Anthropic's annualized revenue rose to $65 billion by end of July, up $18 billion in two months

❯ Anthropic Annualized Revenue Hits $65B by End of July, Up $18B in Two Months

[REVENUE] Anthropic told investors that annualized revenue reached $65 billion as of end-July, up from $47 billion in May and just ~$9 billion at end-2025 — a more than sixfold gain in seven months. Bloomberg first reported the figure; Reuters and CNBC subsequently confirmed it with people familiar with the matter. Preliminary revenue for the company’s latest full quarter came in above $11.5 billion, versus $787 million a year earlier.

[SCOPE] This is an early financial update for investors, not an earnings release. Both Anthropic and OpenAI have confidentially filed IPO papers, with Anthropic seen as likely to hit Wall Street as early as this fall — ahead of OpenAI. On a comparable public basis, OpenAI’s annualized revenue for the same period sits around the $40 billion tier, and Anthropic has already overtaken it. On revenue mix, Anthropic has kept its weight on enterprise API and coding use cases, spending far less on the consumer side than its rival — which is also why its per-unit revenue is richer.

[IMPLICATIONS] Figures like these before an IPO window usually carry a marketing gloss, but a growth rate of this magnitude is hard to fake. Enterprise buyers next need to reassess their pricing leverage: a vendor whose quarterly revenue is up fourteenfold has no incentive to give ground at renewal. At the same time, Anthropic’s open API stance is loosening — reports say it is weighing whether to keep supplying its strongest models to the outside without differentiation. Buyers should ask early: this time next year, can you still buy its best tier?

▪ SIGNAL: Once a seven-month, sixfold curve gets written into the prospectus, pricing power moves away from the buyer.

❯ Nvidia Backstops $105 Billion Lease for OpenAI Ohio Data Center — Not a Direct Investment

[TERMS] Regulatory filings show Nvidia has agreed to backstop up to $105 billion in lease payments for SB Energy’s new data center campus in Ohio, plus a $1.5 billion direct investment in SB Energy. The tenant is OpenAI, under a 20-year lease. The campus sits at the PORTS-Pike technology park in Pike County, with a planned IT load of 8 gigawatts and expected to come online in 2028.

[NOT INVESTMENT] Earlier media coverage widely billed this as Nvidia “investing $105 billion” — a framing that, per reporters familiar with the matter, is wrong. A lease backstop is a credit guarantee: OpenAI’s own credit isn’t strong enough for lenders to accept so long a lease, so Nvidia plugs that gap with its own balance sheet, paying real money only in the event of default. The figure was also cut all the way down from an initially discussed $250 billion, and now covers only the first phase of roughly 5 gigawatts, with the remainder to be negotiated separately. SB Energy and SoftBank will build supporting power infrastructure for 10 gigawatts and invest at least $4.2 billion into the regional grid.

[RISK TRANSFER] Next to watch is how this guarantee lands on the books and whether it alters Nvidia’s own capex pace. The shovel seller is now guaranteeing the shovel buyer — chipmakers’ risk exposure has shifted from inventory cycles onto a 20-year lease. Lenders have to recalculate who they’re really extending credit to: on paper, it’s OpenAI’s lease; in substance, it’s Nvidia’s credit. The thing to watch now is whether the follow-on guarantee beyond the first 5 gigawatts gets negotiated at all. The same day carried another signal set — reportedly, more than 400 data center restriction clauses have now piled up across the Midwest and South, and developers are starting to worry that stalled construction could instead breed a chip glut.

▪ SIGNAL The guarantee stays off the balance sheet — until a default puts it on.

❯ Hedge Fund Situational Awareness Reportedly Sells Anthropic Stake at 20% Discount

[FIRE SALE] According to The Wall Street Journal, AI-focused hedge fund Situational Awareness is selling part of its roughly $5 billion Anthropic stake at a 20% discount to raise cash, per the report. After the news leaked, the fund was “hunted” in the secondary market. The fund was founded by 25-year-old Leopold Aschenbrenner; according to public reports, its net value fell about 67% in July alone.

[CONTEXT] Previously, multiple media outlets reported that before the market opened on July 30, the fund sold its entire public stock portfolio—about $16 billion, including levered positions in SK Hynix and cloud-computing provider CoreWeave—in one block to Citadel, settling a margin call at a roughly 10% discount. At that time it held onto its private assets, especially the Anthropic stake, widely seen as the “crown jewel.” Now even that is being sold piecemeal, showing that cash is still insufficient after the public portfolio was liquidated.

[PRICE DISCOVERY] The 20% discount is an awkward number: it shows there are still takers for the primary market’s pricing of Anthropic, but also that the seller’s bargaining power has hit zero. What is truly brought to light is the liquidity of private AI equity—over the past two years, investors assumed these stakes could be transferred at par on secondary platforms at any time; this trade gives the real quote under stress. Other leveraged funds holding large AI private-equity stakes will need to rerun their risk calculations against this discount.

▪ SIGNAL The 20% discount isn’t a price placed on Anthropic; it’s a price placed on “needing to sell in a hurry.”

❯ White House adviser Sacks hits back at Amodei: Frontier AI is too powerful to be centralized

[CLASH] White House AI and crypto czar David Sacks publicly responded after Amodei released his policy proposals: “Amodei thinks frontier AI is too powerful to be distributed; we think it’s too powerful to be centralized.” The line compresses the two sides’ divergence into a neat antithesis — and it’s the clearest statement yet in this year’s U.S. AI regulation debate.

[DIVERGENCE] Sacks’s argument: Amodei concedes AI is structurally centralized, but the deeper risk lies in who decides which capabilities are opened to whom. He argues that the pre-deployment testing Amodei favors, along with licensing-style oversight modeled on the FAA and financial-industry regulators, would only entrench that centralization rather than hedge against it. Sacks also raked up an old dispute, noting Amodei’s May 2025 statement — “AI will erase half of junior white-collar jobs” — still lacks supporting evidence.

[FALLOUT] This isn’t a personal spat — Sacks holds the pen on executive orders and federal procurement. The legal boundary for open-weight models is the most direct pressure point: if “shouldn’t be centralized” becomes the official narrative, restrictions on open weights get much harder to implement, and any route built on compliance-cost walls loses its policy tailwind. Small and mid-size model vendors should watch the upcoming procurement and export rule details — that’s where it’s written who gets to play.

▪ SIGNAL Distribution or centralization — that’s what decides who actually gets licensed in the next regulatory round.

❯ Axios: Compute Provider Crusoe in Talks With at Least Four Banks; Pre-IPO Round Valued at $35 Billion

[PROCESS] Per Axios reporter Alan Neuhauser, AI data center developer Crusoe has approached at least four Wall Street investment banks about an IPO, with JPMorgan also serving as adviser on its $3 billion pre-IPO round, which targets a valuation of roughly $35 billion. Bloomberg’s July reporting had pegged the figure closer to $30 billion.

[FOUNDATION] Crusoe started out mining crypto with power generated from oil-field flare gas, then pivoted to building and self-powering AI data centers. Its customer and supply contracts span Meta, Oracle, Microsoft, and Google; cumulative equity funding exceeds $2.6 billion, and both Nvidia and Fidelity are on the shareholder roster. Its October 2025 Series E valued the company at just over $10 billion — if the $35 billion figure lands, that’s more than triple in under a year. The public-market anchor for this lane is CoreWeave, which also aimed at the $35 billion tier for its IPO.

[WINDOW] A pre-IPO round at the four-bank stage typically corresponds to a filing window within six months. Whether Crusoe is worth the price hinges on a question public markets will soon judge: is self-powered supply a cost advantage or an asset-heavy drag? Power supply itself is becoming the valuation watershed for compute companies — at a time when data-center restriction clauses abound, developers that can generate their own electricity capture a scarcity premium, not just rack rent.

▪ SIGNAL What compute companies are now competing on is no longer chip orders — it’s grid interconnection permits.

❯ Groq Closes $350M Series A; $3.5B Valuation Halved From Last September

[VALUATION CUT] AI inference chip company Groq closed a $350 million Series A at a post-money valuation of $3.5 billion — roughly half of the $6.9 billion peak reached in September 2025. The round was led by Dallas-based investment firm Disruptive, with Nvidia also participating. Groq says the new valuation reflects the shape of the company following the Nvidia licensing deal.

[WHAT WAS TAKEN] What happened after the peak: Nvidia signed a non-exclusive licensing agreement for Groq’s language processing unit technology, widely reported to be worth about $20 billion, while simultaneously hiring away founder and CEO Jonathan Ross along with his core team. With the technology licensed out and the people gone, the remaining Groq is no longer the chip design company it once was — it has repositioned itself as a data center operator serving inference demand, and just announced a $650 million funding round in June.

[A NEW EXIT] What’s really worth pondering here is Nvidia’s playbook: license first, then poach, then circle back and invest — turning a potential competitor into an inference-cloud customer inside its own ecosystem without ever going through merger review. The next time a startup board is handed a licensing deal by a giant, it should first calculate how much the remaining shell is worth once both the people and the technology walk out the door.

▪ SIGNAL Licensing plus poaching is the standard move for giants to bypass antitrust review this round.

❯ Cursor Launches Code Hosting Service Origin, Opens Early Access to All Paid Users

[LAUNCH] On August 17, Cursor rolled code hosting service Origin into early access, open to all paid plans. Capabilities include native repositories, standard Git clone and push/pull, browser-based code search, merge requests with comments and check items, branch protection, and a set of public REST APIs — plus directly syncing existing GitHub repositories in.

[TIMING] The same day, GitHub suffered a major outage; three days earlier, SpaceX had just closed its acquisition of Cursor’s parent company Anysphere, reportedly at $60 billion. Origin’s stated design goal is blunt: Git hosting built for AI agents, not human developers. Cursor’s stack is now fully closed — editor, cloud agents, code review (Graphite), and hosting, all in-house.

[CUSTODY] GitHub sync lowers the migration barrier, but the trade-off is that the choice of where code lives begins to move away from the platform. Enterprise tech leaders should decide in advance where code is hosted and whose agents read and write it: once agents run directly on the hosting side, repository location is no longer just a remote address — it becomes the permission and audit boundary.

▪ SIGNAL When agents write code, whose repository holds it becomes a security question.

❯ DeepSeek API Adopts Peak/Off-Peak Pricing; V4 Pro Peak Output Rises to 27 Yuan per Million Tokens

[PRICING] Starting at 00:00 on August 17, DeepSeek is rolling out peak/off-peak time-of-use pricing for the DeepSeek API: 9:00–12:00 and 14:00–18:00 daily are peak windows, with the rest off-peak, and off-peak rates set at half the peak price. V4 Pro peak output is 27 yuan per million tokens, off-peak 13.5 yuan; V4 Flash comes in at 9 yuan and 4.5 yuan, respectively.

[HIKES] This is not merely a discount arrangement; it is a genuine price increase. For V4 Pro, off-peak output pricing is up roughly 125% from the earlier initial pricing, and peak pricing is up roughly 350%; cache-hit input posts the steepest peak-time jump, at around 1100%. The official line is that pricing leverage will steer enterprise developers toward staggered scheduling, easing compute congestion and improving platform stability.

[INFERENCE ECON] This marks the first time time-of-use electricity pricing has been ported into a large-model API — a sign that inference-side compute strain has pressed down into the pricing layer. What’s genuinely being rewritten is how batch workloads are scheduled — offline evaluation, bulk cleaning, overnight number-crunching, the kind of work that isn’t time-sensitive, now has a concrete cost rationale for shifting to off-peak windows. Rivals are watching too; once time-of-use proves effective, it’s only a matter of time before other vendors follow.

▪ SIGNAL When APIs start billing by peak and off-peak windows, compute officially becomes a utility.

[TOP] Alibaba’s Qwen3.8-27B has taken the No. 1 spot on Hugging Face’s global trending model chart. According to official and community data, the model was released as open source on August 14 under an Apache 2.0 license, and downloads passed 3 million within three days of release.

[SPECS] It is a 27-billion-parameter dense multimodal model with a native context length of 262,000 tokens, extendable to the million scale via YaRN, native image and video understanding, and an adjustable reasoning-effort setting. Previously, open-source models were either too large to run anywhere but the cloud or too small to be practically useful. Its selling point is that, once quantized, it can run on consumer-grade GPUs and personal workstations — developer tests clock a single RTX 5090 at 115 tokens per second. The company says it outperforms contemporaneous closed-source flagships on coding and agent benchmarks.

[LOCAL] A model that runs on a single consumer card reaching the top of the trending chart shows that demand for local deployment is far deeper than the open-source leaderboards have long suggested. The most direct impact is on small and mid-sized teams’ model-selection logic: previously it was either pay-per-use API calls or giving up entirely. Now there is another viable option — self-hosting with data never leaving the organization, shifting costs from monthly bills to one-time hardware.

▪ SIGNAL Three million downloads in three days measures not model ranking but the real appetite for local deployment.

❯ Meituan’s Wang Puzhong Reviews AI Transformation: All-Hands ‘Shrimp-Farming’ Push Once Cost RMB 10M a Day; CatPaw Now Covers 90,000 Employees

[REVIEW] In public remarks, Wang Puzhong, CEO of Meituan’s Core Local Commerce, reviewed the company’s AI transformation with a rare admission of missteps: the February–March all-hands “shrimp-farming campaign”, which by his account at one point cost around RMB 10 million a day, also muddied real operating judgment. He attributes the difficulty of generating measurable returns from enterprise AI to four misalignments: cognition, efficiency, scenarios, and performance assessment.

[STAGES] In his telling, Meituan’s AI transformation unfolded in four steps: February–March, company-wide deployment at a steep price; April, each business unit set up an AI organization and itemized its transformation steps; June–July, a horse-race mechanism confirmed that the AI transformation is a system-level project uniting business, organization, and technology — not a point tool; July, it genuinely ran end-to-end in internal product workflows and generated value.

[DEPLOYMENT] Taking over this phase is CatPaw, an all-scenario agent platform launched in July. Per the Meituan tech team’s blog, it has now covered 90,000 employees and built 30,000 agents. It keeps mobile and PC in real-time sync: the phone handles task initiation, progress checks, and remote sign-off on key decisions; the PC handles deep local execution — file operations, browser control, and terminal commands — and cloud mode keeps running even when the local device is off or offline. On the scenario front, it builds in industry knowledge spanning the full local-life-services chain, covering store-review diagnosis, product-copy generation, marketing-asset evaluation, campaign planning, and business-data analysis.

[REPLICABLE] The “RMB 10 million a day” figure is more persuasive than any methodology — it marks the true cost of the company-wide rollout path. Companies looking to replicate the playbook should not copy CatPaw — they should copy the sequence: stand up the organization before rolling out the tools, use horse-racing to filter scenarios, align appraisal criteria with AI output; otherwise, the investment just becomes a bill.

▪ SIGNAL Of the four mismatches, the deadliest is assessment — without changing the metrics, even the best tools are just a cost.

[DATE SET] Unitree Technology announced that its shares will list on the STAR Market of the Shanghai Stock Exchange on August 19, 2026, at an issue price of 150.80 yuan per share, issuing 40,446,434 shares.

[PRICING & FORECAST] The issue price corresponds to a diluted static price-to-sales ratio of 35.89 times for 2025. The company expects first-half 2026 revenue of 1.052 billion to 1.128 billion yuan, up 35.62% to 45.41% year-on-year. The online allotment rate was only 0.0181%, making it one of the hardest new listings to get an allocation in STAR Market history.

[PRICING ANCHOR] The humanoid robotics sector had long lacked a public-market valuation benchmark; now that Unitree is listed, it has one. Private-market peers will have to renegotiate valuations against this price-to-sales ratio starting tomorrow—a P/S ratio of 30-plus times is both premium room and pressure to deliver earnings.

▪ SIGNAL The 0.0181% allotment rate isn’t about company quality—it’s that no one is willing to let go before the market opens.

❯ Reuters: Shein Cuts Hong Kong IPO Valuation Target to About $25 Billion

[CUT] According to a Reuters exclusive, Shein has cut its Hong Kong IPO valuation target to about $25 billion, below the $30 billion–$40 billion range from early August. The adjustment came after meetings with investors. The company expects to launch the offering within this week, selling roughly 8% of its shares and raising up to $2 billion.

[GAP] The reference point is the nearly $100 billion valuation from its 2022 funding round — a three-quarter decline in four years. On the fundamentals, Shein posted a net loss of $99 million in the first quarter of 2026, versus a profit of $395 million in the same period a year earlier. The HKEX filing also shows a $328 million loss related to the fair value of convertible shares. Tighter regulatory scrutiny in major markets such as the European Union aimed at e-commerce platforms selling low-priced Chinese goods is the main driver behind the downward revision in growth expectations.

[PRICING REALITY] Cutting the valuation after the roadshow shows that buy-side growth assumptions for cross-border e-commerce no longer match the seller’s narrative. In the same week, Anthropic is preparing to list with a $65 billion revenue curve, while Shein is cutting its price with a loss-making quarterly report — Hong Kong’s pricing patience is currently reserved only for assets that can demonstrate growth. The same cohort of cornerstone investors is watching both deals; the $5 billion valuation gap cut is the price they set for a growth story that doesn’t hold together.

▪ SIGNAL In the same listing window, some are raising prices and others are cutting them — the dividing line is whether growth can be shown.

❯ Financial Times: Singapore Uses AI Model Access to Retain Financial Talent

[RIVALRY] According to the Financial Times, Singapore is using access to advanced AI models as a bargaining chip to retain financial talent, hedging against Hong Kong’s poaching. Financial institutions in Hong Kong face genuine practical difficulties in obtaining the latest US models; Singapore, which enjoys smooth relations with both Washington and Beijing, can access the newest models on the ground without restriction.

[TWO PLAYBOOKS] The two cities are working from entirely different playbooks: Hong Kong has already announced tax incentives for fund managers and private-equity practitioners, while Singapore is betting on access to computing power, models, and technical talent. According to the report, calling up the latest US models in Hong Kong is significantly harder than in Singapore. For quant funds, this is no abstraction — the model directly determines how efficiently researchers process massive datasets, develop trading strategies, and manage risk. Technology accessibility is shifting from a back-office condition to a front-office variable in site selection.

[NEW VARIABLE] For the first time, “can we use the latest model?” has entered the competitive dimensions of financial centers. Location decisions by cross-border asset managers must now weigh one more line item beyond compliance: besides licenses and tax rates, there is the question of which tier of model a quant team can call up locally. This differential cannot be smoothed over with subsidies in the near term — it stems from export controls, not local policy.

▪ SIGNAL Model accessibility is turning from a technical issue into a selling point for financial centers.

❯ Gruber Criticizes Anthropic’s Text Watermarking: Altered Word Probabilities Leave Fingerprints — a Distortion of Writing

[CRITICISM] John Gruber, author of tech blog Daring Fireball, writes that Anthropic’s text watermarking of Claude outputs is “a distortion of writing”, outright calling it “offensive.” The core accusation: by altering word-selection probabilities, the watermark leaves a statistically detectable fingerprint in the text, so Claude is no longer choosing the words that serve the user best.

[MECHANISM] According to public technical documentation, the method follows Google’s earlier SynthID-Text approach: during generation, it adjusts the source of randomness in word selection to create a detectable statistical pattern. Anthropic insists the watermark has no impact on content, creativity, or readability. Gruber’s rebuttal: no two synonyms are perfectly equivalent, and the system sometimes elevates a worse word while suppressing the best one — so “imperceptible” doesn’t hold. The feature was introduced to meet the EU’s AI Act, but since it cannot be restricted by region, it takes effect globally; all Claude models released after August 2 carry the marker.

[WHO PAYS] The compliance obligation originates in the EU, but the cost is spread across users worldwide — a distributional question worth arguing about in its own right. Users who rely heavily on models for long-form text must decide for themselves whether the quality loss from watermarking outweighs the benefit of traceability. For regulators, if the claim that “watermarking necessarily degrades quality” stands, the technical premise of mandatory labeling provisions will need to be re-examined.

▪ SIGNAL The real controversy over watermarking isn’t whether it can be detected — it’s who bears the cost.