S1. The Economics of Zero
Last updated
Last updated
When everything costs near-zero, is abundance a right or a gated privilege? AI promises to demolish scarcity in core necessities: housing, healthcare, education, childcare. The real bottleneck is policy, not technology. Regulatory frameworks designed for an era of scarcity actively prevent abundance through outdated zoning laws, licensing requirements, and accreditation systems that prioritize gatekeeping over access. Without intervention, abundance may become a luxury, not liberation.
Abundance is not about luxury for the few, but freedom from scarcity for all.
— Peter Diamandis The true measure of a society is not how much wealth it creates, but how well it distributes the freedom that comes with that wealth.
— Amartya Sen, Nobel Laureate Economist
Across the ages, technological revolutions have promised abundance only to see their potential captured by regulatory structures designed to maintain artificial scarcity. From printing presses constrained by royal licensing to industrial production limited by guilds, transformative technologies have repeatedly confronted institutional resistance. Artificial intelligence represents the ultimate abundance-creating technology—capable of eliminating scarcity in physical production and intellectual labor. Yet there’s a chance that institutional constraints will hold it back from reaching this potential.
Our economy has bifurcated into two distinct realms—one where technological innovation freely drives down prices while improving quality, and another where regulatory frameworks actively prevent such change. As Marc Andreessen observes in "Why AI Won't Cause Unemployment":
This chart shows price changes, adjusted for inflation, across a dozen major sectors of the economy... We actually live in two different economies. The lines in blue are the sectors where technological innovation is allowed to push down prices while increasing quality. The lines in red are the sectors where technological innovation is not permitted to push down prices; in fact, the prices of education, health care, and housing as well as anything provided or controlled by the government are going to the moon, even as those sectors are technologically stagnant... We are heading into a world where a flat screen TV that covers your entire wall costs $100, and a four year college degree costs $1 million. —
Artificial intelligence represents a different challenge to these regulatory structures than previous technologies. While earlier innovations worked within existing frameworks, AI's ability to perform intellectual labor at near-zero marginal cost directly challenges the premise of scarcity upon which these regulations were built. Previous computational technologies chipped away at scarcity incrementally; AI threatens to shatter it entirely. Once machines can think, the price of anything made of thought—or built by robots guided by thought—plummets toward the cost of the physical inputs and energy required.
This change will affect the necessities that consume the largest portion of family budgets: housing through AI-guided robotic construction; healthcare through automated diagnostics and personalized treatment; education through adaptive digital tutoring; and childcare through intelligent monitoring systems. While certain inputs will remain genuinely scarce—land in desirable locations, clean energy, and advanced computational chips—the intellectual labor that currently dominates these sectors' costs could approach zero.
The economics of zero requires institutions designed for the distribution of plenty rather than the management of scarcity.
Every major technological disruption follows a similar pattern. It doesn't arrive all at once. Instead, it creates small fractures in the existing order that gradually widen until the whole structure transforms.
The steam engine operated for decades before factories completely reorganized. Electricity powered buildings long before industries rebuilt around it. Early computers served as calculation tools before digitally native businesses emerged. AI is entering this same transitional phase—still limited in ways, but already sending tremors through our economic foundations.
These early signals are easy to miss if you're not paying attention. They're just the first cracks in our scarcity-based economy.
Right now, AI mostly handles specific tasks—designing floor plans, analyzing medical images, grading assignments, optimizing schedules. Prices in critical sectors continue rising due to supply constraints and labor shortages. But at the edges, you can see the curve starting to bend. These small deflections are the first signs of a different economic paradigm.
Healthcare shows similar early indicators. AI billing systems are trimming . Mayo Clinic's implementation recovers an estimated $120 million annually while freeing up 15 hours weekly for physicians—time redirected to patient care instead of paperwork.
Most households don't feel much relief yet, but the direction has clearly shifted. Each sector follows its own path toward abundance, with unique constraints determining how quickly change happens:
Housing
↗
Limited supply; early 3-D print & AI design cut costs only at pilot scale
Affordability squeeze; pilots prove tech potential
Fast-track zoning & 3-D-print codes; land-value tax
Healthcare
↗
Aging demand; AI triage & admin save ≈5-10 %
Savings kept as profit; bias & liability fears
Reimburse AI services; data-sharing & privacy laws
Education
↗
Digital divide; cheating & inertia
Fund EdTech access; accredit AI-based credentials
Childcare
↗
Labor-ratio limits; AI only trims admin
High fees push parents from workforce; tech pilots
Subsidies; approve AI scheduling & monitoring
For AI, the period between 2030 and 2045 will likely be a reorganization phase.
This is when AI will move beyond simply augmenting existing systems to completely reshaping economic foundations. We've seen this pattern before: electricity transformed from novelty to necessity between 1900 and 1920, and computing evolved from corporate accessory to economic architecture between 1980 and 2000.
If AGI emerges during this period (which remains uncertain), cost reductions could accelerate. Even without full AGI, continued advances in narrow AI combined with policy reforms should drive cost declines exceeding 50% across critical sectors. This deflation won't happen evenly but will spread in waves of creative destruction reminiscent of previous technological transitions.
Construction sites will shift from primarily human labor to robotic systems guided by autonomous design intelligence. Building costs could fall substantially even in major cities—if regulatory frameworks evolve beyond their current protectionist constraints. Just as affordability through national-standard zoning, reformed building codes could democratize housing abundance beyond wealthy early adopters.
Even childcare—perhaps the most intrinsically human service—faces substantial reorganization as robotic assistance enables higher caregiver-to-child ratios or developmental outcomes. MIT economists project that 30-40% of childcare tasks could be automated by 2035, requiring retraining for an estimated 800,000 U.S. workers.
These fifteen years may well determine the relationship between humanity and material sufficiency—whether abundance becomes universal birthright or exclusive privilege in a world where traditional scarcity no longer constrains production.
Housing
↘
Automated builds, generative design, prefab at scale
Land price capture; construction-job upheaval
Up-zone, land-value tax, public AGI builds
Healthcare
↓
AGI diagnosis, robotic surgery, admin near-zero
Gatekeeper resistance; monopoly on AI models
License AGI doctors; universal coverage of AI care
Education
↓
AGI tutors/assessment; alt-credentials accepted
Prestige lock-in; motivation gaps
Open-source AI curricula; competency exams
Childcare
↘
Robot aides, AI home-care nets, expanded public pre-K
Trust & safety fears; unequal rollout
Certify AI nannies; integrate care into UBS / UBI
The era beyond 2045 may represent humanity's first experience with genuine post-scarcity in essential domains.
When fully realized AGI becomes ubiquitous, it could reconceive our relationship with material necessity. The economics approach zero marginal cost for many essentials, transforming production across all sectors.
Consider housing. Construction could reach hyper-efficiency—buildings emerging fully formed in days for . Singapore's public housing model offers a template for how state land ownership combined with automated construction might reconceive shelter as a fundamental right rather than a speculative asset. We could potentially eliminate homelessness as a social condition rather than addressing it as a temporary problem.
Healthcare would undergo an equally significant shift. could move from managed conditions to eliminated categories. Medical care would evolve from scarce expertise to universal utility. South Korea's single-payer system already demonstrates how integrated AI diagnostics can simultaneously reduce costs while extending specialized care beyond urban centers. Unlike previous medical advances that improved outcomes while increasing costs, AI healthcare promises better results with reduced resource requirements.
Education would transcend institutional constraints as learning approaches the rather than the labor of acquisition. The challenge shifts from knowledge access to credible verification; blockchain-secured skill records and AI-proctored assessments might replace traditional degrees, democratizing entry to professions currently restricted by expensive credentials. This change echoes the democratization that followed the printing press, but with even greater implications for social mobility.
Even childcare—historically among the most labor-intensive services— through humanoid assistants or community AI cooperatives. Yet historical precedent demands caution; previous technological revolutions promised to liberate women from domestic burden but often shifted expectations rather than distribution of labor.
What becomes clear in examining these changes is that the limiting factor won't be technological capacity but governance structure—who owns the land upon which automated housing is built, who determines the rules guiding medical AI, how educational credentials are validated, and how care work is valued. The distinction between post-scarcity as universal birthright or exclusive privilege will be determined not by technological inevitability but by deliberate choices about distributing abundance.
The alternative to democratic oversight is potentially techno-feudalism—a new aristocracy controlling the means of intelligence rather than production.
The post-2045 world may reveal that our greatest challenge was never technological production but philosophical wisdom about the just distribution of plenty. When material scarcity no longer constrains human potential, what purpose will civilization pursue?
Housing
↓
Fully autonomous construction; abundant materials
Land monopoly; asset-value crash
Right-to-housing; land commons governance
Healthcare
↓
Disease eradication; AI medical utility
AI ethics, system failure
Global AI health charter; augmentation equity
Education
↓
Continuous AI mentors, brain-interface learning
Info bubbles; loss of shared canon
Core civics curriculum; open knowledge oversight
Childcare
↓
Humanoid robo-care; community AI co-ops
Over-delegation to AI; alignment of child values
Certify AI caregivers; parental-bond incentives
The big surprise about AGI may be how quickly “cost of living” stops meaning what it does today. When the expensive parts of living can be produced by code and robots, economics shifts from fighting over slices to deciding what to bake next. The hard part won’t be paying for homes, health, schools, or care. It will be agreeing on what we want to do with all the freedom that follows.
The greatest risk of AI abundance is that we lack the collective imagination to share it.
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The central question is whether our regulatory frameworks will evolve to facilitate technological abundance or calcify to protect established interests. The regulations currently limiting innovation in housing, healthcare, education, and childcare were not designed primarily to protect consumers but to maintain artificial scarcity that benefits incumbents—from homeowners protecting property values through to professional guilds limiting competition through licensing requirements.
Consider the evidence: empirical research demonstrates that strict minimum-lot zoning regulations ; costs without corresponding improvements in quality; and educational accreditation requirements force students into expensive degree programs when skills could be verified more efficiently. argues, we must distinguish between regulations that genuinely protect the public and those that just cement incumbent power—technology only spreads prosperity when institutions are designed to distribute rather than concentrate its benefits.
The clearest examples are appearing in housing, where 3D-printed homes already demonstrate . in Austin have achieved these savings while cutting construction time by half—though building codes designed for traditional methods still limit widespread adoption.
Education might be showing the most promising signals. AI-enabled online tutors already without doubling teaching resources. has improved math performance by 23% across socioeconomic boundaries—suggesting technology might reduce rather than reinforce educational inequality.
Even childcare, one of the most human-centered services, is beginning to bend toward affordability through administrative efficiencies. has reduced overhead by 40% for home daycare providers, though state-mandated (essential safety measures) remain the primary cost driver.
AI tutors, auto-grading, admin cuts; remains
Healthcare will be one of the most affected areas, as AI transitions from diagnostic support to comprehensive provider. An AI "doctor" could handle most primary care, . The UK's National Health Service that AI triage systems reduce unnecessary visits by 30% while improving outcomes for urgent cases. The tension between institutional preservation and technological possibility will define healthcare's transformation.
Education will experience similar shifts as AI tutors evolve from supplements to primary instructional mechanisms. Degrees will likely become more affordable as employers rather than traditional credentials. competency-based model, which already reduces costs by 60%, foreshadows a broader disruption of higher education's credentialing monopoly.
The primary risk during this period is societal upheaval—the displacement of workers occurring faster than policy frameworks can evolve to redistribute benefits. Experiments with , land-value taxation, and stakeholder ownership models will likely determine whether technology-driven deflation creates broadly shared abundance or concentrated advantage.
This governance challenge represents both opportunity and risk. Without intentional intervention, historically consistent patterns suggest capital owners—those controlling AI systems and land—will capture disproportionate gains. analysis demonstrates that returns to capital typically exceed economic growth, concentrating wealth unless counterbalanced.
The real political divide of the future may not be left versus right, but abundance enablers versus abundance hoarders. As economist has documented across five technological revolutions, the key question is whether regulatory frameworks evolve to distribute that value widely.
The blue vs. red sectors chart makes for a catchy visual, but reality's messier. on "$100 TVs vs. $1M degrees" ignores how regulation often protects consumers, not just hampers innovation.
25-30% cheaper homes, printed layer by layer. Projects in Texas and show it's already happening, but scaling requires zoning reforms and convincing skeptical builders.
AI's catching billing errors humans miss, but who pockets the savings? 5-10% cost reductions often boost hospital profits rather than reducing patient bills. Trickle-down healthcare remains elusive.
Math scores doubled in some trials. Reading comprehension jumping significantly. on AI tutors shows enormous potential, but the digital divide means wealthier schools benefit first.
Apps streamline scheduling, but the fundamental math remains stubborn. human caregivers' watchful eyes can't be algorithmically replaced. highlight this economic reality.
Half-price urban housing by 2040? construction costs, but displaced workers and land speculation threaten to eat those savings.
30% cheaper diagnostics in pilot programs. shows AI primary care could democratize health access worldwide, but powerful gatekeepers—from doctors' unions to model monopolies—stand in the way.
Google's already on board with AI-verified micro-credentials. this shift threatens traditional degrees, though elite universities won't surrender their prestige premium without a fight.
Robot aides safely stretching caregiver ratios by 15-20% in controlled trials. shows the numbers work, but parents worry. will kids bond with bots instead of humans?
$10,000 homes? Autonomous printing could make housing costs approach raw materials. suggests housing-as-utility, though land remains the real bottleneck.
Routine diseases eradicated, medicine transformed into a public good like water. warns of the flip side. misaligned healthcare AI could amplify biases at unprecedented scale.
Already making quantum physics feel like casual reading. on early AI mentors shows transformative learning potential, alongside the risk of knowledge fragmenting into personalized echo chambers.
Nearly free childcare through humanoid assistance and cooperative models. highlight the central challenge. ensuring AI caregivers truly align with human values and developmental needs.
