Adam Gonnerman

While pursuing my Master’s degree at Salve Regina University , I collaborated with a fellow student on a class project to design a minimalist tabletop role-playing game. Role-playing games (RPGs) are collaborative storytelling games in which players take on fictional characters in a shared world. One participant—often called the Game Master—describes the setting and challenges, while the other players declare what their characters attempt to do. Outcomes are typically determined by simple rules and dice rolls, blending imagination, strategy, and chance. Our goal was to create a system simple enough for beginners but structured enough to feel meaningful. I initially sketched a rules framework built around four core attributes—Strength, Agility, Intelligence, and Charisma—using a single six-sided die to resolve actions. Players would choose streamlined classes like Warrior, Mage, Rogue, or Healer, each interacting differently with combat and problem-solving. The system emphasized fast e...

When most people think about operating systems, they picture the old desktop rivalry between Windows and Mac. But this video looks beyond that familiar battle to tell a different story—one about a quiet contender that reshaped the digital world from behind the scenes. It begins with a hobby project in 1991 and traces how an open-source experiment, rooted in the Unix tradition, began spreading in unexpected places. Without dominating the desktop spotlight, it steadily found its way into the infrastructure that powers the internet, mobile devices, and far more than most people realize. Rather than focusing on who “won” the visible war, the video invites viewers to reconsider what winning actually means—and to discover how the software you rarely see may be running more of the world than you ever imagined.

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 ...