New angle in AI-powered anti-fraud: Actively posing as fraud victims to gather scam intelligence for regulatory agencies to block malicious actors.

Ethereum co-founder Vitalik Buterin published a post noting that roughly half of Bitcoin mining costs come from capital expenditure on mining hardware, with the other half attributed to electricity consumption. By contrast, AI inference costs are almost entirely dominated by computing hardware capital expenditure, a difference that reflects the distinct cost structures of the two computing businesses. Bitcoin miners must cover ongoing electricity bills, with profit margins determined by mining machine efficiency, Bitcoin prices, and changes in network difficulty; AI inference, meanwhile, relies far more on investments in expensive hardware such as GPUs, as well as device utilization rates and computing efficiency. The industrial boundaries between the two sectors are also shifting. As demand for AI computing power grows, some Bitcoin mining firms are repurposing existing power resources and data center facilities for AI and high-performance computing (HPC) services, aiming to transition from pure mining to computing infrastructure services. Reuters previously reported that some mining companies are leveraging power capacity originally built for mining to enter the AI data center market. However, this transition does not eliminate cost pressures: mining businesses are more directly exposed to fluctuations in electricity prices and Bitcoin prices, while AI computing operations face risks including GPU procurement, depreciation, and underutilization. Both sectors compete for power and computing resources, but their ultimate profitability depends on how effectively these resources are converted into revenue.

As U.S. Treasury yields, the U.S. dollar, and oil prices rise, U.S. financial conditions have tightened significantly, leading to a sharp divergence in U.S. stocks: a small number of large tech stocks are propping up index performance, while regular stocks continue to face pressure. Morgan Stanley’s chief equity strategist Mike Wilson warns that tighter financial conditions are having an effect similar to Federal Reserve rate hikes, but the impact is concentrated mainly in regular stocks. Data shows that the median S&P 500 constituent has fallen roughly 15% from its 52-week high, one of the worst performances since the dot-com bubble burst; the S&P 500 Equal Weight Index has underperformed the market-cap weighted index by around 6% over the past six months. Meanwhile, the S&P 500 equity risk premium has fallen below zero, meaning stocks’ valuation appeal relative to U.S. Treasuries has further declined. Wilson notes that current pressure stems mainly from valuation contraction and has not yet clearly evolved into economic growth risks. Nominal GDP growth remains strong, and corporate earnings are still expanding, but if real interest rates continue to rise, market valuations could face further pressure. Goldman Sachs, meanwhile, warns that U.S. stock supply could surge in 2027 due to expiring lock-up periods. Going forward, whether financial stocks can stabilize and U.S. Treasury volatility will ease will be key indicators for judging market direction. U.S. stocks now face two possible scenarios: either regular stocks rebound to catch up with large tech stocks, or large tech stocks pull back, dragging the index to converge with the broader market’s weak performance.

Beating AI Express News: Anthropic has announced performance optimization results for its latest Claude model. From August to October this year, the time to first token (TTFT) for Claude Code local sessions dropped from 4.07 seconds to 1.33 seconds, a roughly 3x speedup. One key optimization is using gzip compression for conversation data sent to the model each round, cutting network transmission overhead from repeated uploads of long conversations. For Claude Code cloud sessions, TTFT fell from 16.99 seconds to 6 seconds, a roughly 2.8x speedup. Beyond data compression, Anthropic also introduced a pre-launch mechanism: as soon as a user begins inputting a task, the system preps the remote code execution environment, removing the need to wait for command submission to initiate it. The wait time for Claude web chat and Cowork dropped from 3.36 seconds to 2.17 seconds, a roughly 1.5x speedup. Claude API’s time to first token fell from 1.53 seconds to 1.01 seconds. Notably, the first three sets of data exclude model computation time, while the API figure includes it.

According to data from Crisil and Coalition Greenwich, global banks are projected to rake in $8.5 billion in investment banking fees from AI-related transactions this year, a sharp surge from $3.5 billion last year. The revenue covers M&A advisory fees and underwriting fees for bond and equity financing. However, as data center financing expands and interest rates climb, Wall Street has grown more cautious in evaluating risks of AI investments. Goldman Sachs CEO David Solomon noted that future capital and computing power demands for the AI ecosystem remain uncertain; Morgan Stanley emphasized it will stay highly selective about risks taken and capital deployed, avoiding transactions that relax credit standards. The bond market has seen a divergence: JPMorgan Chase data shows investment-grade bond issuance cooled in September during the third quarter, while speculative-grade financing stayed weak throughout the period. From the end of June to October 13, the 10-year U.S. Treasury yield rose from 4.47% to 5.21%, and the worst yield on high-yield bonds climbed from 7.16% to 8.30%, pushing financing costs higher. The AI financing boom still brings hefty fees for banks, but issues like high interest rates, data center returns, and borrowers' debt-servicing capacity are prompting Wall Street to tighten risk controls while chasing revenue.

The AI boom has driven semiconductor companies to repeatedly hit record profits, but the market is starting to worry the industry may repeat its cyclical downturn. Cited data shows global semiconductor sales growth in the first half of 2026 reached its highest level since the 1980s. TSMC has broken its profit record for six consecutive quarters, while Samsung Electronics has also posted four straight quarterly profit highs. However, the Philadelphia Semiconductor Index has fallen nearly 14% since October 6, prompting investors to re-examine the sustainability of the AI supercycle. Unlike past cycles, this semiconductor expansion features higher supplier concentration and stronger corporate cash flow. TSMC’s capital expenditure this year is expected to hit around $62 billion, and could rise to $76 billion next year, with its expansion relying mainly on operating cash flow rather than large-scale borrowing. Advanced manufacturing processes and memory chip markets are concentrated among a small number of vendors, boosting suppliers’ pricing power. More notably, chipmakers are shifting some demand risks to customers via long-term contracts. NVIDIA has committed over $250 billion to secure chip supply and related components through January 2029; Micron, meanwhile, has locked in part of its business through "take-or-pay" contracts, with a committed value of $32 billion through 2030. These contracts require customers to pay as agreed even if they do not take delivery. This means even if the AI investment boom cools, chip manufacturers may still have a buffer from advance payments and long-term orders, but customers will face greater procurement and financial risks. Still, if demand plummets and capital expenditure shrinks, chip prices, corporate profits, and high-valuation stocks could still take a hit. The semiconductor industry’s cyclical pattern has not disappeared — it is just that the cost of the next downturn may be shared by both suppliers and customers.

Beating AI News Flash: DeepSeek apparently rolled out an update to its model version V4.1-Flash-1017 today. Per community user feedback, the model identifier deepseek-v4.1-flash-1017 is now accessible on the API platform, though the official has not yet announced specific update content or performance metrics for this version, with further details still pending confirmation.