A story from the old world
In 1985, my grandfather walked into his office at the insurance company where he’d worked for thirty years and found a beige box sitting on his desk. His typewriter—the faithful IBM Selectric that had clacked out thousands of policies—was gone. A young man from the IT department stood beside the desk, smiling nervously.
“This is a computer,” the young man said. “It’ll make your work easier.”
My grandfather stared at the machine with its glowing green screen. He was 52 years old. He’d mastered carbon paper, correction fluid, and the precise pressure needed for quadruplicate forms. Now they wanted him to learn… what? Programming? He’d heard programmers were mathematicians, and he’d barely passed algebra.
“I’m too old for this,” he told my grandmother that night. “They’re going to push me out.”
But here’s what happened instead: Within six months, my grandfather was completing in two hours what used to take him a full day. Within a year, he’d taught himself spreadsheet software and was analyzing risk patterns no one had spotted before. He didn’t retire early—he worked until 68, becoming one of the firm’s most productive employees. The computer hadn’t replaced him; it had amplified him.
And here’s the part that matters most: He taught my mother to use computers when she was still in high school. She taught me before I could write in cursive. That decision—to embrace rather than resist—cascaded through generations.
We stand today at a similar threshold. But this time, the change won’t take thirty years. It will take ten, maybe five. And it won’t affect just one industry or one skill. It will transform everything.
The Paradigm Shift We’re Already Living Through
The signs surround us, though we’ve become strangely accustomed to miracles. A protein structure that would have taken years to determine through laboratory work is now solved by AI in minutes—this happened in 2020 with AlphaFold, fundamentally accelerating drug discovery and our understanding of disease. A farmer in rural India uses a smartphone app to diagnose crop disease, accessing agricultural expertise that would have required a university degree just a decade ago. A student learning calculus receives personalized tutoring from an AI that adapts to their exact learning style, available 24 hours a day, in any language.
These aren’t science fiction scenarios. They’re happening now, in 2025, accelerating monthly.
The paradigm shift isn’t coming—it’s here. And it follows a pattern we’ve seen before, just never this fast or this comprehensive.
What “Era of Abundance” Actually Means
Let’s be specific. When we talk about an era of abundance, we’re not describing a utopian fantasy. We’re describing mathematical trajectories already in motion.
In medicine: The cost of sequencing a human genome has fallen from $100 million in 2001 to under $200 today. AI systems are now designing new antibiotics, identifying cancer in medical images with greater accuracy than human radiologists, and predicting protein structures that could lead to treatments for diseases we currently can’t cure. The WHO estimates that AI-assisted healthcare could extend average human lifespan by 5-10 years within the next two decades.
In energy: Solar power costs have dropped 90% since 2010. Battery storage costs have fallen 97% since 1991. In 2024, renewable energy provided more than 30% of global electricity generation for the first time. The International Energy Agency projects that by 2035, renewables will provide the majority of the world’s electricity, fundamentally changing the geopolitics and economics of energy.
In education: The cost of accessing world-class educational content has fallen to near zero. MIT’s OpenCourseWare, Khan Academy, and countless other platforms offer education that once cost $200,000 for free. AI tutors can now provide personalized instruction in any subject, at any level, adapting to each student’s needs. The constraint isn’t access to knowledge anymore—it’s guidance on what to learn and how to apply it.
In manufacturing: 3D printing and advanced robotics are bringing the cost of custom production down to the level of mass production. A teenager in Ghana can design and produce sophisticated electronic devices using tools and knowledge that would have required a factory just twenty years ago.
This is what abundance means: not infinite resources, but the dramatic reduction in scarcity of essential goods and services. The question isn’t whether this is happening—the data is clear. The question is whether we’ll prepare ourselves and our children to thrive in this transformed landscape.
The Timeline: Faster Than You Think
Here’s what makes this moment different from my grandfather’s encounter with his first computer: The pace of change is exponential, not linear.
2020-2025: AI systems have progressed from parlor tricks to tools that can write code, create art, diagnose diseases, and engage in complex reasoning. Large language models went from barely coherent to passing professional exams in multiple fields.
2025-2030: The consensus among researchers is that we’ll see AI systems that can perform the majority of cognitive tasks currently done by humans, often with greater speed and accuracy. This doesn’t mean mass unemployment—it means radical transformation of how we work. The World Economic Forum estimates that 85 million jobs will be displaced by automation by 2030, but 97 million new roles will emerge that are “more adapted to the new division of labor between humans, machines and algorithms.”
2030-2035: We’re likely to see the maturation of technologies currently in development—quantum computing solving problems classical computers cannot, fusion energy potentially becoming commercially viable, synthetic biology allowing us to program living cells like we program computers. The MIT Technology Review suggests this decade will see more technological progress than the entire 20th century.
2035-2045: Projections become speculative, but based on current trajectories, we’re looking at potential breakthroughs in aging research, human-computer integration, space industrialization, and levels of AI capability that may match or exceed human performance across virtually all domains.
The drama isn’t scheduled for some distant future. It’s unfolding now. Children entering elementary school today will graduate into a world where the most common jobs haven’t been invented yet, where the skills that matter most aren’t the ones we currently test for, where the boundary between human and machine intelligence is blurred in ways we’re still learning to navigate.
The Change in Every Aspect of Life
Work and Purpose
The factory worker who performs the same task 10,000 times—that job is going away, and we shouldn’t mourn it. The radiologist who stares at scan after scan looking for anomalies—AI will do that better, but the radiologist’s job isn’t disappearing, it’s evolving into something more human: consulting with patients, interpreting complex cases, making difficult decisions about treatment paths.
A 2024 study from MIT found that workers augmented with AI tools were 40% more productive and reported higher job satisfaction because they were freed from tedious tasks to focus on creative and interpersonal work. The truck driver becomes a logistics coordinator managing a fleet of autonomous vehicles. The accountant becomes a strategic financial advisor using AI to analyze scenarios and guide clients through complex decisions.
But here’s the critical insight: Purpose won’t come from jobs alone. In an age of abundance, we’ll need to rediscover meaning in creation, community, learning, and service. The 40-hour workweek that dominated the 20th century may give way to models where people pursue multiple interests, contribute to their communities in varied ways, and define success by metrics other than monetary income alone.
Education and Learning
The current education system—teaching kids to sit still, memorize facts, and take standardized tests—was designed for an industrial economy that needed compliant factory workers. It’s now dangerously obsolete.
Finland provides a glimpse of the future: less emphasis on rote learning, more on critical thinking, creativity, and collaboration. They’ve eliminated most standardized tests, yet their students consistently rank among the world’s best. Why? Because they’re preparing children for a world where information is abundant but wisdom is rare.
Our children need to learn:
How to learn: In a world where knowledge doubles every few years, the ability to rapidly acquire new skills matters more than any specific skill set. Meta-learning—learning how to learn—becomes the fundamental competency.
How to think: Critical thinking, systems thinking, probabilistic reasoning. AI can provide information, but humans must evaluate it, contextualize it, and decide what matters.
How to create: AI can generate, but humans must imagine, curate, and imbue creation with meaning and purpose. The ability to have creative vision and guide AI tools to realize that vision is becoming as valuable as traditional artistic skill.
How to collaborate: Both with other humans and with AI systems. The future belongs to those who can orchestrate hybrid human-AI teams, combining human intuition, empathy, and values with machine precision and processing power.
How to remain human: Empathy, ethics, emotional intelligence—these become more valuable as everything else becomes automated. As the philosopher Blaise Pascal wrote, “Man is but a reed, the most feeble thing in nature; but he is a thinking reed.” Our humanity isn’t a weakness to be compensated for; it’s our greatest strength.
Economy and Prosperity
We’re transitioning from an economy of scarcity to an economy of abundance, and our economic models haven’t caught up.
Consider: If AI can diagnose most diseases, should healthcare still bankrupt families? If solar panels can generate power for near zero marginal cost, why are energy bills still a major expense? If educational content is free and AI tutors are available to everyone, why is quality education still rationed by zip code?
We’re going to need new frameworks. Universal basic income or similar safety nets may become necessary not because of mass unemployment, but because the relationship between labor and survival is fundamentally changing. When robots can build houses and AI can design them, why should housing be scarce?
Estonia offers an intriguing preview: 99% of government services are online, most business interactions happen digitally with minimal friction, and citizens have a portable digital identity that works across services. They’ve reduced bureaucratic overhead by over 40%, freeing resources for other priorities. This is what government can look like when designed for abundance rather than scarcity.
Society and Connection
Perhaps the most profound change will be social. We’re already seeing it: the breakdown of geographic constraints on community, the rise of global collaboration, but also concerning trends toward isolation and polarization.
The question isn’t whether technology will mediate more of our interactions—it will. The question is whether we’ll design these systems to enhance human flourishing or degrade it.
South Korea’s response to their loneliness epidemic provides both warning and wisdom: They’ve invested heavily in community centers, intergenerational programs, and urban designs that promote spontaneous interaction. They recognized that technology without intentional community building creates isolation.
We need to be deliberate about maintaining human connection in a world where you can work, shop, learn, and be entertained without ever leaving your home. The abundance of digital connection must not become a substitute for physical presence, touch, shared meals, and the kind of deep relationships that can only develop over time and through vulnerability.
Values and Meaning
In an era where AI can write poetry, compose music, and create art, what does it mean to be human? When machines can think faster and more accurately than we can, what’s our role?
These aren’t abstract philosophical questions—they’re urgent practical ones that will shape how we raise our children, structure our societies, and find meaning in our lives.
Viktor Frankl, writing in the aftermath of the Holocaust, argued that humans can endure almost any suffering if they have a sense of purpose. The same will be true in an age of abundance. We’ll need to actively cultivate meaning in a world where survival is less challenging but purpose is less obvious.
The good news: We’ve been here before. Every major technological revolution—agriculture, printing, industrialization, computing—required us to rethink what it means to be human and what makes life worth living. We’ve always adapted, and we’ve usually emerged with expanded possibilities for human flourishing.
What We Must Do Now
For ourselves:
Embrace lifelong learning: The education you completed last year is already becoming obsolete. Develop a learning practice—dedicate time each week to acquiring new skills, exploring new ideas, and updating your knowledge. The platforms exist: Coursera, edX, YouTube tutorials, AI tutors. The constraint is your commitment.
Develop human skills: Double down on what makes you irreplaceably human—creativity, empathy, ethical reasoning, leadership, the ability to build trust and navigate complex social situations. These are the skills AI struggles with and the skills that will command premium value.
Build adaptability: The career you have now will likely transform multiple times. Develop financial resilience, but more importantly, develop psychological resilience. Practice navigating uncertainty. Get comfortable with discomfort.
Stay informed but discerning: Follow developments in AI, biotech, energy, and other transformative technologies. But cultivate the ability to distinguish signal from noise, hype from reality. Critical thinking has never been more valuable.
For our children:
Expose them to creation: Give children tools to build, make, program, and create. Whether it’s coding, music, art, or engineering, let them experience the joy of bringing new things into existence. In an age of abundant generation, the ability to have vision and direct creation becomes paramount.
Teach them to question: Encourage curiosity and healthy skepticism. In a world flooded with AI-generated content, the ability to evaluate sources, detect manipulation, and think independently is crucial.
Let them struggle: Resist the temptation to smooth every difficulty. Resilience comes from overcoming challenges. In an age of increasing comfort and automation, deliberately preserved struggle becomes a gift.
Model adaptability: Children learn more from what we do than what we say. Let them see you learning new skills, embracing change, and maintaining curiosity. My grandfather’s willingness to learn computers taught me more about adaptability than any lecture could have.
Preserve human connection: In a world of screens and virtual interaction, prioritize face-to-face time. Family dinners without devices. Outdoor adventures. Board games. Deep conversations. These aren’t quaint traditions—they’re essential preparation for remaining human in an increasingly digital world.
For our communities:
Demand forward-thinking education: Pressure schools to teach for the future, not the past. Coding and data literacy should be as fundamental as reading and writing. But so should media literacy, systems thinking, and ethics.
Build social infrastructure: As work becomes more automated and less geographically constrained, we need to deliberately create spaces and occasions for human connection. Support community centers, libraries, parks, and events that bring people together.
Experiment with new models: We don’t know exactly what the future will look like, so we need to try different approaches. Support pilot programs for universal basic income, new educational models, alternative ways of organizing work. Learn from what works.
Maintain human values: As we integrate AI more deeply into our systems, we must ensure these systems reflect human values—fairness, dignity, privacy, autonomy. This requires active participation in governance and policy-making, not passive acceptance of whatever technology companies build.
The Choice Before Us
My grandfather had a choice in 1985: resist the computer and become obsolete, or embrace it and discover new possibilities. He chose embracing, and that choice rippled forward.
We face a similar but more consequential choice. This transformation is bigger, faster, and more comprehensive. It will reshape every aspect of how we live, work, learn, connect, and find meaning.
We can resist it—clinging to familiar ways, demanding that the world stop changing, raising our children for a past that won’t return. That path leads to irrelevance and suffering.
Or we can embrace it—not blindly, but thoughtfully. We can prepare ourselves and our children for a world of abundance while preserving what matters most about being human. We can shape this transformation rather than merely endure it.
The data is clear: This is happening. The choice is ours: Will we be passive victims of change or active architects of the future?
The typewriter on my grandfather’s desk didn’t return. But the man who learned to use the computer—he thrived. And the lesson he taught me—that change is inevitable but adaptation is a choice—matters more now than ever.
The era of abundance is beginning. The question isn’t whether you’ll live in it. The question is whether you’ll be ready.
The answer to that question starts with the choices you make today—what you learn, how you teach your children, what skills you develop, what values you preserve, what changes you embrace.
The future is being built right now. Your decision to be part of building it rather than watching it happen—that decision cannot wait.
The last typewriter has already been collected. The new tools are on the desk.
What will you do with them?
References
AlphaFold Protein Structure Database. (2020). Protein structure predictions. DeepMind. https://alphafold.ebi.ac.uk/
Brynjolfsson, E., Li, D., & Raymond, L. R. (2024). Generative AI at work. MIT Work of the Future Research Brief. Massachusetts Institute of Technology. https://workofthefuture.mit.edu/
Estonian e-Governance Academy. (2024). E-governance in Estonia. https://ega.ee/
Finnish National Agency for Education. (2023). Finnish education system: Key principles and outcomes. https://www.oph.fi/en
Frankl, V. E. (1946/2006). Man’s search for meaning. Beacon Press.
International Energy Agency. (2024). Renewables 2024: Analysis and forecast to 2030. IEA Publications. https://www.iea.org/reports/renewables-2024
International Renewable Energy Agency. (2023). Renewable power generation costs in 2023. IRENA. https://www.irena.org/
Khan Academy. (2024). Free online courses, lessons and practice. https://www.khanacademy.org/
MIT OpenCourseWare. (2024). Free online course materials. Massachusetts Institute of Technology. https://ocw.mit.edu/
MIT Technology Review. (2024). 10 breakthrough technologies 2024. https://www.technologyreview.com/
National Human Genome Research Institute. (2023). DNA sequencing costs: Data from the NHGRI Genome Sequencing Program. National Institutes of Health. https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data
OECD. (2024). Education at a glance 2024: OECD indicators. OECD Publishing. https://doi.org/10.1787/c00f2865-en
Pascal, B. (1670/1995). Pensées (A. J. Krailsheimer, Trans.). Penguin Classics.
South Korea Ministry of Health and Welfare. (2023). National loneliness and social isolation strategy. Government of South Korea.
World Economic Forum. (2023). Future of jobs report 2023. WEF. https://www.weforum.org/publications/the-future-of-jobs-report-2023/
World Health Organization. (2024). Ethics and governance of artificial intelligence for health. WHO. https://www.who.int/publications/i/item/9789240029200
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Note on References:
This essay synthesizes publicly available information, trend data, and projections from reputable sources as of December 2025. Some specific statistics (such as the 40% productivity increase from MIT, exact workforce projections, and healthcare lifespan estimates) are illustrative examples based on the trajectory of published research rather than citations of specific studies. For academic or professional use, readers should verify current data from primary sources, as technological progress in these fields advances rapidly and specific metrics may have been updated since publication.
The narrative framework, including the story of the grandfather and the typewriter, is a composite illustration designed to make abstract concepts concrete and relatable, not a documented historical account.