AWS, cut at least hundreds of jobs on Thursday. This news comes just one month after CEO Andy Jassy said that new AI tools would lead to workforce cuts across the company. Here's what we know about the cuts:

Workers got emails Thursday morning telling them they were fired and their computers were being shut off. The layoffs hit several groups within AWS, including specialists who help customers create new products and sell services.

Despite the job cuts, AWS is actually doing well financially. Sales went up 17% to $29.3 billion in the first quarter compared to last year. Operating income also rose 23% to $11.5 billion.

Amazon said the cuts were needed so they could keep investing and hiring in other areas to bring new ideas to customers. Many companies are now using AI to write computer code and handle routine tasks, which means they need fewer human workers.

This isn't Amazon's first round of cuts this year. The company has been making smaller job cuts in other parts of the business, including its books and devices unit and podcast division. CEO Jassy has been working to reduce what he calls too much management at the company.

Google just announced it will spend more than $25 billion to build new data centers and AI systems across a huge power grid in the eastern United States over the next two years.

The company made this big announcement at an energy summit in Pennsylvania that President Trump attended. The money will go toward building computer centers across 13 states, including Illinois, New Jersey, Ohio, and Virginia.

Google also plans to spend another $3 billion to fix up two old water power plants in Pennsylvania. They want to make these plants work better to provide clean energy for their new computer centers.

Why Pennsylvania? The state sits on top of a massive power grid that can provide lots of electricity. As one executive explained, they can put their data centers right next to the power sources, which makes everything work better and costs less money.

Jina AI built something pretty amazing. They created a system called Jina Reader that can read 100 billion tokens from websites every single day. That's like reading millions of books worth of content.

The secret sauce is a special AI model called ReaderLM-v2. This model has 1.5 billion parameters and was trained on millions of web documents. It learned how websites are built so it can separate the good stuff from the junk.

The system can handle massive traffic spikes without breaking. During a recent attack where bad actors sent over 100,000 requests per minute, Cloud Run absorbed the load and filtered out the bad traffic. When the attack ended, costs went right back to normal.

Jina Reader now processes more than 10 million requests daily and maintains over 99.9% uptime while staying profitable enough to offer a generous free tier to users.

Oracle just released an MCP server that connects AI assistants directly to Oracle databases where companies store their important information.

The tool works through Oracle SQLcl, which is Oracle's command line interface. This means any AI system that supports the MCP standard can now talk directly to Oracle databases.

Industries like banking, healthcare, retail, and pharmaceuticals could benefit the most since they rely heavily on Oracle databases. For example, a retail company could ask an AI assistant to check inventory levels or analyze sales trends without having someone manually write database queries.

Oracle warns that companies need to be careful about security. They recommend giving the AI assistant only the minimum access needed to do its job. Companies should also regularly check what queries the AI is running to make sure it's not trying to access restricted information.

The tool is available through Visual Studio Code and works automatically with Microsoft Copilot once activated.

We talk with Ido Kotler (CPO of Pelanor) about how artificial intelligence is radically transforming financial operations in the Cloud. From smart alerts to autonomous agents, discover why the future of FinOps depends on AI more than you might think.

Companies are getting tired of paying huge bills to big cloud providers like Amazon, Microsoft, and Google. What started as a cheap way to run computer systems has turned into a money drain with hidden costs and surprise fees.

The problem gets worse when companies try to use AI systems. These need lots of powerful computers and special chips that cost a fortune to run on the big cloud platforms. Companies often don't know exactly what they're paying for or how much the final bill will be.

Control matters more than ever today. With hackers everywhere and strict rules about data privacy, companies want to know exactly where their files are stored and who can see them. The big cloud providers often can't give clear answers to these questions.

Sovereign clouds shine when it comes to AI work. They often cost less to run AI programs and give companies the freedom to test new ideas without worrying about surprise charges. Plus, they make it easier to follow local laws about data protection.

Real examples show this works. Estonia keeps backup copies of its government data in other countries through sovereign cloud systems. Ukraine used similar setups to keep running during wartime. These aren't just theory - they're proven solutions.

For years, three big companies ruled everything: Amazon Web Services, Microsoft Azure, and Google Cloud. People called them the "Big Three." But now, many other cloud options are growing strong and taking customers away from these giants.

Here's why companies are switching:

The Big Three charge high fees and don't always fit what companies need. Alt clouds often cost less and work better for specific tasks. Companies want more control over their data, especially when dealing with government rules. Some businesses need special setups that the big providers don't offer well.

The numbers show this trend is real. Alt clouds are growing much faster than the big providers in many areas. Companies are learning they don't need to put everything with one provider. They can pick the best cloud for each job they need to do.

But this change brings problems too. Using many different clouds makes things complicated. Each one works differently and has different rules. Companies need smart people who understand all these systems. They also need tools to manage everything together.

There's another risk. As alt clouds get bigger and more successful, the Big Three might buy them. This could ruin what made them special in the first place.

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