Summary of Key Points
In the past two days, there have been two striking contrasts: NVIDIA, in collaboration with six financial giants, launched an AI infrastructure financing platform worth $500 billion to “build factories” for machines; meanwhile, the International Labour Organization reported that 67 million young people aged 15-24 worldwide are unemployed. The article argues that this is not because AI directly takes jobs away from young people (AI-related job openings account for only 6.1%). Instead, it reflects a society that has established a comprehensive financial support system for machines (loans, mortgages, securitization, etc.) but has failed to provide similar opportunities for young people to grow and develop—especially by restricting their ability to learn from mistakes, preventing them even from taking the first step into the workplace.
1. Why Does Capital Naturally Favor Machines?
It’s not that capitalists are heartless; it’s the nature of capital itself: it always flows towards things that can generate energy and be recycled.
For example, a GPU server can produce computing power as soon as it’s powered on, providing monthly returns. Banks are willing to lend money because there is physical collateral, and investors are interested because the returns can be clearly defined in contracts. But what about a 22-year-old? They need time to gain experience: to work on real projects, make mistakes, and learn from their mentors. These aspects are difficult to quantify. Can we determine if they will be reliable after two years? If the training fails, can the investment be written off? If they switch jobs, all previous efforts are lost.
As a result, the risks associated with machines are spread across the entire financial system (with financing platforms and insurance providing support), reducing the pressure on individuals; in contrast, young people bear all the risks themselves (they struggle to find entry points, and failure to learn from mistakes is seen as a sign of personal incompetence).
2. The Key to Youth Unemployment: The Loss of the Space to “Make Mistakes”
The data from the International Labour Organization reveals a crucial detail: unemployment mainly occurs at the initial stage of entering the workplace, not among those who are already employed.
Many suggest that young people need to improve their skills, but the problem is that training programs may teach them how to draw circuit diagrams or understand legal regulations, yet they lack real-world experience. Companies also often don’t hire entry-level employees despite these skills. What young people really need is an opportunity to start working for the first time.
In the past, entry-level positions served as a learning platform where companies could tolerate their initial inefficiencies as they improved over time. However, AI has taken over many basic tasks (such as data entry and simple drawing), leaving little room for learning. Companies now expect immediate productivity, and the cost of making mistakes falls on the individuals.
3. The Blinding Asymmetry: Machines Have a Complete Set of Support Systems, While Humans Rely on Themselves
What if AI companies can’t afford GPUs? They have access to loans, mortgages, and can package these assets as securities for investment, even attracting $50 billion in funding. The development of machines is supported by significant societal investments (chips, data centers, financing platforms). But what about young people? Without work experience, who will invest two years in training them? There are no specialized financing tools (such as apprenticeship loans), no collateral for their skills, and no guarantees that they will succeed. Human development relies on family, education, and luck—this disparity reflects societal indifference.
4. The Risk of Mistakes May Become a Privilege for Some
If the space to make mistakes continues to disappear, a new gap will emerge: it’s not just about who learns best, but also about who has the means to do so.
Children from wealthy families can study abroad with internships, take gap years to experiment, or get into jobs where mistakes are tolerated. Children from less affluent families struggle to even earn a basic salary and lack opportunities for feedback and growth. Social mobility will become increasingly difficult, as they may never have a chance to start at all.
5. A Way Forward: Creating New Spaces for Learning from Mistakes
Can we rely on companies to change their attitudes? Or on government mandates to expand hiring? For now, small initiatives show promise:
- Paid Apprenticeships: Companies pay apprentices and allow them to learn while working, with the company bearing the cost of mistakes.
- Real-World Projects in Education: Universities integrate real business needs into curricula so students can gain practical experience.
- AI-Assisted Teams: New employees can work on simple tasks with AI support to gradually build their skills.
- Open-Source Communities: Newcomers can participate in small projects with minimal impact if they make mistakes.
The goal of these efforts is not to compete with efficiency, but to find ways to allow young people to grow while still maintaining high productivity—by sharing some of the costs of learning.
Finally, the article raises a poignant question: If $50 billion can fund the future of machines, who will cover the cost of helping young people develop from inexperience to proficiency? Machines are advancing, but humans seem to be falling behind. Is this truly the kind of society we want?
(End of Article)