Today's Newsletter Highlights:

• Amazon axes 16,000 jobs as it pushes AI and efficiency

• The AI infrastructure boom shows no sign of slowing down

• White House compares industrial revolution with AI era

• Best AI Tools

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Amazon axes 16,000 jobs as it pushes AI and efficiency

Amazon is cutting about 16,000 jobs globally. This is the second major layoff in three months, part of a restructuring by CEO Andy Jassy. This comes after about 14,000 jobs were lost in October 2025. So, the total corporate layoffs since late last year are now around 30,000.

Key points :

• 📊 Scale and Scope

• The 16,000 roles account for nearly 10% of Amazon’s corporate workforce. This is just a small part of the company's total headcount, which is about 1.5 million employees globally.

• Affected areas include AWS, Alexa, Prime Video, devices, advertising, and HR, among others.

• 🧠 Why Amazon Says It’s Cutting Jobs

• According to internal communications and company comments:

• The layoffs aim to reduce bureaucracy and simplify the organizational structure.

• Amazon wants to increase ownership and agility within teams.

• The cuts are a response to pandemic-era over-hiring when demand surged.

• The company is adopting AI and automation tools, which affect workforce decisions. CEO Andy Jassy has noted that increased AI use may lead to fewer human roles for some tasks.

• 💼 Worker Impact

• Affected employees will generally receive severance and support. In the U.S., many have up to 90 days to seek internal positions before separation.

• Amazon says it won’t have regular large layoffs. However, some teams might still make adjustments as needed.

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The AI infrastructure boom shows no sign of slowing down

AI infrastructure investment is speeding up. Companies are increasing capacity rather than cutting it. This is clear in hardware orders and semiconductor supply chains. Nvidia, the main supplier of GPUs for AI tasks, sells billions in chips each month. This makes Nvidia one of the world's most valuable companies. Its suppliers, like ASML, are also enjoying record demand. This shows strong confidence in ongoing data centre developments.

Key Points :

• Strong hardware demand: Quarterly orders for advanced chip equipment reached new highs because of AI needs. Long-term infrastructure plans: Big orders suggest companies expect ongoing investment in data centres, networking, and AI computing, not just short-term trends.

• Broader Context on the AI Infrastructure Boom

• Beyond the TechCrunch article, many signs show steady growth in AI infrastructure:

• Huge Capital Investment • Global spending on AI infrastructure remains robust. Big tech firms aim to spend hundreds of billions each year on data centres, chips, and cloud services. • Nvidia’s CEO stated that over $100 billion in AI funding is not a bubble. It marks a structural shift needing significant buildout.

• Global Buildout & Ecosystem Growth • Demand goes beyond the U.S.; firms and regions worldwide are investing a lot. However, startup involvement differs. - AI data centres are being planned or expanded around the world. Trillions are expected to be invested in infrastructure.

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White House compares industrial revolution with AI era

The White House has released a report called “Artificial Intelligence and the Great Divergence.” It compares today’s AI shift to the Industrial Revolution. The report suggests that AI might be a key driver of economic growth, similar to that era.

Key Points:

• AI’s Impact on U.S. GDP

  • U.S. investment in AI infrastructure, such as data centres, software, and computing equipment, increased GDP by around 1.3% in the first half of 2025. This figure is similar to early railway investments in the 19th century.

  • The report outlines various projections for AI’s economic impact. Predictions vary. Some predict modest growth in the single digits. Others expect productivity gains over 20% in ten years. Some models even suggest increases up to around 45% if AI takes over many human jobs.

• AI Adoption & Infrastructure Trends

  • AI has shifted from experimentation to regular use. Most organisations now deploy AI in production.

  • The ability to train AI models has increased quickly, about four times a year lately. Costs per unit of AI output have also dropped sharply. Both trends show the technology’s growing capabilities and economic importance.

• International Economic Context

  • The report says the U.S. leads in AI investment, model performance, and computing infrastructure. This might widen the economic gap between countries if some delay in adopting or investing in AI.

  • Europe and China are important players, but they're developing their AI infrastructure more slowly than the U.S. However, China is still a big contributor and competitor in this field.

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