Google Is Cutting Jobs. ASML Is Paying People to Stay. Which AI Strategy Will Win?

Google layoffs contrasted with ASML employee retention bonuses, illustrating two competing AI workforce strategies and the growing importance of organisational wisdom in the age of artificial intelligence.

Two headlines published just days apart reveal something much bigger than the latest technology news. They suggest that the AI economy is quietly splitting into two very different philosophies of management. At Google, more than 4,500 employees have petitioned CEO Sundar Pichai for stronger protections against layoffs. Their demands are neither radical nor unreasonable. They seek guaranteed severance, voluntary exit packages before compulsory redundancies, and greater transparency in how workforce reductions are handled. Their central argument is difficult to dismiss: how can one of the world's most successful companies continue reducing jobs while investing billions of dollars in artificial intelligence? Across Europe, another technology giant has chosen the opposite path. ASML, the Dutch company whose extreme ultraviolet lithography machines are indispensable to the global semiconductor industry, is reportedly offering employees retention bonuses of around €20,000 if they remain until 2030. One company is reducing headcount. Another is paying to preserve it.

At first glance, these appear to be unrelated corporate decisions. They are not. They represent two competing theories about success in the age of artificial intelligence. The first assumes that AI primarily replaces labour. As models become more capable, organisations require fewer people. Competitive advantage comes from automating faster, reducing costs sooner and continuously reshaping the workforce to match technological progress. The second starts from a very different premise. It assumes that AI makes human expertise more valuable, not less. Not because people compete with machines, but because organisations compete through something machines cannot accumulate by themselves: institutional memory.

That distinction may become one of the defining strategic questions of this decade. According to PwC's 2026 AI Performance Study, just 20% of companies capture 74% of AI's economic value. Their advantage is not that they possess unique AI models. The same frontier models are increasingly available to everyone. What separates these organisations is their ability to redesign workflows, rethink business models, strengthen governance and build cultures that learn continuously. In other words, the leaders are not distinguished simply by better technology. They are becoming better organisations.

Seen in that light, the debate over layoffs changes completely. Large language models are becoming cheaper with astonishing speed. AI capabilities that appear revolutionary today often become widely available within months. Technology diffuses rapidly. Organisational learning does not. Every deployment teaches employees something that cannot simply be downloaded from a cloud provider: how customers behave, where processes fail, which decisions require human judgment, how teams collaborate under pressure, and what risks emerge when theory meets reality. These lessons accumulate slowly, often invisibly, until they become an organisation's most valuable strategic asset.

This is the form of capital that rarely appears on a balance sheet. Every experienced employee who leaves takes more than technical expertise. They carry relationships, institutional context, operational intuition and countless unwritten assumptions built over years of experience. These are not merely individual capabilities; they are fragments of organisational memory. Companies often calculate the immediate financial savings of reducing headcount. They seldom calculate the long-term cost of losing knowledge that competitors cannot purchase, replicate or recover once it has walked out of the door.

History suggests that technological revolutions reward organisations that learn faster, not merely those that automate faster. The industrial revolution did not simply favour factories with the newest machines; it rewarded those that mastered new systems of production. The digital revolution did not crown companies with the biggest servers; it rewarded those that built stronger platforms, cultures and ecosystems around emerging technologies. Artificial intelligence appears to be following the same historical pattern. The technology itself is rapidly becoming accessible. The capability to apply it intelligently remains scarce.

Recent labour market evidence points in the same direction. PwC's latest Jobs Barometer suggests that organisations making the most effective use of AI are not necessarily shrinking their workforces. Instead, many are experiencing stronger productivity alongside continued demand for employees capable of judgment, leadership, creativity and complex decision-making. As routine tasks become easier to automate, distinctly human capabilities become more economically valuable. The paradox of the AI economy may therefore be that machines increase, rather than diminish, the importance of experienced people.

That raises a question every board should now confront. Perhaps the defining strategic issue is no longer how many jobs artificial intelligence can replace. The more important question is how much institutional knowledge an organisation can afford to lose. Those are fundamentally different calculations. One measures short-term efficiency. The other measures long-term competitiveness.

The real scarcity in the AI economy is no longer computing power. Cloud infrastructure can be rented. GPUs can be purchased. Foundation models can be licensed. But organisational wisdom—an institution's accumulated judgment, culture, relationships and collective learning—cannot be bought off the shelf. It must be built patiently and protected deliberately.

Artificial intelligence will become available to almost every company. Organisational wisdom never will. In the coming decade, the companies that lead may not be those that replace people the fastest. They may be those that learn from them the longest.

This article is part of the larger AI, Geopolitics, and Future Civilization series exploring how artificial intelligence may reshape global power through compute infrastructure, semiconductors, energy systems, labor markets, military strategy, industrial ecosystems, and technological competition during the twenty-first century. As the AI age accelerates, the struggle over chips, compute, data centers, talent, and infrastructure may increasingly shape the future architecture of the international order itself. To know more Read: