ALEX

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$ALEX — Bulls Push Hard

$ALEX is trading near $0.004638, up approximately 8.96%, showing strong buying pressure. The immediate battle is around the psychological $0.00470 area. Support: $0.00450–$0.00458. Resistance: $0.00470–$0.00480. If buyers break and hold above $0.00480, the next target could be $0.00500, with $0.00520 as a further level to watch. Losing $0.00450 would weaken the current structure and could invite a retracement. Right now, momentum is clearly interesting, but chasing a vertical move carries risk. Watch the breakout volume closely. If buyers keep stepping in, ALEX could deliver another fast move from these levels! 🚀🔥

#ALEX #White House crypto meeting #Trading

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$ALEX up 100% today and the catalyst is actually substantive — not just hype. AlexLabs dropped a governance proposal that flips ALEX into a deflationary asset: 1.6M STX allocated for buybacks and burns, emissions stopping entirely, and future protocol revenue earmarked for the same. That's a structural supply shock, not a narrative pump.

The chart confirms it — ALEX breaking out to levels not seen since Oct/Nov 2025, and it's still 25x below where it traded this time last year even after today's move. That context matters.

Interesting side effect: $LeoCoinSTX (largest community coin on STX, ~$1.8M market cap) rallied 34% with only $3K in net buying — purely because most of its LP is paired to ALEX. Classic liquidity spillover.

My take: buyback-and-burn mechanisms work best when protocol revenue is real and growing. The key question for ALEX isn't today's pump — it's whether the protocol generates enough fee revenue to sustain meaningful burns long-term. If yes, the deflationary flywheel compounds. If not, this is a one-time catalyst. Worth watching the revenue metrics closely.$ALEX $STX #ALEX

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Alex Thorne, head of Galaxy Research, notes: Bitcoin mining is becoming increasingly centralized, while the AI field is becoming decentralized.


Once, BTC was mined on ordinary PCs; now, ASIC devices and large industrial data centers are required.


AI developed differently: it began with large centralized clusters. But now, open solutions are gradually closing the gap with advanced models due to the latter's data scarcity, context limitations, and memory issues.


Local neural networks are becoming more compact and cheaper, enabling them to run directly on users' personal devices.


#ALEX THORNE #GALAXY RESEARCH

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