Artificial intelligence (AI) promises a future once confined to science fiction: personalized medicine accounting for your specific condition, accelerated scientific discovery addressing the most difficult challenges, and reimagined public education designed around AI tutors suited to each student's learning style. We see glimpses of this potential on a daily basis. Yet, as AI capabilities surge forward at exponential speed, the laws and regulations meant to guide them remain anchored in the twentieth century (if not the nineteenth or eighteenth!). This isn't just inefficient; it's dangerously reckless.
For too long, our approach to governing new technologies, including AI, has been one of cautious incrementalism—trying to fit revolutionary tools into outdated frameworks. We debate how century-old privacy torts apply to vast AI training datasets, how liability rules designed for factory machines might cover autonomous systems, or how copyright law conceived for human authors handles AI-generated creations. We tinker around the edges, applying digital patches to analog laws.
This constant patching creates what we might call "legal tech debt." Imagine trying to run sophisticated AI software on a computer from the 1980s—it might technically boot up, but it will be slow, prone to crashing, and incapable of performing its intended function. Similarly, forcing AI into legal structures designed for a different technological era means we stifle its potential benefits while failing to adequately manage its risks. Outdated privacy rules hinder the development of AI for public good projects; ambiguous liability standards chill innovation in critical sectors; fragmented regulations create uncertainty and inefficiency.
Allowing this legal tech debt to accumulate isn't just about missed opportunities; It breeds public distrust when laws seem irrelevant to lived reality. It invites policy chaos, as seen with the frantic, often ineffective, attempts to regulate social media after years of neglect. It risks a future where transformative technology evolves haphazardly, governed by stopgap measures and reactive panic rather than thoughtful design. With AI, the stakes are simply too high for such recklessness.
We need a fundamentally different approach. Instead of incremental tinkering, we need bold, systemic change. We need to be willing to leapfrog—to bypass outdated frameworks and design legal and regulatory systems specifically for the age of AI.
What does this leapfrog approach look like? It requires three key shifts in thinking:
First, we must look ahead. Policymakers and experts need to engage seriously with plausible future scenarios for AI development, learning from the forecasting methods used by technologists. This isn’t about predicting the future with certainty but about understanding the range of possibilities—from accelerating breakthroughs to unexpected plateaus—and anticipating the legal pressures and opportunities each might create. We need to proactively identify which parts of our legal infrastructure are most likely to buckle under the strain of advanced AI.
Second, we must embrace fundamental redesign. Armed with foresight, we must be willing to propose and implement wholesale reforms, not just minor rule changes. If AI requires vast datasets for public benefit, perhaps we need entirely new data governance structures—like secure, publicly accountable data trusts or commons—rather than just carving out exceptions to FERPA or HIPAA. If AI can personalize education, perhaps we need to rethink rigid grade-based structures and accreditation standards, not just approve AI tutors within the old system. This requires political courage and a willingness to question long-held assumptions about how legal systems should operate.
Third, we must build in adaptability. Given the inherent uncertainty of AI’s trajectory, any new legal framework must be dynamic, not static. We need laws designed to evolve. This means incorporating mechanisms like mandatory periodic reviews tied to real-world outcomes, sunset clauses that force reconsideration of rules, specialized bodies empowered to update technical standards quickly, and even using AI itself to help monitor the effectiveness and impacts of regulations in real-time. We need systems that learn and adapt, preventing the accumulation of new tech debt.
Making this shift won't be easy. It demands a new level of ambition from our policymakers, a greater willingness among legal experts to think beyond established doctrines, and broader public engagement on the fundamental choices AI presents. But the alternative—continuing to muddle through with incremental fixes—is far riskier. It’s a path toward unrealized potential, unmanaged risks, and a future where technology outpaces our ability to govern it wisely.
AI offers incredible possibilities but realizing them requires more than just brilliant code. It requires an equally ambitious upgrade to our legal and regulatory operating system. It’s time to stop patching the past and start designing the future. It’s time to leapfrog.
Kevin Frazier is an AI Innovation and Law Fellow at Texas Law and Author of the Appleseed AI substack.
A deep look at how "All in the Family" remains a striking mirror of American politics, class tensions, and cultural manipulation—proving its relevance decades later.
All in This American Family
There are a few shows that have aged as eerily well as All in the Family.
It’s not just that it’s still funny and has the feel not of a sit-com, but of unpretentious, working-class theatre. It’s that, decades later, it remains one of the clearest windows into the American psyche. Archie Bunker’s living room has been, as it were, a small stage on which the country has been working through the same contradictions, anxieties, and unresolved traumas that still shape our politics today. The manipulation of the working class, the pitting of neighbor against neighbor, the scapegoating of the vulnerable, the quiet cruelties baked into everyday life—all of it is still here with us. We like to reassure ourselves that we’ve progressed since the early 1970s, but watching the show now forces an unsettling recognition: The structural forces that shaped Archie’s world have barely budged. The same tactics of distraction and division deployed by elites back then are still deployed now, except more efficiently, more sleekly.
Archie himself is the perfect vessel for this continuity. He is bigoted, blustery, reactive, but he is also wounded, anxious, and constantly misled by forces above and beyond him. Norman Lear created Archie not as a monster to be hated (Lear’s genius was to make Archie lovable despite his loathsome stands), but as a man trapped by the political economy of his era: A union worker who feels his country slipping away, yet cannot see the hands that are actually moving it. His anger leaks sideways, onto immigrants, women, “hippies,” and anyone with less power than he has. The real villains—the wealthy, the connected, the manufacturers of grievance—remain safely and comfortably offscreen. That’s part of the show’s key insight: It reveals how elites thrive by making sure working people turn their frustrations against each other rather than upward.
Edith, often dismissed as naive or scatterbrained, functions as the show’s quiet moral center. Her compassion exposes the emotional void in Archie’s worldview and, in doing so, highlights the costs of the divisions that powerful interests cultivate. Meanwhile, Mike the “Meathead” represents a generation trying to break free from those divisions but often trapped in its own loud self-righteousness. Their clashes are not just family arguments but collisions between competing visions of America’s future. And those visions, tellingly, have yet to resolve themselves.
The political context of the show only sharpens its relevance. Premiering in 1971, All in the Family emerged during the Nixon years, when the “Silent Majority” strategy was weaponizing racial resentment, cultural panic, and working-class anxiety to cement power. Archie was a fictional embodiment of the very demographic Nixon sought to mobilize and manipulate. The show exposed, often bluntly, how economic insecurity was being rerouted into cultural hostility. Watching the show today, it’s impossible to miss how closely that logic mirrors the present, from right-wing media ecosystems to politicians who openly rely on stoking grievances rather than addressing root causes.
What makes the show unsettling today is that its satire feels less like a relic and more like a mirror. The demagogic impulses it spotlighted have simply found new platforms. The working-class anger it dramatized has been harvested by political operatives who, like their 1970s predecessors, depend on division to maintain power. The very cultural debates that fueled Archie’s tirades — about immigration, gender roles, race, and national identity—are still being used as tools to distract from wealth concentration and political manipulation.
If anything, the divisions are sharper now because the mechanisms of manipulation are more sophisticated, for much has been learned by The Machine. The same emotional raw material Lear mined for comedy is now algorithmically optimized for outrage. The same social fractures that played out around Archie’s kitchen table now play out on a scale he couldn’t have imagined. But the underlying dynamics haven’t changed at all.
That is why All in the Family feels so contemporary. The country Lear dissected never healed or meaningfully evolved: It simply changed wardrobe. The tensions, prejudices, and insecurities remain, not because individuals failed to grow but because the economic and political forces that thrive on division have only become more entrenched. Until we confront the political economy that kept Archie and Michael locked in an endless loop of circular bickering, the show will remain painfully relevant for another fifty years.
Ahmed Bouzid is the co-founder of The True Representation Movement.