# Sequoia cuts its London office and partners, while new data shows AI spending is concentrated in a few heavy users *By VC Tech Radar β€’ November 5, 2026* Sequoia's restructuring leads this brief. Also covered: Menlo and Ramp data on who is actually paying for AI, open-model inference becoming its own business, a16z's argument against tech buyouts, and the period's early-stage deals. ## Sequoia shrinks to its core Sequoia published a restructuring plan: - **Programs cut:** it is sunsetting its Arc batch accelerator and its Scouts program, and simplifying the Sequoia Capital Fund [^1]. - **One team:** Early and Growth are no longer separate. "There is only Sequoia" [^1]. - **London:** the office is closing and several people are leaving. Sequoia says it still expects Europe to produce outlier founders and will support them from the Bay Area [^1]. - **Partnership:** it describes changes that flatten the partnership. It also reports raising about $10B in new funds [^1]. Newcomer, citing unnamed sources, reports that at least three US-based partners were forced out as part of the overhaul [^2]. Luciana Lixandru, who led the London team, will stay in Europe. She remains a Sequoia partner, keeps her board seats, and plans to invest personally in European founders [^3]. She listed early-stage work with Pennylane, STARK, Profound, Rillet, Trade Republic and others [^3]. Reactions split. Nathan Benaich called it "europe in managed decline" [^4]. Martin Casado called it "a lot of really smart moves" [^5]. What it means for investors: the most prominent firm in the business is dropping its accelerator, scout program and European office to concentrate capital on fewer, higher-conviction bets. That leaves room at the pre-seed stage and in Europe for other investors. ## Where AI money comes from, and how concentrated it is **Menlo Ventures (enterprise survey).** Menlo's fourth annual report estimates enterprise AI deployment spend at $176.8B in 2026, up 5.4x from $33B. That is about 30% of public-cloud revenue [^6]. The survey covered about 400 enterprise decision-makers. Their new AI spending came from: - 55% net-new budgets - 22% software budgets - the rest from labor and other budgets [^6] Of the 22% taken from software budgets, incumbents got 13 points and AI-native vendors got 9 points ($12.6B) [^6]. Incumbents win where they already own the workflow. ServiceNow has 91% of AI spend in IT and Workday has 68% in HR. In customer success, AI-native Sierra and Decagon lead the fastest-growing category, at 4.3x [^6]. Coding is a $34B category. Claude Code and Codex take $24B of it, or 70%. Independent coding apps total $10.1B, up 2.9x [^6]. For screening, this means thin layers on top of a frontier model compete directly with the model's maker. Startups have better odds where the customer pays from labor or new budgets than from software seats. **Ramp (business card transactions).** Ramp's data shows the risk that comes with that concentration. The top 1% of firms spent $9,568 per employee per month on AI in September, down 3.1% from a July peak. Over the same period, the top 10% and the median firm kept rising [^7]. Those top-1% firms account for 80% of OpenAI and Anthropic spend, and spend about 600x what the median firm does [^7]. Ramp's lead economist attributes the stall mainly to falling model prices, such as Opus 5.5 cutting cost per task by about 40% [^7]. SaaStr reads Anthropic's still-blank October 31 run rate this way: - above about $75B would mean heavy users came back - about $68–70B would mean they have plateaued [^7] **Two other views:** - Casado, passing on a point from Vitaly Gordon, notes that tokens are still under 5% of labor cost at most companies, which argues against assuming spending has saturated [^8]. - Newcomer's sources, investors who have backed both labs, see Anthropic pulling away from OpenAI. One described "infinite demand" from long-term investors and expects the IPO before Thanksgiving [^9]. That is investor sentiment, not confirmed timing. ## Open-weight inference is becoming its own business Cohere is now following Mistral in offering "sovereign inference": serving frontier open-weight models that it does not train itself [^10]. Benaich's summary: "you either die trying to get to the frontier, or you live long enough to serve inference" [^11]. Several data points this period: - **Fireworks** says it serves more than 40T tokens a day to customers' customized models. Nathan Lambert expects its total to be well above 100T, but that is his forecast, not a reported figure [^12]. - **Baseten** is building a full platform: Grid (gateway and router), agent sandboxes, and Loops (serverless SFT and RL) [^13]. Its new coding router comes within 0.8 points of the pass rate of always using Opus 5.5, at 72% lower cost. That result is on held-out benchmarks, not production workloads [^14]. - **Cognition** launched SWE-2 Flash, which it says is about 3x faster than SWE-2 at one-fifth the price. It has 276B total and 12B active parameters and was adapted from Thinking Machines' open Inkling-Small. Cognition is holding back the weights over cyber-capability concerns. All benchmark figures are Cognition's own [^15]. - **Deep Principle** released Hydrogen-1.0, open weights post-trained from GLM-5.3 for science. It reports 38.3% vs. 33.3% for GPT-6 Astra on FrontierScience Research, at about 72% lower output cost, but trails Astra on NMR [^16]. These are self-reported results. The pattern: frontier capability is increasingly something to buy and post-train, and value is collecting in serving, routing and vertical fine-tunes. ## a16z: tech buyouts are under stress a16z's Jen Kha argues that tech buyouts are a late-cycle asset class and that venture does better when a new technology wave is starting [^17]. Her evidence: - PE's share of global buyout deal value going to tech fell from 34% in 2023 to 11% in Q1 2026 [^18]. - About $140B of risky PE-backed loans mature in 2028 and $160B in 2029. About a third is software [^17]. - A typical deal now needs 10–12% annual EBITDA growth to return 2.5x, compared with about 5% in 2015. Meanwhile, Golub's software borrowers slowed from 9% to under 3% revenue growth [^17]. She concedes that smaller buyouts outside leveraged software can still do fine [^17]. a16z is a venture firm, so this is its own case for venture. ## Deals and teams - **Discovery Loop:** Jeff Dean revealed the company, a public benefit corporation co-founded with Oriol Vinyals, Quoc Le and one other Google colleague [^19]. It aims to automate experimental loops in ML research and hardware design and make domain experts "20x to 50x more productive." It says it has about 20 automated improvement loops running [^19]. - **Phaze:** the team that built Parsec raised an $8.75M seed led by Casado at a16z. The product is remote access to real computers for both humans and agents, with handoff of control between them [^20]. - **Weave (YC W25):** raised a $13.5M Series A led by Standard Capital to measure the output of humans and agents against AI spending. It also launched a model router [^21]. - **Eve:** the plaintiff-law AI company says 1,700+ of about 29,000 US plaintiff firms use it, and its linked hiring post references $164M in funding [^22]. - **Salesforce** signed a definitive agreement to acquire Listen Labs. No terms were disclosed [^23]. - **Talent:** Cat Wu is leaving Anthropic to lead product management for Core Products at OpenAI [^24]. Allie K. Miller says that makes three of Anthropic's top product communicators to leave within weeks [^25]. ## Hustle Fund's October funnel Hustle Fund received 2,718 deals in October [^26]: - **Screening stage:** geography caused about 46% of passes. The fund generally passes on companies not set up as US Delaware C-Corps [^27]. - **Closer review (370 deals):** passes were mostly due to market competition (66%) [^28]. - **Sectors:** AI/ML was the most common category founders selected, at 41% [^29]. - **Valuations (self-reported):** 67% were under $10M [^30]. --- ### Sources [^1]: [𝕏 article by @sequoia](https://x.com/i/article/2117739566904066048) [^2]: [EXCLUSIVE: Sequoia Parts Ways With Half-a-Dozen Partners, Shuts European Office Amid AI-Era Reorg](https://www.newcomer.co/p/exclusive-sequoia-parts-ways-with) [^3]: [𝕏 post by @LucianaLix](https://x.com/LucianaLix/status/2117963625117757926) [^4]: [𝕏 post by @nathanbenaich](https://x.com/nathanbenaich/status/2117975623218217309) [^5]: [𝕏 post by @martin_casado](https://x.com/martin_casado/status/2118162299613745572) [^6]: [AI Spend Hit $177 Billion This Year. Only 22% of It Came Out of Software Budgets. The 4th Annual State of Enterprise AI Report from Menlo Ventures,](https://www.saastr.com/ai-spend-hit-177-billion-this-year-only-22-of-it-came-out-of-software-budgets) [^7]: [The Top 1% of AI Spenders Stalled in Q3. So Did Anthropic’s Dollar Growth.](https://www.saastr.com/the-top-1-of-ai-spenders-stalled-in-q3-so-did-anthropics-dollar-growth) [^8]: [𝕏 post by @martin_casado](https://x.com/martin_casado/status/2118051429739098145) [^9]: [Latest Fundraising Efforts Show OpenAI & Anthropic on Divergent Paths](https://www.newcomer.co/p/latest-fundraising-efforts-show-openai) [^10]: [𝕏 post by @eliebakouch](https://x.com/eliebakouch/status/2117836135535624601) [^11]: [𝕏 post by @nathanbenaich](https://x.com/nathanbenaich/status/2117953596864229484) [^12]: [How big is the open model economy today?](https://www.interconnects.ai/p/how-well-can-we-measure-open-model) [^13]: [𝕏 post by @baseten](https://x.com/baseten/status/2117692175689683235) [^14]: [𝕏 article by @baseten](https://x.com/i/article/2118106781654134785) [^15]: [r/artificial post by u/winer666](https://www.reddit.com/r/artificial/comments/1xo27a9/) [^16]: [r/MachineLearning post by u/AKAMA199](https://www.reddit.com/r/MachineLearning/comments/1xnjhdd/) [^17]: [𝕏 article by @jkhamehl](https://x.com/i/article/2117995817655238656) [^18]: [𝕏 post by @a16z](https://x.com/a16z/status/2118067011943329903) [^19]: [Confronting Our AI Future: Closing Keynote "Discovery Loop" with Jeff Dean and Fei-Fei Li](https://www.youtube.com/watch?v=ooGKNwJWdb8) [^20]: [𝕏 post by @boxerbk](https://x.com/boxerbk/status/2118025491173011943) [^21]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2118040581574213691) [^22]: [𝕏 post by @a16z](https://x.com/a16z/status/2118048262825665018) [^23]: [𝕏 post by @eladgil](https://x.com/eladgil/status/2118112849252020495) [^24]: [𝕏 post by @_catwu](https://x.com/_catwu/status/2118137612577968402) [^25]: [𝕏 post by @alliekmiller](https://x.com/alliekmiller/status/2117899028432130253) [^26]: [𝕏 post by @dunkhippo33](https://x.com/dunkhippo33/status/2118041546905866748) [^27]: [𝕏 post by @dunkhippo33](https://x.com/dunkhippo33/status/2118041547887247728) [^28]: [𝕏 post by @dunkhippo33](https://x.com/dunkhippo33/status/2118041549799928110) [^29]: [𝕏 post by @dunkhippo33](https://x.com/dunkhippo33/status/2118041551750238473) [^30]: [𝕏 post by @dunkhippo33](https://x.com/dunkhippo33/status/2118041550827475014)