Fiscal.ai
It costs $284 million to buy an EUV machine from ASML, on average.

Talk about pricing power.

$ASML https://t.co/5VK7DLbLI0
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Moon Dev
What if Jim Simons showed every step of the way since he started algo trading?

I do.

I'm not there yet, but this is going to be one hell of a ride

all shared on private zooms https://t.co/xJlpXyDfkY
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Moon Dev
I Built a Swarm of 20+ AI Trading Agents So You Don't Have to Trade Alone
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App Economy Insights
📊 This Week in Visuals

$AAPL $META $SAMSUNG $ASML $V $MA $LVMH $UNH $SAP $LRCX $IBM $AXP $KLAC $BA $GEV $TXN $T $VZ $NOW $LMT $SBUX $UPS $GM $SNDK $SOFI $LUV $AAL $APPF
https://t.co/uZvriQAxQj
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Fiscal.ai
29% of Tesla's revenue now comes from non-automotive segments.

Energy Generation & Storage: 15%
Services & Other: 14%

What will this look like in 5 years?

$TSLA https://t.co/Snj79EEFcb
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Brady Long
red pill or blue pill? https://t.co/OSOj9cKIAn
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Dimitry Nakhla | Babylon Capital®
Chris Hohn on AI risk, moats, & disruption:

“The world is changing so much that some of these moats, 𝐚𝐩𝐩𝐚𝐫𝐞𝐧𝐭 𝐦𝐨𝐚𝐭𝐬, 𝐚𝐫𝐞 𝐣𝐮𝐬𝐭 𝐛𝐞𝐢𝐧𝐠 𝐛𝐞𝐚𝐭𝐞𝐧 𝐝𝐨𝐰𝐧 𝐛𝐲 𝐀𝐈 and other disruption forces. So the forces of disruption are actually rising.”

How does Hohn think about navigating that risk?

“You really want something that’s obvious… 𝙨𝙪𝙨𝙩𝙖𝙞𝙣𝙖𝙗𝙡𝙚 𝙗𝙖𝙧𝙧𝙞𝙚𝙧𝙨 𝙩𝙤 𝙚𝙣𝙩𝙧𝙮.”
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So what are barriers to entry?

𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐚𝐥 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬 𝐭𝐡𝐚𝐭 𝐦𝐚𝐤𝐞 𝐢𝐭 𝐝𝐢𝐟𝐟𝐢𝐜𝐮𝐥𝐭 𝐨𝐫 𝐮𝐧𝐞𝐜𝐨𝐧𝐨𝐦𝐢𝐜 𝐟𝐨𝐫 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐨𝐫𝐬 𝐭𝐨 𝐫𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐞 𝐚 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬.

Some of the most important ones 👇🏽

𝐇𝐢𝐠𝐡 𝐬𝐰𝐢𝐭𝐜𝐡𝐢𝐧𝐠 𝐜𝐨𝐬𝐭𝐬: Customers face real friction (cost, risk, workflow disruption) if they leave

𝐈𝐧𝐬𝐭𝐚𝐥𝐥𝐞𝐝 𝐛𝐚𝐬𝐞: Large embedded footprint that compounds over time

𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐈𝐏: Deep patents, proprietary designs, or trade secrets

𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐞𝐟𝐟𝐞𝐜𝐭𝐬: Product becomes more valuable as more users participate

𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 / 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐤𝐧𝐨𝐰-𝐡𝐨𝐰: Decades of accumulated expertise that can’t be shortcut

𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐬𝐜𝐚𝐥𝐞: Massive physical or digital build-out that’s hard to replicate

𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 / 𝐥𝐞𝐠𝐚𝐥 𝐦𝐨𝐚𝐭: Standards, certifications, or regulatory embedment
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Hohn has emphasized that 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐛𝐚𝐫𝐫𝐢𝐞𝐫𝐬 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫.

𝐎𝐧𝐞 𝐦𝐨𝐚𝐭 𝐢𝐬 𝙛𝙧𝙖𝙜𝙞𝙡𝙚. 𝐒𝐭𝐚𝐜𝐤𝐞𝐝 𝐦𝐨𝐚𝐭𝐬 𝐚𝐫𝐞 𝙙𝙪𝙧𝙖𝙗𝙡𝙚.

Examples 👇🏽

$ASML → Installed base, extreme IP complexity, knowledge moat, high switching costs, infrastructure scale

$FICO → Regulatory embedment, network effects, switching costs, data advantage

$MSFT → Switching costs, network effects, ecosystem lock-in, scale infrastructure

$SPGI → Regulatory reliance, switching costs, network effects, brand trust

$AMZN → Infrastructure scale, switching costs (AWS + Prime), ecosystem lock-in, data advantages

$ICE → Regulatory licenses, switching costs, network effects, mission-critical infrastructure

$TDG → Proprietary IP, certification barriers, sole-source positions, high switching costs
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𝐁𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: 𝐀𝐬 𝐝𝐢𝐬𝐫𝐮𝐩𝐭𝐢𝐨𝐧 𝐩𝐫𝐞𝐬𝐬𝐮𝐫𝐞 𝐫𝐢𝐬𝐞𝐬, 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬 𝐩𝐫𝐨𝐭𝐞𝐜𝐭𝐞𝐝 𝐛𝐲 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐫𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐢𝐧𝐠 𝐦𝐨𝐚𝐭𝐬 𝐚𝐫𝐞 𝐟𝐚𝐫 𝐦𝐨𝐫𝐞 𝐥𝐢𝐤𝐞𝐥𝐲 𝐭𝐨 𝐜𝐨𝐦𝐩𝐨𝐮𝐧𝐝 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐜𝐡𝐚𝐧𝐠𝐞.

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Video: Money Maze Podcast (11/13/25)
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Quiver Quantitative
BREAKING: A pro-Trump Super PAC just filed massive ad spending against Thomas Massie.

This comes just hours after the new Epstein file release.

Look at this screenshot from Quiver: https://t.co/FxJ7K2VdQu
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Moon Dev
since we cant use the twitter api to track sentiment

i am tracking HLP who is hyperliquids market maker

they turned $1,000 to $140,000,000 btw https://t.co/jblgpuPuat
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