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.
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.
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.
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.
A turbulent geopolitical and economic environment is here to stay. Allocating capital in today’s economic and geopolitical landscape requires a sharp focus on macro trends, a disciplined approach to risk, and an ability to anticipate shifts in policy and global power dynamics. The investment landscape has never been more complex, with heightened tensions between the U.S. and China, uncertainty surrounding Taiwan, and Europe’s economic fragility. The new reality is that trade realignments, subsidized industrial policies, and emerging trading blocs characterized by protectionism and localization are rising. Now What?Geopolitical risk is no longer an afterthought. The US-China rivalry, Taiwan’s strategic importance, Europe’s economic fragility, and shifting trade policies will shape the next decade of global markets. Savvy investors will anticipate these changes and allocate capital to industries and regions positioned for sustained growth. The key to success is flexibility, resilience, and the ability to recognize macro trends before they materialize fully. The future is uncertain but full of opportunities.
The “Thucydides Trap” occurs when a rising nation-state—for the Greek historian Thucydides, it was Athens—must eventually have a violent confrontation with the existing dominant nation-state—Sparta in his time. It is a zero-sum game where there can be only one dominant nation-state as the eventual winner, and it is usually assumed that the rising nation-state will outdo the dominant nation-state resolved only by military conflict.The United States and China are today’s Sparta and Athens. For several decades, their geopolitical relationship has been fundamentally based on collaboration and healthy competition, raising the bar for both countries. Now, it is turning into discordant competition, trade restrictions, and embargoes. The combined benefits of global collaboration and competitiveness, not trade restraint, will only enhance the benefits for the United States and China. The government creates friction and potential conflict, which is the biggest reason we fall into the Thucydides Trap. If appropriate, oversight, sensible regulation, and enforceable trade agreements do not interfere with fair competition and collaboration. There is no “Trap” to avoid. The sooner China and the US realize this, the better off each country (and the world) will be.
AI is not a data problem; it is a cognitive architecture problem. Data and computing power will become insurmountable hurdles for transformer-based models. A new generation of AI models requires fundamental breakthroughs. Large data models can’t learn, transfer knowledge or understanding, understand the relevance, or use analogous learning to transfer that relevance and predict. Current AI models require massive and increasing data and learn from reinforcement. This cannot scale and is massively inefficient. Real learning based on cognitive architecture, focused dynamic data, and referential data sets is a better solution. This is closer to real human learning, more effective and efficient, and offers a significantly better solution. Understanding the natural learning process — referential and analogous data, categorization, transferring and building upon that data, and creating knowledge applicable to new situations — learning builds upon itself and is exponentially effective. That is the real AI solution.
The paranoia is building. From the CHIPS Act to proposals for absurdly high tariffs (60% on goods from China isn’t going to help anyone) to banning TikTok, the world is on the verge of reversing decades of progress and exchanging real progress for delusionary gains.
Efforts to localize production and economic development with vast government subsidies are being proposed or enacted in the United States, the EU, China, India, and any other economic center that can think of it.
Hiding behind walls has never worked and makes life worse for everyone.
Global technological transformation and disruptive technologies create extraordinary opportunities – and magnified risks. Headline-grabbing hyperbole dominates each news cycle, and some forecasts and bewildering futuristic projections can mostly be dismissed. However, meaningful substance and catalytic disruptive change are permeating all industries.
A context to understand these developments – a broader, methodical, and disciplined way to think about disruption and transformation- shows that extraordinary opportunities on a highly competitive global scale are emerging.
Artificial intelligence and AI-generated tools, digital assets, blockchain-based businesses, gene editing, and DNA sequencing profoundly impact the world’s most important industries. New technological innovations and platforms enable unprecedented disruption to all business and economic models.
Artificial intelligence is poised to significantly impact various fields and activities, transforming how we approach creativity, professional activities, science, and many more domains. Disruption will accelerate the development of new innovative businesses and strategies in finance, medicine, data management, systems engineering, materials science, art, and other industries. AI’s impact will be profound and multifaceted, driving innovation and efficiency and posing challenges regarding ethics, job displacement, and new skills and regulations. As AI continues to evolve, its integration into these areas will likely shape the future of human society in significant ways.
