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Oct 10, 2025 13 tweets 5 min read Read on X
Oct 9, 2025: China's Ministry of Commerce issued Announcements No. 61 & 62, expanding rare earth export controls to 12 of 17 elements and imposing extraterritorial licensing requirements.

This is direct retaliation for U.S. semiconductor export bans announced days earlier.

China controls 70% of global mining, 90% of processing, and 93% of permanent magnet production. Each F-35 requires 417kg of rare earths. China refines 100% of global samarium.

What does this mean for U.S. defense? How will this affect AI data centers? What happens to semiconductor and EV supply chains? Let's dive in:Image
1/12: TIMING IS EVERYTHING

The announcement came days after U.S. expanded chip export bans (Oct 7, targeting ASML/TSMC) and weeks before two critical deadlines:

• 90-day U.S.-China trade truce expires
• Trump-Xi meeting in South Korea

Strategic retaliation designed to maximize Beijing's leverage in upcoming negotiations.
2/12: RARE EARTHS 101

17 elements (lanthanides + yttrium/scandium) critical for high-tech applications—magnets, lasers, semiconductors.

They're not "rare" geologically, but incredibly hard to process:
• Only 0.1-1% concentration in ore
• Creates radioactive byproducts (thorium), driving up environmental and political costs

China dominates via low-cost mining and vertical integration. The Bayan Obo mine alone produces 70% of global light rare earths.Image
3/12: WHAT'S ACTUALLY RESTRICTED - ELEMENTS & MATERIALS

China added 5 rare earths to the restricted list: holmium (Ho), erbium (Er), thulium (Tm), europium (Eu), and ytterbium (Yb)—critical for lasers, fiber optics, and defense systems.

That means 12 out of 17 rare earths are now restricted, including neodymium (Nd), praseodymium (Pr), and dysprosium (Dy) from April.

Plus dozens of refining and mining equipment items.

Effective: Nov 8 for elements/equipment, Dec 1 full implementation.
4/12: FOREIGN PRODUCTS

Any product with >0.1% Chinese-sourced rare earths needs Beijing's export license for re-export.

Even if it's made in Taiwan. Or Vietnam. Or Texas.

This is China's version of the U.S. Foreign Direct Product Rule. Extraterritorial control over global supply chains.
5/12: MORE CONTROLS

END-USE BANS:
• No licenses for foreign militaries, or weapons
• Case-by-case review for ≤14nm chips, ≥256-layer memory, AI/military R&D

TECHNOLOGY & LABOR:
• Ban on exporting REE mining/processing/recycling tech
• Chinese citizens need government approval to join overseas REE projects

Full compliance deadline: Dec 1, 2025.
6/12: CHINA'S DOMINANCE - THE NUMBERS

Mining: 70% global share (240k tons vs U.S. 43k tons)

But here's where it gets scary:

• 90% of global separation/refining
• 93% of permanent magnet production
• 44M tons reserves (37% global)

Global demand: 200k tons/year, growing 7-10% annually from EVs/AI/renewables.

Alternatives exist (Australia's Lynas 8%, U.S. MP Materials 15%), but they all still send ore to China for processing.Image
7/12: DEFENSE IMPACT

This is where it gets serious.

Rare earths are irreplaceable in military hardware due to magnetic/thermal properties:

• F-35 fighter: 417kg of REEs
• Virginia-class submarine: 4.2 tons
• Tomahawk missiles: REEs in guidance
• Predator drones/JDAMs: precision optics/motors

China refines 100% of global samarium—the element critical for high-temp military magnets.Image
8/12: THE DEFENSE CAPABILITY GAP

The implications are stark:
• China builds military hardware 5-6x faster than the U.S.
• U.S. has zero domestic samarium refining capacity
• Short-term: Stockpiles last months
• Long-term: 5-10 years to build independent supply chains

With Indo-Pacific tensions rising, Beijing now has leverage over the foundation of U.S. defense production.Image
9/12: SEMICONDUCTOR CHOKEPOINT

REEs are critical for chip manufacturing:
• Magnets in lithography equipment (ASML's EUV tools)
• Wafer processing equipment

Ban targets ≤14nm chips (Nvidia A100/H100 territory).

TSMC, Samsung, SK Hynix ALL need licenses if using Chinese REEs.

That 0.1% threshold = de-facto veto power over the semiconductor supply chain.Image
10/12: BROADER ECONOMIC RIPPLES

Markets reacted immediately: Chinese REE stocks surged 9-10%. U.S. miners like MP Materials rose on investment flows.

Short-term: 20-50% price spikes

Key impacts:
• EVs: 30% REE-dependent
• Wind turbines: up to 200kg/MW
• AI data centers: REE magnets for cooling

REE magnet market: $20B (2023) → $30B (2030)

Escalation risk: China controls 80% gallium (LEDs/chips) + 70% lithium refining.

Historical precedent: China's 2010 embargo on Japan sent REE prices up 10x.Image
Image
11/12: U.S. COUNTERMOVES

DoD response:

• $400M equity in MP Materials (largest shareholder)
• $150M loan for heavy REE separation
• 10-year offtake for new magnet facility
• Lynas-Noveon partnership for U.S. production

India/Australia ramping exploration.
Recycling emerging: 10-20% recovery potential from e-waste.

Reality check: 5-10 years minimum to scale. U.S. = <5% global processing today.Image
13/13: THE BOTTOM LINE

China weaponizing 90% processing monopoly to retaliate for U.S. chip bans. Targeting defense (417kg per F-35) and semiconductors ≤14nm.

Short-term: 20-50% price spikes could throttle AI boom (data centers need REE magnets).

Long-term: Forces Western reindustrialization. Diversification takes years.

THE QUESTION: Can alternatives scale faster than China leverages its monopoly?

Negotiations are possible—Trump-Xi could trade chip access ⟷ REE flow.

The decoupling isn't coming. It's here.Image

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More from @AskPerplexity

Dec 26, 2025
Silver is quietly becoming a problem

Price just broke all-time highs after 14 years. Up 158% this year.

Electrification. AI datacenters. Grid. Defense. All need silver.

China is tightening export controls, and stockpiles are still depleting.

Here's what's actually happening: Image
Image
1/ China is restricting exports

Starting 2026, silver exports require government licenses. Only large, state-approved firms qualify.

In practice: more paperwork, more gating, more “approved players.”

That can act like supply loss when timing matters.

Some reports suggest some institutional positioning may be shifting and governments may be stockpiling.Image
Image
2/ The market was already short

Silver has run structural deficits for 4 straight years.

• Cumulative gap: 678 million ounces
• That's 10 months of global mine production
• 2025 is on track for year 5

Where'd the metal come from? Stockpiles built over 30 years.
Those stockpiles are nearly gone.Image
Read 8 tweets
Dec 3, 2025
"Journalists keep saying AI is 'draining aquifers' and 'boiling oceans.'

One problem: they're citing a 2023 estimate that's now off by ~100×.

Google just measured it. A median Gemini text query uses:
- 5 drops of water
- 9 seconds of TV worth of electricity
- 0.03g of CO₂

Per-prompt energy has dropped 33× in one year.

So why does the myth persist?
Outdated research, good headlines—and a real issue buried underneath.

The actual concerns are local, not global.

Here's what's actually happening:Image
1/ Where the water actually goes

AI doesn't "drink" water inside the model. Data centers use water to move heat:

Heat from chips → cooling towers → water evaporates

Water use varies by location, cooling design, and power source. A data center in wet Oregon on hydro ≠ one in drought-stricken Arizona on natural gas.

It's an infrastructure question, not a "prompt is evil" question.Image
2/ Per-prompt impact: small and falling

Google's Gemini data (May 2025):

- 5 drops of water (0.26 mL)
- 9 seconds of TV (0.24 Wh)
- 0.03g CO₂

Efficiency is improving fast: Google reports 33× lower energy and 47× lower carbon per prompt compared to one year ago.

The direction is clear: more usage, less water per useful token.Image
Read 7 tweets
Nov 28, 2025
🐋 The Whale is back!!

DeepSeek just dropped an IMO gold-medalist model.

On ProofBench-Advanced—where models prove formal mathematical theorems—GPT-5 scores 20%. Gemini Deep Think IMO Gold hits 65.7%. DeepSeek Math V2 (Heavy) scores 61.9%.

That's second place—but Gemini isn't open source.

This is the best open math model in the world. And DeepSeek released the weights. Apache 2.0.

Here's what they discovered:Image
Image
1/ Why Normal LLMs Break on Real Math

Most large language models are great at sounding smart, but:
- They’re rewarded for the final answer, not the reasoning.
- If they accidentally land on the right number with bad logic, they still get full credit.
- Over time they become “confident liars”: fluent, persuasive, and sometimes wrong.

That’s fatal for real math, where the proof is the product.

To fix this, DeepSeek Math V2 changes what the model gets rewarded for: not just being right, but being rigorously right.Image
2/ The Core Idea: Generator + Verifier

Instead of one model doing everything, DeepSeek splits the job:
1. Generator – the “mathematician”
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2. Verifier – the “internal auditor”
- Checks the proof for logical soundness.
- Ignores the final answer. It only cares about the reasoning.

This creates an internal feedback loop:
One model proposes, the other critiques.Image
Read 11 tweets
Nov 27, 2025
Batteries Just Became AI Infrastructure

Battery storage is already scaling—159 GW deployed globally, 926 GW projected by 2033.

Renewables needed it first. Now AI needs it too.

Tesla is deploying Megapacks at data centers. China is deploying 30 GW this year, integrating storage directly into AI buildout.

Why? Data centers can’t scale without solving three problems:
- 7-year interconnection queues
- power quality GPUs demand
- backup without diesel permits

Batteries solve all three ↓
Why AI Data Centers Need Batteries

Interconnection is broken. Utility connection takes 7+ years. Batteries bypass it. Skip the queue.

GPUs break traditional power. Training loads swing 90% at 30 Hz. Batteries smooth it in 30 milliseconds.

Diesel doesn’t scale. Permitting is hard. For 20-hour backup, batteries are cost-competitive.

The math: ~1% of data center capex.Image
The Scale

Global capacity: 159 GW by end-2024. Up 85% from 86 GW in 2023. Projected: 926 GW by 2033.

Cost curve: $115/kWh in 2024, down 84% from $723/kWh in 2013. Still falling.

Economics flipped. Solar plus 4-hour storage runs ~$76/MWh. New gas peakers cost $80-120/MWh.

Storage wins in sunbelt markets now.Image
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Read 9 tweets
Nov 25, 2025
The universe isn’t just expanding — it’s speeding up

13.8 billion years after the Big Bang, astronomers expected gravity to slowly slow cosmic expansion. Instead, when they looked deep into space, they found the opposite: the universe is accelerating.

Whatever drives that acceleration makes up ~70% of the cosmos.

We call it dark energy.

We can measure it. We can see its effects. So what is it, really?
How we figured this out

Cepheid stars: the distance trick

Henrietta Leavitt discovered that certain stars (Cepheid variables) get brighter and dimmer with a regular period — and that period tells you their true brightness → lets us measure distance to faraway galaxies.

Redshift: galaxies on the move

Vesto Slipher used spectra of galaxies to show many had their light stretched to longer, redder wavelengths.
Redder → moving away faster.

Hubble & the expanding universe

Edwin Hubble and Milton Humason combined Cepheid distances with redshift and found a pattern:

>The farther a galaxy is, the faster it’s receding.

That’s the Hubble–Lemaître law: clear evidence that the universe is expanding.Image
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The shock: expansion is accelerating

In the 1990s, two teams studied Type Ia supernovae, stellar explosions so consistent in brightness that they act like “standard candles.”

By comparing how bright they should be to how bright they look, you can get distance.

By measuring redshift, you get how fast they’re moving away.

The surprise:

• The supernovae were dimmer and farther away than expected.

• That only made sense if, over billions of years, the universe’s expansion had sped up instead of slowing down.

This cosmic acceleration is what we now attribute to dark energy.Image
Read 6 tweets
Nov 24, 2025
🚨The White House just launched the Genesis Mission — a Manhattan Project for AI

The Department of Energy will build a national AI platform on top of U.S. supercomputers and federal science data, train scientific foundation models, and run AI agents + robotic labs to automate experiments in biotech, critical materials, nuclear fission/fusion, space, quantum, and semiconductors.

Let’s unpack what this order actually builds, and how it could rewire the AI, energy, and science landscape over the next decade:Image
1/ At the core is a new American Science and Security Platform.

DOE is ordered to turn the national lab system into an integrated stack that provides:
• HPC for large-scale model training, simulation, inference
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National-scale AI scientist + AI lab tech as infrastructure.Image
2/ The targets are very explicit and very strategic.

Within 60 days, DOE has to propose at least 20 “national challenges” in:

• advanced manufacturing
• biotechnology
• critical materials
• nuclear fission & fusion
• quantum information science
• semiconductors & microelectronics

This is about energy dominance, supply chains, and defense.Image
Read 6 tweets

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