Sequoia cuts its London office and partners, while new data shows AI spending is concentrated in a few heavy users

November 5, 2026 •

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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 .
One team: Early and Growth are no longer separate. "There is only Sequoia" .
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 .
Partnership: it describes changes that flatten the partnership. It also reports raising about $10B in new funds .

Newcomer, citing unnamed sources, reports that at least three US-based partners were forced out as part of the overhaul .

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 . She listed early-stage work with Pennylane, STARK, Profound, Rillet, Trade Republic and others .

Reactions split. Nathan Benaich called it "europe in managed decline" . Martin Casado called it "a lot of really smart moves" .

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 . 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

Of the 22% taken from software budgets, incumbents got 13 points and AI-native vendors got 9 points ($12.6B) .

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 .

Coding is a $34B category. Claude Code and Codex take $24B of it, or 70%. Independent coding apps total $10.1B, up 2.9x . 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 . Those top-1% firms account for 80% of OpenAI and Anthropic spend, and spend about 600x what the median firm does .

Ramp's lead economist attributes the stall mainly to falling model prices, such as Opus 5.5 cutting cost per task by about 40% . 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

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 .
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 . 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 . Benaich's summary: "you either die trying to get to the frontier, or you live long enough to serve inference" .

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 .
Baseten is building a full platform: Grid (gateway and router), agent sandboxes, and Loops (serverless SFT and RL) . 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 .
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 .
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 . 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 . Her evidence:

PE's share of global buyout deal value going to tech fell from 34% in 2023 to 11% in Q1 2026 .
About $140B of risky PE-backed loans mature in 2028 and $160B in 2029. About a third is software .
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 .

She concedes that smaller buyouts outside leveraged software can still do fine . 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 . 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 .
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 .
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 .
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 .
Salesforce signed a definitive agreement to acquire Listen Labs. No terms were disclosed .
Talent: Cat Wu is leaving Anthropic to lead product management for Core Products at OpenAI . Allie K. Miller says that makes three of Anthropic's top product communicators to leave within weeks .

Hustle Fund's October funnel

Hustle Fund received 2,718 deals in October :

Screening stage: geography caused about 46% of passes. The fund generally passes on companies not set up as US Delaware C-Corps .
Closer review (370 deals): passes were mostly due to market competition (66%) .
Sectors: AI/ML was the most common category founders selected, at 41% .
Valuations (self-reported): 67% were under $10M .

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