Ann Kemp retweetledi
Ann Kemp
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Ann Kemp retweetledi

🚨 Do you understand what Oracle just did..
they fired 30,000 people.. via 6 AM email.. while reporting a 95% increase in net income last quarter..
Oracle isn't a struggling company .. Oracle made MORE money than ever.. and still fired 30,000 people because they're spending $156 billion on AI data centres instead..
and Larry Ellison.. the guy who just fired 30,000 families.. is worth $200 billion.. the 3rd richest person on earth.. he owns an entire Hawaiian island.. Lanai.. 98% of it.. bought it for $300 million like it was a vacation home..
this is the same playbook every single time..
IBM fired 7,800 and replaced them with AI in 2023.. Amazon cut 27,000 the same year while reporting record revenue.. Atlassian cut thousands while profits climbed.. Google laid off 12,000 while sitting on $100 billion in cash..
they told you to learn to code.. you learned to code.. they told you to upskill.. you upskilled.. and then they replaced you with the thing you helped build and sent the termination letter before you woke up..
the company made record profits and decided the reward for that was firing the people who made it happen.
unusual_whales@unusual_whales
BREAKING: Oracle has reportedly begun layoffs, with 30,000 employees likely to be fired, per the Deccan Herald.
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China is spending billions on robot training farms.
Here’s why that is one of the smartest strategic moves in AI today:
1. Data is the real bottleneck
The biggest constraint in training reliable, generalizable VLAs is diverse real-world embodied data — robots interacting with objects, environments, and edge cases at scale. That data is slow and expensive to generate.
2. State funding changes the economics
In China, this data is not only subsidized, but effectively shared across the ecosystem.
The result is a much larger training base and a faster learning cycle across the industry.
Competition is no longer about who can afford to collect the most data, but who can process, label, train on, and productize that data best.
3. It strengthens the manufacturing flywheel
Last year, 61% of humanoid robot sales went to R&D and data collection.
Each state-funded lab that buys a humanoid helps scale manufacturing volumes, which lowers hardware costs, improves supply chains, and makes the next round of deployment cheaper.
How defensible this advantage becomes will depend on the quality and diversity of the data being collected.
The more varied the data, the more valuable it is. That’s why lab-generated data is fundamentally more limited than real-world data collected in homes, public spaces, and commercial environments.
And it’s not hard to imagine China extending this same state-backed strategy beyond labs into exactly those settings.
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