DeepSeek reportedly launches V4.1-Flash-1017, with the new version now available via API.

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 via Bloomberg: Jump Trading and DRW are ramping up their positions in the AI compute power-linked derivatives market, planning to trade compute futures and other contracts to help enterprises hedge against GPU rental price volatility. Both firms entered the cryptocurrency market early, and now aim to replicate their market-making experience there. Jump has set up a dedicated trading team, will use its own balance sheet to take on trading risks, and plans to provide market-making services for compute futures being prepared by institutions like CME and Silicon Data, while also monitoring related products from ICE and Ornn. DRW, meanwhile, plans to participate in compute trading via three teams, and has already invested in Silicon Data and Ornn. CME’s relevant futures products still await regulatory approval. This push comes amid growing skepticism toward AI infrastructure investments. Stocks and bonds of compute firms like CoreWeave are under pressure, as markets reassess whether AI revenues can support data center construction costs. Boston Consulting Group projects the global AI compute market could reach $2.3 trillion by 2030. However, the compute derivatives market remains in its early stages, with insufficient price transparency. GPU rental prices vary widely by region, configuration and supplier, which may cause futures indices to diverge from enterprises’ actual procurement costs. Additionally, compute power cannot be stored like oil or copper, and unused GPU compute time cannot be reserved for future use. Whether Jump and DRW can turn compute power into a tradable, hedgeable standardized asset remains to be seen.

Former US President Trump posted a graphic on his social media platform, claiming that during his tenure, the S&P 500 surged from 5,881 points in December 2024 to 7,651 points in September 2026, a cumulative gain of roughly 30%. He captioned the post: "The strongest in history!" However, recent risks tied to AI trading have pressured US equities, which fell broadly on Friday. Concerns over AI investment returns, paired with soaring international oil prices, triggered fresh sell-offs in tech stocks. The Nasdaq Composite closed down 2.15%, the Nasdaq 100 dropped 2.5%, and the S&P 500 slipped 1.1%. The Nasdaq notched a 4.48% weekly loss, while the Dow Jones Industrial Average rose 0.68% — their weekly performance gap was the largest since July 2002. The Philadelphia Semiconductor Index declined for the eighth straight trading day, marking its longest losing streak since 2008. Earlier today, Trump delivered his latest comments on the Russia-Ukraine and Middle East issues: "I know some details of the Russia-Ukraine conflict, and I will make them public at some point. I hope it ends. The Biden administration did a lot of bad things regarding the Ukraine issue." When asked about inflation, Trump again asserted: "Oil prices will drop soon. Israel's support for Saudi Arabia is very substantial, and the Abraham Accords are back on Saudi Arabia's agenda."

According to Arkham's monitoring, whale trader 0x9c6 holds a 20x leveraged long position on SOL on Hyperliquid, with the current position value totaling around $57.5 million. The entry price for the position is $80.86. Based on SOL's current price of $104.43, the position has an unrealized profit of roughly $13 million. The trader opened the position approximately one month ago and has not closed it to date. Their Hyperliquid account is currently valued at about $24.2 million.