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Exits: who pays, and how much
Most of today's exit news comes from one source, Jason Lemkin's SaaStr recap of 20VC. Salesforce is reportedly buying Listen Labs for $2B. The company is three years old and has about $30M of annualized revenue. It walked away from a signed $125M Series C at a $1.5B valuation to take the deal . Lemkin's test for whether a founder should sell: is the company "generational"? If not, a $2B offer within five years is hard to beat on net present value. He adds that in a meeting he attended this year about a similar offer, every VC at the table said don't sell .
The public market looks harder. Oura postponed a $2.2B IPO even though orders came in for about four times the shares on offer. Revenue for the first nine months of its fiscal year was $1.21B, up 74%. Most of the shares were coming from existing holders, and Forerunner was selling its entire 9.3% stake .
Other liquidity notes from the same recap:
- Nvidia–Groq ($20B): the deal was a $17B non-exclusive license plus $3B in Nvidia stock for the 150–200 engineers who moved over. Two former Groq engineers who still hold shares have sued in Delaware . Lemkin thinks the legal exposure could end license-and-hire deals. Employees will start asking what happens to their equity when the tech and half the team are bought but the company is not .
- ElevenLabs reached $22B in a $300M employee tender, double its $11B valuation in February. Reference customers described paying hundreds of thousands of dollars a year for voice and calling it a good deal .
Bending Spoons as the buyer for slow-growth SaaS. The company is worth $24B and trades at about 8x ARR, while buying B2B companies at 2.3–2.7x ARR . In its first 102 days as a public company, revenue grew 126% but organic growth was 3%. Nearly all of the $393M of added Q2 revenue came from six acquisitions, and EPS rose 167% . Miro and Airtable together brought $1.08B of ARR for $2.64B. The company underwrites to a 25% unlevered return, which at its 54% margin supports about 2.2x ARR . AI writes more than 90% of its code. The gain shows up as capacity, not pricing: customers pay about 10% more, and the same 60 people run a business four times larger . Likely targets are companies with $300–800M ARR growing under 25%, but Bending Spoons does only a handful of deals a year . Jason Calacanis predicts "ten more Bending Spoons" .
What it means for portfolios: a strategic sale can beat a priced round. An IPO with 74% growth still failed to price. And slow-growth SaaS now has a clear price ceiling set by a buyer that underwrites around 2–3x ARR.
Agents are starting to pick vendors
Vercel says agent-triggered deployments rose from under 3% to more than 50% . When Lemkin asked his agent where to host an app, it ranked Render first and Vercel fifth . His first agent-driven purchase was Resend, after SendGrid kept breaking. By the numbers cited on the show, Resend's MCP calls grew from 106,000 in April to 3M in September. The warning for startups: incumbents have 10–15 years of content for agents to read, so a startup's docs and API have to make it a first-class choice for a buyer that never sees its homepage . Lemkin says he knows of no product yet that tracks how agents rank vendors in a category . That gap is an open startup opportunity.
Specialist models and cheaper inference
Brian Armstrong says Coinbase fine-tuned open-weight Qwen3.5-9B on internal data for fraud prevention. He says it beat frontier models on that task at much lower cost. He expects specialist models to replace some backend services, and Coinbase has built an internal skill to make fine-tuning easy . Scott Kupor cited it as evidence for open weights . Nathan Benaich pushed back: "nothing new here," and the comparison is against an old model .
Jerry Liu argues that frontier agents can now generate ground-truth training data for tasks with checkable answers. In his workflow, humans mainly define the evals, agents gather training signals, and teams then train specialized models. That would reduce the need for deep ML expertise and manual data labeling . Garry Tan called replacing LLM calls with deterministic code "a no brainer" . He was endorsing a Jev-based harness that cut 100,000 compliance alerts from more than $290K on Opus 5 to under $26K .
Fight over inference funding. Jaya Gupta argues inference is now a "permanent reverse auction." In her view, app companies move millions of dollars of spend with "one line of code," which makes inference one of the least defensible layers even as it raises $100M seeds . Sarah Guo calls that "half total bullshit": inference is a scale game, and market leaders rarely lose customers except to other leaders . Her test for new entrants is whether they have a path to scale supply. Selling $20M of inference at negative gross margin without one does not work .
Competing views on where AI progress stands
The Investing in AI Substack argues frontier progress is flattening: each gain costs exponentially more compute, and most high-quality data has been used up . It estimates current models could transform up to 80% of standard knowledge work, while enterprise adoption is about 5% . It recommends vertical workflow AI, AI-enabled services and proprietary-data businesses over cash-burning model startups, summed up as "Model intelligence is rented. Workflow, margins, and data are owned."
Andrej Karpathy describes the opposite experience at the top. By his estimate, about 20M people use frontier models professionally for math and code. Around 5,000 have internal access to systems that run agent swarms working for weeks, and human review is starting to fall behind . Stripe's John Collison says Stripe is seeing the highest rate of new company formation "in recent memory." He ties it to cheaper "intelligence on tap" .
Product and policy notes
- Meta Muse: Alexandr Wang says the prototype took one to two weeks and the launch came seven months later . Fewer than 200 people worked on it . Meta has publicly said "millions" of users but no exact number .
- UK AISI: per The Telegraph, the White House reversed its order, and the institute will again get pre-release access to models from Anthropic, Google DeepMind and OpenAI . The UK also plans tougher reporting of AI-related security breaches for critical-infrastructure companies .
- Anthropic debate continues: David Sacks highlighted Mustafa Suleyman's essay. Suleyman argues that training Claude to see itself as having an inner self, wellbeing and rights is a potential existential risk .
- Energy: Heron Power's Drew Baglino says US electricity demand is growing faster than ever, driven by compute, transportation and industrial electrification .
- Infrastructure risk: BBC reports that Ukraine hit three Yandex data centers in a week. Yandex says it can't predict when its chatbot and smart-home services will return .
- OCR: Jerry Liu calls the 3B-parameter Unlimited OCR a milestone, but it ranks #115 on ParseBench at about 46.2% and ignores visual elements .
Early-stage notes
- Base44 as a case study: Wix bought it four months after launch. It reportedly passed $200M ARR and 10M users by August 2026 . Only $18.1M was paid in cash at close; earnouts run quarterly through 2029 .
- Elizabeth Yin: round names no longer signal risk; some AI companies raise $100M+ before having a product . She recommends angels build portfolios of 50–100 companies .
- a16z Speedrun Alpha: applications closed today. The fellowship invests up to $250K in students, dropouts and recent grads before they have a company, idea or team. Fellows get an automatic final interview for up to $1M from Speedrun .