As we look back on 1776 after this July 4th holiday, it's a good opportunity to skip forward and predict what our forebears will think of us. When our descendants assess our policies, ideas, and culture, what will they see? What errors, born of myopia, inertia, or misplaced priorities, will they lay at our feet regarding today's revolutionary technology—artificial intelligence? From their vantage point, with AI's potential and perils laid bare, their evaluation will likely determine that we got at least ten things wrong.
One glaring failure will be our delay in embracing obviously superior AI-driven technologies like autonomous vehicles (AVs). Despite the clear safety benefits—tens of thousands of lives saved annually, reduced congestion, enhanced accessibility—we allowed a patchwork of outdated regulations, public apprehension, and corporate squabbling to keep these life-saving machines largely off our roads. The future will see our hesitation as a moral and economic misstep, favoring human error over demonstrated algorithmic superiority.
They will also criticize our stubborn refusal to integrate AI-based policy forecasting into our legislative processes. While AI models could have analyzed the long-term societal and economic impacts of proposed laws, helping us anticipate unintended consequences and optimize for human flourishing, we largely relied on antiquated, human-limited methods. This neglect meant our policies often lagged behind technological change, undermining the very notion of effective, responsive governance.
Crucially, they will likely question our failure to establish new intellectual property frameworks even after it became evident that current copyright and patent laws disproportionately favored incumbents and no longer served their intended purpose in the age of AI. Contemporary delay reinforced monopolies, rather than fostering a vibrant, decentralized ecosystem of innovation that truly benefited independent creators and inventors.
The future will equally lament our oversight in adjusting our schools and workforce development programs. They will see our delay in instituting widespread AI literacy for the general public as a critical blunder. We did not take the requisite steps to equip citizens with the fundamental understanding to navigate an AI-saturated world—to ensure they had access to the latest tools, discern AI-generated misinformation, and grok the foundational technical aspects of AI so that they could contribute to AI policy conversations. This lapse compromised our collective pursuit of an informed, participatory democracy. Compounding this, our sluggishness in adjusting reskilling and upskilling programs meant we left vast segments of the workforce vulnerable to displacement, rather than proactively empowering them with the skills to thrive alongside AI.
Perhaps more fundamentally, they will indict our failure to see data sharing as a social good. In an era where data is the new oil (or even the new water!), we allowed its collection and control to remain highly fragmented and proprietary. We did not establish robust, ethical frameworks for data cooperatives or public data trusts that could have fueled innovation for the common good—in healthcare, urban planning, and scientific research.
From an innovation perspective, the future will see our lack of sufficient investment in basic AI research as a monumental strategic error. Our focus skewed heavily towards optimizing existing models, rather than dedicating resources to more elementary inquiries that could uncover the next generation of transformative AI systems. This shortsightedness potentially limited humanity's long-term scientific and technological trajectory. This misallocation of resources will be underscored by our prioritization of Artificial General Intelligence (AGI) over the development and deployment of robust, beneficial generic AI applications. The speculative pursuit of an arbitrary, unspecified goal often overshadowed the immense, tangible benefits that could have been realized through focused development of practical, specialized AI solutions for pressing societal problems.
Finally, our descendants will not forgive our inadequate investment in public digital infrastructure and universal access. As AI became a foundational layer for economic opportunity and civic life, we allowed a significant digital divide—now an algorithmic abyss—to persist, denying equitable access to the very tools needed to participate in the new economy. From places like New Braunfels, Texas, to rural Virginia, the future will look at our massive, energy-hungry data centers and transmission lines and ask why we also showed a lack of adequate support for the communities disrupted by the immense physical requirements of AI development. These energy-intensive facilities placed environmental and social burdens on local populations without integrating them into the AI ecosystem's benefits.
As things stand, the ledger of future complaints against us concerning AI will be long. But this prophecy need not be our destiny. By confronting these potential failures now, by prioritizing sustained innovation and adaptive governance, we can still pivot towards a future where AI serves humanity's highest aspirations. The time for foresight and courageous action is now, before the future passes its final judgment.
Kevin Frazier is an AI Innovation and Law Fellow at Texas Law and Author of the Appleseed AI substack.
