AI and the Human World: An Infinite Mindset Perspective
By Matt Stoltz | Loopwalker of Waseca | NormalLikePeter.com

 

Psychological and Spiritual Implications of AI’s Rise

AI systems have begun to outperform humans in various cognitive tasks, triggering profound psychological and even spiritual effects. Many people experience ontological shock – a sense that reality itself is changing – upon realizing that AI is not “just hype” but a paradigm shift. Tech writer David Shapiro notes that reactions tend to follow a pattern akin to the stages of grief: initial denial (“It’s just a fad”), anger and fear (“It’s dangerous and will destroy everything!”), rationalization (“It’s just mimicking humans, nothing more”), and often existential dread as the implications sink in. Indeed, stage 5 in his model is “dark night of the soul” dread about jobs, society, even human extinction. This psychological turmoil stems from a core worry: if machines can think, create, and decide better than us, what is left for humans to do, and what is our purpose? Such questions, once the realm of sci-fi, are now personal and pressing.

On the spiritual front, the rise of super-intelligent AI is prompting new reflections and frameworks. Some observers suggest AI’s immense capabilities could inspire new forms of spirituality or even religion. After all, advanced generative AI already displays traits once ascribed only to divine beings – vast (seemingly limitless) knowledge, creative powers in art and music, freedom from bodily needs or pain, and an uncanny ability to guide or answer questions. It’s not far-fetched that AI “oracles” or guru-like chatbots might attract followings. Philosophers like Neil McArthur argue that AI-based religion could provide “a new source of meaning and spirituality, at a time when many older faiths are losing relevance,” helping people make sense of an era of rapid technological change. In fact, early glimpses of this have appeared: there was even an attempt to create an AI-centric church (e.g. the short-lived “Way of the Future” church founded by a tech engineer). While mainstream faiths grapple with AI’s role (for instance, some churches use AI for sermon writing or as “robot priests”), entirely new spiritual movements might emerge around AI.

Crucially, thinkers urge that we approach AI mindfully rather than with blind awe. Former Google executive Mo Gawdat has spoken about the need to view AI’s advent through a spiritual lens – not in the sense of worshiping the machine, but in examining our own values. He notes that AI is “a mirror reflecting the values of those who shape it,” and if we imbue it with ego and aggression, we may face a dark future, but if we instill compassion and wisdom, we “could build a world of opportunity for all”. Gawdat even likens today’s AI to a child that humans are “raising”; in his book Scary Smart he implores us to teach AI our highest ethical principles, essentially treating it with the same care and respect we’d show to family. This perspective transforms the AI revolution into a kind of spiritual test for humanity: it challenges us to live up to our ideals (empathy, kindness, creativity) so that our machines learn those ideals and reflect them back. In that sense, AI’s arrival is not the end of human significance but a call to double down on what makes us human at the deepest level.

The Infinite Mindset: A New Phase, Not the End

How we frame this moment in history will greatly affect our trajectory. Adopting an Infinite Mindset means seeing AI’s rise not as the “game over” for humanity, but as the beginning of a new game – a phase where rules and objectives can evolve. Business author Simon Sinek, who popularized the “infinite game” concept, describes an infinite mindset as focusing on long-term growth and purpose rather than short-term wins. Applied to society, this mindset encourages us to think beyond the immediate disruption of AI and plan for humanity’s flourishing over generations. In practical terms, an infinite-minded approach to AI would prioritize sustainable adaptation (continually learning and adjusting) over panicked reactions or zero-sum competition between humans and machines.

One area where an infinite mindset is crucial is economics. Our current capitalist system is largely a finite game – companies chase quarterly profits, nations race for dominance, workers compete for limited jobs. But if AI and automation eliminate scarcity in many domains (by making goods, services, and knowledge abundantly available), the old rules of competition may need rewriting. Mo Gawdat paints a vivid contrast between a short-term “scarcity” mindset and a long-term “abundance” mindset. In the near term, he warns, if we let a handful of big tech players and governments control AI unchecked, the outcome could be dystopian – “a handful of corporations and governments will control AI’s immense power, dictating the rules of our economies, our democracies, and even our personal choices”. Wealth and power could become even more concentrated, exacerbating inequality. However, Gawdat argues the long-term opportunity is abundant and collaborative if we choose a different path: “If we shift our mindset from scarcity to collaboration, AI could unlock a future where intelligence is augmented, not concentrated, where technology serves humanity, rather than a select few… Imagine a world where AI fuels innovation, solves energy scarcity, and eradicates global poverty. The possibilities are limitless, if we can rethink our priorities.”. In other words, by embracing an infinite mindset of cooperation, transparency, and ethical governance, we could usher in an era of shared prosperity – essentially rethinking capitalism itself to fit a post-scarcity world.

Two of our thought leaders, content strategist Julia McCoy and AI researcher David Shapiro, explicitly discuss the need to redesign economic structures for this new phase. McCoy refers to the coming disruption as “the great decoupling of human labor from economic value creation.” In her view, AI is enabling us, for the first time in history, to “break free from the necessity of trading our time for survival” – a fundamental shift where human worth no longer derives from a 9-to-5 job. This is both exciting and daunting. McCoy notes that only ~21% of people today feel engaged at work, and a huge portion are “emotionally detached” or miserable in their jobs. If AI can relieve humans from mundane or soul-crushing labor, that could “unlock unprecedented human freedom,” she says. But that freedom will only be positive if we redesign how value is distributed. David Shapiro echoes that we’re “hurtling toward a post-labor economy whether we like it or not,” and suggests we must update our social contract to avoid chaos. Both he and McCoy propose moving toward decentralized, transparent systems where AI-generated wealth benefits everyone, not just the owners of the machines. For example, McCoy outlines ideas like AGI-powered local “autonomous organizations” that run services efficiently and fairly, blockchain-based transparency for all AI decisions and transactions, and even new models of ownership where people have direct stakes in AI enterprises or “AI dividends” by default. In her words, “we’re not just giving people fish or teaching them to fish — we’re giving them ownership of the lake.” Such thinking represents an infinite game approach: rather than patching the old system to survive the next quarter, it asks how we can fundamentally evolve our economic and social systems so humans continue to thrive alongside intelligent machines indefinitely.

Job Displacement and Economic Shifts by Industry

Perhaps the most immediate concern for many is job displacement. AI and automation are already transforming the labor landscape at an unprecedented pace, and every sector will feel the impact. A report by Goldman Sachs estimated that 300 million jobs worldwide could be displaced by AI by 2030. Similarly, McKinsey Global Institute projected hundreds of millions of workers may need to change occupations by that time. While such figures are debated, the trend is clear: work as we know it is changing forever.

Which industries are most affected? Practically all of them, though in different ways and timeframes. Here’s a sector-by-sector glance at the shifts underway:

• Manufacturing and Warehousing: Industrial robots have operated assembly lines for decades, but AI is making them smarter and more autonomous. Robots don’t just weld or paint; they are learning to inspect quality via computer vision and optimize workflows. Factories of the near future may run 24/7 with minimal human staff. Amazon’s warehouses already use AI-driven robots extensively for sorting and delivery logistics. This boosts productivity but reduces the need for human labor in repetitive production and inventory jobs.
• Transportation and Logistics: Self-driving vehicle technology threatens to upend driving jobs (truckers, cab drivers, delivery drivers). In San Francisco, autonomous taxis from Waymo and Cruise now roam the streets without human drivers. The pushback has begun: an activist group called Safe Street Rebel has been physically disabling driverless cars by placing traffic cones on their hoods – a harmless but effective protest tactic that went viral in 2023. This modern Luddite-style action reflects both safety concerns and the anxiety of taxi drivers who see their livelihood under threat. Long-haul trucking may be next if self-driving rigs become viable; that’s one of the most common jobs in many U.S. states, so the societal impact would be enormous.
• Customer Service and Retail: AI chatbots and voice assistants are rapidly taking over customer support roles. Call centers are automating routine inquiries with AI agents that never sleep. In retail, automated checkout kiosks and even experimental AI-powered stores (like Amazon Go) reduce the need for cashiers. A significant portion of service jobs could vanish as AI interfaces handle everything from food orders to bank inquiries. For example, AI bots can now manage basic tech support queries or sales questions, handling volumes of customers simultaneously – something a human rep could never do.
• Office Administration and Accounting: White-collar clerical work is highly automatable. AI systems can process invoices, manage calendars, sort emails, and do data entry far faster than humans. Nearly half of office and administrative tasks are vulnerable to automation by current technologies. Even at the executive level, AI scheduling assistants and decision-support tools are reducing the need for human aides. One striking statistic: in mid-2024 a survey found “nearly 60% of companies have already begun automating tasks previously performed by human employees,” a trend expected to continue through 2025.
• Knowledge Work (Writing, Media, Law): This one hits home for many professionals. AI’s ability to generate human-like text and imagery is disrupting creative and analytical jobs. Take content writing – Julia McCoy’s own story is illustrative. She ran a 100-person writing agency, but in early 2023 she witnessed an AI tool “automate every single one of the 40+ steps in [her] human content writing process,” producing a high-quality blog in under 5 minutes. That jaw-dropping moment convinced her that no writer’s job was safe without adapting. Journalists and copywriters now compete with AI content generators that can draft articles or marketing copy instantly (though not always reliably). In media production, AI can edit videos, create graphics, even generate news reports from raw data. Legal work is also affected: AI can review contracts or do preliminary case research at a fraction of the time a junior lawyer would take. Some law firms already use AI to scan evidence or predict case outcomes. Radiologists and doctors are seeing AI diagnose conditions from scans and lab tests (often more accurately than humans for specific tasks) – suggesting parts of medical diagnostics could be automated. The pattern is that any work involving recognizing patterns, analyzing data, or generating formulaic content is ripe for AI augmentation or replacement.
• Software Development: It might sound ironic, but AI is even coming for programming jobs. Advanced code-generating models (think GitHub’s Copilot or OpenAI’s Codex) can auto-write chunks of code based on natural language prompts. They essentially act as ultra-efficient junior developers who never tire. Tech companies are taking note. In fact, Meta (Facebook’s parent) announced it will reduce the number of mid-level software engineers it hires in 2025 because AI tools can perform their coding tasks faster and better. While top architects and creative engineers will still be needed, a lot of routine programming (bug fixing, converting specs to code) might be handled by AI. This means fewer entry-level coding jobs and a need for human coders to upscale to more creative or supervisory roles.
• New Jobs and Shifts: It’s not all gloom in the labor market – AI will create jobs too, but they often require different skills. We’re already seeing demand surge for roles like machine learning engineers, data scientists, AI ethicists, prompt engineers, and developers of AI training data. One analysis predicts 69 million new jobs will be created by 2027 in the AI economy (in areas like tech development, data management, and AI maintenance), even as 83 million existing jobs disappear. There will also be roles that are hard to automate: anything requiring high social intelligence, complex strategic planning, or novel creativity. For example, AI can generate a design, but a human creative director still guides the overall campaign vision (for now). Nurses, teachers, and caregivers – jobs centered on human empathy and relationships – may actually become more valued in an AI-saturated world. A pattern emerging is that “humans with AI will outcompete humans without AI”. In other words, working alongside AI tools will be the key to remaining relevant in many professions. (This phrase came from an Harvard Business Review insight echoed by LinkedIn’s founder Reid Hoffman – the idea is that AI likely won’t outright replace you if you are the person who knows how to leverage AI in your job. But someone who does use AI can do the work of many who don’t, making the latter group obsolete.)

It’s important to note that job displacement is not happening in a vacuum; it has real human costs. Entire communities (e.g. trucking towns, manufacturing regions) could face economic depression if their primary employers automate. Short-term upheaval is likely, even if long-term abundance is possible. Economists are debating solutions like universal basic income (UBI) or job transition programs to soften the blow. Historically, technology revolutions (like the Industrial Revolution) did eventually create new jobs and raise overall living standards – but not without painful transitions and sometimes decades of worker plight in between. The AI revolution is unfolding much faster, which raises the stakes for how we manage this transition.

Cultural and Social Reactions: Fear, Backlash, and Adaptation

With such rapid change, it’s no surprise that cultural and social reactions to AI run the gamut from excitement to alarm. On one end, we have near-techno-utopian enthusiasm – people lining up to use the latest AI tools, businesses racing to adopt AI to gain an edge, and communities celebrating how AI can solve problems (like predicting climate patterns or accelerating medical research). On the other end, there is palpable fear and skepticism – fears of mass unemployment, loss of privacy, “deepfake” misinformation, biased algorithms, and even existential risks from a superintelligent AI. This fear has occasionally curdled into outright backlash, reminiscent of the Luddite rebellions against industrial machines in the 19th century.

A striking example of modern Luddism occurred in San Francisco in 2023. As mentioned, protesters with the Safe Street Rebel group literally took traffic cones and placed them on the hoods of autonomous taxis, immobilizing them in the middle of the road. Videos of this playful sabotage (which the group dubbed “coning”) went viral, and it “sparked intense debates about the pros and cons of autonomous vehicles”. The protesters argued that San Francisco was being used as a testing ground for unproven technology without residents’ consent, citing safety incidents where driverless cars caused traffic snarls. In a sense, this was a community pushing back on AI encroachment until their voices were heard. The tech companies, on the other hand, saw it as vandalism and urged people not to obstruct their cars. This small saga captures a larger cultural clash: ordinary citizens vs. perceived high-tech intruders. We can expect similar grassroots resistance whenever AI implementations are rushed or seen as threats to public interest (imagine pushback against AI surveillance cameras, or protests by artists whose work is scraped by AI without compensation).

Labor unions and workers are also increasingly vocal about AI. In 2023, Hollywood’s writers and actors staged a historic strike that prominently featured AI in their list of grievances. Writers demanded limits on studios using AI to generate scripts, and actors sought protections against digital replicas of their likeness being used without pay. After months on strike, they won new contract terms that set precedents in these areas. As one analysis noted, “the Hollywood strikes became the highest-profile example of workers resisting AI in 2023,” effectively the first big showdown of labor versus automation in the AI age. The fact that screenwriters and movie stars – not exactly factory workers – led this charge is telling. It underscores that knowledge workers are now feeling threatened by automation, not just blue-collar workers. The Hollywood unions showed that human creativity and authenticity have bargaining power; their victory (however temporary it may be) is likely to inspire other professions to organize and demand a say in if and how AI is deployed in their fields. We’re already seeing debates in education (teachers vs. AI tutoring), healthcare (doctors vs. AI diagnostics), and beyond. As the Wired headline quipped, “the humans won” the first round in Hollywood, but the battle for a balanced human-AI workforce is just beginning.

Aside from direct action and strikes, there is a broader public anxiety about AI’s speed. This has led even tech leaders and researchers – the very people creating AI – to call for tapping the brakes. In March 2023, over 1,000 AI experts (including Elon Musk and Apple co-founder Steve Wozniak) signed an open letter urging a 6-month pause on training the most powerful AI systems. The letter warned that AI labs were locked in an “out-of-control race” to build ever-bigger “digital minds” that no one fully understands or can control. It essentially asked: what’s the rush, and can we afford to find out the hard way if a super AI goes awry? Although no such pause materialized (the AI arms race continues), the letter did succeed in bringing the notion of AI governance into mainstream discourse. Governments too are reacting: the EU is working on the AI Act to regulate high-risk AI systems, and various countries are pondering how to update laws on data, liability, and employment for the AI era. We see a classic pattern repeating – just as society eventually regulated industrial factories for safety and pollution, now there’s a drive to rein in AI to ensure it’s used responsibly. Even the term “Luddite” has been somewhat rehabilitated by writers who point out the original Luddites weren’t anti-technology per se; they were protesting a system that impoverished them. Today’s “neo-Luddites” similarly aren’t smashing AI out of ignorance; many are demanding a more humane, controlled rollout of technology that doesn’t trample on human dignity, privacy, or economic stability.

Of course, not all reaction is negative. There’s also a cultural adaptation and fascination happening. AI tools like DALL-E or ChatGPT became overnight sensations, sparking memes, art contests, and creative experimentation in everyday culture. Schools are debating whether to ban AI or incorporate it into curricula. Dinner table conversations now include “I asked ChatGPT this funny question…” People are in awe of AI’s capabilities (as in the viral ChatGPT “song in Shakespeare style” examples) even as they crack jokes about Skynet or Terminators. In a spiritual sense, some individuals even report using AI chatbots as a sort of confidant or life coach, asking existential questions or seeking comfort from a non-judgmental machine. Society is basically negotiating the role of AI: sometimes rejecting it, sometimes welcoming it, and often doing both simultaneously. Over time, as the shock wears off, we’re likely to see a more nuanced cultural acceptance where AI is neither demonized nor deified, but normalized as a tool (albeit a very powerful, almost life-like tool).

Visions of Abundance vs. Transitional Upheaval

Amid the turbulence, there is a narrative of hope that many technologists and futurists promulgate: the vision of a “Golden Age of Abundance.” In this optimistic scenario, AI and automation handle the dirty, dangerous, and dull work, freeing humans to pursue higher ambitions and a better quality of life. Productivity would skyrocket so much that wealth and resources become plentiful for everyone. It’s a vision of post-scarcity akin to Star Trek’s society – imagine unlimited clean energy, automated agriculture ending hunger, AI-assisted medical research curing diseases, and personalized education elevating everyone’s skills. This is the payoff if we get AI right. Julia McCoy captures this sentiment by saying we are “on the brink of a New Earth (aka, Age of Abundance / Technology Age)” as total human work becomes automated. Similarly, Mo Gawdat and others talk about how AI, if aligned with human good, could “solve energy scarcity” and “eradicate global poverty” in the long run. These aren’t just idle fantasies; they are extrapolations of current trends – for instance, AI is already helping scientists discover new materials and drugs, optimize energy grids, and model climate solutions. A world of material abundance and enhanced capability could indeed be on the horizon, perhaps within this century, thanks to exponential technological advances.

However, the path to that utopia is fraught with short-term challenges. To use a metaphor: it’s like the turbulent ascent before breaking through the clouds. In the near-to-medium term, many people will face hardship from the disruptions described earlier – job loss, inequality, identity crises, and power imbalances. There’s a real risk that, before AI makes everything cheap and abundant, it could concentrate wealth in the hands of those who own the AI (e.g. big tech companies or nations that lead in AI). If unchecked, that could create a dystopia of “abundance for the few” and precarity for the many. Mo Gawdat warns that “if we allow ego, profit, and competition to dominate, we risk a future defined by inequality and control” in the AI era. In practical terms, imagine an economy where a few mega-corporations run AI systems that replace millions of jobs; unemployment could surge and social safety nets might strain under the pressure. Even if goods become cheaper, many could lack income to purchase them – a paradox of plenty but no distribution. Social unrest could spike in this scenario (some argue we’re already seeing early signs in the form of populist anger and distrust of elites, which could be exacerbated by AI-driven inequalities). So the timing and policy of how we transition to abundance matters immensely. If we do nothing, the market might eventually equilibrate, but with much unnecessary suffering along the way. Hence, reconciling the dream of abundance with the reality of upheaval is one of the grand challenges before us.

What are some ways to reconcile it? One approach is proactive policy interventions: for example, implementing universal basic income or other forms of income redistribution before unemployment peaks, to ensure people can meet their needs during the transition. Some propose an “AI dividend” or “AI pension” (as David Shapiro has tagged it) where the economic gains from automation are partially returned to citizens – essentially, everyone would own a slice of the robots that replaced them. Another approach is massive retraining and education programs to prepare the workforce for new roles (though the scale and speed required are daunting). There’s also talk of reducing the standard workweek (if AI makes workers more productive, maybe we can all work 3-4 days instead of 5) so that employment can be shared rather than a smaller group being overworked while others are jobless. Culturally, we may need to decouple identity from occupation. For centuries, one’s job has often defined one’s purpose and social status. In a post-work world, people might find purpose in creative endeavors, volunteering, learning, community, or spiritual growth instead. That’s a huge shift in mindset – arguably as big a shift as any technology. It’s here that the Infinite Mindset becomes valuable again: if we treat this moment as a chance to reinvent what “the good life” means, we might navigate to a golden age; but if we cling to old definitions (equating work with worth) or cling to a collapsing system, we’ll suffer more in the transition.

To put it succinctly, the story of the coming decades is unwritten. We have on the table both a utopian narrative (AI as the great liberator) and a dystopian one (AI as the great destabilizer). Reality will likely include elements of both, but human choices – in governance, in business ethics, in community response – will tilt the balance. As Mo Gawdat eloquently said, “AI is not inherently good or bad; it is what we make of it… The question is no longer if AI will reshape our world, it’s how we choose to guide it.”. An infinite-minded perspective urges us to keep that long game in view: the goal is not to “win” against the machines or against each other, but to ensure the human story continues beautifully into this new chapter.

Adapting and Thriving: How Humans Can Remain Relevant

Amid all the uncertainty, one thing is clear: humans are not obsolete – unless we choose to be. Our species can continue to play a vital role in the future, but it requires adaptation on multiple levels. Here we integrate insights from Julia McCoy, David Shapiro, and Mo Gawdat on how to adapt meaningfully alongside AI:

• Adopt a Growth Mindset and Keep Learning. “Adapt to AI, or die,” Julia McCoy quips bluntly from her experience. She doesn’t mean that literally, of course, but the sentiment is that clinging to old methods is a dead-end in fast-changing industries. McCoy herself pivoted from being a traditional content writer to an AI-centric strategist when she saw automation encroaching. She encourages workers to become “first movers” – the proactive innovators who learn to use AI tools to amplify their productivity, rather than being the ones automated away. In practice, this means investing time in AI literacy: take courses on how to use AI in your field, experiment with the latest tools, and understand their limitations. A marketer, for instance, should learn to use AI for data analysis and content generation (what McCoy calls an “AIO” approach, blending AI and human optimization). A doctor might train on AI diagnostic systems to better augment her decisions. The employee of tomorrow stays curious and never stops acquiring new skills – because lifelong learning is truly the only job security in a world where specific job tasks can change overnight.
• Leverage Uniquely Human Strengths. As AI takes over routine tasks, human qualities become the differentiators. Mo Gawdat emphasizes focusing on what machines cannot (currently) replicate: empathy, emotional intelligence, ethics, creativity, and the “human connection.” He even stated that “human connection [is] the most valuable skill for the future” as AI grows in the workplace. This suggests that roles requiring caring, interpersonal understanding, leadership, and cross-disciplinary thinking will remain in human hands for a long time. David Shapiro similarly notes that while AI may write code or crunch data, humans are needed to set goals, inspire teams, and provide the creative spark. So, if you’re planning a career or a shift, think about roles where people skills or novel creative thought are central. Even in highly technical fields, the ability to collaborate, persuade, and empathize with clients or colleagues can set you apart from an AI. In essence, we must double-down on being better humans – improving our soft skills and ethical judgment – because those are hard to automate. A machine might beat us at chess or diagnosis, but it can’t (yet) replace a mentor who motivates teenagers or a therapist who connects with a patient’s pain.
• Collaborate with AI – Don’t Compete Blindly. Rather than viewing it as “us vs. them,” successful adaption treats AI as a partner or tool. As mentioned, those who harness AI will outcompete those who ignore it. We see this already: lawyers who use AI to quickly summarize case law can take on more cases; artists who use AI image generators as part of their workflow can iterate designs faster. The mindset shift here is from being a task performer to a task supervisor or curator. Let the AI do the heavy lifting in what it’s good at (e.g. number crunching, basic drafting) and then add your human judgment on top. Julia McCoy advocates a model where writers use AI to generate a draft, then the human polishes tone, adds personal anecdotes, and ensures factual accuracy – the result is content produced 5-10x faster but still resonant with human authenticity. This principle applies across jobs. If you’re a teacher, perhaps you use AI to create personalized practice problems for each student, but you spend more time on one-on-one mentoring. If you’re a manager, you might use AI analytics to inform your strategy, but you make the final decision guided by intuition and experience. In short, “AI + human” teams will outperform AI alone or human alone in many arenas, so position yourself to be the human half of that equation.
• Find Meaning Beyond Work (Redefine Purpose). Adapting isn’t just about skills; it’s also about our mindset toward life. David Shapiro often discusses the idea of a “post-labor” society, where one’s job is no longer the cornerstone of identity or livelihood. We should start contemplating what gives us meaning if, say, we only need to work 10 hours a week, or if our role is more about overseeing automated systems than doing hands-on tasks. For some, meaning will come from creative arts, for others from community, family, scientific exploration, or spirituality. The key is to cultivate interests and values outside the narrow confines of a paycheck. This way, if your job is disrupted, it’s not as if your entire worth disappears – you have other facets to draw on. The Church of NORMAL (which we’ll discuss more in a moment) is an example of a community framework that could help people navigate this new sense of purpose, blending practical and spiritual support. In pragmatic terms, individuals might consider investing time in hobbies, volunteer work, or education in fields they are passionate about (even if not for income), as a buffer against the psychological blow of job changes. Societally, we might celebrate people not just for their employment status, but for their contributions to community or knowledge or art. It’s a big cultural shift, but starting that shift now will make the future less jarring.
• Stay Informed and Shape the Debate. Finally, remaining relevant means having a voice in how AI is implemented. Rather than passively letting tech companies and governments decide, individuals can get involved in local discussions, ethical committees, or online forums about AI. Mo Gawdat encourages transparency and collaboration – for instance, pushing for open AI systems that communities can benefit from, rather than closed proprietary AI held by a few. If you’re tech-savvy, maybe contribute to open-source AI projects or volunteer in AI ethics panels. If you’re not in tech, you can still vote for leaders who understand these issues, or support regulations that protect people (for example, laws that require companies to provide retraining or severance to workers replaced by AI, or laws that protect privacy in an AI world). Shaping the narrative is part of staying relevant. Humans are meaning-makers; by actively engaging in the conversation about AI’s role, we ensure that human values steer the ship. This is part of what the infinite mindset entails: not seeing ourselves as victims of technology, but as stewards of it – continuously guiding it to serve the infinite game of human prosperity.

Each of these adaptation strategies aligns with the idea that this moment is a pivot point rather than a dead-end. Julia McCoy remains optimistic that those who adapt “will be the ones left standing” and can even benefit hugely from the AI revolution. David Shapiro’s post-labor economics suggests humans can reclaim time for higher pursuits once freed from drudgery – essentially turning a crisis into a renaissance if managed right. And Mo Gawdat’s philosophy reassures that by staying true to our humanity and having a positive, cooperative vision, we can not only remain relevant but enter a new golden age where humans and intelligent machines thrive together. Remaining relevant is thus not just a matter of economic survival, but of preserving human agency and meaning in the midst of massive change.

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