We’re all pretty excited about the potential of artificial intelligence (AI), right? The idea that AI could improve productivity, reduce costs, and drive growth has been a major theme for investors and businesses alike. But what if the future of AI is actually more bearish than bullish?
This article isn’t about spreading fear or doom and gloom, but about exploring a scenario that has been largely under the radar. Our friend Alap Shah asked a critical question: What if AI’s rise leads to unintended consequences that hurt the economy? What if AI’s success actually leads to job losses, a drop in consumer spending, and a deeper economic crisis? This isn’t a prediction—just a scenario that’s worth considering.
Here’s a look at the events that CitriniResearch outlined in their Macro Memo from June 2028, detailing the progression and fallout of what they call the Global Intelligence Crisis.
The Shift Begins: A Strong Start for AI, But at What Cost?
By the end of 2026, everything seemed great. Corporate profits were soaring, driven by AI’s efficiency. The S&P 500 had hit a high of 8000, and the Nasdaq had broken 30k. Nominal GDP was growing at a solid pace, and productivity was through the roof. But here’s the catch: the human workers were disappearing fast.
In early 2026, AI began replacing white-collar workers across sectors. “Layoffs due to human obsolescence” were happening everywhere. The result? Margins expanded, earnings beat expectations, and stocks rallied. But the real problem began when the human economy started falling apart.
“The headline numbers were still great. Nominal GDP repeatedly printed mid-to-high single-digit annualized growth. Productivity was booming. Real output per hour rose at rates not seen since the 1950s, driven by AI agents that don’t sleep, take sick days, or require health insurance.”
The wealthy owners of AI compute saw their wealth explode. Meanwhile, real wage growth collapsed. White-collar workers, especially in tech, finance, and consulting, found themselves out of jobs and forced into much lower-paying roles.
The “Ghost GDP” Problem
As AI improved and businesses cut more jobs, consumer spending began to decline. People simply weren’t making enough to buy goods and services, and the velocity of money slowed down. There was a growing disconnect between the record-setting output of AI and the real economy.
“In every way AI was exceeding expectations, and the market was AI. The only problem…the economy was not.”
“Ghost GDP” became the phrase of the day, referring to the economic output that was recorded but didn’t actually circulate through the economy. It’s like having lots of goods and services produced, but no one is buying them because there’s no money in the hands of consumers.
The human economy, which used to drive 70% of GDP, began to wither. The vast amount of economic value created by AI didn’t lead to the consumer spending that’s so crucial for economic growth. AI’s capabilities were exceeding expectations, but it was still a negative feedback loop that just got worse.
The Jobless Spiral: A Negative Feedback Loop
AI didn’t stop at just displacing white-collar workers; it created a negative feedback loop that no one had planned for. As AI’s role grew in business, companies needed fewer workers. The fewer workers there were, the less money they spent. And the less money spent, the harder it became for businesses to thrive.
As AI became more advanced, companies invested more in AI tools to replace human workers. But this only led to more layoffs, creating a vicious cycle that nobody had anticipated.
“AI capabilities improved, companies needed fewer workers, white-collar layoffs increased, displaced workers spent less, margin pressure pushed firms to invest more in AI, AI capabilities improved… It was a negative feedback loop with no natural brake.”
What Happened to AI-Powered Companies?
The AI revolution wasn’t just limited to replacing workers. Even software companies started to feel the heat from AI’s progress. Companies like ServiceNow were already starting to show signs of stress by the end of 2026.
“SaaS wasn’t ‘dead.’ There was still a cost-benefit analysis to running and supporting in-house builds. But in-house was an option, and that factored into pricing negotiations.”
The emergence of AI-driven alternatives had disrupted pricing power. Companies that had relied on recurring contracts for SaaS products now had to deal with the fact that businesses could build similar systems in-house with AI. This forced companies to cut prices just to stay competitive.
The Impact on Consumer Behavior
As AI became more integrated into everyday life, it started disrupting consumer behavior in ways that were difficult to track. By 2027, people were no longer the primary decision-makers in consumer purchases. AI agents began optimizing purchases for individuals, making decisions for them behind the scenes.
The result was “agentic commerce”—where purchases were no longer made through traditional shopping platforms but by AI agents that shopped for consumers based on their preferences. As AI took over, businesses that relied on consumer inertia (like DoorDash and Uber Eats) began to face serious competition from new, more efficient entrants.
“The market fragmented overnight and margins compressed to nearly nothing. Agents went looking for better deals and lower prices, and companies that relied on consumers’ laziness were no longer competitive.”
The Systemic Risk: When AI Breaks the Economy
By 2027, the tech sector’s problems spread beyond just software. The AI-driven job displacement spiral began affecting every business model that relied on white-collar productivity.
“The U.S. economy is a white-collar services economy. White-collar workers represented 50% of employment and drove roughly 75% of discretionary consumer spending. The businesses and jobs that AI was chewing up were not tangential to the U.S. economy, they were the U.S. economy.”
The crisis didn’t remain sector-specific. As white-collar jobs disappeared, so did the spending power needed to support the economy. The equity markets still cared more about the tech sector, but the bond markets had already started pricing in the deflationary effects of the crisis.
What’s the Takeaway?
The rise of AI has fundamentally changed the way we look at technology. While it promised to bring about greater efficiency and productivity, it also introduced new risks—structural risks that could severely affect economies that rely on human labor.
If AI continues to thrive, it might not just replace jobs—it could disrupt entire industries. As CitriniResearch pointed out, AI isn’t just a sectoral issue anymore; it’s a systemic risk. The future of AI might be more complicated than we originally thought, and its consequences could be much broader and deeper than expected.
As Alap Shah and the team at CitriniResearch say, it’s important to prepare for the risks ahead and understand that AI’s success might not be the economic boon we once hoped for.