Adam Gonnerman

When we think about modern finance, we picture sleek apps, real time payments, and cloud infrastructure humming in the background. What we rarely picture is the decades old code quietly processing trillions of dollars every day. This video pulls back the curtain on the surprising reality that much of global banking still runs on COBOL, a programming language built for precision and reliability long before most modern developers were born. It explores why banks continue to trust COBOL for mission critical systems, how an aging workforce has created what some call the Greybeard Crisis, and whether artificial intelligence can realistically step in to maintain or modernize the backbone of the financial system. Beneath the surface of innovation lies a fragile stability, and understanding that tension is essential to understanding the hidden risks inside global finance today.

Several years back I was in a program role supporting high-visibility digital platform launches. As usual for such a role, I was responsible for driving an on-time release tied to contractual distribution milestones. Meanwhile, a senior engineer was focused on refactoring part of the integration layer to improve long-term maintainability. We were seemingly at cross-purposes, because my goal was to schedule adherence and certification readiness, and the senior engineer was technical sustainability and reducing future defects. These priorities seemed to be in tension, as deeper refactoring risked delaying launch. How could we move forward? Thinking it through, I decided that instead of forcing a trade-off, I would facilitate a working session to break the work into phases. That is, we would identify what refactoring was critical for launch stability versus what could be sequenced into a fast-follow release. Through this process we aligned on a minimal, high impact improvement set that re...

The recent surge in the stock market has been driven largely by seven technology giants often called the Magnificent 7, whose valuations have soared on the promise of artificial intelligence. At the center of this rally sits Nvidia , now one of the largest companies in the S&P 500 and responsible for more than seven percent of the index. Its high price to earnings ratio reflects investor confidence that AI demand will continue to expand rapidly. Yet beneath this optimism lies a structural vulnerability that receives far less attention than quarterly earnings and product announcements. Nvidia’s dominance rests on its ability to design cutting edge GPUs such as the H100 and B200, which power modern AI systems. However, Nvidia does not manufacture these chips. It designs the architecture and software stack, then outsources production. The physical fabrication of nearly all of its most advanced chips is handled by a single company, Taiwan Semiconductor Manufacturing Company . The same...

 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....