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The agents helping users snag iPhone foldables are shaping the future of e-commerce in the AI era

Shopping agents such as Meta's Muse and xAI's Grok Bot snapped up inventory during the iPhone Duo pre-order; Amazon banned external agents on the grounds of violating its terms of use, while Shopify chose deep collaboration with Muse. Shopify disclosed that in the first quarter of this year, orders driven by AI search grew nearly 13-fold year over year and AI-driven store traffic grew 8-fold, but in the second quarter these two multiples fell back to 3-fold, and agentic commerce transaction volume remains very small relative to the platform's GMV. The article argues that Amazon guarding its advertising shelves and Shopify building an open pipeline are the most rational choices for each of the two business models, and outlines the position of the UCP protocol, Shopify Catalog and Shop Pay in agent transactions.

Should AI Hit the Brakes: Divergent Positions and the Battle Over Commercial Motives Among Tech Giants

The debate over whether the AI industry should slow its research and development pace continues to heat up. On September 12, Anthropic CEO Dario Amodei published an article calling on the industry to proactively slow the pace of capability improvements in frontier models, warning that recursive self-improvement could spiral out of control. OpenAI CEO Altman, xAI founder Musk, DeepMind founder Hassabis and others publicly expressed support. On September 16, Meta CEO Zuckerberg said that devoting the vast majority of compute to serving people rather than racing to pursue recursive self-improvement is one of the best ways to develop this technology safely, and said labs that do not focus on alignment will fall behind. Opposing voices are equally strong: Trump called AI safety concerns a hoax, and Nvidia CEO Jensen Huang believes innovation and safety can go hand in hand and that no new legislation is needed; while big short Michael Burry pointed out that executives talk about slowing down out of self-interest, and said large language models are not AI and will not become AGI.

Who Is Really Eating the AI Dividend: Looking at Profit Distribution Through Tech Giants' Financial Reports

The article reviews the latest financial reports of tech giants such as Meta, Amazon, and Google, noting that AWS revenue grew 39% year-over-year, but the group's free cash flow over the past 12 months turned negative at -$42.4 billion, and Alphabet's stock price fell 12.64% in a single day. Anthropic's prospectus shows revenue of $16.286 billion in the first half of 2026, a year-over-year increase of 1300%; compute and infrastructure costs during the same period reached $8.945 billion, and it signed minimum purchase commitments totaling $269.4 billion with Google, Amazon, and Microsoft. In a McKinsey survey, 80% of respondents said AI improved work efficiency, but only 37% believed AI made a positive contribution to corporate EBIT.

Who Is Really Eating the AI Dividend?

Combining the latest financial reports from giants such as Amazon, Google, and Meta with surveys from multiple institutions, the article sorts out how the AI dividend is distributed among leading model companies, upstream chip and cloud vendors, enterprises adopting AI, and ordinary workers. Anthropic's prospectus shows revenue of $16.286 billion in the first half of 2026, up 1,300% year over year, but compute and infrastructure costs reached $8.945 billion, accounting for 55% of revenue. In a McKinsey survey, 80% of respondents said AI improved efficiency, while only 37% believed AI made a positive contribution to their company's EBIT.

a16z Survey of 100 CISOs: How AI Is Rewriting the 2027 Security Agenda

a16z's survey of 100 CISOs at companies with more than 1,000 employees shows that 87% expect cybersecurity spending to increase, driven mainly by AI-related threats and the expanded attack surface from enterprises' own AI deployments. All seven of the most-watched security initiatives for 2027 are AI-related; SOC automation received the most first-priority votes, but only 18% have put AI SOC tools into production. 45% plan to make new purchases or upgrades within 12 months for internal AI agent security, and 42% plan to procure offensive security capabilities.

SemiAnalysis Analyst Interprets Anthropic S-1: The Ledger Behind the Valuation and AGI Narrative

The Verge's Decoder podcast invited SemiAnalysis analyst Joey Brookhart to break down the S-1 filed by Anthropic, discussing its potential Nasdaq listing at a valuation of $1.5 trillion to $2 trillion. Brookhart said Anthropic has achieved over $70 billion in ARR, with inference gross margins turning from negative 94% two years ago to positive 85%, and revenue per megawatt of inference at about $65 million, but revenue is highly concentrated in coding scenarios, and about 70% of compute is used for training and research. He also mentioned that Anthropic has $500 billion to $600 billion in compute commitments over the next 7 to 10 years, and compared OpenAI's more consumer-oriented business structure.

Ed Zitron's Long-Form Article Questions Anthropic IPO: S-1 Conceals Q3 Data, $430 Billion Compute Commitment About to Take Effect

In his long-form article, Ed Zitron analyzes Anthropic's S-1, arguing that the document only discloses Q2 2026 data, and that the quarter's $974 million in profit came from two months of SpaceX compute discounts, while Q3 revenue, cash balance, and payment schedule were not disclosed. He tallies Anthropic's non-cancelable compute commitments with Google, Amazon, Microsoft, SpaceX, Broadcom, and others at approximately $269.4 billion, with an additional $125 billion to $150 billion in TPU purchase commitments not included in the contract table, and notes that Q3 revenue grew 45% quarter-over-quarter to $16.8 billion, while compute spending grew 76.3% to $9.88 billion. The article also mentions that 5% to 7% of revenue comes from customers found to have violated rules by using its models to train their own models, as well as lock-up arrangements that allow insiders to sell down relatively quickly.

a16z Long Read: Databricks and the Enterprise in the Agent Era

a16z partners Ben Horowitz and Michael McGuiness wrote the article, using Databricks' four generations of technological evolution—from Spark and Lakehouse to Lakebase and agents—as a thread to discuss how agents are changing enterprises. The article states that Databricks has surpassed $7 billion in annualized revenue, up 80% year over year, at a valuation of $190 billion, with more than 17 million databases launched by agents every day. The authors argue that enterprises need an agent-facing system of record that captures definitions, permissions, and evaluation results, and believe that in the future more people's work will shift toward specifying tasks, delegating execution, and checking results.

After hedge funds' record AI stock selloff, Goldman Sachs recommends selective allocation to AI assets

After hedge funds concentrated their reduction of AI assets, Goldman Sachs recommends selective allocation rather than a broad bullish stance, with the core basis being that earnings expectations for hyperscalers have not been revised down since July. Goldman Sachs' Hedge Fund VIP basket posted its worst single-day performance since 2011 on Thursday, and the net leverage ratio of fundamental long/short funds fell to 47%, in the 1st percentile of the three-year lookback range. Goldman Sachs strategist Louis Miller also cautioned that momentum exposure remains in the 73rd percentile of the historical distribution, and trading has not fully escaped crowded conditions.

Unprecedented trillion-scale investment—is NVIDIA the only winner?

Huxiu reviews the global AIDC construction wave, noting that combined computing power investment is expected to reach $830 billion in 2026, up 79% year over year, but as of July 2026 more than 95% of NVIDIA's Grace Blackwell capacity has not yet been put into use. Industry consensus is that about 60% or more of giant capital expenditure corresponds to clear customers, while the remaining 30-40% is preemptive capacity grabbing. The article cites cases such as CoreWeave's Q2 revenue of $2.575 billion and net loss of $626 million to show that the three conditions—single-park revenue covering costs, stable utilization and rental prices, and follow-on financing continuing—are currently not broadly verified, and the certain winner may only be equipment seller NVIDIA.

Where the money of the top 100 US VCs and CVCs flowed in the first three quarters of 2026

In the first three quarters of 2026, global startup funding reached $679 billion, a record high for the same period, of which US companies took about $515.8 billion, up about 44% year over year. Tracking 100 US VC and CVC institutions, the report finds that capital is shifting from picking winners to directly purchasing capacity such as data centers, GPUs, power, and defense contracts, with 27 billion-dollar financings in Q3. Corporate venture capital already accounts for 87.9% of the amount of US AI venture deals; among the 25 VC sample, the top 10 companies by amount took 39.2% of funding, and the average deal size of $137 million is 5.7 times the median of $23.98 million.

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English translation is being prepared.