The Genius Act

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The U.S. Treasury is likely to need to borrow approximately $15 trillion over the next decade. As incredible as that seems, the U.S. Treasury cares about the price of that debt, and therefore, since they are the seller, they need to attract as many buyers as possible to keep the cost of that debt as low as possible. Basic supply and demand at work. Reliable treasury bond buyers are pulling back, primarily China and, to a certain degree, Japan. The Genius Act permits private entities to issue their own stablecoins, provided they are fully backed by U.S. treasuries. That will open up a new set of investors for the U.S. Treasury as it issues its required bonds. On the one hand, it’s an interesting solution because the Treasury can access an expanding market that exists outside the banking system. Expect to see much more international capital flows in US dollars, as well as significantly increased shadow banking among large US-based corporate entities that engage in business with each other and internationally.

The AI Supercycle

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Artificial intelligence is driving technological disruption and economic transformation. It is a unique opportunity and, like PCs, the Internet, mobile, and cloud computing before it, AI is driving a new supercycle. Unlike previous technological revolutions, the current transformation is exponential, creating new industries and markets and impacting existing economic structures, costs, distribution, and employment. While productivity and economic growth are expected to surge, the most significant opportunity arises for capital owners, and therefore, investors. AI will be the most significant economic catalyst of the 21st century, fundamentally altering how we work, innovate, and create value.

Time for Hard Things

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With better models, more effective benchmarks, and a framework for constant improvement, now is the time to focus AI on complex, innovative, and transformational tasks. Essentially, AI and models should focus on hard tech. Hard tech refers to businesses rooted in advanced engineering and scientific innovation, often involving the development of physical products or systems that address complex challenges. Beyond drones, robots, and AI-driven hardware, the following are prominent examples of hard tech opportunities across industries. AI-driven hard tech is creating new business models and industries, such as personalized medicine, autonomous logistics, smart infrastructure, and agentic AI platforms that autonomously manage complex operations, reshaping the competitive landscape and unlocking new avenues for value creation. As a result, businesses and professionals who embrace interdisciplinary skills and continuous learning will thrive in the hard tech ecosystem.

Is AI Any Good?

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So far, we’ve attempted to answer that question through benchmarks. These give models a fixed set of questions to answer and grade them on how many they get right. But just like exams, these benchmarks don’t always reflect deeper abilities. Lately, it seems as if a new AI model is released every week, and each time a company introduces one, it comes with fresh scores showing it surpassing the capabilities of its predecessors. AI research is a hypercompetitive infinite game. An infinite game is open-ended—the goal is to keep playing. However, in AI, a dominant player often produces a significant result, triggering a wave of follow-up papers that chase the same narrow topic. This race-to-publish culture puts enormous pressure on researchers, rewarding speed over depth and short-term wins over long-term insight. If academia chooses to play a finite game, it will lose.

This “finite vs. infinite game” framework also applies to benchmarks. So, do we have a truly comprehensive scoreboard for evaluating the true quality of a model? Not really. Many dimensions—social, emotional, interdisciplinary—still evade assessment. But the wave of new benchmarks hints at a shift. As the field evolves, a bit of skepticism is probably healthy.

What? So What? Now What?: Uncertainty, Transformation, and Upheaval 

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Uncertainty and decisions. This book helps readers better understand a situation (What), determine why it’s important (So What), and decide what to do next (Now What).

The world is uncertain, and all decisions are made in an uncertain environment with unpredictable outcomes. This challenge transcends disciplines, industries, and professions. An increasingly complex modern world shaped by artificial intelligence, geopolitical instability, data overload, and rapidly evolving technology can overwhelm decision-makers who rely on outdated ways of thinking.

Uncertainty is unavoidable. It is not the enemy. It can be navigated with structure and discipline. Critical thinking, multiple perspectives, and decision tools help prioritize, forecast, and adapt decisions, but cannot dictate outcomes. “Decision Intelligence” is vital because it combines data, models, and human judgment, all augmented with new technologies, especially artificial intelligence. Better decisions come from clarity, not certainty. This is the foundation of resilience, agility, and better decision-making during volatile, unpredictable, and transformative environments. It’s not simply a matter of having a formula. Uncertain circumstances are not simple mathematical problems but require systematic and structured thinking. Understanding these structures and the motivations behind the various approaches will be essential. This approach is more of a way to think about thinking.

As Einstein said, “Give me 60 minutes to solve a problem, and I will spend 55 minutes defining it. Then the solution will be obvious.”

This book is about those 55 minutes.

AI is Not Magic

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Artificial intelligence is often imagined in extremes — utopian dreams of salvation or dystopian fears of extinction. More realistically, AI should be viewed as a normal technology. AI will be transformative, like electricity or the internet. Still, it will unfold over decades, shaped by human institutions, policies, and societal adoption patterns, not by sudden leaps into autonomous superintelligence. AI is not miraculous and unpredictable. It is transformative and will impact many lives for many decades. AI will not create extreme utopian or apocalyptic visions. It will be part of a continuum of human technological advances, powerful and transformative but ultimately shaped by human choices, institutions, and values.
Focusing on resilience, gradual adaptation, institutional innovation, and evidence-based governance can help society maximize AI’s benefits while managing its genuine risks. The future of AI will not be determined by the technology alone. We will determine it.

A New Perspective

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The convergence of volatile geopolitics fragmented and unpredictable markets, disruptive technologies, and unique opportunities.

Understanding geopolitical issues, developing innovative and insightful investment strategies, and navigating political and economic volatility are now essential to achieving investment success.

China’s Emerging AI

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Significant VC activity and AI development opportunities are emerging in China. DeepSeek is the Vanguard of innovation from the artificial intelligence “moonshot” encouraged by the Chinese government. Not only will we see ongoing developments from Alibaba and Tencent, but there will also be a layer of elite AI companies at the forefront of China’s AI sector. US sanctions and restrictions have only increased innovation and groundbreaking AI development activity in China. Those sanctions will amount to nothing and encourage accelerated advancement.

The AI Wars

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The announcement of a $500 billion commitment to building AI infrastructure in the United States, is another major salvo in the AI wars. At this point, it’s hard to distinguish whether this is just hyperbole from hyperventilating technology executives or something with real substance.But, more importantly, it indicates an agenda to “win” in artificial intelligence. OpenAI, Softbank, and others are pushing the narrative to “beat China” and align themselves with the Trump administration. Fundamental is a belief that such a race exists, the US can gain advantage by dedicating computer resources, and it’s worth winning at all costs – whatever that means.Unfortunately, computer resources don’t define a sustainable advantage anymore. A decoupling of resources and cooperation between the United States and China have forced the Chinese to develop near-equivalent models while using only a fraction of resources. bigger data centers, substantial computing resources, and overwhelming numbers of GPU production won’t win this arms race. It’s A Zero-Sum Game That Amounts to Nothing.

AGI Is Not Coming Soon

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This podcast discusses my article on the current state of artificial intelligence, focusing on the limitations of large language models and the unrealistic expectations surrounding the development of artificial general intelligence (AGI). I argue that AI systems are not on a trajectory to match or exceed human intelligence because LLMs lack common sense and rely on regurgitating information rather than understanding. Despite the hype surrounding AGI, it is decades away, and the current focus on LLMs is misguided. Instead, I advocate for a different approach to AI that incorporates real-world interactions and visual data.