🌉How Can Ordinary People Avoid Being Left Behind in the AI Wave?

— Bridging the Intelligence Gap: Ensuring Equal Access to AI for All

Introduction: A Quiet 'New Inequality'

In July 2026, at the World Artificial Intelligence Conference (WAIC) in Shanghai, a set of figures sparked heated discussion: global enterprise AI adoption has reached 88%, and generative AI has covered 53% of the world's population in just three years. AI is no longer a laboratory concept — it is reshaping the fundamental logic of every industry.

Yet the other side of the coin is equally alarming.

A joint report by CCID Think Tank and MYbank, Observations on Small and Micro Business Operators in the AI Era, reveals that among China's 190 million small and micro business operators, 97.4% have heard of AI tools and 53.2% have tried them, but only 33% are sustained users. More strikingly, 58.5% have abandoned or discontinued AI tool subscriptions due to cost.

This is not a uniquely Chinese dilemma. Across the ocean, the average development cost of a production-grade AI project has surged to $120,594; mainstream AI subscription services charge $20 to $200 per month; and in Q1 2026, domestic cloud AI service prices rose by 5% to 34%.

AI is creating a form of inequality more hidden and systemic than the traditional digital divide — the intelligence gap.

The 2026 Global Intelligence Gap: Evolution and Inclusive Pathways report, released by the World Internet Conference in April 2026, states clearly: the imbalanced distribution of AI development dividends has given rise to a new form of inequality more systemic and hidden than the traditional digital divide, becoming a critical issue affecting global equitable development.

So, are ordinary people truly destined to be left behind?

The answer is no. But only if we confront the problem honestly and seek solutions across multiple dimensions.

I. Economic Dimension: How High Is AI's 'Price Wall'?

1.1 The Current Global Cost of AI

Let us begin with a straightforward comparison:

For an American middle-class household, a $20 monthly subscription may seem trivial. But for an ordinary worker in a developing country earning $300–$500 per month, this expense represents 4% to 7% of household income — and that is before factoring in stable internet access and hardware costs.

1.2 Price Hikes Make Matters Worse

In March 2026, Alibaba Cloud, Baidu Cloud, and Tencent Cloud in China successively announced price increases: AI model-related services rose by 5% to 34%, and storage products by 30%. This is not an isolated phenomenon but a microcosm of rising global computing costs.

The energy crisis transmitting to the computing industry, tight chip supply chains, and soaring data center electricity costs — these converging factors create headwinds for AI democratization.

1.3 A Positive Signal: Inference Costs Plummet

Yet there is light in the darkness. According to Global magazine under Xinhua News Agency, AI inference costs have dropped by 95% in two years. A task that cost 100 yuan two years ago now costs just 5 yuan. From 2026 to 2027, inference costs are projected to fall by more than 300 times.

The exponential decline in costs is the greatest hope for ordinary people. But 'cheaper' does not mean 'free,' nor does it mean 'usable.' Price is only the first barrier.

II. Educational Dimension: Not Knowing How to Use AI Is More Dangerous Than Not Affording It

2.1 The Skills Gap: The Real 'Invisible Wall'

At the 2026 World Digital Education Conference, experts from China and abroad reached a core conclusion: the central contradiction in AI-era talent development is not the technology itself, but the dynamic balance between technological empowerment and educational equity.

Shi Yigong, President of Westlake University, articulated a clear principle at the conference: encourage AI empowerment in research contexts, but strictly limit it in teaching and assessment contexts. This is not conservatism — it is a defense of the fairness baseline. If some students receive AI literacy education from childhood while others lack even basic digital skills, AI will not narrow the gap but tear it wider.

Data confirms this concern: universities in the Beijing region have opened 459 AI general education courses, while many underdeveloped regions still struggle with a lack of AI faculty and curricular resources. 22.8% of rural students still rely entirely on traditional learning methods, having never touched any AI-assisted tool.

2.2 A Global Perspective: The Gradient of AI Readiness

Stanford HAI's 2026 report reveals a harsh reality:

• The United States, China, and Western Europe — the major AI-developing nations — have not participated in UNESCO's Readiness Assessment Methodology (RAM);
• Countries that have completed or begun assessments are concentrated primarily in Latin America, sub-Saharan Africa, and parts of South and Southeast Asia.

This means that the regions most in need of help are precisely those with the weakest AI governance and infrastructure.

2.3 Breaking Through: From 'Giving Tools' to 'Building Capability'

True educational equity is not about giving everyone an AI account — it is about ensuring everyone has the ability to use AI, understand AI, and critically evaluate AI. Specific pathways include:

• Integrating AI literacy into basic education: not teaching children to code, but teaching them how to prompt AI, verify AI outputs, and identify AI biases;
• Building community-level AI learning centers: deploying free AI terminals in libraries and community service centers, staffed with facilitators;
• Developing localized AI educational resources: writing materials in local languages with local case studies, rather than simply translating English content;
• Corporate training responsibility: AI companies should provide free or low-cost training slots for low-income groups.

At the 2026 WAIC, China announced 5,000 digital and AI training slots for developing countries over the next five years — a practice of 'building capability' rather than merely 'giving tools.'

III. Infrastructure Dimension: Without Connectivity, There Is No AI

3.1 The Contest for Computing Sovereignty

AI does not run on thin air. It requires data centers, high-speed networks, and stable electricity. Stanford's 2026 report shows:

• From 2018 to 2025, public and public-private AI supercomputing clusters in Europe and Central Asia grew from 3 to 44;
• North America grew to 41;
• China leads the world with 85 clusters.

Zimbabwe, through partnership with NVIDIA, built Africa's first dedicated AI factory. This is a milestone, but it also highlights the extreme scarcity of AI infrastructure across the African continent.

3.2 The Divergence of Data Sovereignty

As of 2024:

Data localization is intended to protect privacy and sovereignty, but it objectively increases the complexity and cost of AI deployment, imposing additional burdens on resource-constrained developing nations.

3.3 The Infrastructure Dilemma for Ordinary People

For a small-town sole proprietor, a young person returning to a rural village, or a freelancer in a developing country, the barrier to AI is not just the subscription fee but also:

• Stable broadband internet (still unavailable in many rural areas);
• Adequate terminal devices (a computer or phone capable of running AI applications smoothly);
• Basic digital literacy (knowing how to register, input prompts, and assess output quality).

The first step in bridging the gap is making infrastructure as universally accessible as water and electricity.

IV. Institutional and Policy Dimension: Who Safeguards Fairness?

4.1 The Lag in Global Governance

Stanford's 2026 report notes that Responsible AI (RAI) infrastructure is developing, but progress is uneven and has not kept pace with AI deployment. AI safety incidents surged by 55%, yet frontier models rarely report their RAI benchmark results, and the transparency of foundation models declined in 2025 after improving the previous year.

In July 2026, APEC Digital Week was held in Chengdu, where economies jointly called for: establishing unified regulatory baselines, bridging the technology gap, and building an inclusive and secure global AI governance framework.

4.2 China's Practical Exploration

At the 2026 WAIC, China announced a series of concrete measures:

• Providing 5,000 digital and AI training slots for developing countries over the next five years;
• Establishing AI application cooperation centers with ASEAN, the African Union, BRICS, and the Shanghai Cooperation Organisation;
• Promoting the AI meteorological early warning system 'MAZU' to 30 countries;
• The formal establishment of the World Artificial Intelligence Cooperation Organization (WAICO) in Shanghai.

The conference chair's statement also explicitly proposed 'cultivating a new Token Economy model based on large models and driven by intelligent agents,' with daily token invocations reaching 175 trillion. This signifies that AI is transitioning from toolification to economic ecosystemization, enabling ordinary people to directly participate in value creation through 'one-person companies,' data services, and agent customization.

4.3 Policy Recommendations: Building an Institutional Framework for AI Inclusion

V. Personal Dimension: What Can Ordinary People Do Right Now?

Macro narratives matter, but as individuals, we cannot wait for institutional perfection. Here are actions ordinary people can take today:

5.1 Leverage Free and Low-Cost Resources

• Open-source models: by early 2026, the top ten open-source large model architectures are fully available, allowing local deployment without subscription fees;
• Free tiers: nearly every mainstream AI platform offers free usage quotas, sufficient for daily learning and light use;
• Public digital services: pay attention to free AI training and tools offered by local governments and libraries.

5.2 Build an 'AI + Your Profession' Skill Combination

AI will not replace everyone, but people who use AI will replace those who do not. The key is not to become an AI expert, but to integrate AI into your existing professional skills:

• Teachers use AI to assist lesson planning and personalized tutoring;
• Small shop owners use AI for inventory management and marketing copy generation;
• Freelancers use AI to improve delivery efficiency;
• Farmers use AI to analyze weather data and optimize planting strategies.

5.3 Join Communities, Share Knowledge

AI's learning curve is not steep, but the information gap is enormous. Joining local or online AI learning communities — sharing usage tips, prompt techniques, and tool reviews — is an effective, low-cost way to improve rapidly.

5.4 Maintain Critical Thinking

AI is a tool, not truth. Even in 2026, AI still 'confidently states nonsense.' The most important AI literacy for ordinary people is not writing prompts, but judging whether outputs are reliable, whether biases exist, and whether they fit your specific context.

VI. Outlook: AI Inclusion Is Not a Utopia

Let us return to the most fundamental question: In the AI era, do ordinary people still have opportunities?

Yes, but the window is limited.

The exponential decline in inference costs, the flourishing open-source ecosystem, inclusive policies from governments worldwide, and the emergence of new models like the 'Token Economy' — these trends are opening doors for ordinary people. The 2026 WAIC theme, 'AI Partnership for a Brighter Future,' is not empty rhetoric but a reality in the making. But we must also see clearly:

• If education does not keep pace, no cost reduction will help if no one knows how to use the tools;
• If infrastructure does not reach the last mile, no model will be powerful enough to matter;
• If institutions do not impose constraints, AI dividends will concentrate solely in the hands of capital and power;
• If individuals do not act, the best tools will remain merely someone else's productivity.

AI inclusion requires the convergence of five forces: technological cost reduction, educational empowerment, infrastructure coverage, institutional safeguards, and personal action.

This is not one person's responsibility — it is a shared challenge for all of society. But every ordinary person can take the first step, starting today.

Because in this era, the greatest inequality is not being unable to afford AI — it is not even knowing what AI can do for you.

Data sources: Stanford HAI 2026 AI Index Report, World Internet Conference 2026 Global Intelligence Gap Report, CCID Think Tank Observations on Small and Micro Business Operators in the AI Era, 2026 WAIC Chair's Statement, Clutch 2026 AI Development Cost Survey, Xinhua Global Magazine April 2026 cover story.