Alibaba is raising $10.2 billion to deepen its AI push, even as heavy spending pressures profits and shareholders. In India, another AI opportunity is emerging: giving every child access to personalized tutoring, potentially making quality academic support cheaper and more accessible. But as AI enters classrooms, educators are highlighting an equally important lesson: students must understand what AI cannot do, especially when human judgment, lived experience, empathy, and original thinking matter most.

In today’s post:

• Alibaba just made its AI bet much bigger

• The skill AI still cannot replace

• What if every Indian child had a tutor?

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PROFITS

Alibaba is sacrificing today’s profits for tomorrow’s AI position

Alibaba shares fell sharply in Hong Kong Monday. The trigger was a massive new share placement. The company is raising about $10.2 billion. Every dollar is earmarked for artificial intelligence. That makes this more than another funding round. It shows how expensive the AI race has become.

• Alibaba will issue 710 million new shares. The price is HK$112.70 each.

• That price sits below Friday’s HK$123 close. Investors responded quickly, pushing shares down sharply.

• The timing matters. Alibaba just reported profits falling 75% year-over-year.

• AI spending is already putting pressure on those profits. Quarterly capital expenditure jumped 75% to 67.7 billion yuan.

• Yet Alibaba appears unwilling to slow down. It previously committed 380 billion yuan toward AI and cloud infrastructure.

• The strategy reaches beyond building better AI models. Alibaba controls cloud infrastructure that can distribute and monetize them.

• Tencent is spending aggressively too. That suggests China’s AI race could demand increasingly deeper pockets.

Alibaba is making a difficult trade. Shareholders absorb dilution and weaker profits today. Alibaba gets more capital to build its AI position. Neither outcome guarantees the strategy works. But the bigger signal is hard to ignore. AI competition is becoming a capital allocation decision. The winners may not build the smartest models alone. They may be companies willing to finance them longest.

EDUCATION AND AI

Students should learn AI’s limits, not just its capabilities

AI can summarize, imitate, and generate convincing language. That makes it useful inside classrooms. It also makes dependence dangerously easy. The deeper educational challenge is not banning AI. It is teaching students where human thinking still matters.

• AI recognizes patterns, but it does not have lived experience.

• That difference matters when interpreting emotion, culture, and ambiguity.

• In literature, fluent answers can still miss essential context.

• AI can also produce confident factual mistakes inside polished writing.

• Students need practice comparing machine output with human interpretation.

• Writing remains valuable because it develops judgment, not just prose.

• Education should teach when AI helps and when it weakens thinking.

The goal should not be protecting students from AI. That battle is probably already lost. The better goal is making students harder to fool. They should recognize shallow reasoning behind polished language. They should know when intuition and experience matter. And they should keep doing difficult intellectual work themselves. AI may become better at producing answers. Education must become better at producing judgment.

RESEARCH

AI could make personal tutoring available beyond wealthy families

India has a tutoring problem hiding in plain sight. Millions of students already pay for extra coaching. Yet better outcomes are far from guaranteed. AI could change the economics of that system. The bigger question is who builds the infrastructure.

• India’s test-preparation market reached about $14.8 billion in FY26.

• Around 17 million to 20 million secondary students use coaching.

• That demand shows schools alone are not meeting every need.

• Private platforms cannot realistically serve every Indian student alone.

• A national AI tutor could offer personalized explanations at scale.

• The strongest model may be public infrastructure, not one vendor.

• Success should be measured through learning outcomes, not AI adoption.

The most interesting part is not the technology. It is what happens when tutoring becomes nearly free. Today, personalized help often depends on family income. AI could weaken that link considerably. But access alone will not improve education. The tutor must understand curricula, languages, and individual gaps. If India gets those basics right, this could matter enormously.

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