Introduction – from scarcity to digital abundance
Classical economics was built on the notion that scarcity is inevitable. Wealth was measured in commodities with limited supply and labour was seen as the core input to production. In the 21st century, however, the world is undergoing a phase transition as digital technologies, artificial intelligence (AI) and intangible assets upend the conditions under which the economy operates. This paradigm shift challenges many of the assumptions that have guided policy and personal decisions for decades. The following essay examines seven “lies” or outdated beliefs and uses recent evidence to illustrate why they no longer hold, what new dynamics are taking their place, and what the consequences may be for the fate of humanity.
Lie 1 – Scarcity is fundamental to economics
In the industrial age, scarcity underpinned supply–demand models. Digital goods break this model because they are non‑rival and infinitely replicable. Once created, a piece of software or a digital song can be duplicated at virtually zero marginal cost. Economists Danny Quah and Diane Coyle describe digital goods as infinitely expansible and note that their non‑rival nature means “quantity can be arbitrarily large at no cost” . Similarly, digital goods are non‑excludable unless producers impose technological restrictions, so natural scarcity does not apply . A commentary on the digital economy highlights that companies often manufacture scarcity through digital rights management and planned shortages; for example, Nintendo restricted availability of “Super Mario 3D All‑Stars” despite the game being a digital download .
Even the looming technological singularity—a hypothetical point when AI could autonomously produce all goods—raises the possibility that advanced automation might “solve the fundamental economic problem of scarcity” . While these scenarios remain speculative, they underscore that scarcity is not an immutable law but a design choice. A system predicated on scarcity becomes obsolete when abundance is possible; clinging to artificial scarcity simply sustains outdated profit models.
Lie 2 – Human labour is the ultimate source of value
Industrial societies equated a person’s worth with their capacity to work; both identity and income were tied to jobs. Mass education in the factory school model trained people to fit into repetitive roles. Automation first replaced physical labour; now AI is replacing cognitive labour. A 2025 MIT study created a digital twin of the U.S. labour market and estimated that existing AI systems could perform tasks equivalent to about 11.7 % of U.S. jobs, representing roughly $1.2 trillion in wages . McKinsey research similarly concluded that more than half of current work activities could be automated, though the firm emphasised that automation augments jobs rather than causing immediate unemployment . The World Economic Forum’s Future of Jobs 2020 report projected that 85 million jobs could be displaced globally by 2025 but also expected 97 million new roles to emerge . These forecasts show that human labour is no longer the sole source of productivity.
In many sectors AI is already making decisions that once required human judgement. An AI governance report notes that algorithms now decide in milliseconds who qualifies for a loan, which applicants are shortlisted for a job, and whether insurance claims are approved, often without human oversight . In the insurance industry, regulators observe that AI is “transforming areas such as underwriting, customer service, claims, marketing and fraud detection” and is already being used to assess damage severity and predict repair costs. A comprehensive review of AI applications in insurance warns that data quality issues can lead to biased risk assessments and stresses the need for explainable AI for ethical decision‑making . These developments reduce the market value of human labour and raise ethical questions about dignity and agency.
Lie 3 – Growth requires more physical resources
Traditional economic growth is defined as producing more goods and services using capital, labour and natural resources. However, modern growth increasingly comes from intangible assets—software, databases, intellectual property, R&D, branding and design—rather than physical capital. The World Intellectual Property Organization reports that global investment in intangible assets grew more than three times faster than tangible investment between 2008 and 2024 . By 2024 these assets formed a large and growing share of world GDP; software and data are the fastest‑growing categories . In the United States, the digital economy reached $4.9 trillion in 2024, representing 18 % of U.S. GDP, and digital economy employment grew 12 times faster than overall employment .
Such figures illustrate a shift from hardware to code and from atoms to bits. When factories become cloud platforms, output is no longer constrained by land or raw materials. Instead, network effects and data drive returns. Measuring this growth with tools like GDP—which values goods at their market price—becomes challenging because digital goods have zero marginal cost and often generate value through attention or data rather than direct sales. The concept of growth must therefore adapt to intangible production and environmental sustainability rather than simply counting physical outputs.
Lie 4 – Markets always find equilibrium
Classical economics assumes that free markets are self‑correcting. In digital markets, however, network externalities, economies of scale and winner‑takes‑all dynamics create self‑amplifying feedback loops. A UN Conference on Trade and Development (UNCTAD) report warns that digital platform markets—dominated by firms such as Amazon, Google, Apple and Facebook—benefit from large economies of scale and data‑driven network effects that entrench dominance . These platforms leverage vast amounts of user data to maintain market power, and because entry barriers are high and switching costs low, competition does not spontaneously restore equilibrium .
Even in the corporate landscape, profit concentration reveals disequilibrium. Analysis of U.S. corporate profits in 2025 shows that nearly 99 % of after‑tax profits are captured by the top 10 % of firms, with the top 1 % alone accounting for more than 93 % . In knowledge‑intensive sectors such as technology and pharmaceuticals, the top 1 % consistently pocket around 92 % of total profits . Such winner‑takes‑all dynamics are reinforced by intellectual property rights and global tax arbitrage, making digital markets volatile and prone to monopoly rather than self‑balancing. Without regulatory intervention, the system accelerates toward concentration rather than equilibrium.
Lie 5 – Money measures all value
Capitalist markets assign price tags to everything from labour to land. In the digital era, personal data itself has become a commodity. Data‑driven firms monetize user behaviour through targeted advertising and algorithmic recommendations, often without compensating the individuals whose data fuel the system. A privacy advocacy article observes that “a small number of companies disproportionately profit from consumer data,” and warns that consumers are no longer recipients of free services but the product . The same article cautions that proposals to pay consumers for their data may further entrench commodification . Meanwhile, our most cherished values—honour, dignity, pride, freedom, time—are increasingly monetized. Social media platforms sell the right to influence your vote, dating apps charge premiums for visibility, and gig‑economy platforms convert leisure time into micro‑tasks. When intangible goods and behavioural data drive profits, using money as the sole metric of value misses community well‑being, ecological health and human autonomy.
Lie 6 – Humans are rational actors making free choices
Neoclassical economics assumes that individuals rationally maximize utility. Behavioural science and AI reveal a different picture. Humans are biologically biased and easily influenced, and AI systems can exploit these biases at scale. Researchers studying microtargeting found that personalized ads tailored to individual vulnerabilities are significantly more persuasive than non‑targeted messages, and that warnings about being targeted do not eliminate the persuasive advantage . Another study on social‑media marketing notes that AI predicts consumer behaviour and automatically generates content to match user tastes , showing how platforms adapt in real time to shape desires.
Beyond persuasion, AI increasingly makes decisions on behalf of humans. The AI governance report cited earlier lists numerous arenas—credit scoring, recruiting, insurance claims and healthcare diagnosis—where algorithms decide outcomes . This capacity to model individual psychology, predict preferences and control access to opportunities shifts power from people to machines. Unless transparency and accountability are built into these systems, AI could entrench biases, manipulate behaviour and erode autonomy.
Lie 7 – Distribution follows contribution
Market ideology holds that rewards flow to those who create value. In practice, the digital economy channels returns to those who own the platforms rather than those who contribute content or labour. As noted earlier, profit concentration in the United States is extreme: the top 1 % of firms capture over 93 % of after‑tax profits , and in knowledge industries like technology and pharmaceuticals the top 1 % grab around 92 % . Traditional industries fare only slightly better; even there, the top 1 % hold about 79 % of all profits . Such inequality is not confined to firms. The income share of the top 1 % relative to the bottom 50 % in the United States has climbed from 27 times in the 1980s to 81 times by 2014 .
A report on corporate profits argues that the U.S. economy has morphed into a winner‑takes‑all system where network effects, intellectual property and tax arbitrage allow a tiny elite to extract global rents . Since intangible assets scale globally with minimal cost, a firm that establishes early dominance can capture markets worldwide. Meanwhile, wages for ordinary workers stagnate and labour’s share of income declines. This divergence between contribution and reward fuels social tension and political instability.
Implications for humanity’s future
Phase transition and paradigm shift
The preceding evidence illustrates a phase transition from industrial scarcity to digital abundance. Scarcity is no longer natural but engineered; labour’s centrality gives way to automation; physical resources are supplanted by intangible assets; markets no longer self‑correct but self‑concentrate; value escapes monetary measures; rationality is supplanted by algorithmic manipulation; and distribution becomes decoupled from contribution. Collectively, these transformations constitute a paradigm shift. The economic model that grew out of the Industrial Revolution is ill‑equipped to handle the dynamics of a digital, AI‑driven economy. Persisting with the old mindset leads to destabilizing inequalities, environmental unsustainability and loss of human agency.
Challenges and opportunities
- Redefining value and work. With AI performing many cognitive tasks, societies must decouple identity from employment. Policies such as lifelong learning, shorter work weeks and universal basic income may help individuals thrive in a world where value is created by ideas rather than labour.
- Democratizing data and platforms. Data generated by individuals should benefit them collectively. Data trusts or cooperatives could redistribute profits from personal information and ensure fair compensation, while privacy regulations limit commodification.
- Antitrust and competition policy. To prevent self‑amplifying concentration, regulators must enforce antitrust laws, limit anti‑competitive mergers, and encourage interoperability and open standards. Progressive taxation of digital rents could fund public goods.
- Transparent and accountable AI. High‑impact AI systems—credit scoring, insurance underwriting, hiring—should be subject to oversight and explainability. Bias audits, ethics boards and open algorithms can reduce discriminatory outcomes and preserve human agency.
- New metrics for prosperity. GDP must be supplemented with measures of well‑being, environmental health and equity. Recognizing the value of care work, ecological services and civic engagement ensures that digital abundance benefits society rather than eroding it.
Takeaways
Humanity stands at a crossroads. A phase transition from scarcity‑based industrial economics to an information‑rich, AI‑powered paradigm is under way. The seven “lies” examined in this essay—beliefs in scarcity, labour‑centric value, resource‑based growth, market equilibrium, monetary metrics, rational actors and meritocratic distribution—no longer describe reality. Abundance, automation, intangibles, network effects, data commodification, behavioural manipulation and extreme inequality define the emerging landscape. Recognizing these shifts is the first step toward designing institutions and values that harness technological progress for the common good rather than entrenching new forms of domination. The fate of humanity hinges on whether we adapt our worldview and systems to the realities of the digital age.
- Purdue OWL (Online Writing Lab) – General Format Guidelines
- This page describes the basic setup for an APA 7th edition paper, including double‑spacing, 1-inch margins, and how to format the running head. These guidelines are cited in lines 152–158 .
- Link: https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/general_format.html
- Purdue OWL – Reference List: Basic Rules
- This resource explains the general rules for formatting a reference list in APA 7th edition, noting that it is revised according to the 7th edition of the APA Publication Manual .
- Link: https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html
- Northern Michigan University (NMU) APA Style Guide
- The NMU guide provides an overview of APA 7th edition and notes that it is based on the Publication Manual released on October 1, 2019 . It also directs readers to the official APA Style website .
- It contains tips on finding or verifying DOIs using the CrossRef query form .
- Link: https://nmu.libguides.com/apa-style-7th-edition
- National University Academic Success Center – APA Style Basics 7th Edition
- This guide covers the basics of APA 7th edition, including line spacing (double‑spacing and no extra lines between paragraphs) , citation practices for multiple authors, rules for lists, and reference list formatting (e.g., listing up to 20 authors and using DOIs as hyperlinks) .
Links to Relevant Resources
Video Resources
What’s New in APA Style – In this video, members of the APA Style team provide an overview of the key updates made in each chapter of the Publication Manual.
Creating References Using Seventh Edition APA Style – In this webinar, members of the APA Style team demonstrate how to format references and explain why references are easier because of the changes.
Citing Works in Text Using Seventh Edition APA Style – This webinar provides information on how to create and format in-text citations. Experts also answer many of the most common citation questions.
A Step-By-Step Guide for APA Style Student Papers – This webinar provides information on how to set up papers, with an emphasis on how default word-processing software settings align with seventh edition style.
Tutorials
Academic Writer Tutorial: Basics of Seventh Edition APA Style – This tutorial covers the basics of seventh edition APA Style. This tutorial was adapted from the tutorial featured in Academic Writer®, APA’s tool for teaching and learning effective writing.
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