Pavel Durov unveils Cocoon — a decentralized AI compute network

Cocoon is a new platform designed to handle AI computations through a distributed network of GPUs, eliminating the need for large centralized servers.

Here’s how it works:
- GPU owners connect their machines to Cocoon and contribute computing power.
- AI developers send processing tasks to the network.
- The system automatically assigns these tasks to available GPUs.
- Contributors are rewarded in TON for every completed job.

The standout feature of Cocoon is data privacy, all information is encrypted, ensuring no participant can view the contents of a request. In essence, it’s private AI infrastructure secured by the TON blockchain. Telegram will be the first major adopter, planning to route its AI-related operations through Cocoon to promote both privacy and network adoption.

In short:
- GPU providers earn TON tokens.
- Developers gain affordable, secure compute power.
- Users benefit from fast, private AI performance.

The AI job crisis is real

The U.K. is facing a severe Gen Z job crisis: around 1.2 million graduates are competing for just 17,000 available entry-level positions, the worst ratio since the late 1990s.

AI automation and hiring freezes have wiped out many entry-level roles, while universities keep producing degrees misaligned with market needs—leaving thousands of young workers stranded between education and employment.

XPENG came out of nowhere and is taking the market by storm: two robots run in perfect sync!

In China, autonomous vehicles are already ubiquitous, relieving people of the last mile. This also allows for the continued generation of massive amounts of data for even better autonomous driving.

In Germany and across Europe, however, the focus remains on discussion and the creation of new regulations; essentially, there is stagnation. Europe could never catch up, even if they started today.

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Musk Predicts Optional Work Future

Elon Musk predicted at the U.S.–Saudi Investment Forum that within 10–20 years, work will become optional thanks to widespread automation and humanoid robots, creating a post-scarcity world where money may lose relevance.

Economists agree full automation is the long-term direction but say Musk’s timeline is unrealistic due to slow robotics adoption, high costs, and the massive political and social challenges of supporting billions of people without traditional jobs.

Will humanoid robots become superhuman soldiers?

A San Francisco–based robotics startup, Foundation, is in discussions with the U.S. Department of Defense, aiming for its humanoid robot, Phantom MK1, to assist or even replace soldiers in dangerous missions. The company has already secured about $10 million in government contracts and plans to enable the robot to operate around the clock within the coming months.

Phantom MK1 can learn skills through various defense-related applications, such as maintaining and refueling aircraft or breaching obstacles in combat zones. It stands 5 feet 9 inches tall, weighs 176 pounds, features eight cameras on its head, supports a continuous payload of 44 pounds, and can handle up to 80 pounds at maximum.

Foundatio was founded by Sankaet Pathak, former CEO of the fintech company Synapse, along with former U.S. Marine Mike LeBlanc.

Its getting worse day by day. An increasing number of studies are reaching serious conclusions.

"Most of the roles at risk about 40% involve the kinds of drafting, processing information and routine reasoning that AI agents can do"

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🗣 DeepMind co-founder Shane Legg: AI won't bring sudden disruption; it will quietly take over economic work, domain by domain

Within a few years, sectors like software engineering will see massive efficiency gains, with 20 engineers using AI doing the work of 100

NOETIX has developed bionic humanoids that could serve as receptionists or guides in museums and other public spaces.

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⚠️ Doctors trust AI but fear who controls it

In early 2025, AI systems crossed a symbolic threshold: large language models began outperforming human doctors in diagnostic accuracy. The expectation was clear, better outcomes, faster care, less burnout. Reality turned out messier.

Doctors actively use AI and see real clinical value. Yet they are deeply unhappy with how AI is being introduced into medicine.

What doctors actually think
• AI improves decision-making and efficiency.
• It makes them better clinicians.
• But control over AI is drifting away from physicians and toward administrators, vendors, and bureaucratic processes.

Why dissatisfaction is so high
1. Doctors are excluded from decisions
• AI policy, procurement, and deployment are discussed without the people who treat patients.
2. Bottom-up adoption, top-down control
• Many doctors pay for AI tools out of pocket because hospitals lag behind.
• When institutions act, they often impose tools doctors didn’t choose.