[Reading level: C1 – Advanced]
For many years, artificial intelligence has been promoted as a stimulant for a tired global economy. Productivity was expected to surge and become a major driver of global growth.
But the deeper AI is implemented in reality, the harder it becomes to avoid a fundamental question: what if productivity is no longer the central issue of the economy?
This is not an argument against technology. AI is real, it is being deployed, and in many sectors it genuinely improves efficiency. Yet, the issue lies elsewhere. AI is increasing productivity in places that were already strong, while the sectors that need a productivity boost the most—public services, primary healthcare, mass education, elderly care, and public administration—are seeing limited benefits. And even when overall productivity rises, it no longer guarantees economic growth in the traditional sense.
The world today does not lack the ability to produce; it lacks the ability to consume.
For most of the twentieth century, rising productivity was almost synonymous with expanding prosperity. Producing more created more jobs, higher wages, and a middle class large enough to absorb the additional output. But that link has weakened. In many advanced economies—and increasingly in emerging ones—potential output has exceeded society’s capacity to absorb it. The question is no longer how to produce more, but who will buy everything that has been produced.
One important indicator revealing this trend is the declining share of labor income relative to total output—the so-called labor share—a pattern observed across many developed economies. Recent reports suggest that in the United States, the labor share fell to around 53.8% in 2025, the lowest level on record, while labor productivity has risen sharply since the 1980s.
Income inequality is the hinge of this structural break. As income and wealth become increasingly concentrated among a small group at the top of the distribution, the consumption capacity of society as a whole cannot keep pace with productivity growth. The reason is not moral or psychological; it lies in the very physical limits of consumption.
The wealthy, even when their wealth increases rapidly, cannot keep eating lobster at the same pace as their asset growth. They cannot buy unlimited clothes, cannot own houses in numbers proportional to their investment portfolios, and cannot multiply material consumption tenfold simply because their assets have increased tenfold. At a certain threshold, the consumption of high-income groups saturates.
At that point, the additional income no longer flows into the real economy but into financial assets. Money moves toward stocks, real estate, bonds, investment funds, and increasingly complex financial structures—places that can absorb capital without requiring additional buyers of goods or services. This is a very basic economic mechanism, yet it has strong explanatory power: it explains why asset prices continue to rise while mass consumption and productive investment remain sluggish. When demand for goods is insufficient, money is forced to find other outlets.
AI, rather than breaking this cycle, risks making it worse. Technology-driven productivity—especially AI—does not distribute gains neutrally. It rewards capital, high skills, and intellectual property, while putting pressure on middle-income workers and jobs that are easily automated. Income from capital grows faster than income from labor; median wages rise more slowly than GDP—a pattern consistently documented by international organizations and scholars for more than two decades.
There is a simple economic principle often forgotten in discussions about AI: the marginal propensity to consume. Low- and middle-income households tend to spend nearly all of any additional income, while very high-income individuals do not. For them, most additional income is saved, invested financially, or accumulated as assets.
When income concentrates at the top, total demand grows more slowly than total income—even while productivity, corporate profits, and stock market indices continue to rise. This is not a paradox; it is the mathematics of income distribution. An economy can become increasingly efficient at producing while becoming increasingly inefficient at creating buyers.
An economic term that describes this condition is “secular stagnation”—a concept brought back into prominence by Lawrence H. Summers after the global financial crisis of 2008. It refers to an economy suffering from long-term demand deficiency, where even interest rates near zero—or below zero—cannot stimulate enough investment and consumption to achieve potential growth.
The paradox of the AI era is visible in striking numbers: while NVIDIA’s revenue surged by 262% in just one year due to the global hunger for AI chips, on the opposite side of the economy the global agricultural sector—responsible for feeding and employing billions of people—is projected to see a significant decline in net income. This is a clear manifestation of secular stagnation: money is not lacking, productivity is not lacking, but both are “trapped” upstream in the financial system. When the value created by AI remains on the balance sheets of Big Tech or is converted into stock buybacks, mass consumer demand withers, and the promise of a broadly shared prosperity becomes an illusion.
This also explains why skepticism about the promise that “AI will boost productivity” is spreading. It is not because AI cannot perform its tasks, but because the places where AI works best are not the places where the economy needs it most. Optimizing advertising, financial trading, global logistics, or software programming certainly generates benefits, but these improvements do not automatically expand mass purchasing power.
Meanwhile, the sectors that determine the long-term quality of growth—public education, primary healthcare, social care, and public administration—are much harder to “AI-ize” quickly because they require human interaction, institutions, and social trust rather than algorithms.
Therefore, the core problem of the AI economy does not lie in whether AI increases productivity, but in who receives that productivity gain, and whether it translates into demand. If it does not, the world may enter a strange condition: high productivity, high profits, and high asset prices—but low growth and rising social instability.
The paradox of the AI era is visible in the numbers themselves: AI may allow a few corporations such as NVIDIA to achieve extraordinary revenue growth, but if most workers do not see their real incomes improve, the mass consumer market will have limited absorption capacity. In that case, high productivity will no longer be a promise of growth, but a reminder that production only has meaning when consumers are able to absorb it.
For an economy like Vietnam, where agriculture still occupies a significant share and incomes remain relatively modest, this could serve as an important reminder.
Source: https://vnexpress.net/ai-va-cai-bay-nang-suat-5043292.html
WORD BANK:
artificial intelligence /ˌɑːr.t̬əˈfɪʃ.əl ɪnˈtel.ə.dʒəns/ [B2] (n): trí tuệ nhân tạo
stimulant /ˈstɪm.jə.lənt/ (n): chất kích thích
surge /sɝːdʒ/ (n): sự tăng vọt
driver /ˈdraɪ.vɚ/ (n): cú hích, động lực thúc đẩy
implement /ˈɪm.plə.ment/ [B2] (v): thực hiện, triển khai
fundamental /ˌfʌn.dəˈmen.t̬əl/ [B2] (adj): cơ bản, nền tảng
deploy /dɪˈplɔɪ/ [C1] (v): triển khai, bố trí
genuinely /ˈdʒen.ju.ɪn.li/ (adv): thực sự, chân thành
boost /buːst/ (v): thúc đẩy, tăng cường
mass /mæs/ (adj): quy mô lớn, đại trà
administration /ədˌmɪn.əˈstreɪ.ʃən/ (n): chính quyền, sự quản lý
guarantee sth /ˌɡer.ənˈtiː/ [B2] (v): đảm bảo điều gì
sense /sens/ (n): ý nghĩa
synonymous with sth /sɪˈnɑː.nə.məs wɪð/ (adj): đồng nghĩa với
prosperity /prɑːˈsper.ə.t̬i/ (n): sự thịnh vượng
absorb /əbˈzɔːrb/ [B2] (v): hấp thụ
output /ˈaʊt.pʊt/ (n): sản lượng
emerging economy /ɪˈmɝː.dʒɪŋ ɪˈkɑː.nə.mi/ (n): nền kinh tế mới nổi
exceed sth /ɪkˈsiːd/ (v): vượt quá
indicator /ˈɪn.dɪ.keɪ.t̬ɚ/ (n): chỉ số, dấu hiệu
labor share /ˈleɪ.bɚ ʃer/ (n): tỷ phần lao động
relative to sth /ˈrel.ə.t̬ɪv tuː/ (prep): so với
the so-called /ðə ˌsoʊˈkɔːld/ (adj): cái gọi là
pattern /ˈpæt̬.ɚn/ (n): xu hướng, mô hình
hinge /hɪndʒ/ (v): phụ thuộc vào
structural break /ˈstrʌk.tʃɚ.əl breɪk/ (n): sự thay đổi cấu trúc
concentrate /ˈkɑːn.sən.treɪt/ (v): tập trung
distribution /ˌdɪs.trəˈbjuː.ʃən/ (n): sự phân phối
as a whole /æz ə hoʊl/ (adv): xét tổng thể
keep pace with sth /kiːp peɪs wɪð/ (v): theo kịp
moral /ˈmɔːr.əl/ (adj): thuộc đạo đức
psychological /ˌsaɪ.kəˈlɑː.dʒɪ.kəl/ (adj): thuộc tâm lý
lobster /ˈlɑːb.stɚ/ (n): tôm hùm
proportional to sth /prəˈpɔːr.ʃən.əl/ (adj): tỉ lệ thuận với
investment portfolio /ɪnˈvest.mənt pɔːrtˈfoʊ.li.oʊ/ (n): danh mục đầu tư
multiply /ˈmʌl.tə.plaɪ/ (v): nhân lên
tenfold /ˈten.foʊld/ (adv): gấp mười lần
threshold /ˈθreʃ.hoʊld/ (n): ngưỡng
saturate /ˈsætʃ.ɚ.eɪt/ (v): làm bão hòa
flow into somewhere /floʊ/ (v): chảy vào đâu đó
stock /stɑːk/ (n): cổ phiếu
real estate /ˌriː.əl ɪˈsteɪt/ (n): bất động sản
bond /bɑːnd/ (n): trái phiếu
explanatory /ɪkˈsplæn.ə.tɔːr.i/ (adj): mang tính giải thích
mass consumption /mæs kənˈsʌmp.ʃən/ (n): tiêu dùng đại chúng
sluggish /ˈslʌɡ.ɪʃ/ (adj): trì trệ, chậm chạp
insufficient /ˌɪn.səˈfɪʃ.ənt/ (adj): không đủ
outlet /ˈaʊt.let/ (n): đầu ra, kênh tiêu thụ
technology-driven /tekˈnɑː.lə.dʒi ˈdrɪv.ən/ (adj): được thúc đẩy bởi công nghệ
distribute /dɪˈstrɪb.juːt/ (v): phân phối
gain /ɡeɪn/ (n): lợi ích, khoản tăng
neutrally /ˈnuː.trə.li/ (adv): một cách trung lập
intellectual property /ˌɪn.təˈlek.tʃu.əl ˈprɑː.pɚ.t̬i/ (n): sở hữu trí tuệ
median /ˈmiː.di.ən/ (n): trung vị
consistently /kənˈsɪs.tənt.li/ (adv): một cách nhất quán
scholar /ˈskɑː.lɚ/ (n): học giả
principle /ˈprɪn.sə.pəl/ (n): nguyên tắc
marginal /ˈmɑːr.dʒə.nəl/ (adj): cận biên, không đáng kể
accumulate /əˈkjuː.mjə.leɪt/ (v): tích lũy
corporate profit /ˈkɔːr.pɚ.ət ˈprɑː.fɪt/ (n): lợi nhuận doanh nghiệp
indices /ˈɪn.də.siːz/ (n): các chỉ số
paradox /ˈper.ə.dɑːks/ (n): nghịch lý
mathematics /ˌmæθ.əˈmæt̬.ɪks/ (n): toán học
secular stagnation /ˈsek.jə.lɚ stæɡˈneɪ.ʃən/ (n): tình trạng đình trệ dài hạn
bring sth back into prominence /ˈprɑː.mə.nəns/ (v): đưa điều gì trở lại nổi bật
deficiency /dɪˈfɪʃ.ən.si/ (n): sự thiếu hụt
interest rate /ˈɪn.trəst reɪt/ (n): lãi suất
stimulate sth /ˈstɪm.jə.leɪt/ (v): kích thích điều gì
striking /ˈstraɪ.kɪŋ/ (adj): đáng chú ý, nổi bật
revenue /ˈrev.ə.nuː/ (n): doanh thu
feed sb /fiːd/ (v): nuôi, cung cấp cho ai
be projected to do sth (v): được dự đoán sẽ làm gì
manifestation /ˌmæn.ə.fesˈteɪ.ʃən/ (n): biểu hiện
upstream /ˌʌpˈstriːm/ (adj): thượng nguồn
balance sheet /ˈbæl.əns ʃiːt/ (n): bảng cân đối kế toán
stock buyback /stɑːk ˈbaɪ.bæk/ (n): mua lại cổ phiếu
wither /ˈwɪð.ɚ/ (v): suy yếu, tàn lụi
broadly shared prosperity /ˈbrɔːd.li ʃerd prɑːˈsper.ə.t̬i/ (n): sự thịnh vượng được chia sẻ rộng rãi
illusion /ɪˈluː.ʒən/ (n): ảo tưởng
skepticism /ˈskep.tɪ.sɪ.zəm/ (n): sự hoài nghi
optimize sth /ˈɑːp.tə.maɪz/ (v): tối ưu hóa
determine /dɪˈtɝː.mɪn/ [B2] (v): xác định
institution /ˌɪn.stɪˈtuː.ʃən/ (n): tổ chức, thể chế
algorithm /ˈæl.ɡə.rɪ.ðəm/ (n): thuật toán
extraordinary /ɪkˈstrɔːr.dən.er.i/ [B2] (adj): phi thường
ỦNG HỘ READ TO LEAD!
Chào bạn! Có thể bạn chưa biết, Read to Lead là một trang giáo dục phi lợi nhuận với mục đích góp phần phát triển cộng đồng người học tiếng Anh tại Việt Nam. Chúng tôi không yêu cầu người đọc phải trả bất kỳ chi phí nào để sử dụng các sản phẩm của mình để mọi người đều có cơ hội học tập tốt hơn. Tuy nhiên, nếu bạn có thể, chúng tôi mong nhận được sự hỗ trợ tài chính từ bạn để duy trì hoạt động của trang và phát triển các sản phẩm mới.
Bạn có thể ủng hộ chúng tôi qua 1 trong 2 cách dưới đây.
– Cách 1: Chuyển tiền qua tài khoản Momo.
Số điện thoại 0947.886.865 (Chủ tài khoản: Nguyễn Tiến Trung)
Nội dung chuyển tiền: Ủng hộ Read to Lead
hoặc
– Cách 2: Chuyển tiền qua tài khoản ngân hàng.
Ngân hàng VIB chi nhánh Hải Phòng
Số tài khoản: 012704060048394 (Chủ tài khoản: Nguyễn Tiến Trung)
Nội dung chuyển tiền: Ủng hộ Read to Lead
Lớp luyện thi IELTS online
Bạn đang có nhu cầu thi chứng chỉ IELTS cho đầu vào đại học, đi du học, xin việc hay xin cư trú và đang phân vân chưa biết học ở đâu?
Nếu bạn đang tìm kiếm dịch vụ luyện thi IELTS online với giáo viên uy tín và chất lượng, cũng như học phí phải chăng, thì thầy Trung và Cô Thủy (Admin và dịch giả chính của Read to Lead) có thể là một lựa chọn phù hợp dành cho bạn.
Hãy liên hệ (nhắn tin) tới trang Facebook cá nhân của mình (https://www.facebook.com/nguyen.trung.509) để tìm hiểu về lớp học và được tư vấn cũng như được học thử nha!
