Amid the AI Boom: Survival Strategies for Ordinary People Through Three Technological Revolutions

Recently, news of NVIDIA’s market cap surpassing $3 trillion sent shockwaves through the industry. This company, which three decades ago focused solely on gaming graphics cards, now holds a valuation exceeding the GDPs of Germany and Japan. Its growth trajectory is undeniably a case of explosive expansion by precisely catching the wave of the era.

The AI boom is equally palpable in daily life: corporate executives push all employees to learn AI tools, industry discussions are filled with talk of “AI startup opportunities,” and even cashiers at corner coffee shops mention “AI-written promotional copy boosting efficiency.”

Yet beneath the clamor, a pervasive anxiety lingers: Will AI replace existing jobs? Could blindly following trends lead to being left behind? Will the time and costs invested yield reasonable returns?

With these questions in mind, I delved into the historical trajectories of three technological revolutions: railways, electricity, and the internet. Research reveals that every major technological wave in human history follows a similar pattern: “frenzy — chaos — bubble — restructuring — maturity.”

Facing today’s AI wave, rather than blindly following trends amid anxiety, it’s wiser to distill patterns from historical experience and find a path forward for ordinary people.

I. The Railway Revolution: Fever Without Standards Ultimately Leads to Internal Strife

In 1825, Britain’s first railway officially opened. At the time, no one could foresee how this “steam-belching machine” would reshape human society — a scenario mirroring the 2022 debut of ChatGPT. Initially, most viewed it merely as an “interesting tool,” failing to recognize its potential to spark a transformative revolution.

During the early railway boom, Britain plunged into a nationwide “track-laying frenzy.” Capitalists scrambled to monopolize rail resources, while people across all social strata invested in railway stocks. By 1846, railway investments accounted for a staggering 15% of Britain’s GDP.

This scenario mirrors the current AI landscape: tech giants race to build data centers, startups swarm to develop AI applications, and investors chase every “AI concept” project.

Yet this frenzy concealed a fatal flaw — the absence of standards. British railways lacked a unified gauge: some companies adopted the 1,435-millimeter standard gauge, others insisted on the 2,140-millimeter broad gauge, and some even deliberately used special specifications to monopolize routes.

This chaos forced trains to halt mid-journey, requiring passengers to carry luggage between connections and goods to be repeatedly loaded and unloaded. What appeared to be a dense rail network was, in reality, a collection of isolated, disconnected facilities.

Even more severe was the confusion over time standards. With no unified time zones, each railway company set schedules based on local time. Passengers departing from London planned connections according to London time, only to arrive at their destination and discover missed connections due to time differences, drastically reducing travel efficiency.

This chaotic state of “innovation outpacing regulation” persisted until the 1847 railway bubble burst. Following the bankruptcy of hundreds of railway companies, government intervention standardized track gauges, time zones, and signaling systems. Only then did Britain’s railways transform from a “battleground of corporate competition” into the “backbone of the national economy,” providing critical support for the Industrial Revolution.

Today’s AI industry is reenacting the “gauge war” of yesteryear: AI models from different companies feature incompatible interfaces, disparate data formats, and lack unified standards for energy consumption and security protocols.

Content generated by Company A’s AI requires repeated format conversions when imported into Company B’s tools for layout. Enterprises purchasing multiple AI tools often see efficiency decline rather than rise due to data incompatibility.

This history offers a crucial lesson for ordinary individuals: in the AI era, focus on “standardization” rather than blindly pursuing niche skills. Just as professionals skilled in maintaining standard gauge tracks and unified signaling systems became most competitive after railway standardization, today’s AI landscape favors those proficient in universal interface adaptation and data interoperability — skills that better withstand industry disruption.

For instance, when learning AI-powered drawing, prioritize mastering universal design specifications and data formats rather than limiting yourself to operating a niche tool. When leveraging AI for office work, the core lies in using AI to reconstruct workflows and achieve efficient multi-tool collaboration, not merely memorizing shortcuts for a single application.

History has long proven that technology’s ultimate value lies in “connection,” and the prerequisite for connection is “standardization.” In the current phase where AI industry standards remain evolving, continuously tracking these norms holds far greater long-term value than mastering isolated skills.

II. The Power Revolution: The Core of Technological Value Lies in Breakthroughs in “Restructuring Capability”

In September 1882, New York’s Pearl Street Power Station first switched on its power, illuminating 400 street lamps and dozens of buildings in the financial district. Yet at the time, this event was briefly reported by The New York Times under the “City News” section — much like AI today, the long-term impact of emerging technologies is often difficult to discern immediately.

Economic historian Paul David’s research reveals a crucial phenomenon: after electricity’s invention, it took nearly half a century before it truly drove significant increases in social productivity. The core reason was that industries had only achieved “energy substitution” without undergoing “structural transformation.”

Late 19th-century factories predominantly employed the “steam engine-central shaft” model: a large steam engine drove a central shaft, connecting all machinery via belts and gears. Workers were stationed around the shaft, with machine speeds decreasing as the distance from the shaft increased; if the steam engine stopped, the entire factory ground to a halt.

After the advent of the electric motor — despite its advantages of compact size, high efficiency, and distributed deployment — business owners merely treated it as a replacement for steam engines, preserving the traditional central shaft layout. Electricity’s flexibility remained largely untapped, viewed merely as “cleaner steam power,” naturally hindering productivity gains.

True transformation began with a deep understanding of electric motors’ characteristics: distributed motors eliminate the need for centralized drives. Factories were redesigned around production flows, allowing machines to be dispersed across zones with independent operation and maintenance. This structural overhaul directly spawned the Ford assembly line, fully unleashing electricity’s productive potential and propelling industrial efficiency to a qualitative leap.

Today, many AI adopters are repeating the factory owners’ mistake: merely replacing tools without rethinking processes. I know an operations professional who bought AI writing tools but stuck to traditional workflows: drafting outlines manually, using AI to fill content, then meticulously editing each word. After a significant time investment, efficiency improved by only 20%, leading to complaints about AI’s practicality.

In contrast, another colleague leveraged AI to handle topic selection, outline creation, and draft generation, focusing instead on strategy development and performance reviews — boosting efficiency by over 300%.

The fundamental difference lies in perspective: the former treats AI as an “auxiliary tool,” while the latter uses it to achieve “workflow restructuring.” The lessons of the electrical revolution clearly demonstrate that the core of technological transformation lies not in tool upgrades, but in the systematic restructuring of thinking and structures. For ordinary individuals, the core competitiveness in the AI era does not lie in “whether one can use AI,” but in “whether one can leverage AI to redefine the boundaries of one’s capabilities.”

Programmers should not merely rely on AI to generate code, but should use it to handle repetitive tasks like debugging and vulnerability analysis, freeing their energy for architectural design and technological innovation. Educators should not be confined to using AI for grading assignments but should leverage it to build personalized teaching plans and precisely track student progress. Newcomers to the workforce need not fear the risk of AI-driven job displacement but should proactively use AI to learn new skills and optimize work methods, turning it into an “accelerator” for personal growth.

Technological iteration is endless, but the “ability to reinvent oneself” remains the core competency that transcends cycles. Just as entrepreneurs who pioneered factory restructuring became the winners of the industrial era, those who harness AI for self-reinvention today will ultimately secure a dominant position in the new age.

III. The Dot-Com Bubble: Bubbles Will Burst, Value Lies in the “Ruins”

Late 1990s Silicon Valley pulsed with a feverish “disrupt everything” ethos: “.com” logos plastered everywhere, taxi drivers debating dot-com stocks, and college students securing funding by registering domain names from their dorm rooms. In 1999, the Nasdaq surged nearly 80%, with an internet company going public every two business days. On average， the peak of capital frenzy.

Much like today’s AI frenzy, the internet sector was rife with “irrational exuberance”: companies prioritized user numbers over profitability; investors chased “conceptual narratives” while downplaying cash flow metrics; entrepreneurs routinely proclaimed they would “disrupt industries” without clear business models.

In March 2000, after hitting an all-time high, the Nasdaq index plummeted. By October 2002, it had fallen 78%, wiping out trillions of dollars in market value and forcing thousands of internet companies into bankruptcy. Countless servers were discarded as “obsolete equipment,” while newly laid fiber-optic cables lay idle underground, becoming what scholars termed “dark fiber.”

Media sentiment shifted dramatically. BusinessWeek even ran a cover story titled “The Internet? Pfft!” mocking the once-promising industry. At the time, most believed the internet was merely a fleeting speculative bubble.

Yet history’s irony lies in how bubble bursts often mark the beginning of value reconstruction: After its stock price plummeted 90%, Amazon shed unprofitable ventures, focused on supply chain optimization and customer experience enhancement, turned profitable within three years, and laid the foundation for its e-commerce and cloud computing empire.

Google emerged from the industry’s trough by leveraging its PageRank algorithm and clear search advertising business model, establishing the value logic of the internet era.

Those abandoned “dark fiber” cables and servers, acquired cheaply and repurposed, became the core infrastructure for subsequent cloud computing and streaming media industries.

By 2010, the internet industry had risen again, successfully weathering the impact of the 2008 global financial crisis. Looking back, the 1999 bubble wasn’t the internet’s end but a “constructive destruction” — weeding out speculators while solidifying critical infrastructure and business models.

Today, questions about whether the AI industry is in a bubble frequently arise, and the answer is almost certainly yes: some AI startups lack core technology, merely repackaging open-source models while claiming “billion-dollar valuations”; Some practitioners rush to launch AI projects without viable business models. The bursting of such bubbles seems almost inevitable.

For ordinary individuals, the bubble itself isn’t frightening — what’s terrifying is “blindly following the frenzy and exiting after the crash.” The lesson from the dot-com bubble is that the ‘infrastructure’ and “industry insights” left behind after a bubble burst are the true core assets.

In the AI domain, the enduring legacy after the bubble burst will center on three areas: widespread AI literacy, mature computing infrastructure, and market-validated application scenarios.

Against this backdrop, the strategic direction for ordinary individuals becomes clear: Those planning to enter the AI industry should focus on mastering foundational technical principles and data-driven thinking — skills unaffected by market fluctuations. For AI entrepreneurship, abandon the “get-rich-quick” mentality and start with niche scenarios — such as using AI to optimize traditional industry workflows. These market-validated needs become more competitive after the bubble.

For those solely aiming to enhance workplace competitiveness, learn actionable skills relevant to your industry — e.g., e-commerce professionals mastering AI product selection, educators adopting AI-powered personalized tutoring methods. The value of such skills remains intact regardless of market fluctuations.

History often rhymes: After the 19th-century railroad bubble burst, merchants who leveraged the rail network reaped substantial rewards; following the 20th-century dot-com bubble, companies adept at revitalizing infrastructure grew into giants. Should the AI bubble burst as expected, those who master AI cognition and infrastructure will seize the next wave of opportunity.

IV. The Ultimate Survival Rule in the AI Era: Insight into Trends, Commitment to Core

After tracing the historical threads of three technological revolutions, the anxiety sparked by the AI boom gradually dissipates. Whether it’s the AI craze or a tech bubble, these are fundamentally routine manifestations of technological iteration in the evolution of human civilization. For ordinary individuals, there’s no need to become technical experts or precisely predict when the bubble will burst. Grasping the following three core principles is sufficient to stand firm amidst the waves of the era.

First, embrace “standardization” and avoid the “niche skill trap.” The ultimate direction of technological evolution is “unification and connectivity.” The standardization of railway gauges, electrical voltages, and the Internet’s TCP/IP protocol all validate this principle. While universal standards in the AI industry are not yet fully established, the trend is clear.

Against this backdrop, avoid expending energy on specialized operations of niche tools. Instead, focus on core competencies like data processing, logical analysis, and adapting universal tools — these are the “hard currency” that transcends industry cycles.

Second, proactively “reconstruct” to break free from “path dependence.” The lessons of the electrical revolution show that a technology’s value lies not in the tool itself, but in its ability to enable the reconstruction of thinking and processes.

When confronting AI, one should not passively fear being “replaced,” but proactively leverage it to “upgrade oneself”: delegate repetitive tasks like data entry and formatting to AI, while concentrating efforts on creative generation, strategy formulation, and interpersonal communication — areas where AI struggles to compete. This transforms AI into an efficiency enabler rather than a rival

Third, rationally “assess bubbles” and position for “long-term value.” Technological revolutions and bubbles are inseparable companions. The bubble itself is not a disaster; the irrational pursuit of it is.

Amid the AI fervor, maintain rational judgment: assess whether demand is genuine and evaluate whether skills possess lasting value. Focus on core, enduring elements — industry knowledge, essential skills, and quality networks — these are the “true wealth” you can carry forward after the bubble bursts.

NVIDIA’s high market valuation is merely a snapshot of the era’s tide, while the power of AI tools serves only as a stepping stone in human progress. History does not repeat itself, but it often follows similar patterns: the railway revolution broke spatial constraints, the electrical revolution unleashed energy potential, the internet revolution enabled information exchange, and the AI revolution will ultimately transcend the boundaries of human thought.

In this era of transformation and uncertainty, maintaining clear-eyed awareness and drawing wisdom from history — neither blindly optimistic nor excessively anxious — is the most rational choice. After all, the core competency that transcends all technological revolutions has never been any single skill, but rather the ability to adapt to change and reinvent oneself.

The AI era has arrived. It is not a passive race to participate in, but an active opportunity to create. Only by discerning trends and staying true to core values can one find one's footing amidst the technological tide.