Adam Gonnerman

 The current AI boom is often framed as an inevitable technological revolution, but a closer look at how it is being financed tells a more fragile story. Major technology companies are borrowing at historic levels to fund AI infrastructure, even as real demand remains uncertain. In 2024 alone, companies such as Amazon , Google , Meta , and Oracle raised tens of billions of dollars each in new debt to expand data centers, buy chips, and scale AI capabilities. Total tech debt issuance has surpassed six trillion dollars. The scale of borrowing is unprecedented, especially for an industry still searching for durable, widespread profitability. Some prominent financial voices are openly skeptical. Michael Burry, known for predicting the 2008 financial crisis, has criticized the earnings narratives surrounding companies like Nvidia , questioning whether accounting practices obscure underlying risks. Investors also point to the imbalance between massive spending commitments and comparati...

The AI boom has become so dominant that it now poses a structural risk to the global economy. A handful of technology companies focused on artificial intelligence account for nearly half of global stock market value. The so-called Magnificent 7, including companies like Nvidia and Microsoft , collectively represent market capitalizations comparable to the entire economy of China . When so much financial weight is concentrated in so few firms, the system becomes fragile. A modest shock to demand, regulation, geopolitics, or technical capability could ripple outward with outsized consequences. At the core of this concentration is a strange economic inversion. Historically, technology companies benefited from scale. The more customers they served, the lower their marginal costs and the higher their profits. In AI, the math often runs in reverse. Each additional query processed by a large model consumes vast amounts of electricity and specialized hardware. Training and inference require ...

AI is no longer just a technology story. It is a market structure story, a governance story, and increasingly a global economic risk story. By multiple estimates, capital concentration around AI now exceeds prior speculative cycles by a wide margin. Comparisons place the current AI-driven market expansion at many times the scale of the dot-com bubble and several times larger than the global real estate bubble that preceded the 2008 financial crisis. Unlike those earlier cycles, this one is not confined to a single sector. That difference matters. Market Concentration and the Illusion of Growth Seven companies now dominate the public markets: Apple, Microsoft, Nvidia, Amazon, Meta, Google, and Tesla. Together, these firms represent roughly 34 percent of the total value of the S&P 500. Each is deeply entangled in AI infrastructure, tooling, data, or distribution deals. This concentration masks a troubling reality. Strip out these firms, and broad market growth over the past two ...

 The story most often told about AI and jobs is one of mass replacement. The reality looks different and in some ways more damaging. Only a small fraction of recent layoffs have been directly attributed to AI. What AI is actually doing is reshaping hiring behavior in ways that quietly undermine the workforce. The most striking effect is at the entry level. Rather than replacing existing workers, AI is being used as a justification to stop hiring new ones. Service firms have implemented hiring freezes tied to AI far more often than they have eliminated jobs outright. This shifts risk away from companies and onto workers trying to enter the labor market. Graduates Caught in the Middle The consequences are already visible. Recent college graduates face elevated unemployment and widespread underemployment. The traditional on-ramp into professional life is eroding. Entry level roles that once trained workers, built experience, and transmitted institutional knowledge are disappearing....

 Much of the public conversation about artificial intelligence treats it as a creative breakthrough or a new form of intelligence. A more sober analysis suggests something far less inspiring. AI is being adopted primarily as a labor strategy. Its main appeal to corporations is not innovation or delight, but the ability to reduce wage costs by eliminating or devaluing human workers. This framing matters because it cuts through the hype. When companies talk about AI as inevitable or transformative, they are often masking a much simpler goal. They want fewer people on the payroll. Large tech firms consistently present AI adoption as unavoidable, as if there were no viable alternatives. This rhetoric echoes an older political move that insists there is no alternative to the current system. Framing AI this way shuts down debate before it begins and discourages exploration of different economic or technological paths. Even the branding around AI reinforces this narrative. Corporate la...

 The current wave of excitement around generative AI is hard to miss, but a closer look suggests a widening gap between promise and reality. The dominant story says AI is on the verge of awakening, that it will suddenly become reliable, intelligent, and transformative if we just feed it more data and more money. That story does not hold up. The more convincing analysis is that today’s AI systems consistently underperform relative to the hype. They are often best understood as sophisticated add-ons to existing tools rather than world-changing breakthroughs. Video editing, text generation, and image creation have improved, but not in ways that justify the breathless claims being made on their behalf. Capabilities, Errors, and Incentives One of the defining traits of current AI systems is their tendency to hallucinate. They produce confident, incorrect outputs in ways that are difficult to predict or prevent. From a user’s perspective, this is a serious flaw. From a company’s persp...

Most people sense that digital platforms have gotten worse. Search results are noisier, social media feels hostile, and basic features are locked behind paywalls or buried under ads. What often gets missed is why this happened. The decline of major platforms is not a natural market outcome or the fault of users choosing convenience. It is the result of policy decisions that removed meaningful discipline from powerful firms. This process has a name: enshittification. It describes how platforms begin by serving users well, then pivot toward extracting maximum value once they are dominant and difficult to leave. I find this framework convincing because it explains patterns we see across nearly every major tech company. How Platforms Decay Enshittification is driven by policy failure, not just greed or bad leadership. Platforms like Facebook, X, Amazon, Apple, and Google followed a similar arc. Early on, they competed for users by offering useful, even delightful experiences. Once they...