” effort despite work-life trade-offs.