orxbit @propxTech

@LearnWithSubhan Fable

The RWA chart that doesn't get enough attention. $5B to $22B in just over a year, with the sharpest move early this year. Traditional assets are moving onchain faster than most people expected... dont ignore this sector.

In Kerala, apparently you can now call a coconut harvester the same way you book a cab. A uniformed professional arrives on a cycle, equipped, trained, and ready to work. We often speak about India’s services economy in terms of IT exports or global capability centres. But we’re digitising even our most traditional, hyper-local services. There was another detail from this video that stayed with me. The young man who climbed those trees was from Chhattisgarh. When I began my career in our Group’s steel business, many of of our associates working in our furnace and foundry shops had come from states like Bihar and Madhya Pradesh, travelling far from home in search of opportunity. Today, it seems those same aspirations are finding avenues not just in heavy industry, as in the past, but in new-age, tech-enabled services. People moving, adapting and rising are a powerful economic force. And also a force for integration. As long as they’re welcomed by the host states!

Google proves the math for cracking Bitcoin's encryption in 9 minutes now exists — the only missing piece is the hardware. 🤯 With precomputation, an attacker could derive a private key from a public key in roughly 9 minutes. Bitcoin's average block time is 10 minutes. The core finding is precise and unsettling: Shor's algorithm can now be compiled to solve the 256-bit elliptic curve discrete logarithm problem with roughly 1,200 logical qubits and 90 million Toffoli gates. Translated to hardware, that means fewer than half a million physical superconducting qubits, nearly a 20-fold reduction over the best prior estimates from Litinski's 2023 work. It means "on-spend" attacks, where a transaction is intercepted in the mempool and forged before confirmation, become plausible for fast-clock quantum machines. The paper introduces a critical architectural distinction. Superconducting and photonic platforms, with microsecond-scale error correction cycles, can threaten active transactions. Neutral atom and ion trap systems, two to three orders of magnitude slower, can only threaten static holdings with long-exposed keys. Approximately 6.9 million BTC sit in addresses with exposed public keys today. Around 1.7 million of those are locked in early P2PK scripts from the Satoshi era, almost certainly with lost private keys, forming a permanent multibillion-dollar quantum target. Ethereum faces distinct risks: its account model, BLS-based validator signatures, and KZG commitment scheme in data availability sampling each present separate at-rest attack surfaces. The authors validated their resource estimates using a zero-knowledge proof, a first for quantum cryptanalysis, allowing verification without revealing the attack circuit itself. The margin between quantum capability and cryptographic failure is narrowing faster than most migration timelines assume. ---- quantumai. google/static/site-assets/downloads/cryptocurrency-whitepaper.pdf

🚨‼️ BREAKING: Anthropic has decided to open source their entire codebase and is rebranding their AI to OpenClaude. Anthropic CEO Dario Amodei said: "Yesterday was no slip-up. If we disappear just like OpenAI is vanishing right now, our code can live on through the community."

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Brilliant economic paper, directly models the "Structural Jevons Paradox" happening right now in the AI industry. The cost of running an LLM is dropping, but total computing energy is exploding anyway. It mathematically proves that as the unit cost of digital intelligence and coding drops, the aggregate demand for complex AI agents and the infrastructure to support them surges exponentially, creating a massive new downstream ecosystem that requires human management. Reveals a massive paradox where dropping the price of AI usage does not save money, but instead encourages developers to build vastly more complex agents that eat up exponentially more computing power. Because of this relentless progress, small companies building simple applications on top of these models get completely crushed as the core AI naturally absorbs those exact same features over time. They also discovered a brutal dynamic where a perfectly working LLM becomes economically worthless the moment a competitor releases a smarter version. Ultimately, the researchers prove that this combination of massive computing costs and the need for constant user data naturally pushes the entire AI industry toward an unavoidable monopoly. --- arxiv. org/pdf/2601.12339v1 "The Economics of Digital Intelligence Capital"

Rohan Paul @rohanpaul_ai

7 months ago

Citadel Securities published this graph showing a strange phenomenon. Job postings for software engineers are actually seeing a spike. The graph here is short term but still it's super interesting and really strange. Is it Jevons paradox at play. When AI makes coding cheaper,

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Private credit default rate just hit 9.2%. That's higher than 2008 bank loan peaks. $1.8 trillion in assets, $100B in secondary liquidity. 18:1 mismatch. When exits close, panic starts.

🤯BREAKING: Alibaba just proved that AI Coding isn't taking your job, it's just writing the legacy code that will keep you employed fixing it for the next decade. 🤣 Passing a coding test once is easy. Maintaining that code for 8 months without it exploding? Apparently, it’s nearly impossible for AI. Alibaba tested 18 AI agents on 100 real codebases over 233-day cycles. They didn't just look for "quick fixes"—they looked for long-term survival. The results were a bloodbath: 75% of models broke previously working code during maintenance. Only Claude Opus 4.5/4.6 maintained a >50% zero-regression rate. Every other model accumulated technical debt that compounded until the codebase collapsed. We’ve been using "snapshot" benchmarks like HumanEval that only ask "Does it work right now?" The new SWE-CI benchmark asks: "Does it still work after 8 months of evolution?" Most AI agents are "Quick-Fix Artists." They write brittle code that passes tests today but becomes a maintenance nightmare tomorrow. They aren't building software; they're building a house of cards. The narrative just got honest: Most models can write code. Almost none can maintain it.

my OpenClaw woke me up at 3:47 AM with one message: "found 6 markets resolving in next 90 minutes while US is asleep, need approval for $12K deployment" i typed "yes" and went back to sleep woke up to +$43,800 been running an agent that hunts timezone arbitrage for 9 days never thought it would actually wake me up the setup: gave OpenClaw access to global news feeds in different timezones: > Japanese government RSS > European parliament calendars > Australian financial wires > Middle East flight trackers > Asian central bank announcements told it: "find markets that will resolve during US sleep hours (2 AM - 6 AM EST), alert me if edge exceeds 30%" what happened at 3:47 AM: agent detected 6 markets resolving between 4 AM - 6 AM across different timezones all had same pattern: > crowd priced them like normal markets > but resolution would happen while americans sleep > official sources in those countries already showing signals the alert: > "Japan rate decision - 68% YES per BOJ leak, polymarket at 23¢" > "EU emergency vote - live stream shows YES winning, polymarket at 31¢" > "South Korea policy - government RSS confirmed, polymarket at 19¢" > "Australia trade deal - minister quoted 2 hours ago, polymarket at 27¢" > "UAE production cut - OPEC meeting notes public, polymarket at 15¢" > "Singapore regulation - parliament session live, polymarket at 22¢" - total edge detected: $43K potential - window: 90 minutes before -capital needed: $12,000 my phone buzzed i opened telegram half asleep saw "approve or miss" typed "yes" closed my eyes 7:30 AM - woke up to notifications: all 6 markets resolved during asian/european morning > US traders woke up to already-closed markets > my positions entered at 15¢-31¢ > all resolved at 95¢-100¢ profit breakdown: - Japan: $8,200 - EU: $6,900 - Korea: $11,400 - Australia: $7,100 - UAE: $5,800 - Singapore: $4,400 - total: +$43,800 checked the logs: agent had been watching these markets for 8-14 hours tracking official sources in real-time waiting for US to go to sleep then finding the moment when: > outcome is basically confirmed overseas > but US crowd hasn't updated prices > resolution is imminent the edge is stupid simple: polymarket is 70% american traders world events don't care about EST timezone while you sleep, markets resolve if you want to copy wallets running this 24/7: thetradefox.com/?ref=AUTOCOPY am i the only one making money while literally unconscious?

zer @zerqfer

8 months ago

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Atlassian just confirmed 1,600 layoffs with 900+ coming from engineering But I'm hearing the real story from inside Sources say they've been running "knowledge extraction sprints" for 6 months - recording every senior engineer's screen, logging their prompts, documenting their debugging workflows One architect told me they made him walk through his entire microservices decision tree while they filmed it. Called it "knowledge transfer for the transition team" The transition team? 47 contractors in Bangalore with access to his recorded sessions and a Claude Enterprise subscription Same architect just found out his replacement starts Monday. Guy makes $28k annually and ships code 40% faster using the exact prompt libraries they extracted They're not just cutting headcount - they're systematizing 15 years of engineering expertise into training data The "strategic AI focus" isn't about building AI products It's about replacing their entire engineering culture with agents trained on their senior engineers' knowledge Word is the CTO replacement already has the playbook: extract, document, offshore, automate If you're still there and they ask you to "document your processes for the team" - RUN The knowledge extraction is complete

Our new report Stablecoins: The Next Frontier in Financial Infrastructure is live! Stablecoin supply sits at roughly $306B today. Around $100B of that was added in the last year alone while BTC sold off. For the first time stablecoin growth has completely decoupled from the broader crypto market. Sending $1,000 from the US to Turkey costs 2.5-6% on SWIFT. On stablecoin rails it costs around one basis point plus on/off-ramp fees. In corridors like Nigeria, Kenya, and Argentina over 80% of remittance costs aren't even FX risk. It's pure infrastructure overhead from maintaining correspondent banking chains that stablecoins eliminate entirely. That's where adoption is taking hold first. Stablecoin issuers are now the 19th largest holder of US treasuries. Those treasuries yield around 3.89% while a standard savings account pays 0.39% and interest checking pays 0.07%. Banks are capturing nearly the entire spread on deposits. If stablecoin issuers start passing that yield through to holders deposits migrate and banks lose the cheap funding they depend on to extend credit. Yield-bearing stablecoins are already here and more issuers are moving in that direction. Stablecoins grow whether or not crypto does now.

Arcana @0x_Arcana

8 months ago

Over the last 6 months we’ve been thinking about stablecoins extensively. In our 2026 Infra year ahead report stablecoins were the main narrative going into the year. The thesis is that stablecoins represent more than just a fiat-denominated digital form of money. They represent

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Banger of a piece, from start to finish. 🔥 "We have handed agents the codebase, the tests, the docs, the specs, the commit history." "We haven't handed them the one artifact that actually survived — the record of what users have been asking the system to do, every day, for years." "Until agents can read that layer — not as log lines, but as behavioral contracts, as the accumulated promise a system has made to the people who depend on it —harness engineering in brownfield and blackfield will remain a human problem with an AI assistant bolted on." linkedin.com/pulse/producti…

Neo Finance: The Convergence of Three Big Trends

The Strait of Hormuz has been closed for 8 days. Everyone thinks this is about oil. This is about what oil becomes. 92% of the world's sulfur comes from refining oil and gas. Close the Strait of Hormuz and you don't just lose 20 million barrels of crude per day. You lose the feedstock for sulfuric acid, the single most produced chemical on Earth. Sulfuric acid is how we extract copper. It's how we extract cobalt. Without it, you can't make transformers, EV batteries, or the substrates inside every data center on the planet. One chemical, made from one feedstock, shipped through one chokepoint. The cascade goes further: Qatar ships 30% of Taiwan's liquefied natural gas through Hormuz. Taiwan has 11 days of reserves left. TSMC, the company that makes 90% of the world's advanced chips, draws 8.9% of Taiwan's total electricity. No gas, no power, no chips. Then food. 33% of the world's nitrogen fertilizer feedstock moves through the Strait. Half of all humans alive today exist because of synthetic nitrogen. Sulfur, semiconductors, food. That makes three supply chains, one 21-nautical-mile chokepoint, and zero domestic alternatives at scale.

🚨PREPARE FOR A -20% MARKET DROP: Everyone thinks the Iran conflict is an oil story. It’s not. Let me explain what this is really about. The Strait of Hormuz has been closed for 8 days. Markets are focused on crude prices. That’s the wrong variable. The real cascade nobody’s mapping: 92% of the world’s sulfur comes from refining oil and gas. Close Hormuz, you don’t just lose 20 million barrels of crude per day. You lose the feedstock for sulfuric acid m, the single most produced chemical on Earth. Sulfuric acid is how we extract copper. How we extract cobalt. Without it, you can’t make transformers, EV batteries, or the substrates inside every data center on the planet. One chemical. One feedstock. One 21-nautical-mile chokepoint. It gets worse. Qatar ships 30% of Taiwan’s LNG through Hormuz. Taiwan has 11 days of reserves. $TSMC, the company making 90% of the world’s advanced chips, draws 8.9% of Taiwan’s entire electricity grid. No gas → no power → no chips. Then food. 33% of global nitrogen fertilizer feedstock moves through that same strait. Half of all humans alive exist because of synthetic nitrogen. Sulfur. Semiconductors. Food. Three supply chains. One chokepoint. Zero domestic alternatives at scale. The economic math from here: Oil holds $80-100+ per barrel if closure persists beyond weeks. Inflation climbs 0.5-1% above baseline. Fed delays rate cuts, 1-2 reductions instead of 3. GDP growth slows to 1.5-2%. Stagflation risk over the next 3-6 months is real. S&P/Nasdaq: 5-10% correction base case. Tech/growth down 10-15% on higher yields and risk-off. Energy and defensives up 5-10%. Market is currently pricing a 4-week conflict duration. If this extends? 15-20% drawdown. What I’m watching: The US objective isn’t just degrading Iran’s military. It’s economic strangulation, destroy the refinery infrastructure, induce blackouts, impair logistics, accelerate regime instability without a full ground invasion. The short-term pain is intentional and accepted. The strategic calculus: weaken Iran’s ability to project power, sever proxy support, and neutralize a nuclear threat permanently. China feels this differently. Iran was supplying 1M+ barrels daily of discounted sanctioned crude. That’s gone. Now Beijing is forced into costlier alternatives while already under U.S. economic pressure. This isn’t about oil. Oil is just the vector. The real targets are the supply chains that run through it. How I’m positioning into this: If this escalates and markets reprice, here’s my expected drawdown map on BETA stocks: > $ASTS, -15 to -35% (beta amplification, rate sensitivity in space telecom) > $IREN, -20 to -30% (rising energy costs crushing margins) > $CIFR, 15-20% (rising energy costs crushing margins) > $AMPX, -15 to -30% (cobalt + sulfur supply chain disruption hits batteries hard) > $RKLB, -10% to 25% (higher yields compressing aerospace valuations) > $ONDS, -10% to 25% (industrial wireless demand slowdown in tight credit) > $NBIS, -5% to 20% (AI cloud risk-off but lower beta buffers the downside) > $KRKNF, -5% to 15% (low beta, robotics holds relatively well) > $OSS, -5% to 15% (hardware stability, limited tech sector contagion) I still hold cash. That cash exists for exactly this scenario. My plan: I don’t hold enough cash as of now, which is why my strategy will be to buy the hardest-hit names on the way down, DCA monthly through the pressure, and let the timeline work. If this plays out as I expect, escalation through summer, then resolution, the relief rally sets up Oct/Nov. That’s 7-8 months of accumulation before the market re-rates. The biggest mistakes in geopolitical dislocations are panic selling and waiting for the all-clear. By the time the all-clear comes, the move is already over. Note: This is not financial advice.

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Gaurab Chakrabarti @Gaurab

8 months ago

The Strait of Hormuz has been closed for 8 days. Everyone thinks this is about oil. This is about what oil becomes. 92% of the world's sulfur comes from refining oil and gas. Close the Strait of Hormuz and you don't just lose 20 million barrels of crude per day. You lose the

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Stablecoin yields making the WSJ. Traditional savings accounts still average ~0.50%, money markets ~0.51%. Meanwhile some crypto users are earning 5%+ on stablecoins,

Why Bitcoin’s Long-Term Price Follows a Power Law Bitcoin looks chaotic short term. But zoom out and a clear structure appears. Growth proportional to size. In many systems the rate of change scales with the size of the system: dP(t)/dt ∝ P(t) When growth depends on current size, it compounds. You see this everywhere: • populations • cities • internet networks • capital markets Bitcoin adoption behaves the same way. Users → liquidity → security → capital → more users. That feedback loop produces multiplicative growth. Now add the constraint. Bitcoin’s supply is fixed: ≈ 450 BTC per day ≤ 21 million total When demand compounds but supply cannot expand, price must absorb the imbalance. The network grows. Supply cannot. So the adjustment happens in price. Scale invariance. Systems that grow through multiplicative processes often become scale invariant. That means the structure looks similar across different sizes. On a log-log plot, Bitcoin’s long-term fit is remarkably strong: R² ≈ 0.96 Examples: • city size distributions • lightning branching • river networks • crack propagation in materials Different systems. Same scaling logic. Mathematically this property leads to a power law. The scaling law. If growth scales with system size over time, the differential relationship becomes dP(t)/dt ∝ P(t) / t The solution is a power law: P(t) = a · tᵇ On log-log axes this becomes a straight line. That is why log-log charts reveal the structure that linear charts hide. Why cycles happen. Real markets experience shocks. Liquidity leverage news macro events These create deviations from the structural trend. Those deviations behave like a mean-reverting process: dz = −κz dt + σ dW Large deviations create stronger restoring drift. So price oscillates around the structural growth path. The path is noisy. The structure is not. Bitcoin combines four ingredients that naturally produce power laws: • multiplicative network growth • a hard supply constraint • scale-invariant adoption • periodic supply halvings The cycles are noise. The power law is the signal.

Striking image from the new Anthropic labor market impact report.

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