## Introduction Human history has been shaped by revolutions in productivity that turned scarce resources into abundant commodities. The Agricultural Revolution transformed food into a commodified resource through farming, while the Industrial Revolution did the same for energy and manufactured goods through mechanization. Today we stand amid an **“Intelligence Revolution,”** where artificial intelligence (AI) and information technology promise to commodify cognitive skills and knowledge itself. In this emerging paradigm, **intelligence** – the ability to process information and solve problems – may become as ubiquitous and tradable as grain or electricity. By examining the root mechanisms of past revolutions, we can better understand how AI-driven intelligence is produced, distributed, and consumed, and what systemic dependencies and societal shifts might follow. This report takes a first-principles look at how intelligence could become a commodity, identifying historical parallels, macro trends, and potential risks from a global perspective. It aims to go beyond surface analogies (“data is the new oil”) to uncover deeper structural insights from history, and to map out both the opportunities and vulnerabilities of treating **intelligence-as-a-commodity** in the 21st century. ## **Historical Parallels: From Agriculture to Industry to Intelligence** **Commodification Mechanisms Across Revolutions:** Each productivity revolution expanded the supply of a once-scarce resource via new technology and organization, turning it into a common commodity. In the Agricultural Revolution, the domestication of plants and animals and innovations like the plow allowed humanity to produce food surplus at scale. What was once obtained by precarious foraging became a reliable product – **“food-as-a-commodity”** – grown on farms, stored in granaries, and traded in markets. Similarly, the Industrial Revolution harnessed fossil fuels and machines (steam engines, then electricity) to multiply human labor output. Mechanical power supplanted muscle power, **mass-producing goods and energy** that could be bought and sold cheaply. This “mechanical muscle” revolution eventually gave nearly everyone access to manufactured products and electric power, resources formerly limited to labor-intensive production. **Turning Intelligence into a Commodity:** The **Intelligence Revolution** follows a similar pattern. Advances in computing, algorithms, and data harvesting are rapidly increasing the supply of machine-based cognitive work. **Artificial intelligence can perform tasks that once required human intellect** – from calculations and record-keeping to driving vehicles and diagnosing illnesses. AI thus holds the potential to become a **“mechanical mind”** analog to the mechanical muscle of the industrial age. As AI technologies improve and proliferate, they enable cognitive skills to be productized and scaled. For example, a single machine-learning model can provide language translation or medical image analysis for millions of users, far outpacing what individual experts could do. Cloud computing platforms now distribute AI capabilities globally on-demand, much as the electric grid distributes power. Economist Nicholas Carr’s insight that **IT’s ubiquity reduces its strategic importance** – making it a utility rather than a differentiator – is now being applied to AI . In other words, AI is on track to become a base commodity that every business and person can tap into, rather than a niche capability. Indeed, researchers note that _“the commoditization of AI has already begun”_. The notion that _“AI is the new electricity”_ captures this parallel: just as electrification made energy universally available, AI could make **intelligence universally accessible**. **Surplus and Ubiquity:** Past revolutions dramatically increased output and lowered costs. Farming boosted food production so vastly that the share of people working in agriculture plummeted over time – falling from roughly 90% of the U.S. workforce in 1790 to under 2% today. Industrialization similarly scaled up manufacturing; in the U.S., manufacturing employment peaked mid-20th century and then fell to under 9% by 2019 as productivity soared. In both cases, technology made a scarce good abundant, freeing most people to shift into other roles. We now see signs of an intelligence surplus in the making. **AI systems can handle growing volumes of work** – reading legal documents, optimizing logistics, answering customer queries – often at lower marginal cost than human labor. As AI deployment expands, we may find that basic information services and cognitive labor (from translation to data analysis) become **cheap or even essentially free commodities** bundled into our devices and workflows. Just as computing power (measured by transistor cost) became abundant due to Moore’s Law, so might **AI-driven intelligence become abundant** due to better algorithms and scalable cloud infrastructure. Notably, a 2024 study suggests the AI/big data boom could be _“almost as transformative to the economy as the Industrial Revolution,”_ potentially boosting productivity while reducing labor’s share of income. In short, intelligence might follow food and energy on the path from scarcity to ubiquity – with profound implications for how our economies and societies function. ## **Systems of Production and Distribution** Every commodity needs a **production system** (how it’s generated) and a **distribution system** (how it reaches people). These systems create new dependencies and vulnerabilities, as history shows. - **Agricultural Systems:** Once food production centralized into farms, people increasingly depended on farmlands, the climate, and trade networks. Societies built granaries, canals, and later railroads to store and distribute grain. This brought resilience (fewer people starved when local harvests failed, because food could be imported) but also new risks: crop monocultures could fail catastrophically and long supply chains meant local failures could cascade. Human diet and survival became tied to a **vast production network** of fields, livestock, and markets rather than personal hunting skills. - **Industrial Systems:** Industrialization created factories, mines, power plants, and global supply lines for raw materials. People came to rely on the **energy grid, fuel supply, and mass production chains** for their daily needs. For example, the average citizen no longer built their own tools or generated their own power – they purchased goods and electricity delivered over complex systems. This yielded huge gains in efficiency and comfort, but also **structural dependencies** on these networks. An oil embargo or a power blackout could cripple entire cities. Geopolitically, nations without energy resources became dependent on those with oil and coal, reshaping international relations around energy security. - **Intelligence Systems:** In the AI era, the production of machine intelligence happens in **data centers** – the new factories of the mind – and is distributed via digital networks (the internet and cloud platforms). Already, **colossal server farms and cloud computing hubs generate AI services** used for search engines, personal assistants, navigation, and more. Humans are becoming deeply dependent on these systems for everyday decision-making and knowledge. For instance, billions rely on search engines or AI assistants to retrieve information instead of memorizing facts or consulting libraries. This convenience creates reliance on a **global “knowledge grid”** managed by tech companies. If the service goes down or gives faulty information, most users are left helpless – a scenario analogous to a power outage in the electric age. Our cognitive autonomy is intertwined with AI production systems. **Data** is a key input to these systems, much like raw materials for a factory. Tech giants harvest vast datasets (often from users) to train models, and this **data supply chain** spans the globe. Workers in one country might label images or moderate content to improve an AI model that users in another country will consume. The distribution of AI intelligence is predominantly through APIs, software, and devices – often controlled by a few major platforms (for example, the **cloud market is dominated by three companies holding ~65% share**). This concentration means that much like a handful of utilities once controlled electricity, a few corporations now control large portions of the **“intelligence grid.”** Individuals, businesses, and governments find it hard to function if cut off from these AI-driven information networks. **Evolving Dependence:** With each revolution, humans surrendered some autonomy in exchange for efficiency. Hunter-gatherers were self-reliant in procuring food, but farmers depended on the stable functioning of their agricultural society. In industrial cities, people depended on jobs and wages to buy life’s necessities from the system. Now, we depend on connectivity and data flows. The internet’s outage or a cyber-attack on critical AI systems could disrupt everything from transportation to healthcare, illustrating a new form of **systemic fragility**. At the same time, these networks increase resilience in other ways: data can be backed up across continents, and AI systems can reroute around local failures, potentially making knowledge distribution more robust than pre-digital systems. The key takeaway is that intelligence-as-a-commodity will come from **complex socio-technical systems** – data centers, algorithms, power grids, telecom infrastructure, and skilled personnel – and society will be deeply reliant on the smooth interplay of all these components. We must therefore analyze the **structural dependencies** and single points of failure in this emerging system, just as we do for food security or energy grids. ## **Short-Term Disruptions and Labor Shifts** Major productivity revolutions tend to bring **turbulent transitions** in the short term. In the near future, as AI commmodifies intelligence, we can expect significant disruption in labor markets, economic balance, and societal attitudes: - **Job Displacement vs. Creation:** Already AI and automation are displacing certain jobs, especially routine cognitive work (data entry, basic accounting, customer support chat, etc.). This echoes how textile machines displaced weavers in the early 1800s, triggering the Luddite protests. In the short run, many workers feel threatened as **machines encroach on skilled white-collar roles** (for example, AI writing assistants in journalism or contract analysis in law). However, history shows new technologies also create new roles. The Industrial Revolution eventually led to entirely new industries (electrical, automotive, telecommunications, etc.) that employed millions in jobs previously unimaginable. Likewise, the AI revolution is spawning new occupations: prompt engineers, AI model trainers, data curators, and AI maintenance specialists. **New industries and services are expected to emerge** around AI, and early evidence suggests that while some jobs are lost, others will be created or transformed rather than eliminated. For instance, rather than rendering physicians obsolete, AI diagnostics might shift doctors’ focus to more human-centric care, effectively creating a new model of AI-augmented healthcare jobs. - **Productivity Boost and Transition Pain:** In the short term, companies adopting AI are seeing **productivity gains** – one study found AI and big data tools can boost worker output and even raise annual income potential by tens of thousands of dollars for those who upskill to use them. Such productivity jumps can increase economic growth. But they also can **reduce the labor share of income**, meaning relatively more wealth accrues to owners of AI capital versus workers. This mirrors early industrialization, where factory owners gained fortunes even as many workers toiled in poverty until labor reforms caught up. We may witness a widening income inequality in the near term: a tech-savvy, highly paid minority (those who build or leverage AI) and a larger group facing stagnant wages or unemployment. Policymakers and society often react slowly, so we could see a period of **heightened inequality and social stress** before adjustments occur. The late 19th century, for example, saw surges in inequality and the rise of robber barons, eventually countered by progressive reforms. In our time, talk of measures like universal basic income or job guarantee programs is already emerging to cope with possible mass displacement by AI. - **Skills Mismatch and Education:** In the immediate future, a key challenge is that **education and skills training lag behind technology**. Many workers and students are learning skills that AI may render obsolete. As AI becomes a commodity service, knowing how to effectively _use_ AI may become more valuable than performing the raw task that AI can do. There is a short-term scramble for skills like data science, machine learning engineering, and AI-augmented decision making. Those with these skills enjoy a seller’s market (hence the intense “talent war” for AI experts), while those without them may struggle. We are likely to see increased demand for retraining programs and lifelong learning as standard career advice. Yet retraining millions of workers quickly is a massive undertaking; historically, societies have struggled with this (e.g., many older manufacturing workers never found equally good jobs after factories closed). Thus, the short term could involve **significant frictional unemployment and underemployment** – people willing to work but not possessing the skills that growing industries need. - **Market Concentration and “Intelligence Barons”:** In early stages of a new commodity, we often see dominant players due to economies of scale. Just as a few industrialists controlled steel or oil, a few tech companies today control much of the AI capacity. They benefit from network effects (more data leads to better AI, which attracts more users generating more data) and large capital requirements (training frontier AI models can cost tens of millions of dollars, affordable only to big firms). In the near term, this **concentration of AI power** could intensify. This risks **monopolistic practices** where these companies set terms for access to AI (pricing, data policies) much as early energy monopolies did before antitrust interventions. It also creates strategic vulnerabilities – if something happens to one of these “AI barons” (say a major cloud provider suffers an outage or hack), the ripple effects would be felt economy-wide. Already, the top three cloud-AI providers account for well over half of global cloud infrastructure, reflecting this concentrated control of distribution. The short term may thus require grappling with whether and how to regulate AI utilities to ensure fair access and reliability, akin to treating them as public utilities or essential infrastructure. - **Social Perception and Resistance:** It’s typical for new tech to be met with public anxiety. We see that pattern repeating: on one hand, there’s **exuberance and hype** around AI’s possibilities; on the other, fear of job loss and dystopian outcomes. Short-term, we can expect social pushback, from calls to _“pause AI development”_ to worker strikes against automation or popular movements demanding a human role in decision-making. During the Industrial Revolution, anxieties about mechanization sometimes turned violent (the Luddite machine-breaking). Today’s resistance might take the form of legal challenges, increased unionization in tech-augmented workplaces, or demands for algorithmic transparency. Society is essentially negotiating the terms under which this new commodity (AI intelligence) will integrate into daily life. In summary, the immediate horizon of the intelligence revolution is marked by **both opportunity and upheaval**. Productivity and economic output may surge, but so may inequality and insecurity for many workers. The key will be managing this transition – through education, social safety nets, and perhaps novel economic models – to ensure that the gains from commodified intelligence eventually benefit the many, not just the few. ## **Long-Term Macro Trends and Evolution** Looking beyond the turbulent transition, we ask what the **mature state** of an AI-driven intelligence economy might look like. History suggests that in the long run, productivity revolutions can lead to higher overall living standards, new social classes, and different economic structures – albeit not without persistent challenges. Several long-term macro trends can be anticipated if intelligence becomes as commonplace as food or electricity: - **Near-Zero Marginal Cost Intelligence:** In the mature phase, **AI services could become extremely cheap and widespread**, akin to a public utility. Just as today even poor regions eventually gained access to mass-produced clothing and basic electric lighting, tomorrow even small businesses or remote communities might access powerful AI analysis or automation at low cost. The commodification drives cost toward the marginal cost of production, which for digital goods is very low. Imagine AI tutors for every child, AI medical consults for every patient, and on-demand translation between any languages – these could become standard offerings embedded in devices or community services. This ubiquity can massively boost human capabilities and free humans from many menial cognitive tasks, potentially sparking **new waves of creativity and innovation** built atop the AI utility (much as electrification enabled appliances, radio, computers, etc.). A PwC analysis estimates AI could contribute up to $15.7 trillion to the global economy by 2030 through efficiency and new products, indicating the scale of long-term growth at stake. - **New Industries and Roles:** Over decades, entirely new industries will likely form that we can only partly envision now. Historical analogy: someone in 1800 could hardly predict the automotive or software industries of the 20th century. Similarly, by 2050 we might see sectors like **“personal creativity services,”** **robotic caregiving industries,** or **AI ethics management** become significant employers. Humans will gravitate toward roles that complement AI: the uniquely human skills of creativity, complex strategic planning, craft aesthetics, empathy, and leadership. **Human-AI collaboration** will be the norm in most fields – for instance, teams of human doctors and AI diagnostics, human teachers with AI assistants, human judges informed by AI evidence analysis. Rather than pure replacement, there will be a symbiosis in many areas, with AI handling the commoditized analytical substrate and humans providing guidance, values, and final-mile execution where needed. This is one optimistic long-term scenario: a larger pie with new slices of economic activity, and humans still finding meaningful work alongside intelligent machines. - **Wealth Distribution and “Winner-Take-Most” Dynamics:** A less positive trend to monitor is the **distribution of the enormous wealth generated by AI**. Without intervention, there is a risk that the owners of AI platforms and intellectual property could capture a disproportionate share of the value, exacerbating inequality. In an extreme case, if AI and robots eventually do the bulk of economically productive work, and ownership of these assets is concentrated, we could see a societal split between an elite who own the “intelligence means of production” and a majority who rely on redistribution (through taxes, UBI, etc.) to share the benefits. Thomas Jefferson once worried about an “aristocracy of wealth” emerging from industrial capitalism; in the long run, we must guard against an **“aristocracy of data and AI”** – a small class controlling the key algorithms, data, and computing infrastructure. This could harden into a new form of class stratification if not mitigated. However, democratic societies might respond by redistributive policies, expanding public ownership of AI utilities, or recognizing data as a labor input (and thus compensating people for the data they generate). The trajectory of inequality in the long run is not set in stone – it will depend on policy choices and social movements, much as it did in the 20th century when progressive taxation, antitrust laws, and welfare states emerged to counteract the worst excesses of industrial-era inequality. - **Human Capital and Continuous Reinvention:** A defining feature of the intelligence era may be the need for **continuous learning and adaptation** by the workforce. Unlike past revolutions where a skill could last a lifetime (a master craftsman’s techniques, or an engineer’s single specialization), AI’s rapid improvement could obsolete skills faster than ever. Historian Yuval Harari warns that by 2050 we might have billions of people forming a “useless class” – not because there is absolutely no work left, but because the work landscape changes so fast that people cannot adapt quickly enough and become _unemployable_. In his words, _“most of what people learn in school will be irrelevant by the time they are 40 or 50… people will have to reinvent themselves again and again”_. The long-term trend could be a labor market in flux, where lifelong employment in one profession is rare. Societies might shift to models of lifelong education, with mid-career sabbaticals for re-skilling, and a cultural acceptance of multiple career paths. There is also the possibility that the very concept of “having a job” changes; if AI-driven productivity is high enough, societies may decouple income from employment (via universal basic income or other mechanisms) and encourage people to pursue more individualized vocations, crafts, or care work without the pressure of economic survival. Long term, human purpose and fulfillment might be sought more in creative, social, or recreational endeavors once the struggle for material productivity is largely handled by machines – a scenario some refer to as **“post-scarcity”** for intellectual labor. This is speculative, but it is a logical extension of an intelligence-abundant world. - **Integration and Autonomy:** As intelligence becomes a commodity, it will be integrated into virtually every device and process – much as microprocessors ended up everywhere from thermostats to toothbrushes. We’ll likely interact with subtle AI dozens of times a day without even realizing (from traffic systems optimizing flow to personalized AI content curators). Over decades, this pervasiveness could lead to a **blurring between human and machine intelligence** in decision-making loops. A long-term concern is maintaining human **autonomy and agency**: we must avoid a future where humans become passive consumers of AI-driven decisions, with critical thinking atrophying. Ideally, the long-run state is one where humans are _amplified_ by AI, not automated away. But achieving that balance will require conscious design choices now (such as keeping humans “in the loop” for important decisions and cultivating skills that complement AI). In broad strokes, the long-term future of an intelligence-based economy holds immense promise – productivity and knowledge could reach levels that solve many of humanity’s pressing problems (disease, hunger, environmental management). Yet it also poses the challenge of **ensuring inclusivity and human dignity** in a world where intelligent machines are ubiquitous. History’s lesson is that **productivity gains do not automatically equate to shared prosperity**; deliberate effort is needed to spread benefits and redefine social contracts. The coming decades will likely be a negotiation between these forces, determining whether the Intelligence Revolution becomes a true boon for global civilization or a source of new divisions. ## **Infrastructure and Structural Dependencies of Intelligence** When intelligence is produced and delivered as a commodity, it rests on a **vast physical and digital infrastructure** that comes with its own constraints. It’s easy to think of AI as “software” or ethereal cloud services, but in reality AI is built on very tangible foundations: data centers, semiconductors, power grids, and supply chains crisscrossing the globe. Understanding these structural dependencies is crucial, because they represent potential **choke points and vulnerabilities** for the intelligence economy: _Figure: The resource intensity of AI – illustrated by a laptop (representing an AI service interface) connected to rows of data center servers (the computational backbone), all drawing heavy electric power (lightning bolts) and standing on a green floor symbolizing environmental impact. This metaphor highlights that AI’s seemingly virtual services depend on massive energy-consuming hardware infrastructure._ - **Energy and Data Centers:** AI may operate in the digital realm, but it runs on electricity. The compute power required for training and running AI models has been skyrocketing. **Data centers – the factories of AI – consume about 1.5% of global electricity** (as of 2024) and this share is expected to **double by 2030** due to the AI boom. In 2022, data centers worldwide used roughly 460 terawatt-hours of electricity, putting them on par with a mid-sized country like France in energy consumption . By 2026, data centers (driven largely by AI workloads) could consume around 1,050 TWh, which would rank **fifth in the world** in electricity usage (between Japan and Russia) if data centers were a country. This massive energy appetite creates dependence on power infrastructure: AI services are only as reliable as the electricity grid. It also ties the intelligence commodity to the fossil fuel economy (unless grids become predominantly renewable). If energy prices spike or shortages occur, the cost of AI services will rise and capacity could be rationed. Conversely, widespread AI adoption could itself exacerbate energy strain, potentially creating a feedback loop of **higher energy demand requiring more power plants or grid upgrades**. There are also environmental implications: training one large AI model can emit hundreds of tons of CO₂, contributing to climate change if the electricity isn’t clean. Furthermore, cooling the thousands of servers in data centers requires huge amounts of water. Generative AI models’ lifecycle involves _“a great deal of water”_ usage for cooling, which can **strain local water supplies and ecosystems**. For example, a data center supporting AI in a drought-prone region could compete with agriculture or cities for water. Thus, **AI’s commodity infrastructure inherits the vulnerabilities of the energy and water systems**: outages, price volatility, and climate-related disruptions are all risks. Efforts are underway to improve efficiency (better chips, liquid cooling, renewable-powered data centers) to mitigate this, but the fundamental dependency remains. - **Semiconductor Supply Chain:** At the heart of AI infrastructure are advanced microchips – specifically, high-performance processors (like GPUs and AI accelerators) that train and run neural networks. The supply of these chips is highly concentrated. **Over 90% of the world’s most advanced microchips are manufactured in one country: Taiwan**. One Taiwanese company, TSMC, produces more than half of global supply for cutting-edge chips. This concentration is a profound structural dependency. It means the entire global AI industry relies on the stability of one geographic region and a few facilities. Geopolitical tensions heighten the risk: analysts warn that a conflict involving Taiwan (for instance, a disruption or blockade) would have a **“devastating impact on the global economy – far greater than the havoc wrought on food and energy supplies”** by recent wars. Such an event could trigger an “intelligence famine” – a sudden shortage of AI chips crippling everything from tech companies to military systems worldwide. Even without a geopolitical crisis, the complexity of the semiconductor supply chain (which involves raw materials from Africa, equipment from Europe, factories in East Asia, and R&D in the U.S.) means it is vulnerable to disruptions (pandemics, trade disputes, natural disasters). The **2020–2021 global chip shortage** hinted at these fragilities, when car manufacturers and others had to halt production due to lack of semiconductors. For the intelligence economy, securing the chip supply is as vital as securing oil was in the 20th century. This might drive long-term strategies like countries investing in domestic chip fabrication (e.g., the US CHIPS Act, Europe’s semiconductor initiatives) to reduce single-point failure risk. Nonetheless, for the foreseeable future, advanced AI computation is an **resource chain with weak links** – a reminder that even the most digital of commodities depend on physical manufacturing that can become a bottleneck. _Figure: Taiwan’s exports by commodity (Q1 2023). The large green slice (42%) represents electronic components (primarily semiconductors). This chart underscores Taiwan’s outsized role in supplying the world’s electronics and chips, indicating how modern intelligence systems (which require these chips) are tied to global trade patterns._ - **Global Data Networks:** The distribution of intelligence relies on the internet – a vast network of undersea cables, cellular towers, satellites, and servers. This network is generally robust due to its redundancy, but localized failures can occur (an undersea cable cut can blackout internet for a region, for instance). Moreover, **access to the network is uneven globally**, which creates a dependency asymmetry: developed regions assume constant high-bandwidth connectivity and can commodify intelligence easily via cloud services, whereas rural and less developed regions might lack the connectivity to fully participate. In effect, the **“intelligence grid”** doesn’t reach everyone equally, which could entrench a divide (addressed more in the global section). From a security standpoint, the reliance on networks opens up cyber vulnerabilities – hacking or malware in critical AI systems could spread rapidly. If intelligence services are centralized (say, a popular AI model hosted on the cloud), a single cyber incident could compromise service for millions. This is analogous to how a virus in a monoculture crop can wipe out a huge food supply: a monoculture of AI systems could be risky. Building resilience might require decentralizing some intelligence processing (e.g., running AI on local devices at the “edge” to reduce total dependency on central clouds). - **Maintenance and Human Expertise:** Often overlooked is the human infrastructure behind AI. Behind automated AI services lies an army of engineers, data scientists, content moderators, and data labelers who build and maintain these systems. Many AI models require continuous updating (for new data, handling new user demands, fixing errors or biases). Thus there is a **skilled workforce dependency** – if the pipeline of AI talent stops or if a company loses its key experts, the quality and safety of the intelligence service can degrade. We’ve already seen how _outsourced labor_ in the Global South plays a role: for example, **OpenAI relied on Kenyan workers earning under $2/hour to filter toxic content** and make ChatGPT safer. This highlights a potential vulnerability and ethical issue: the commodification of intelligence may be propped up by undervalued human labor in unseen roles. If those workers face burnout (content moderation can be psychologically traumatic) or revolt against poor conditions, it could shock the system supporting “automated” intelligence. Long-term stability of the intelligence commodity might require treating this human layer as critical infrastructure too – ensuring fair labor practices and a stable supply of expertise. In sum, the intelligence economy is not a weightless cloud; it is built on **industrial-age foundations of silicon, electric power, and global trade**. Each foundation comes with dependencies analogous to the farms and oil wells of previous eras. A prudent approach to the intelligence revolution requires fortifying these foundations: investing in sustainable energy for data centers, diversifying chip manufacturing, strengthening network infrastructure, and training a robust workforce to support AI. The lesson from past revolutions is that the _commodity is only as secure as the system that produces and delivers it_. We will need to monitor these systemic dependencies and address weak links to prevent disruptions in the flow of this new commodity, intelligence. ## **Social and Economic Impacts: Inequality, Autonomy, and Stratification** Turning intelligence into a commodity will reverberate through social structures and everyday life. As with prior revolutions, there will be profound impacts on who benefits, how people live and relate to each other, and the overall resilience of society. Here we explore several key dimensions: inequality, human autonomy, social stratification, and community resilience. - **Inequality and Power Dynamics:** Technological revolutions often initially widen economic inequalities, and the AI revolution shows signs of this pattern. Those who control the **means of producing intelligence (AI models, computing power, data)** stand to gain tremendous wealth and influence. Already, we see the concentration of AI development in a few tech giants and elite research labs. This risks creating a new **oligopoly of intelligence providers**, analogous to how a few oil companies once dominated energy. If left unchecked, wealth could concentrate further in tech hubs and among investors backing AI firms. Meanwhile, some jobs eliminated by AI may never return, potentially leading to long-term unemployment or underemployment for certain skills groups. The _labor share of income_ – the portion of economic output paid as wages – could decline, as noted in recent findings where AI adoption in firms led to around a **5% drop in labor’s share**. A grim scenario (per Harari’s “useless class” concept) is a mass of people finding their skills obsolete and unable to compete economically. However, these outcomes are not inevitable; they depend on policy and collective action. Societies could choose to redistribute AI-generated wealth (through progressive taxation or equity schemes), or encourage models of AI development that are open-source and widely accessible rather than proprietary. The inequality issue also has a global aspect: countries leading in AI (with more capital and talent) will leap ahead economically, whereas those lagging may become **dependent consumers of foreign AI**. Without intervention, AI could amplify global wealth gaps – effectively a form of **digital or data colonialism** where the Global South supplies raw data or cheap labor while the benefits accrue in the Global North. Recognizing data as a strategic resource and ensuring all countries can develop AI capacity are steps being discussed to avoid this neocolonial dynamic. - **Human Autonomy and Cognitive Dependency:** As we integrate AI into daily decisions, from navigation to news curation, there is a subtle social impact on autonomy and human agency. If intelligence is commodified and delivered conveniently, people may rely on it **unquestioningly**, potentially diminishing their own skills. For example, widespread GPS navigation has already eroded people’s map-reading and wayfinding abilities. With AI, this could extend to remembering information (since any fact can be looked up or asked from an AI assistant) or making judgments (if algorithms recommend financial investments or medical treatments, will individuals just follow orders?). There is a risk of **over-reliance on automated advice**, leading to a populace that is less practiced in critical thinking or decision-making. The extreme end of this spectrum is a kind of **algorithmic paternalism** – where key life decisions (hiring, loan approvals, dating matches, legal sentencing) are heavily influenced or even made by AI systems. That raises ethical issues about transparency and accountability. If an AI denies someone a loan or parole, who is responsible and how can the decision be questioned? Societies will need to safeguard **autonomy** by setting boundaries on AI’s role, insisting on human oversight in critical matters, and educating citizens to understand and critically evaluate AI outputs. Maintaining human autonomy also means ensuring diversity of options – not every service should be channeled through one AI system. Just as we value diverse news sources for a healthy democracy, we may need diverse AI systems to avoid monoculture of thought. - **Social Stratification and Class Structure:** With intelligence becoming a commodity, we might see the emergence of new social strata defined by access to or control of AI. One possible stratification is between those who are **augmented by AI vs. those who are not**. Consider education: wealthier students might use AI tutors, personalized learning algorithms, and achieve far better outcomes than those in under-resourced schools without such tools. This could create a feedback loop where the augmented class continuously outperforms and out-qualifies others. Within workplaces, those adept at using AI could become a higher tier of workers (handling more complex tasks and supervising automated processes) while those who cannot adapt either settle for menial jobs or exit the workforce. In essence, a cognitive elite augmented by AI could form, somewhat analogous to how the Industrial Revolution initially created a divide between industrial capitalists, skilled managers, and unskilled factory laborers. Another stratification could be **ownership-based**: if we move toward a world where many people don’t have traditional jobs, the division might be between those who own productive AI/robotic assets and those who rely on social support. This resembles the distinction between capital owners and laborers, but if labor as a contribution diminishes, owning a share of the AI-driven economy (through stocks, data ownership, etc.) might become crucial for personal wealth. It’s also worth considering regional stratification: entire cities or regions that become AI hubs will prosper (drawing talent, investment, advanced services) while others might stagnate, similar to how some rust-belt cities declined when industrial jobs left. Social stratification could thus increase both at the individual and geographic level. However, awareness of these potential rifts could spur policies to counteract them – such as **universal AI access programs** (making AI tools a public good, like libraries), or education reforms so that _everyone_ is taught to use AI competently, narrowing the augmentation gap. - **Community Resilience and Local Autonomy:** One under-discussed impact of commodified intelligence is on community resilience. As communities (be it a town, or an organization) become dependent on globally provided AI services, their local capacity to solve problems might weaken. In prior eras, local self-sufficiency was given up in exchange for efficiency (people stopped growing their own food and relied on supermarkets; most can’t fix their own electronics, they buy new ones, etc.). In the intelligence era, local knowledge could be outsourced to the cloud. Imagine a small city’s administration relying on an AI service for managing traffic, utilities, emergency response – if the service fails or the provider withdraws support, is the community able to take over those functions? This concern speaks to **resilience**: systems should be designed with fail-safes or manual override modes for when the commodity service isn’t available. Communities might consider investing in some level of local AI capability (edge computing, local data backups, training local talent) much as some communities maintain backup generators for when the electric grid fails. Additionally, the **loss of local knowledge** could have cultural implications – human knowledge that isn’t regularly practiced can disappear over generations. If future generations grow up with AI always available, will they maintain the same cognitive skills or will those atrophy? This is analogous to how the widespread use of calculators has reduced mental arithmetic skills in many people. One positive counter-trend could be that by freeing people from routine tasks, AI allows more focus on community-building, relationships, and creative endeavors, potentially strengthening social bonds if used wisely. But if mismanaged, it could also lead to social isolation (people interacting more with AI than neighbors) or erosion of certain social roles (for instance, if elder care is done by robots, the intergenerational interactions could diminish). - **Surveillance and Control:** Commoditized intelligence also raises the prospect of widespread surveillance and data collection, since AI thrives on data. There is a risk that the same systems providing helpful services could also be used to monitor and control populations, intentionally or unintentionally. Already, everyday tools generate data – smartphones track movements, smart assistants can listen at home – and AI can aggregate and analyze this data at scale. Societies will have to grapple with the **balance between utilizing data for good (public health, safety, convenience)** and protecting individual privacy and freedom. In a negative scenario, authoritarian regimes could use AI as a commodity of control – employing facial recognition and predictive analytics to stifle dissent and micromanage citizens. Even in democratic societies, if unchecked, the combination of corporate and government AI might lead to a _“surveillance capitalism”_ or surveillance state where people’s behavior is heavily influenced by algorithmic nudges or watched for deviance (as some credit scoring or policing algorithms already do). Maintaining a healthy society in the intelligence age will require robust **data governance, privacy protections, and ethical AI frameworks** to ensure this powerful commodity doesn’t undermine civil liberties. In summary, the social impacts of making intelligence widely available and tradable are double-edged. We could see a more empowered society where humans focus on higher pursuits and inequalities are leveled by access to AI. Or we could see greater divides and new forms of dependency and control. The outcome will depend on proactive measures: addressing inequality through inclusive access and fair economic models, emphasizing human agency in the loop, fostering digital literacy for all, and setting rules that prioritize human values in AI deployment. The **human factor** remains central – even as intelligence is commodified, it should serve human ends and not the other way around. ## **Shifts in Human Skillsets and Roles** One of the clearest ways to understand the impact of productivity revolutions is to look at how they change the skills people need and the roles they play in the economy. The commodification of intelligence will almost certainly demand a major realignment of human skillsets, comparable to the shifts seen when we went from hunting to farming, or from craftwork to factory assembly lines. - **From Physical to Cognitive to Creative:** Historically, we’ve seen a progression: the Agricultural Revolution reduced the need for nomadic survival skills and increased the need for farming know-how; the Industrial Revolution reduced the need for agrarian skills and increased the need for operating machinery and performing repetitive specialized tasks in factories. Many analysts frame the current transition as moving humans up another rung: as AI takes over routine **cognitive** tasks, humans will shift toward **creative, innovative, and empathetic** tasks. In essence, jobs will emphasize the _soft skills_ and _abstract thinking_ that AI cannot easily replicate. For example, while an AI may handle data bookkeeping (as spreadsheets did to clerical work), a financial advisor’s role may shift more to understanding a client’s life situation and providing personalized advice with a human touch. Engineers might rely on AI for technical calculations but spend more time on inventive design and cross-disciplinary thinking. We already see early signs: education curricula are debating greater focus on creativity, critical thinking, and collaboration, anticipating that memorizing facts or learning routine procedures will be less relevant when AI can assist those. - **Continuous Learning as Norm:** One can argue that **life-long learning will become not just a slogan but a necessity**. As mentioned earlier, Harari’s warning that people will need to _“reinvent themselves… faster and faster”_ may define the future career path. It won’t be unusual for someone to undergo several rounds of retraining in their working life – for instance, a truck driver displaced by self-driving trucks might retrain as a drone fleet operator, and a decade later retrain again as a robot maintenance technician. The half-life of skills is shortening. This places a big onus on both individuals and educational institutions. We may need to overhaul how education is delivered: instead of front-loading 20 years of schooling in youth, there could be intermittent educational sabbaticals. Online learning, micro-credentials, and vocational programs will likely expand to cater to adults cycling through new skills acquisition. One positive aspect is that AI itself can be an aid in this process – AI tutors and personalized learning programs can help workers pick up new competencies more efficiently. The concept of _“learning how to learn”_ becomes perhaps the most critical meta-skill, along with adaptability and resilience in the face of change. Societies that foster flexible, modular education systems will handle this transition better than those clinging to a one-and-done schooling model. - **Redefining Work and Purpose:** As AI takes over many tasks, humans might find that their **work roles need to be redefined** not just in skill but in purpose. In previous eras, while jobs changed, the notion that most adults would work to earn a living remained constant. Now, we face a potential scenario where there simply may not be enough _traditionally defined jobs_ for everyone, if AI and automation become extremely efficient. This raises questions: Do we reduce working hours significantly and share the remaining work (as John Maynard Keynes imagined, a 15-hour workweek)? Does society provide universal income so people can pursue non-economic contributions (art, community, learning, caregiving) without financial hardship? The identity and self-worth people derive from work might shift; more people might find purpose in arenas outside their paid employment. Indeed, we may see a renaissance of hobbies, arts, civic engagement – activities that were somewhat sidelined in the industrial chase for productivity – becoming central to human life again if material production is largely handled by machines. In the long run, the skill most needed might be **self-directed motivation and creativity** to use one’s increased leisure or flexibility meaningfully, avoiding the dystopian image of bored masses addicted to distraction (the VR-and-drugs scenario Harari alludes to). - **Human-Machine Teaming Skills:** Rather than purely technical skills, a lot of emphasis will likely go to **“interfacing” skills** – the ability to effectively work with AI tools. This includes formulating the right questions or prompts for AI (a skill in itself), critically evaluating AI outputs, and combining human insight with machine suggestions. Think of it like being a good pilot in an age of advanced autopilot: one must know when to trust the system and when to take manual control. Professions across the board will incorporate AI such that almost everyone becomes a sort of **AI-augmented worker**. Training for future doctors, lawyers, engineers, etc., will incorporate AI literacy – knowing AI’s strengths, weaknesses, and ethical considerations. Emotional intelligence and ethics will be highlighted, because humans will be needed to do what AI cannot: navigate ambiguous situations, understand human emotions, and make value-based judgments. For instance, an AI might flag a set of job candidates as optimal based on data, but a human HR manager will need the people skills and ethical lens to decide who truly fits the team and to ensure biases are checked. So, **judgment, ethics, and interpersonal skills** become more prominent in many roles. - **Care and Empathy Economies:** Many experts predict growth in jobs that require empathy and human connection – areas least likely to be fully automated. These include healthcare (nurses, therapists, elder care), education (mentors, special ed, early childhood education), and creative fields (artists, entertainers, writers who bring a human perspective). AI might assist in these but the human element is the core value. So we might encourage more people to enter care professions or creative industries, which historically have been undervalued or underpaid. Society might need to re-evaluate how it rewards such roles if they become a larger portion of employment. It’s conceivable that as raw intelligence becomes cheap, _emotional intelligence becomes comparatively more valuable_. - **Revival of Craft and Niche Skills:** A curious side effect of mass commodification of a capability is often a counter-trend valuing the artisanal and unique. For example, industrial food production led to movements for organic farming and local food; fast fashion’s ubiquity revived interest in handmade crafts. Similarly, if AI can generate art, music, writing at scale, there may be a greater appreciation for **human-made, unique outputs** precisely because they are human. We see a bit of this already: despite AI art’s rise, there’s a growing market for handmade goods on platforms like Etsy, and people tout “handcrafted” or “human-curated” as a mark of quality. Thus, some humans may deliberately cultivate niche skills that AI finds hard to replicate or that carry a human-authenticity premium. In education, while AI tutors might teach math, a human mentor might be valued for inspiring a student. In entertainment, AI might churn out formulaic screenplays, but audiences might cherish the auteur director or novelist’s work more. This dynamic suggests that **human originality and authenticity** could become a selling point in a future saturated by AI-generated commodities. In conclusion, the skill landscape will transform: **routine cognitive skills (like memorizing, calculating, straightforward programming) will diminish in value**, while **adaptability, creativity, ethical reasoning, and interpersonal skills will rise in value**. Humans will continually co-evolve with machines, and success will depend on our ability to carve out a complementary niche – doing what machines can’t, or doing what machines do in a uniquely human way. Societies that anticipate these shifts and re-skill their populations accordingly will navigate the intelligence revolution more gracefully, minimizing the pain of transition and maximizing the utilization of human potential. ## **Global Developments and Asymmetries** No analysis of a revolution is complete without considering its global dimensions. The Agricultural and Industrial Revolutions played out differently across regions – some societies surged ahead, others were colonized or marginalized, and global power balances shifted. The Intelligence Revolution is likely to be similarly uneven, with distinct **leaders and laggards**, and new forms of interdependence (and possibly exploitation) emerging. - **The US-China AI Race and Geopolitics:** A major theme of the current intelligence era is the strategic rivalry in AI between superpowers, notably the United States and China. These nations view leadership in AI as key to economic and military power in the 21st century. Currently, the U.S. holds a significant edge in certain areas: for example, by one estimate the U.S. has roughly **10 times more computing capacity for AI R&D than China** , and hosts many of the top AI software platforms. This advantage in “compute” is crucial – it means the US can train more advanced models and deploy them widely, reaping network effects and economic benefits. The U.S. also houses a large share of the world’s AI talent and research institutions, and leads in semiconductor design (although, as discussed, manufacturing is global with Taiwan as a linchpin). China, however, is investing heavily and has advantages of its own: a huge population generating vast amounts of data, a government-driven strategic focus on AI, and increasingly, homegrown innovations. By 2025 China has closed some gaps – for instance, it has produced competitive AI models and significantly increased its high-performance computing capabilities, partly by circumventing export controls on chips. In terms of publications and patents, China is at or near the top globally in AI research output. The global implication of this rivalry is a potential **bifurcation of the AI ecosystem**: different standards, networks, and even a “splinternet” scenario where Chinese-developed AI tools dominate in parts of the world (especially Global South countries linked to China’s Belt and Road digital initiatives) while U.S./Western tools dominate elsewhere. Much like the Cold War saw a split in technology systems, we might see competing AI infrastructures. On the other hand, there is interdependence – for example, U.S. firms rely on Chinese rare earth materials for electronics; Chinese firms rely on Western chip designs. A disruption (like cutting off trade in advanced chips) is a double-edged sword. The power asymmetry also invites **AI arms control talks**: there is growing discussion of international agreements to manage the risks of military AI and ensure one side’s pursuit of superintelligent AI doesn’t endanger all (similar to nuclear treaties). In short, the intelligence commodity is entangled with global power contests, and how this plays out will shape international relations profoundly – possibly determining which country or bloc sets the rules for the global intelligence economy. - **Opportunities for the Global South:** Historically, productivity revolutions have sometimes allowed leapfrogging – countries that industrialized late could adopt the latest technology without the legacy constraints (e.g., some African nations skipped landlines and went straight to mobile phones). The Intelligence Revolution could offer similar **leapfrog opportunities**. For instance, an emerging economy with good internet access might bypass building expensive brick-and-mortar universities by using AI-powered online education to upskill its population. AI could help address doctor shortages in rural areas via telemedicine and diagnostic tools. Agricultural AI could optimize crop yields for subsistence farmers. If accessible, AI as a commodity might empower developing regions by compensating for gaps in human expertise or infrastructure. However, there is also risk of these regions becoming **data mines and testbeds** without capturing value. Many AI datasets (for facial recognition, language models, etc.) draw from global users, often without compensation. There’s concern of _digital colonialism_, where the Global South’s data is extracted by Big Tech in exchange for free services, and then monetized in developed markets. To counter this, some countries are exploring data sovereignty laws – asserting that data generated by their citizens is a national resource (analogous to oil or minerals) that should be protected or paid for. Furthermore, local AI innovation is being encouraged – for example, African AI researchers have formed communities to work on AI solutions for African problems (like crop disease detection via mobile images). India is investing in AI for inclusive development and has strengths in its IT workforce to leverage. The global intelligence market might thus see **new players rising** outside the traditional tech hubs, especially if open-source AI models lower the entry barrier for innovation. Nonetheless, disparities in infrastructure (electricity, connectivity), education, and capital mean the asymmetry could widen if not addressed. As of 2022, only 25% of people in low-income countries used the internet, versus over 90% in high-income countries – this digital divide could translate into an AI divide. Bridging it requires international support, knowledge transfer, and perhaps treating AI capacity-building as part of development aid. - **Global Supply Chain Interdependencies:** We’ve touched on chips and data, but broadly, the intelligence commodity ties nations into a **global supply web** in new ways. Countries with abundant energy (for running data centers) might become more important in the AI era; for example, Iceland has become a hub for data centers using its cheap geothermal energy. Countries rich in certain minerals (cobalt, lithium, rare earths) find those resources now critical for AI hardware (batteries, chips), impacting their geopolitical importance – e.g., Democratic Republic of Congo’s cobalt is vital for tech batteries, raising issues of labor exploitation in mining. On the flip side, countries heavily reliant on human labor exports (like call centers, manufacturing assembly) might see their comparative advantage eroded by AI automation, forcing them to rethink their economic model. **Trade patterns will adjust**: we may see less trade in some services (if AI can do it domestically) but more trade in data and AI products. Data might even be conceptualized as an export; for instance, think of a country allowing its medical data to be used by a foreign pharma company’s AI in exchange for some benefit. Such arrangements raise questions of privacy and consent across borders. As intelligence becomes a commodity, ensuring fair terms of trade for data and AI services becomes a diplomatic topic, possibly necessitating new international frameworks (just as we have treaties for trade in goods, intellectual property, etc.). - **Cultural and Linguistic Impacts:** AI systems often carry the cultural biases or values of their creators or the data they’re trained on. If the intelligence market is dominated by Western-developed AI, then Western languages and norms could be disproportionately represented. This might lead to a **cultural homogenization** effect, where smaller languages and cultures get sidelined (since commodified AI might not support them well). Conversely, there’s an opportunity to use AI to preserve and even revitalize languages by creating translation and education models for them. There is already work in AI translation that can help bridge language divides cheaply. The key will be whether the intelligence commodity is inclusive or if it reinforces existing cultural power dynamics. Global AI ethics discussions emphasize the need for diversity in AI development to avoid a one-size-fits-all model of intelligence. - **Global Governance and Cooperation:** Finally, the global dimension will force new forms of governance. Just as the industrial era led to things like the International Telecommunication Union or climate accords, the intelligence era might require global cooperation on issues like AI safety (to prevent catastrophic misuse), cybersecurity (since AI can be weaponized or used in cyber attacks globally), and economic stability (managing the impact on global labor markets). There have been calls for a **“Geneva Convention for AI”** or UN-led initiatives to ensure AI benefits humanity broadly. The challenge is that unlike climate change (where there’s a clear common threat), AI’s risks and benefits are distributed unevenly, making consensus harder. Still, as AI permeates global systems (finance, health, environment modeling), there’s a shared interest in reliability and preventing destructive outcomes. We may see nascent efforts in the coming years to set international norms for AI use in warfare (to ban autonomous lethal weapons, for example) and agreements on data sharing for global good (like using AI for pandemic monitoring). How the leading nations handle their rivalry will heavily influence whether cooperation or competition dominates the global intelligence landscape. In essence, the Intelligence Revolution is a **global story with uneven chapters**. Some will harness it to great effect; others might be left behind or exploited. Our task is to recognize these asymmetries early and strive for a more equitable distribution of this new form of power. Just as global institutions had to adapt to a world of industrial economies, they will need to evolve (or be created) to manage a world of AI economies. ## **Interdependencies, Blind Spots, and Systemic Vulnerabilities** A first-principles analysis would be incomplete without probing the less obvious interconnections and potential **blind spots** – those facets of the intelligence revolution that might be overlooked until a crisis hits. By examining these, we can identify where proactive measures are needed to shore up the system’s resilience. - **Energy-Intelligence Feedback Loop:** One interdependency to highlight is between AI systems and the energy system. As discussed, AI needs energy; but increasingly, energy systems use AI. Smart grids, predictive maintenance for power plants, and AI-optimized energy trading are becoming standard. This creates a **feedback loop**: the stability of the power grid might depend on AI, while AI depends on power. A failure in one could cascade to the other – for example, a widespread power outage would disable AI services, but also an AI malfunction in grid management could cause an outage. This tight coupling is a systemic risk. Similar loops exist in other domains (e.g., AI manages supply chains that produce AI hardware). We must identify these loops and build redundancies. For instance, critical grid controls should have fail-safe manual modes, and vital AI data centers might need independent power backups (beyond just typical generators, perhaps even local renewable sources). Thinking in terms of **system-of-systems engineering** will be crucial to avoid vicious cycles of failure. - **Concentration Risk and “Monoculture” Dangers:** If the intelligence commodity is delivered by only a few dominant models or platforms, the entire ecosystem is exposed to any flaw in them. This is akin to a monoculture in agriculture – efficient but fragile. An example would be if one AI language model became the backbone of thousands of applications, and then an unexpected exploit or bias was discovered in it, it could propagate errors or vulnerabilities everywhere. One could imagine a scenario where a widely used AI system makes subtle errors in financial calculations that accumulate risk in global markets, leading to a financial crisis – not unlike how complex financial algorithms contributed to the 2008 crisis. Another angle is security: a virus targeting a specific AI platform could cause widespread outages. **Diversity in AI approaches and models** is therefore a resilience strategy. Encouraging a competitive and open ecosystem (rather than all software calling the same proprietary API) might reduce monoculture effects. It’s a bit of a paradox because commodification tends toward standardization (for efficiency and compatibility), but over-standardization becomes a vulnerability. Striking a balance – say, having multiple major AI providers with interoperable systems – could help. The blind spot would be not realizing we’ve put too many eggs in one basket until it breaks. - **Ethical and Legal Lag:** Another vulnerability is the lag in ethical and legal frameworks relative to AI deployment. We might roll out AI systems that make decisions affecting people’s lives (hiring, policing, medical triage) without fully understanding their biases or failure modes. By the time we catch up legally (through courts or regulation), harm could be done – e.g., certain groups consistently disadvantaged by an algorithm. An historical parallel is how industrial pollution was rampant before environmental laws caught up. In AI, biases and unfair outcomes are the “pollution” that can accumulate. Additionally, the intellectual property regime for AI (who owns an AI-generated invention or artwork? who is liable for an AI’s mistake?) is still a gray area. If not addressed, this legal ambiguity could result in either chilling effects (hesitancy to use AI due to liability fears) or injustice (people harmed with no recourse). Proactively updating laws and corporate practices (like requiring AI audit trails, transparency, and bias testing) is critical. The blind spot would be deploying AI at scale without these guardrails and then facing a legitimacy crisis for the whole commodification project if public trust is lost. - **Over-reliance and Skill Atrophy:** We touched on this in autonomy, but it bears repeating as a systemic risk: if human skills atrophy, we lose our _backup_ for when AI fails. For example, pilots still train on manual flying for emergency situations when autopilot disengages. In society, do we have people who can do critical calculations if the software is down? Can doctors diagnose without AI decision support if needed? Maintaining a baseline of human expertise is like keeping seeds in a seed bank – you hope not to need them, but they’re invaluable if you do. A blind spot would be to assume AI will never fail catastrophically and thus stop training humans in foundational skills. Already, some educational curricula debate reducing emphasis on things like arithmetic or writing structure because “AI can do it.” But completely externalizing competence to machines is risky. We might consider requiring “practice outages” – drills where systems are turned off to ensure humans can cope (similar to fire drills). Resilience might mean sometimes deliberately not using AI to keep human skills sharp. - **Data Quality and Availability:** Commoditized intelligence depends on a steady diet of data (for training and updating models). What if that data dries up or degrades in quality? This could happen due to privacy backlash (regulations limiting data collection, people opting out) or due to behavioral changes (if AI writes more content, the internet may become flooded with AI-generated text that is recycled in training, potentially causing quality to plateau or degrade – a kind of auto-cannibalism of the data commons). There’s also the issue of _data monoculture_: if everyone trains on the same huge datasets (like the internet), niche or local knowledge might be missing. A future blind spot could be an overemphasis on quantity of data over quality – leading to very powerful but homogenized intelligence that might have critical blind areas. To counter this, efforts in curating diverse, high-quality datasets, and ensuring AI can incorporate expert knowledge (not just brute-force learning from raw data) will be important. The open data movement internationally and initiatives to include marginalized knowledge in AI training are good steps. - **Environmental and Climate Feedback:** As climate change progresses, physical risks (extreme weather, wildfires, water scarcity) could impact the infrastructure of AI (data centers need cooling and can be shut down by heatwaves or need to evacuate in fires). Conversely, the choices made by AI systems could impact the environment (AI optimizing for certain outcomes might neglect sustainability if not explicitly coded to consider it). We have a blind spot if we treat AI deployment as separate from environmental planning. For example, building huge server farms in areas with cheap land but limited water is a short-sighted approach. Climate resilience needs to be built into the expansion plans of the intelligence infrastructure – backup cooling, relocation strategies, etc. Likewise, AI algorithms themselves might need guidelines to operate under resource constraints or emergency conditions. - **Social Cohesion and Psychological Impact:** A softer, but important, interdependency is social cohesion. Work and shared economic roles have been a glue for society (despite class conflicts, there was also interdependence – e.g., workers needed employers and vice versa). If AI breaks some of these bonds – say, if a large portion of people feel they are not needed in the economy – there could be psychological and social fallout: increased alienation, identity crises, or susceptibility to extreme ideologies. Societies under stress can fracture. Thus one could argue a systemic vulnerability of the intelligence revolution is the **mental and social well-being of humans** living in an AI-dominated environment. We might need new social structures (like community groups, creative societies, etc.) to provide purpose and belonging outside traditional work. Recognizing this early is important so that we strengthen social fabric (promoting arts, sports, volunteering, etc.) as we transition. The blind spot would be focusing purely on economic and technical metrics (GDP growth from AI, etc.) and ignoring signs of social strain (e.g., rises in depression or substance abuse in communities with job losses, or generational tensions if young adapt and older don’t). By scanning for these interdependencies and blind spots, we adopt a holistic risk management view. The goal is to ensure that the commodification of intelligence, for all its benefits, does not create an Achilles’ heel that could lead to a large-scale failure or backlash. Just as engineers harden critical infrastructure against rare but catastrophic events, we should harden the intelligence production system against systemic risks, and keep our societal **“insurance policies”** – in the form of human capabilities, diverse systems, and ethical guardrails – in force. ## **Conclusion** The commodification of intelligence through AI is a transformative process on par with the greatest shifts in human history. By viewing it through the lens of the Agricultural and Industrial Revolutions, we see both **reassuring patterns and cautionary tales**. On one hand, it holds the promise of unprecedented abundance: just as we solved the challenge of feeding billions and powering civilizations, we might soon solve the challenge of providing expert knowledge and problem-solving ability on demand, to everyone. This could be a great equalizer and amplifier of human potential – the tool that helps cure diseases, educate the masses, and manage our planet sustainably. On the other hand, history teaches that such revolutions **upend existing structures**. They create winners and losers, test the adaptability of institutions, and often proceed faster than society’s moral and legal compass. In the coming decades, we must be intentional in how we navigate the intelligence revolution. **Key insights and imperatives include:** - _Build resilient systems:_ Acknowledge the **dependencies (energy, hardware, data networks)** and invest in their robustness. Diversify the “intelligence supply chain” to avoid single points of failure, whether that means more distributed energy for data centers or multi-national cooperation to secure chip supplies. Plan for worst-case scenarios (geopolitical conflict, blackouts, cyber-attacks) that could disrupt the flow of intelligence, just as we do for oil shocks or crop failures. - _Ensure inclusivity:_ Work to spread the benefits of AI widely, both within societies and across the globe. This means education and re-skilling programs so workers can transition to new roles instead of being left behind. It means investing in digital infrastructure in developing regions to close the connectivity gap. And it means considering mechanisms like data dividends or shared ownership in AI platforms so that wealth generated by AI doesn’t pool in only a few hands. - _Preserve human agency:_ Even as we embed AI in everything, maintain **human oversight and control** where it matters. This is both to ensure ethical outcomes and to keep humans in the loop such that our skills and judgment remain sharp. We should treat AI as a powerful tool, not an infallible oracle. A future where humans become too passive or overly dependent on AI guidance would diminish the very autonomy and creativity that this revolution should empower. - _Anticipate social change:_ Proactively address how work and society will change. This could involve experiments with reduced work weeks, job guarantee programs in emerging sectors, or strengthening social safety nets like universal basic income if job displacement becomes severe. Psychological and community support will be important in a time of identity shifts – we should celebrate and enable new forms of productivity (creative, caregiving, civic) beyond the traditional paycheck paradigm. - _Govern wisely:_ Finally, governance – both national and international – needs to keep pace. This includes updating regulations on AI transparency, bias, and accountability. It also involves global dialogues to prevent misuse of AI (whether in autonomous weapons or mass surveillance) and to manage competitive dynamics so that an AI race doesn’t compromise safety. The more AI becomes a foundation of society, the more its governance should be a public, democratic concern, not left solely to private companies. Just as utilities and essential services are regulated, **AI may need a framework that treats it as a public good** in many respects. The intelligence market of the future may evolve to look as ordinary to us as today’s markets for food or electricity – a background utility we take for granted. Reaching that point of normalcy and stability will require **navigating a complex transition**. By learning from the past and being clear-eyed about current trends, we can steer this revolution toward a outcome where enhanced intelligence enriches humanity as a whole. In the end, the measure of success will not be just the sophistication of our algorithms or the trillions added to GDP, but whether this new commodity of intelligence truly enables a more knowledgeable, prosperous, and equitable world – avoiding the pitfalls of past revolutions and forging new paths to resilience and inclusion. **Sources:** 1. Abonamah, A. A., et al. (2021). _“On the Commoditization of Artificial Intelligence.”_ Frontiers in Psychology – _AI’s ubiquity vs. strategic importance_. 2. Walden, S. (2024). _“Does the Rise of AI Compare to the Industrial Revolution? ‘Almost,’ Research Suggests.”_ Columbia Business School – _AI’s economic impact on labor share and productivity_. 3. MIT News (2025). _“Explained: Generative AI’s environmental impact.”_ – _Data center energy use, carbon and water footprint of AI_. 4. Interos Report (2023). _“G7 Confronts China’s Designs on Semiconductor Supply Chain.”_ – _Taiwan’s 90% share of advanced chips; geopolitical risk of supply disruption_. 5. RAND Corporation (2025). _“China’s AI Models Are Closing the Gap — but America’s Real Advantage Lies Elsewhere.”_ – _US vs. China AI compute capacity (10x advantage)_ . 6. UNESCO Inclusive Policy Lab (2024). _“Addressing digital colonialism: A path to equitable data governance.”_ – _Global North-South data exploitation and inequalities_. 7. Harari, Y. N. (2016). _Interview in The Guardian:_ “AI will create ‘useless class’ of humans” – _on labor obsolescence and need for constant re-skilling_. 8. Human Progress (2023). _“The Changing Nature of Work.”_ – _Historical data on decline of agricultural and manufacturing employment_. 9. MHR (2018). _“Artificial intelligence and the future of work.”_ – _“Mechanical muscle” vs “mechanical mind” metaphor for industrial vs AI revolution_. 10. TIME (2023). _“OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic.”_ – _AI’s hidden human labor and ethical issues_.