🚨BERITA TERKINI: PRESIDEN TRUMP MEMBELI BURGER DENGAN BITCOIN TRANSAKSI PERTAMA KALINYA $BTC OLEH SEORANG PRESIDEN A.S. | Bluechip di Binance Square

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🚨BERITA TERKINI:PRESIDEN TRUMP MEMBELI BURGER DENGAN BITCOIN
TRANSAKSI PERTAMA KALINYA $BTC  OLEH SEORANG PRESIDEN A.S.

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JAPAN'S MONETARY REVOLUTION: THE BITCOIN EXPERIMENT THAT WILL REDEFINE GLOBAL FINANCE

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Price the stack, not the story. OpenAI says it will finish the year >$20B run-rate and is “looking at” ~$1.4T in long-dated infra commitments by 2030. The Information’s Q3 charts still show deep negative free cash flow through the late-2020s. Both can be true: revenue scales S-curve, cash burns linear until unit economics flip. Receipts that matter: • Demand shock: Nvidia just swung ~–$800B in five days, then ~+$500B back within hours; a +5.8% day was its biggest since April. That’s repricing at the margin, not new cash in .. market cap is a stock, not a flow. • Supply race: Hyperscalers are diverting an unprecedented share of operating cash to AI capex. Orders rumored in the hundreds of billions don’t clear unless power, racks, and grid lead-times do. • Unit economics: Today’s inference must get 50–100× cheaper and agents must run real workflows, or model providers stay cash-flow negative even as top-line grows. How to actually value this cycle: 1. Track revenue per watt and revenue per GPU-day (not DAUs). 2. Watch data-center utilization and AI gross margin by layer (chips → platforms → models → apps). 3. Follow cash conversion: ARR → gross profit → free cash flow—because you pay for $1.4T with cash, not headlines. 4. Energy is the bottleneck: time-to-power, not just time-to-model, will separate winners. Simple test: Profit per token = Price per token – (Power cost + Compute amortization + Bandwidth + Labor). If profit per token × usage < capex service, narratives die; if it clears and compounds, this is the most profitable build-out since the internet. Bottom line is that AI isn’t a bubble or a guarantee. It’s a margin war with a power bill. Price the cash flows, stress the bottlenecks, and stop confusing market cap with money in the bank.

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The Great AI Illusion is over. McKinsey's data reveals the silent collapse. 88% of companies use AI. A mere 1% have mature systems. This is not adoption. This is mass delusion. We are witnessing the largest failed transfer of technology in human history. Companies are bolting superintelligence onto broken processes. The result is catastrophic waste. But a schism is emerging. A top 6% elite are not just adopting AI. They are rebuilding their entire enterprise around it. They are not measuring efficiency. They are measuring agent autonomy. For everyone else, the clock is ticking. 30% of companies forecast workforce reductions. This is not a simple shift. It is the beginning of a cognitive winter for roles built on the old world. The pilot phase is dead. The transformation era has begun. The 67% stuck in experimentation are not behind. They are already obsolete. The next twelve months will separate the architects of the new world from the caretakers of the ruins. This is the single greatest recalibration of capital and talent in a generation. You are not deciding on a technology. You are deciding on a future. The data is the warning. The consequence is inevitable. Adapt or be absent. The choice is that stark.

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Blockchains will never be perfectly fair Physics prevents it. Protocols can price it. Different nodes see different orders. Light has a speed. Clocks drift. In an adversarial network, total fairness is impossible. The honest goal is auditable ε-fairness. Design that gets you close • Frequent batch auctions so races end inside the batch, not between blocks. • Threshold encryption to hide contents until the batch is sealed. • Verifiable random shuffle to permute order inside each batch. • Proposer–builder separation with open order-flow auctions so private reordering is capped and rebated. • MEV containment so reordering profit is returned to users, not hoarded by a few. Tests that decide if it is fair enough 1. Inclusion latency for honest transactions ≤ 2Δ across regions. 2. Expected rank error ≤ k, published per batch. 3. Builder profit from reordering < ε of total fees over a rolling window. 4. Censorship rate < 1 percent, with provable non-inclusion proofs and penalties. 5. A public fairness ledger that posts seeds, threshold keys, rank metrics, and rebates every epoch. Hashgraph’s median timestamps reduce skew but a single bad reporter can still bias order. Aequitas and Themis group conflicts rather than worship first-arrival. The right question is not “perfect or bust” but what is the bound, who pays, and can we verify it live. One line to carry forward: Fairness is not a timestamp. It is a budgeted advantage with receipts. If your chain cannot print its ε, it has not solved fairness. $BTC

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$UNI Notable volume. Nice structure. Could be a runner.

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