LAIKA

July 15, 2026

Indeed, shunning AI might mean missing out on the opportunity for industrial advancement, yet placing blind faith in AI is equally a perilous wager.

Recently, at an annual conference, SoftBank's founder, Masayoshi Son, criticized AI skeptics, asserting that they are 'overestimating their own importance.' He likened resisting artificial intelligence to rejecting cars and airplanes in their respective eras—an act against the tide of progress.

This perspective, which draws historical parallels between AI transformation and the industrial and transportation revolutions, is highly thought-provoking within the business community.

However, in today's era of rapid technological evolution, as business leaders and observers, we must maintain a level-headed view: Simply likening AI to cars and airplanes not only underestimates the complexity of this transformation but may also lead firms into the trap of 'technological determinism,' marked by blind adherence or passive apprehension.

01 Historical Reflections: The Inevitability and Nuances of Productivity Leaps

Undeniably, Son's analogy carries some weight at a macro level.

From the steam engine to electricity, and then to the internet, every technological revolution in human history has fundamentally reshaped the components of productivity.

Just as factories that clung to steam power and rejected electricity in the late 19th century were eventually phased out, in the AI era, companies that refuse to embrace efficient tools indeed risk being left behind.

AI is evolving from a mere digital tool to digital labor, replacing not just manual tasks but also infiltrating the intellectual work of white-collar professionals. From this vantage point, the inevitability of AI reshaping the global economy is undeniable.

Yet, AI is fundamentally distinct from cars and airplanes.

Cars and airplanes are extensions of the physical realm, valued for enhancing 'mobility efficiency,' with relatively clear technological limits. AI, as a commoditized form of intelligence, essentially distills human cognition and knowledge. Large models encapsulate widely accepted common knowledge but lack access to cutting-edge, original tacit knowledge.

If companies merely treat AI as an omnipotent black box and blindly relinquish core thinking and decision-making authority, it is akin to trading their unique and scarce assets for cheap, homogeneous commodities in the market.

02 The Reality Gap: When Cost Reduction Clashes with Trust Crises

The underlying logic of business has never been mere technological adoration but value creation and trust preservation.

Currently, the global business landscape is witnessing a stark disconnect: On one hand, numerous companies are attempting to flatten management structures and replace mid-level positions with AI; on the other hand, leading brands like L'Oréal, Unilever, and LEGO are establishing AI-free zones and even marketing themselves as 'handcrafted, AI-free.'

The emergence of this 'anti-AI' sentiment underscores the challenges of technological implementation. When AI-generated content becomes formulaic, lacks warmth, or even produces 'hallucinations' and misinformation, consumer trust erodes. In sectors like beauty, healthcare, and high-end services, where emotional connection and trust are paramount, AI's mechanistic approach contradicts the authenticity and naturalness that brands strive for.

If companies blindly adopt AI across the board, disregarding their business nature, the outcome may not be a surge in efficiency but a collapse in brand value.

03 Breaking Through: Establishing Trust Boundaries and Fostering Human-Machine Synergy

Facing the AI surge, companies should neither blindly embrace technology like fanatics nor reject it entirely like Luddites. Instead, they should establish rational trust boundaries and learning mechanisms.

Firstly, companies must retain control over their evaluation systems and data ownership. Core business logic should not be dependent on any single model, and unauthorized intelligent 'interference' should not breach trust boundaries. Only by firmly grasping interaction records and decision-making contexts can companies build genuine competitive advantages.

Secondly, redesign human-machine task allocation. For highly calculable and standardized tasks, let AI take the lead; for tasks requiring subjective judgment, empathy, and complex decision-making, human oversight must be maintained. For instance, in medical diagnostics, requiring doctors to submit their own judgments before consulting AI recommendations—a mechanism that promotes critical thinking—can significantly enhance decision quality.

Conclusion

Rejecting AI might mean missing out on the opportunity for industrial advancement, yet placing blind faith in AI is equally a perilous wager.

Cars and airplanes transformed how humans traverse distances, while AI will transform how humans think and create value. In this transformation, the ultimate victors will not be those who merely utilize AI tools but those who preserve human uniqueness, master 'human-machine synergy,' and continuously uncover new demands in open markets.

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