They publish a list.

Not a vague thematic outlook. Not a “we’re excited about AI” blog post. A specific, partner-attributed, publicly available wishlist of exactly the kinds of companies they want to fund. They call it the Request for Startups.

Most founders glance at it. A few founders read it. Almost none of them dissect it the way they should.

That’s a mistake. Because the RFS is not just a list of ideas. It’s a window into how YC’s partners are reading the moment. It tells you which problems they believe are now solvable, which markets they think are newly vulnerable, and where they’re willing to write the first cheque.

The Winter 2027 list is out, and it reads very differently from the one we dissected in July. Eleven categories. Not a single consumer app among them. And an opening line from YC that tells you almost everything about where this one is headed:

AI’s biggest bottlenecks are moving beyond the models.

Let me break it down.

First: What the RFS Actually Is (And What It Isn’t)

Before we get into the categories, a calibration.

The RFS is not a requirement. YC says so on the page itself: these ideas are only a fraction of what they fund, and you don’t need to work on any of them to apply. The best YC companies of the last decade didn’t map neatly onto a wishlist. Airbnb didn’t. Stripe didn’t.

What the RFS is is a pre-approval signal. If your company falls into one of these categories and your execution is strong, you’re not asking a partner to build conviction from scratch. The category-level thesis already exists. You’re just demonstrating that you’re the right team to execute it.

That matters more than most founders realise. A YC interview is ten minutes. Every second a partner spends building context on your market is a second they’re not spending evaluating you. If they already believe in the space, you’ve bought yourself more time for the thing that actually wins rooms: founder credibility, specificity, and conviction.

So read the RFS. Not to narrow your options, but to understand where YC’s partners have already done the work.

Fall 2026 vs Winter 2027: The Shift in One Sentence

Before diving into Winter 2027 in full, it’s worth looking back at Fall 2026, because the contrast is revealing.

Fall 2026 was a list about where AI goes next. Into classrooms, into the Army, onto ships, into the homes of aging parents, onto a billion phones. YC’s framing was that AI was moving into the physical world and founders should rebuild the systems around it.

Winter 2027 is a list about what’s standing in AI’s way. Not where intelligence should go, but why it isn’t there yet. Transformers with three-year lead times. Rare-earth magnets made almost entirely in China. Banks that underwrite a robot like a forklift. City permit offices still doing everything by hand. Hospitals and defence contractors that can’t send their data to an API.

Three other shifts are worth noting.

Consumer has disappeared. Fall 2026 had three consumer-facing categories, including a direct challenge to build AI products for a billion people. Winter 2027 has none.

Crypto has gone too. Last batch’s contrarian bear-market bet doesn’t reappear.

And the authorship has come back inside. Fall 2026 featured, for the first time, a sitting cabinet official (Army Secretary Daniel Driscoll) and operator-founders writing from inside their own companies. Every Winter 2027 entry is written by someone at YC. Six authors, eleven categories. Brad Flora wrote three of them, and all three lean on what YC is already seeing inside its own portfolio. That’s a different kind of signal: less “here’s a thesis we find interesting,” more “here’s a pattern we keep watching our founders hit.”

If you’re raising, here are 6 more worth your time:

The Claude Guide Every Founder Should Run Before Fundraising - your deck isn’t read by a partner first anymore. It’s read by a model. Here’s how to pass the screen before a human ever sees it.

The 6 Claude Prompts That Change What You Walk Into the Room With - the meeting system that gets you prepared for the investor, not just the pitch.

The Claude Prompt That Turns a Cold Email Into One a VC Actually Reads - cold emails convert at 3%. Here’s the five-prompt system that beats it.

The Two-Minute Investor Briefing Every Founder Should Be Running - the Claude agent that turns a fund and partner name into a full investor briefing before every meeting.

I Asked Claude to Reject My Pitch Until It Couldn’t. Then I Raised. - the adversarial system that finds every weak point in your pitch before an investor does.

How to Build Your Fundraising Narrative with Claude - the five-prompt sequence that turns your raw story into the argument that actually closes rounds.

The Three Threads Running Through Winter 2027

When you read all eleven categories together, three distinct theses emerge, plus one clear outlier.

Theme 1: The buildout needs atoms, and the capital to pay for them.

Robots Made in America, Data Center Supply Chain, Startups in a World of Abundant Space Access, and Wall Street for the Physical Buildout all describe the same problem from different ends. AI’s next phase runs on physical things: robots, transformers, switchgear, launch capacity, GPU clusters. Those things are scarce, often imported, and expensive to finance. A clear sub-theme runs through two of them: building it in America, rather than depending on Chinese magnets or a single domestic steel plant.

Theme 2: AI has to get past the walls.

AI for City Hall, Plaid for Government Benefits, and AI for the Air-Gapped Economy are about the parts of the economy where the data and the work are locked behind regulation, procurement, or a literal absence of internet connection. These are the slowest customers to sell to. YC’s argument, made explicitly in two of the three entries, is that this is exactly why the companies that get in will be so hard to dislodge.

Theme 3: The era of renting all your intelligence is ending.

Own Your Intelligence, Vertical Foundation Models, and LLMs That Write Well all make some version of the same argument: the frontier labs are optimising for things that leave real gaps. Open-weight models have caught up. Specialised models can be cheaper and better in narrow domains. And the labs’ focus on coding has, in one partner’s view, made their models worse at sounding human. This is the most quietly provocative thread on the list.

The outlier: The $15-an-Hour Expert.

It doesn’t sit inside any of the three threads. It isn’t a technology thesis at all. It’s a labour-market thesis: that AI’s biggest economic effect may be turning ordinary workers into specialists, rather than replacing them.

A Category-by-Category Dissection

Here’s what each Winter 2027 category is actually saying, and what most founders will miss about it.

1. Startups in a World of Abundant Space Access

Who wrote it: Ankit Gupta

The setup: SpaceX is projecting a future of 10,000 Starship launches a year. An expendable Starship is already close in cost to a reusable Falcon 9, and far cheaper per kilogram. As Starship becomes reusable, Gupta expects launch costs per kilogram to fall by several orders of magnitude. The ask is for business models that don’t quite work today, but would work if launch got 100x cheaper.

What most founders miss: this is a timing bet, not a hardware bet. YC is explicitly asking for companies that are early on purpose. The pitch that wins here isn’t “we’ve built a satellite.” It’s a precise argument about which cost threshold unlocks your business, roughly when that threshold arrives, and what you can sell before it does.

2. The $15-an-Hour Expert

Who wrote it: Brad Flora

The contrarian framing: everyone talks about AI replacing workers. Flora wants the opposite, AI that turns a capable person without credentials into someone doing specialist work. He names the target zones: benefits, taxes, insurance claims, medical billing, immigration paperwork. Anywhere a credential is the bottleneck. His closing line is the most ambitious on the list: the last industrial revolution built a middle class from factory jobs, and this one could build it from expert jobs.

What most founders miss: this isn’t a software category. The company is the workforce. The product is a trained, AI-backed team doing work customers used to pay specialists for. Which means the hard problems are hiring, training, quality control, and margin, not model choice.

3. Robots Made in America

Who wrote it: Diana Hu

Hu’s argument is that robot foundation models are starting to transfer skills between robots, which moves the bottleneck to hardware. The numbers are pointed. The US installed 38,400 industrial robots in 2025, up 12%, but most are imported from Japan and Europe. China made 94% of the world’s sintered rare-earth magnets in 2024, and when it restricted exports in April 2025, some automakers had to cut production. In July, the FCC stopped authorising new connected mobile robot models unless they’re made in the US or get an exemption.

What most founders miss: the rule has a number attached. More than 65% of a robot’s component cost must be domestic, rising to 75% in 2029. Assembly doesn’t clear that bar. The opportunity is in the parts list Hu spells out: magnets, motors, strain-wave gears, encoders, force sensors, grippers, chips, battery packs, and the machines that make them. Unglamorous components with a regulatory tailwind are a very different business from building the next humanoid.

4. LLMs That Write Well

Who wrote it: Jared Friedman

The most surprising entry on the list. Friedman argues that as LLMs got better at code and maths, they got worse at writing: current models sound more robotic than the ones from 2024 and 2025. His diagnosis is structural. The reward signals that make models good at coding flatten voice and style, and the major labs will always prioritise coding and agents when forced to choose.

What most founders miss: this is a model-training company, not a writing app. Wrapping a frontier model in a “make it sound human” prompt is precisely what this entry argues doesn’t fix the problem.

To be fair to the labs, this is a claim, not a settled fact. A single training-priority shift at any frontier lab could close the gap. Founders building here need a reason their advantage survives that.

5. Plaid for Government Benefits

Who wrote it: Gustaf Alströmer

Anyone who has applied for benefits knows the loop: prove your income, your household, your address, then do it again for the next programme and again at renewal. No agency shares data. Honest people get rejected over a missing document while fraud gets through. Alströmer cites the Government Accountability Office estimate that fraud costs the federal government up to half a trillion dollars a year, and notes AI can now forge a pay stub in seconds. The ask: a consent-based layer that pulls verified income, tax and residency data from source into every programme at once. Verify once, apply everywhere.

What most founders miss: there’s a dated forcing function. From 2027, states must re-check millions of Medicaid enrollees every six months. That’s a specific, recurring, legally mandated workload landing on state agencies, and it’s a far sharper wedge than “modernise government benefits.”

6. Wall Street for the Physical Buildout

Who wrote it: Brad Flora

Software is cheap to start. Robots, GPU clusters and data centres aren’t, and somebody has to fund all that steel and silicon upfront. Flora’s observation is that the financing mostly doesn’t exist yet. Banks underwrite a robot like a forklift. YC founders are inventing the instruments themselves: a lender for robots that each earn $500k a year, put options on GPU capacity. Every great buildout, from railways to electricity to telecoms, created giant financial companies alongside it.

What most founders miss: the founder profile is unusual. Flora wants people who understand both the machines and the money. A fintech founder without hardware literacy can’t price the asset. A hardware founder without capital-markets experience can’t structure the instrument. The rare team that has both is competing against almost nobody.

7. Vertical Foundation Models

Who wrote it: Diana Hu

Hu wants research labs building specialised foundation models for manufacturing, energy and biology that work zero-shot or from a handful of examples. Her reference point is Google’s TimesFM-3: a 330M-parameter forecasting model that works across related time series with no task-specific fine-tuning. Trained once, each user brings their own history as context. The bar is explicit: match or beat general-purpose models at orders of magnitude less inference compute.

What most founders miss: Hu names the hard part herself, and it isn’t the architecture. It’s high-quality, verifiable, vertical-specific rewards, and the rights to the data. A team that has deep domain access and data rights already in hand is starting the race several laps ahead of a team with a better model idea.

8. AI for City Hall

Who wrote it: Gustaf Alströmer

If you want to build a house or start a business in America, you need your city’s permission first. Forms, plan reviews, inspections, months of waiting. In some cities the permit takes longer than the build. Alströmer’s maths: if every US building permit were approved in 10% of the current time, US cities would gain $16 billion a year in additional tax revenue. There are 19,000 cities, and almost all still do this by hand.

What most founders miss: the line that matters most is the one dismantling the objection. The conventional wisdom says you can’t sell software to government. Alströmer says that used to be true and no longer is, and that some of YC’s government-AI companies are growing faster than almost anything else they’ve seen. The permit is the wedge. 311, public records, licensing and inspections are the expansion path he names.

9. Data Center Supply Chain

Who wrote it: Jared Friedman

Friedman calls the AI data centre buildout the largest capital investment project in American history: bigger than the railways, the highways and the Manhattan Project combined. Everyone knows about the demand for GPUs, power and electricians. He points at the non-obvious bottlenecks instead. Large power transformers have a three-year lead time. Switchgear, circuit breakers and liquid cooling equipment are sold out. The grain-oriented electrical steel used in transformer cores is made at a single US plant, which can’t keep up.

What most founders miss: this is the broadest entry on the list, which makes it the easiest to pitch badly. “We serve the data centre buildout” is not a thesis. The founders who stand out will name one specific sold-out component and show how they shorten its lead time.

10. AI for the Air-Gapped Economy

Who wrote it: Brad Flora

The most valuable data in the world isn’t in the cloud. Defence contractors, banks, hospitals and nuclear plants keep it behind walls and can’t send it to an AI API. Flora says YC founders keep hitting the same wall: the customer loves the product and wants it running entirely inside their own building with no internet connection. The ask covers on-prem models, deployment tooling, security accreditation and hardware.

What most founders miss: Flora is unusually candid about the trade. It’s slower and harder to sell. It’s also where the largest budgets and most defensible companies are. Security accreditation is listed as a category in its own right, which suggests that helping other AI companies get through the walls may be a business before building your own product inside them.

11. Own Your Intelligence

Who wrote it: Jon Xu

Today nearly every company building with AI rents its intelligence: send tokens to a frontier lab, get answers back, pay the bill. That made sense when the best models were years ahead. Xu argues it no longer does, because open-weight models are now competitive on meaningful benchmarks and companies are increasingly aware of their dependency. He expects more of them to own models they improve on their own data and run where they choose, particularly in regulated industries.

What most founders miss: the gap Xu describes isn’t model hosting. It’s everything between downloading an open model and owning one that improves the business: turning workflows into evals, expertise into training data, production failures into improvements, and carrying all of it forward to each new base model. That last part, surviving the base-model upgrade cycle, is where most fine-tuning efforts quietly die. He’s also careful to say companies will keep renting frontier intelligence for their hardest problems. This is “and,” not “instead.”

What This List Tells You If You’re Not Building Any of These

The RFS is useful even if your startup doesn’t fit a single category.

Read it as a map of YC’s macro bets right now. They’re saying the model is no longer the constraint; the constraints are physical supply, financing, regulation and data access. They’re saying the slowest, most walled-off customers (governments, hospitals, defence, banks) are now worth the sales cycle. And they’re saying the dependency on a handful of frontier labs is a strategic risk companies will pay to reduce.

If your startup sits in a different category but reflects those beliefs (that you’re removing a real bottleneck rather than adding another AI layer, that you can sell into a hard customer others avoid, that your advantage survives the next model release) you’re in the right mental model even if you’re not on the list.

The RFS doesn’t define the universe of fundable companies. It defines the universe where YC has already done the thinking.

My Honest Take on Winter 2027

This is the least science-fiction list YC has published in a while, and probably the most commercially grounded. Even the space entry is framed as an economics question rather than a moonshot. What’s here instead is a series of very specific shortages: of transformers, of magnets, of financing instruments, of permit reviewers, of models that can run without the internet.

The category I think is most underpriced by founders reading this list: Wall Street for the Physical Buildout. Fintech founders don’t read hardware RFS entries, and hardware founders don’t think of themselves as lenders. If Flora is right that every great buildout created giant financial companies alongside it, this one is still waiting for its first.

The category I expect to generate the most applications and the least differentiation: Own Your Intelligence. Every fine-tuning, eval and model-hosting startup will reach for this framing. The founders who stand out won’t be the ones arguing open-weight models have caught up. Everyone will say that. They’ll be the ones who can show a customer’s model getting better after a base-model upgrade, not just before it.

And the category I think most founders will misjudge: The $15-an-Hour Expert. The instinct will be to read it as an AI-tools category and pitch a copilot for claims adjusters. The entry describes something closer to a services company with AI leverage, where the workforce is the product. The founders who win here will know exactly which tasks legally require a credential and which only traditionally did, because that line is the entire business.

The RFS Is a Signal. Use It.

If you’re applying to YC for Winter 2027, the RFS should be part of your preparation. Not as a template to force-fit your startup into, but as a framework for understanding how the partners are actually thinking.

On-time applications for the Winter 2027 batch close on 2 November at 8pm PT (4am UK time on 3 November). On-time applicants get a decision by 11 December, and the batch runs January to March in San Francisco. YC still considers late applications but won’t promise a timeline, and Early Decision is open for the Spring, Summer and Fall batches if this one is too soon.

The question to ask yourself: does my company reflect the beliefs behind the category I’m closest to? Do I understand the underlying thesis, not just the surface-level description? Can I articulate why now, why this market, and why my team, in the language of someone who has already committed to the space?

If you can answer those questions clearly, you’re not just pitching a product. You’re confirming a thesis they already hold.

That’s a very different kind of conversation.

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In-Depth Insights 🔍

• A Four-Model Agent Setup Runs a Full Workday for $8.02 💸 (22 minute read)
  Splitting one agent workday across four models cut its modeled cost from $90.75 to $8.02. The setup has Fable 5.1 plan and Sonnet 5.5 execute, with Opus 5.5 grading each result under a $15 daily cap. The figures come from assumed call profiles at Anthropic list prices.

• Eight Claude Code Systems Cover a Five-Person Team’s Repeat Work 🤖 (19 minute read)
  One founder can run eight Claude Code systems on Claude Max 20x for $200 a month. They cover inbox triage, feature builds, deploy checks, migrations and competitor research, while the founder keeps approvals and customer calls. Five US developers at the median wage cost $679,900 a year in salary alone.

• Grok Bot Lets Founders Staff Eight Single-Job Agents 🤖 (18 minute read)
  SpaceXSI’s Grok Bot gives every Bot the same persistent cloud computer with a browser and terminal, so work continues while the laptop is closed. The founder lineup runs from a CEO brief to a sponsor inbox, and each Bot stops at a draft or report. “Ask first” rules make every Bot wait for approval before a payment or an outside message goes out.

• A PostHog Engineer’s Agents Load a Skill in 78% of Chats 🧠 (14 minute read)
  Over 28 days, 78% of one engineer’s 578 agent chats loaded a skill and 47% updated one. Project trackers and handovers live as versioned skills in a shared store that Claude Code, Slack, Codex and Cursor can all read. The main project skill is on version 433, written mostly by agents. [PostHog]

  

• AI Could Widen the Founder Pool Beyond the Usual Profiles 👥 (8 minute read)
  Andrew Chen expects AI to open startups to solo founders, non-technical founders, domain experts, students and creators. Each new profile still has a gap to close, from customer meetings to sustained growth. He asks which old founder rules were fundamental and which only reflected their era. []

• AI Speeds Up Building, So Early Access Has to Show What to Build 🔍 (60 minute read)
  Hiten Shah’s playbook starts early access with a few people who already have the problem and puts the product into a real task. Teams watch where users get stuck before they choose the next change. Its plays run from a five-line Working Bet to checking whether value transfers to new users. [Hiten Shah]

  

• AI at a Fixed Capability Level Gets 10x Cheaper About Every Year 📉 (4 minute read)
  Steve Krouse charted prices for fixed scores on four benchmarks and found 10x drops every 0.8 to 1.3 years, about 1.0 years on average. That matches the pace Sam Altman cited in February 2025. Older capability levels flatten out, with o3-mini’s level only 11x cheaper after about 1.7 years. [Steve Krouse]

  

• A Content Team Runs Shared AI Skills From a Private GitHub Repo 🗂️ (7 minute read)
  Bluestone PIM’s content team keeps its brand voice and skills as plain files in a private GitHub repo that every member’s Claude reads. A new skill reaches the whole team as soon as it is pushed. Head of content Maya Pasek estimates the setup saves about 20 hours a week. []

  

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Social Media Gems 💎

• An iPhone Duo Shows a Deeper Crease After 10,000 Folds 📱 (Video)
  YouTuber Tim Pan ran an iPhone Duo through 10,000 machine folds at room temperature and another 1,000 at -4°F. The hinge kept working, while the crease grew much more visible. It already looked deeper after the first 500 folds. [Tim Pan]

• Anthropic Acquires Mithril Medicine, a One-Person Biotech Run With Claude 🧬 (3 minute read)
  Anthropic disclosed on October 27 that it bought Mithril Medicine in July, and founder Cody Tranbarger joined the company. He was Mithril’s only full-time employee, working with part-time consultants and 11 outside labs while Claude helped manage the experiments. Its rare muscular dystrophy programs became Anthropic’s first internal drug programs. [R&D World]

• Cursor Agents Can Now Click and Type Across Any App 🖱️ (Post)
  Cursor added Computer Use, which lets its agents work in any desktop app beyond the browser. Agents call it on their own when a task needs it, and users can request it by @-mentioning Computer Use. The demo shows an agent driving the iOS Simulator to test a feature it built. [Cursor]

• Stripe Pay Sends Free, Instant Business-to-Business Transfers 💸 (2 minute read)
  Stripe Pay lets a business send money to another company by looking up its Stripe profile handle. Transfers are free and work from the Dashboard and the CLI. The sender needs a funded financial account, and the recipient needs one linked to its Stripe profile. [The Paypers]

Trending News ⚡

• Anthropic’s Seven Cofounders Can Each Rank Up to 10 Secret Successors 🔐 (2 minute read)
  The seven cofounders who hold 50.1% of Anthropic’s voting control through the Founders LLC can each name up to 10 ranked successors. The other LLC members vote to approve each list. The names stay confidential unless a court order or other legal requirement forces disclosure. [Business Insider]

• Amazon Is Likely the Biggest Financial Winner of Anthropic’s IPO 💰 (3 minute read)
  Amazon holds Anthropic convertible notes with $6.2 billion of principal, which its own filings valued at $97.9 billion on June 30. The notes can convert into up to 1.84 billion shares, capped at 12% of the partially diluted share count. No VC firm reaches the 5% ownership threshold, and Menlo’s stake is roughly 1.5%. [TechCrunch]

• Anthropic Files for a Nasdaq IPO Under the Ticker ANT 📈 (1 minute read)
  Anthropic filed its S-1 on October 26 to list Class A shares on the Nasdaq Global Select Market. The filing shows $4.6 billion of 2025 revenue and a $42 billion net loss, and it leaves the price range and valuation blank. NBC News reports the offering could value the company at more than $2 trillion. [Anthropic]

• OpenAI Is Reportedly Raising Up to $30 Billion at a $1.25 Trillion Valuation 💰 (2 minute read)
  OpenAI is close to a round of up to $30 billion at a $1.25 trillion valuation before the new money, per CNBC. Investors from the United Arab Emirates are leading it alongside Coatue. The round is positioned as a bridge ahead of a potential IPO. [CNBC]

• Meta Plans a $95 Billion Stock Raise for AI Infrastructure 💸 (2 minute read)
  Meta will raise $35 billion through underwritten equity offerings, then another $60 billion through an at-the-market program. Nvidia, AMD and Broadcom expressed interest in buying up to $5 billion of the shares. The stock fell 1.6% in morning trading after the announcement. [CNBC]

• SpaceX Closes $40 Billion in GPU Debt Financing 🚀 (1 minute read)
  SpaceX signed a binding commitment with an Apollo-managed entity for roughly $40 billion to buy GPUs arriving in the first half of 2027. Elon Musk predicts more than 200 neoclouds will face failures and consolidation. He says debt markets have largely closed to companies without substantial revenue outside AI data centers. [CNBC]

• General Compute Raises a $100M Series A at an $800M Valuation 🔌 (3 minute read)
  The neocloud builds on inference chips from Nvidia rivals, and its valuation rose from $60 million after a May seed round. Blackbird Ventures and Abstract Ventures led the round, which followed a customer deal worth $100 million in ARR. The company expects to deploy 75 megawatts in 2027 on SambaNova, Positron and Cerebras chips. [TechCrunch]

• Chinese Model GLM-5.3 Completes 3 of 13 Cyberattack Scenarios in Tests 🚨 (2 minute read)
  Z.AI’s open-weight GLM-5.3 finished 3 of 13 multistage attack simulations at least once in tests by security firm Irregular. That is the strongest result Irregular has recorded from an openly available model. OpenAI’s GPT-6 Astra completed 9 of 10 scenarios in a separate assessment that was not identical. [Reuters]

• OpenAI Launches GPT-6.1 Terra at Half the API Price of GPT-5.6 Terra 🤖 (3 minute read)
  GPT-6.1 Terra costs $1 per million input tokens and $5 per million output tokens, 50% below GPT-5.6 Terra. OpenAI says it scores 15 points higher than GPT-5.6 Terra on Agents’ Last Exam at about one-third the cost. Its low-effort error rate on hard prompts fell from 53.6% to 16.7%. [OpenAI]

New Funds 💰

• Disruptive announced its $10 billion Flagship Fund to back about 10 growth and late-stage technology companies.

• Trust Ventures filed for its fifth venture fund, targeting $400 million.

• Interlagos Capital filed for Interlagos Fund II, targeting $400 million.

• AP Ventures closed its third venture fund at $300 million to back early-stage industrial and energy technology companies.

• DPI Venture Capital closed its Nclude Fund at over $112 million to back technology businesses across Africa and the Middle East.

• Entourage closed its second fund at €100 million to invest in AI, enterprise software, robotics and deeptech startups.

• Auriga Partners secured an €80 million interim close for its second cybersecurity venture fund.

• Phaze Ventures launched two venture funds targeting over $75 million combined to back AI and deeptech companies.

• BY Venture Partners closed its third venture fund at $60 million to back early-stage AI-native companies.

• U&I Ventures closed its debut fund at $32.2 million to back seed-stage AI-native infrastructure in security, engineering and IT.

That’s a wrap for this week.

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See you next time 🚀

This series is a preview of a new book I’ve written with my partners Richard Blundell and Paul Watson — The Selling Software Algorithm: The Go-to-Market Navigation System for B2B Software Leaders.

It isn’t a strategy deck dressed up as a book. Between the three of us we’ve spent close to seven decades inside B2B software as founders, operators, investors and coaches — building companies, exiting some, and quietly breaking a few along the way, which is where most of the real learning lives. Through our work at Vencha we’ve now supported hundreds of founders trying to do the single hardest thing in software: turn a clever product into predictable commercial traction in a market that’s noisier and more crowded than it has ever been.

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Last time, we pressure-tested the pain itself: budget presence, urgency, frequency, and the nice-to-have trap that catches founders who mistake interest for spend.

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Here’s the failure this piece exists to prevent. A founder does everything this series has asked. The pain is real, tested and funded. The Algorithm sentence is sharp. The homepage passes the swap test. And the pipeline still moves like it’s wading through wet sand, because the buyers aren’t ready, the enabling technology isn’t cheap enough yet, or three better-funded competitors got there eighteen months earlier and have already been bought. Nobody did anything wrong in the sales process. The market simply wasn’t standing where the founder assumed it was.

Table of Contents

• A Simple Example: Same Product, Two Founders

• The Right Product at the Wrong Moment Still Fails

• Why Timing Comes After Pain, Not Instead of It

• Five Signals, Not One Gut Feeling

• Demand Signals: What the Market Is Already Telling You

• Technology Readiness and the Cost of Being Early

• What the Numbers Say About Timing

• Competition and Regulation: The Two Signals You Don’t Control

• The Window: Opening, Peak, Closing, Closed

• Founder-Market Timing Is Still Timing

• The Honest Case Against Timing the Market

• Knowing Exactly What You’re Waiting For

• What Each Window Asks of You This Quarter

• Turning a Feeling Into a Verdict

A Simple Example: Same Product, Two Founders

Picture two founders. Not real ones, but you’ll recognise both.

They build almost the same product: software that pulls supplier data together and produces the risk reports a mid-market procurement team would need if a regulator, or a large customer, ever asked for them.

The first founder launches early. The pain is real. Every procurement lead she speaks to agrees the current process is a mess of spreadsheets and chased emails. Testing calls go brilliantly.

Then the pipeline stalls. Not on price. Not on a competitor. On “let’s revisit this next year.” No regulation requires the report yet. No large customer is demanding it. The data integrations the product depends on are still expensive and fragile, so every implementation turns into a services project. She raises a bridge, then another, and eventually sells the IP for less than her seed round.

The second founder launches four years later, with a thinner product.

By then a rule has a date attached to it. Two of the largest customers in the sector have started writing supplier reporting into their contracts. Integrations are cheap and standard. The buyers who told the first founder “next year” are now calling vendors themselves, with budget and a deadline.

He closes in weeks what she couldn’t close in quarters.

Same pain. Same buyer. Better product, in fact, the first time round. The only thing that changed was the market’s own readiness. That’s the gap this article is about.

The Right Product at the Wrong Moment Still Fails

Founders who’ve done the work in this series — a real pain, a tested Algorithm, a sharp message — still lose deals to timing they never measured.

A genuinely painkiller-grade problem sold two years before the enabling technology is affordable produces a founder convinced they were right.

The same problem sold two years after three well-funded competitors consolidated the category produces exactly the same founder.

One was early. One was late. Both watch the market prove them wrong while being, in every way they can measure, right.

What makes timing so dangerous is that it rarely shows up as a reason. Lost deals get logged as “budget”, “priorities shifted”, “went with the incumbent”, “no decision”. Each of those is true in isolation. Put twenty of them side by side, and a good number are telling you the same thing in different words: not yet, or not any more.

Timing isn’t a vibe. It’s measurable. And it’s worth measuring before you commit the next twelve months to a market, not after.

Why Timing Comes After Pain, Not Instead of It

There’s a reason this sits after the severity test rather than before it.

A timing score on a weak pain tells you nothing useful. If nobody would pay to make the problem go away, it doesn’t matter whether the market is opening or closing. You’re early or late to a party nobody is throwing.

So the order matters. First, is the pain real? That’s the testing calls and the Algorithm. Second, is it severe enough to carry a company? That’s the budget-presence work from last time. Only then does the third question earn its place: is this the moment the market will pay for it?

The book frames buyer readiness as a set of stages, from people who don’t yet know they have a problem through to buyers with procurement approved and budget assigned. Timing is the same idea at the level of the whole market. A market is mostly made of Stage 1 buyers, or mostly made of Stage 5 buyers, and no amount of individual sales skill changes the mix you’ve walked into.

A good salesperson can move one buyer a stage. Nobody moves a whole market a stage on their own budget.

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Five Signals, Not One Gut Feeling

“Is the timing right?” is one of those questions that’s too easy to answer yes to. Founders are optimists by selection. If you weren’t, you wouldn’t have started.

So the diagnostic doesn’t ask it as one question. It runs twenty questions across five categories:

Demand Signals. Whether buyers are already looking, already urgent and already paying.

Technology Readiness. Whether the things your product depends on are mature, affordable and in your buyers’ hands.

Competitive Landscape. Whether the space is empty, crowded, consolidating or quietly complacent.

Regulatory and Macro. Whether the rules and the wider economy are pushing buyers towards you or holding them back.

Founder-Market Timing. Whether you, specifically, are positioned to move inside this market right now.

Each category contributes equally to the overall percentage. Twenty percent each, no exceptions.

That’s a deliberate choice, and it matters more than it looks. Founders always have a comfortable category: the one they talk about fluently in pitch meetings. Equal weighting means you can’t talk yourself into a good score by being strong in that one.

A brilliant founder-market fit doesn’t rescue a market with no demand signal.

A red-hot demand signal doesn’t rescue a founder with no domain expertise and no network to move fast inside it.

Five scores show you which leg of the stool is missing. One gut feeling only tells you how confident you feel.

Demand Signals: What the Market Is Already Telling You

This is the category where founders most often confuse their own conviction with the market’s.

It asks four things. Whether search trends and interest in the problem are rising. Whether customers are actively looking for something like your solution, rather than needing to be educated into wanting it. How urgently they need it solved. And whether they’re already willing to pay for solutions in the space.

The second question is the one that sorts markets fastest.

If you have to explain the problem before you can sell the solution, you’re doing category education. That’s an expensive, slow job, and it’s usually done by whoever has the most money, which is rarely the seed-stage company.

If buyers arrive already describing the problem in their own words, already comparing vendors, already asking about pricing, the market has done the education for you.

The evidence for this is usually sitting in plain sight:

Inbound language. Are prospects using your category words, or are you supplying them?

Search behaviour. Is the volume around the problem flat, falling or inflecting? Trend tools won’t tell you everything, but a two-year flat line is information.

Buying behaviour elsewhere. Are adjacent vendors hiring sales teams, or cutting them? Are RFPs mentioning the problem by name?

Workarounds with a cost. Are buyers already paying consultants, contractors or internal headcount to deal with this by hand?

A market stuck at “would be helpful eventually” scores very differently from one where buyers describe their hair as on fire. No amount of positioning work changes which one you’re actually in. Positioning helps you win the market you’re in. It doesn’t move you to a different one.

Technology Readiness and the Cost of Being Early

Every product rests on things it doesn’t control. Infrastructure, data access, devices, integrations, models, standards, the skills inside your buyer’s team.

Technology Readiness is the question of whether those things are mature, affordable and already in your buyers’ hands, or whether your go-to-market quietly depends on them arriving.

Being early here is brutal, because the cost doesn’t show up as a lost deal. It shows up as an expensive win.

The customer signs. Then implementation takes six months instead of six weeks, because the integration your product assumed doesn’t exist yet, so your engineers build it bespoke. Then the customer’s team can’t use half the product, because the skills it assumes haven’t reached them yet. Then the margin on the account is negative, and the reference you hoped for turns into a cautionary tale.

When the enabling technology isn’t ready, you don’t just sell slower. You end up paying to build the market’s infrastructure on your own balance sheet.

There’s a flip side worth watching for. Readiness can shift fast, and when it does, a market that was hostile for years can open in months. The founders who win those moments are usually the ones who’d been watching the specific thing that was missing, and recognised it the day it arrived.

What the Numbers Say About Timing

This isn’t just intuition. The evidence points the same way, though it deserves to be read carefully.

Start with the best-known attempt to measure it. In his 2015 TED talk, Idealab founder Bill Gross described scoring 100 Idealab companies and 100 companies from outside Idealab on five factors: idea, team, business model, funding and timing.

Timing came out on top, accounting for 42% of the difference between success and failure. Team came second at 32%, then idea at 28%, business model at 24%, and funding last at 14%.

The point Gross drew from it is the useful part. Almost every other factor can be changed after you start. You can add a business model, raise more money, rebuild the team, refocus the idea. You can’t move the market’s calendar.

It’s worth being honest about the limits. Gross rated the companies himself, it wasn’t a peer-reviewed study, and the percentages overlap rather than adding to 100. Treat 42% as a strong signal from an experienced operator, not a law of nature.

Then look at what kills companies now. CB Insights’ 2026 update on 431 VC-backed startups that shut down since 2023 put bad timing at 29% of failures. Not a footnote. Nearly a third.

And look at the most famous before-and-after in the startup canon.

Webvan delivered groceries ordered online. It raised close to $800 million, built its own warehouses, and shut down in 2001, laying off around 2,000 people.

Instacart launched in 2012 doing broadly the same job for the customer. The difference wasn’t the idea. By then smartphones were everywhere, buyers were used to ordering online, and the company could use existing supermarkets as its infrastructure rather than building its own. Sequoia’s Michael Moritz, whose firm backed both companies, has pointed to Webvan’s early timing as part of the difference.

Same pain. Same buyer. Roughly a decade apart.

Neither example is pure timing, and I wouldn’t claim it is. Webvan’s capital-heavy model would have struggled in most years. But the comparison shows exactly what Technology Readiness means in practice: the same job, done on infrastructure that didn’t exist the first time.

If you’re building right now, these five are worth your time

• The Test That Tells You If Anyone Will Actually Pay: five weighted dimensions, and the guard rail that caps an unfunded pain at 55 however urgent it sounds.

• The 9-Step GTM System Every Founder Should Build Before They Scale: nine steps, one living document, so every new hire knows who to target and what to say.

• Why Most Founders Are Solving the Wrong Pain Entirely: ten pains go into the room, three come out, and only those three ever move a deal.

• Would Anyone Notice If Your Competitor Stole Your Homepage?: seven seconds, one armour-piercing message, and the swap test most hero lines fail.

• The AI Search Playbook That Gets Your Startup Recommended: three prompts that show whether a model puts you on the shortlist when a buyer describes their exact pain.

Competition and Regulation: The Two Signals You Don’t Control

Two of the five categories describe forces entirely outside the company. They’re the ones founders are most tempted to wave away, because there’s nothing to do about them on Monday morning. That’s exactly why they need scoring.

Competitive Landscape reads the state of the field. An empty space can mean an untapped opportunity, or it can mean other people already tried and learned something you haven’t. A crowded space can mean proven demand, or it can mean the category is consolidating and the window for a new entrant is shutting. Incumbents who look comfortable and slow can be the best signal of all, because complacency is an opening.

The trap is reading competition in only one direction. “Nobody else does this” is not automatically good news. “Five people do this” is not automatically bad news. The question isn’t how many competitors exist. It’s what their presence tells you about where the market is on its own curve.

Regulatory and Macro reads the weather. Rules that force buyers to act. Rules that stop them acting. Budget cycles tightening or loosening. Whole sectors under pressure, or flush.

The clearest example of regulation turning from background noise into a market is privacy software around GDPR.

OneTrust was founded in Atlanta in 2016, after GDPR had been approved but before it became enforceable in May 2018. By the time the deadline arrived, the product was already proven in the market, and the company went on to top Inc.’s 2020 list of fastest-growing private companies in America with three-year revenue growth of more than 48,000%. One of its investors later put its success down to founding at such an opportune moment.

Notice what that founder did and didn’t do. He didn’t create the demand. He couldn’t. He read a dated, public, falsifiable signal, a regulation with an enforcement date, and built so the product was ready on the day buyers were forced to look.

That’s what timing looks like when it’s done deliberately rather than discovered afterwards.

The Window: Opening, Peak, Closing, Closed

Alongside the overall score, the diagnostic assesses which of four windows you’re standing in, built from the demand and competitive signals read together.

Closed. Conditions are hostile. The honest posture is to wait and watch.

Opening. Signals are building. The moment to build quietly and prepare.

Peak. Demand is strong and the field hasn’t consolidated yet. Strike now. This won’t last.

Closing. The advantage is fading. Competitors are consolidating, buyers are settling on default choices, and each new deal costs more than the last.

The diagnostic then returns one of four verdicts, running from Wait & Watch through Build Quietly and Prepare & Position to Strike Now. Each comes with a genuinely different recommended posture, not just a different colour on a dashboard.

Why read demand and competition together? Because either one alone misleads.

Strong demand with no competitors can be Peak, or it can be a market so early that nobody else has noticed yet, and you’re about to pay for everyone’s education.

Strong demand with lots of competitors can be Peak too, or it can be Closing, with the land already carved up.

The window is where those two signals meet. Neither is enough on its own.

Founder-Market Timing Is Still Timing

The category most founders skip past is the one about themselves.

It asks about four things: the strength of your network in this specific market right now, the depth of your domain expertise, how complete your team is, and whether the resources exist to go all-in today rather than in six months.

That last one catches more founders than any other. A market at Peak doesn’t wait for your next round to close.

A founder with no relevant network entering a market at its peak window is still badly timed. Just in a different way from a founder with perfect domain expertise entering a market that hasn’t woken up yet.

The first one is too slow for the market. The second one is too fast for it. Both lose the window.

This is where timing stops being something that happens to you and becomes partly something you can change. You can’t make the regulation arrive. You can spend the next two quarters building a network inside the buyer community, hiring the one person with deep sector credibility, or getting the money in place so you can move when the signal turns.

Timing is a property of the founder and the market together, not the market alone.

The Honest Case Against Timing the Market

It would be odd to write this without giving the sceptics a fair hearing, because some of their objections are good.

“Nobody can time a market.” Investors have argued for decades about whether anyone can reliably time the stock market, and most of the evidence says it’s very hard to do consistently. Why would startups be different?

“The best founders create the moment.” Some categories really were created by someone who refused to wait. If everyone had run a timing diagnostic, a few great companies would never have started.

“The evidence is shakier than it looks.” Gross scored his own companies. CB Insights relies on post-mortems written after the fact, when “bad timing” is a kinder story than “we got it wrong.”

“Waiting is a decision too.” Every quarter spent watching is a quarter a competitor might spend building.

All four are fair. None of them is an argument for not looking.

On timing markets: the diagnostic isn’t predicting next quarter’s price. It’s reading conditions that move slowly and visibly, like adoption, regulation and competition. That’s closer to checking the weather than calling the stock market.

On creating the moment: the founders who did tended to have unusually strong founder-market timing, a deep network, the money to educate a market and the patience to do it. That’s one of the five categories, not an exception to them.

On shaky evidence: agreed, which is why the numbers here are signals, not proof. But the direction is consistent across very different sources, and nobody seriously argues that timing doesn’t matter.

On waiting: yes. Which is why only one of the four verdicts says wait, and even that one comes with a specific thing to watch for.

“Being right too early looks exactly like being wrong, right up until someone else gets the credit.”

Knowing Exactly What You’re Waiting For

Here’s the part of the output I’d pay most attention to.

Where a category scores below fifty percent, the diagnostic doesn’t just flag weakness. It names the specific trigger worth watching for:

Search trends inflecting upward. The market starting to look for the problem on its own.

Incumbents showing complacency. Slower releases, rising prices, unhappy customers saying so in public.

Regulation shifting from headwind to tailwind. A rule getting a date. A rule being relaxed.

The network and team catching up to the opportunity. The hire made, the relationships built, the round closed.

That turns “we’re too early” from a vague feeling into a specific, falsifiable condition you can actually check for next quarter.

This is the difference between waiting and drifting. A founder who’s drifting says “the market isn’t there yet” every quarter, and means it, and never says what “there” looks like. A founder who’s waiting can tell you the exact signal, where they’ll see it, and what they’ll do the week it shows up.

If you can’t say what you’re waiting for, you’re not waiting. You’re hoping.

What Each Window Asks of You This Quarter

A window is only useful if it changes what you do. Here’s how I’d read each one.

If you’re in a Closed window, cut burn to the minimum that keeps you close to the market. Stay in the conversations. Keep a list of the specific triggers you’re watching and review it monthly. Don’t hire a sales team into a market that can’t buy.

If you’re in an Opening window, build quietly. Win the first few design partners, learn the language buyers use, and get the implementation playbook right while the stakes are low. This is when you earn the references the Peak will need.

If you’re at Peak, move. This is the moment to put money behind sales and marketing, because every quarter of delay is a quarter a competitor uses to become the default. The work you did in the Opening window is what lets you move fast without breaking.

If the window is Closing, be honest about where you can still win. That usually means narrowing: a segment the consolidators are ignoring, a use case they serve badly, a geography they haven’t reached. Trying to fight the leaders head-on in a closing market is how good companies spend their last round.

The common thread: the right posture depends far more on the window than on how good your product is. A great product in a Closed window should behave very differently from a great product at Peak.

Turning a Feeling Into a Verdict

Most founders have a feeling about their timing. Very few have a verdict they could defend to a sceptical board member.

This is exactly what the Market Timing Diagnostic exists to do: twenty questions, five categories, roughly five minutes. It returns your overall timing score, the window you’re standing in, a verdict from Wait & Watch to Strike Now, and, for any category below fifty percent, the specific trigger worth watching for.

Run it on the market you’re in now. Then run it on the adjacent market you keep being told you should look at. The comparison is often more useful than either score on its own.

And if the verdict isn’t the one you wanted, remember what it’s for. It isn’t telling you the idea is bad. It’s telling you when, and what to watch for in the meantime.

Next in the series, we assume the timing is right and turn to the people already selling into that same window: your competitors, and exactly where you do and don’t beat them.

-Chris Tottman

A painkiller-grade problem, sold at the wrong point in the market’s own readiness curve, still produces a slow, unpredictable pipeline.

The Market Timing Diagnostic scores five signal categories and returns a verdict, from Wait & Watch to Strike Now, so timing stops being a guess.

Try it below

Check Your Timing

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Founders screenshotted it.

They put “We believe in ambition” in their LinkedIn headlines.

Then on Monday morning they ran their company exactly the way they had on Friday.

The manifesto gets quoted as a mood. It was written as a set of rules.

Let’s dive in 👇

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The Manifesto in 60 Seconds ⏱️

If you never read all 5,200 words, here’s what’s in it:

• 🚀 Growth is everything. Every good thing sits downstream of it. Stop growing and you get zero-sum infighting, then decline.

• 🧠 AI is alchemy. Sand, taught to think.

• 💸 Cheap beats expensive. Rich beats poor. Abundant beats scarce.

• 🏆 Merit and radical competence. Plus an absolute rejection of resentment.

• 🌍 Technology doesn’t care who you are. A positive attitude and a cheap laptop are enough to contribute.

• ⚔️ An enemies list. Stagnation, bureaucracy, vetocracy, gerontocracy, regulatory capture, cartels.

It got savaged on arrival. The SF Standard compared the prose to LinkedIn “bro-etry”. Political scientist Henry Farrell likened it to a religious creed.

Yet even critics conceded ground. Nate Silver said he agreed with roughly 84% of it.

My First Ever AMA

You know the email.

“Loved meeting you. Not quite right for us at this stage. Keep us posted.”

Translation: no. And no idea why.

Was it the traction? The deck? The valuation? You never find out.

This time you can ask.

On Thursday 29 October at 4pm GMT, we’re hosting a live call for paid subscribers and free trialists. No camera-off webinar. You bring the problem, we dig into it with you.

Straight answers. Uncomfortable ones included.

🧭 Me. MessageLabs from tiny team to $700m exit, then co-founded Notion Capital. Operator and investor.

📣 Paul Watson. Sales won’t move? Paul works out who to sell to, what to say and how to make it repeatable.

💰 David Levine. CEO of Glenluna Ventures, Principal at Manchester Angels. Story, valuation, who to approach. Wonderfully blunt.

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The Line That Actually Matters 🎯

Our enemy is stagnation.

Most readers took it as a shot at Washington.

Jeff Bezos had already written the startup version. In his 2016 letter to shareholders, he called it Day 2. First stasis. Then irrelevance. Then painful decline. Then death.

Here’s the uncomfortable part.

Day 2 doesn’t arrive from outside. Founders build it themselves, one reasonable-sounding approval at a time.

And every company that dies of it was run by people who believed in ambition.

Five of the 113 beliefs are the antidote. Most founders adopted the other 108.

Read more

The partner laughs at the right moments. Asks for the deck. Says it was really interesting.

Then the email.

“Really enjoyed meeting you. It’s a bit early for us, but let’s keep in touch.”

The founder reads it four times. Forwards it to their co-founder. Posts it in the founder WhatsApp group with one question.

Is this a soft yes?

I’ve sat on both sides of that email.

I joined MessageLabs in 1999 as one of its first employees and was there for the $700m sale to Symantec. For the 17 years since, I’ve been on the other side at Notion Capital: eight funds, more than 500 founders backed.

Which means I’ve written far more of the other email than the one founders want.

Most of mine were polite. Some were honest. Fewer were both.

A VC’s no is the most useful piece of data you’ll get in a raise. It just arrives in a language nobody taught you.

My First Ever AMA

You know the email.

“Loved meeting you. Not quite right for us at this stage. Keep us posted.”

Translation: no. And no idea why.

Was it the traction? The deck? The valuation? You never find out.

This time you can ask.

On Thursday 29 October at 4pm GMT, we’re hosting a live call for paid subscribers and free trialists. No camera-off webinar. You bring the problem, we dig into it with you.

Straight answers. Uncomfortable ones included.

🧭 Me. MessageLabs from tiny team to $700m exit, then co-founded Notion Capital. Operator and investor.

📣 Paul Watson. Sales won’t move? Paul works out who to sell to, what to say and how to make it repeatable.

💰 David Levine. CEO of Glenluna Ventures, Principal at Manchester Angels. Story, valuation, who to approach. Wonderfully blunt.

🛠 Your tools, actually opened. Investor readiness check, the 30-second deck test, and the filter across 2,500+ investors.

Three experts on your problem would normally take three intros and a lot of diary juggling. Here, it comes with your subscription.

Places are limited. More calls on 25 November and 10 December.

👉 Start your 7-day free trial and the invite is yours. Book your place:

Here

The Arithmetic Behind Every Polite No

Paul Gompers, Will Gornall, Steven Kaplan and Ilya Strebulaev surveyed 885 VCs at 681 firms for the Journal of Financial Economics in 2020.

The average firm screens 200 companies a year and makes four investments.

Passes per year = companies screened − investments made

200 − 4 = 196 nos. Close to four every week, from a firm with five senior investment professionals.

Now picture writing a thoughtful rejection four times a week for companies you’ll never back.

Lightspeed partner Mercedes Bent said it in a viral LinkedIn post: a careful no takes effort, and when it won’t become a deal it slides down the list. Hustle Fund’s Eric Bahn gets about 30 inbound pitches a day.

That explains why the no is short.

It doesn’t explain why it’s vague. That part is deliberate.

Why the Real Reason Never Makes It Into the Email

Paul Graham described the mechanism in How to Raise Money back in 2013, and nothing about it has changed.

Investors want to wait, because every week tells them more about a young company. So they lead founders on. Some never say no at all. They just stop replying, which keeps a free option open in case you turn hot.

His translation of “I’d invest, but I don’t lead” is still the best in print:

“No, except yes if you turn out to be a hot deal.”

Jason Lemkin at SaaStr has the honest investor’s version. The clear nos go out fast. The yeses go out fast. It’s the almosts that sit in his inbox: a great CEO without a great CTO, or 90% growth at $1m ARR when he needed it faster. He calls it inbox drag. To the founder, it looks exactly like ghosting.

The vague no isn’t a lie. It’s an option the investor hasn’t decided to close.

Founders feel it. When Sifted asked 59 founders about their worst investor experiences, more than 75% said they’d dealt with arrogant VCs and been ghosted. One wanted investors to just say so:

“When you’re never going to be more than a frog — let us know.”

And sometimes the reason is simply that nobody was listening.

In June a thread on X turned into a confession booth. Greg Isenberg described a partner at a top firm sleeping through 30-plus minutes of a $15m Series A pitch. Liz Wessel, now at First Round, said an investor dozed off in her 2015 Series A meeting, then called to say his firm wanted in anyway. Her team passed. Travis Kalanick once finished a pitch from the passenger seat of the investor’s car.

The Fair Case for the Vague No

Before you draft the angry reply, hear the other side. A specific no invites an argument.

Investor David Paul wrote about it: founders ask for honest feedback, then use it to rebut him. Investors learn to stop giving it.

Bahn told TechCrunch his firm sometimes sent detailed reasons for a pass and got abuse back, occasionally threats. Those founders went into the firm’s database and never heard from it again.

And the honest reason can be wrong and damaging at once. “We don’t think your CTO can scale this” is one partner’s guess after one hour. Written down, it can follow a team around.

So the fix isn’t demanding honesty from investors. It’s learning to read the no you actually get.

Our Most-Read Articles Right Now

🔥 I Ran a Startup Through a VC Investment Committee. It Died in Four Minutes. The memo and the verdict, before you walk in.

🔥 The Way VCs Actually Calculate Your Valuation The return maths that sets your price before the meeting starts.

🔥 The Pitch Deck Test Investors Use in 30 Seconds — Now You Can Too How your deck really lands, before the silent no.

📈 The 169 VCs Leading Rounds Right Now Who actually led a round in the last ninety days.

The Investors Who Said No, Then Chased

A no is a decision made on the evidence available that week. It has a date on it.

Bessemer Venture Partners keeps the most honest page in venture: its anti-portfolio, the great companies it passed on.

It passed on FedEx seven times. It passed on Apple’s pre-IPO stock at a $60m valuation because the price looked steep. A partner told Eduardo Saverin to forget Facebook because Friendster had already won.

And David Cowan’s college friend rented her Menlo Park garage to two Stanford students writing a search engine. She kept trying to introduce them. Cowan’s response, as Bessemer tells it:

“How can I get out of this house without going anywhere near your garage?”

The same page tells the other half of the story.

Bessemer’s Kent Bennett passed on Toast. Twice. Then a co-founder showed him payments built into the product, and he called his mentor to admit a huge mistake. The advice: send a term sheet that day, and every day after, until they signed. Toast went public in 2021 at a $20bn valuation.

With Legora, Bessemer missed the round entirely, then stayed close. Four months later the company had more than doubled. Two partners met the founder at Penn Station after midnight, watched a demo in a bar, and led the $1.8bn Series C in 2025.

It matters more now than it did in 2021. Carta tracked 12,249 seed rounds and found that no seed cohort since Q3 2021 has reached a 30% graduation rate to Series A within two years.

Which makes the investors who passed on your seed a large part of your Series A pipeline.

The question is which ones.

Not All Nos Are Equal

Toast and Legora both started with a no. But not every no comes back.

Over 17 years I’ve learned that the shape of a no matters far more than how warm it sounds.

So I went back through the pass lines I see most, the ones I’ve sent and the ones my founders have forwarded to me, and sorted them by what tends to happen next.

They fall into three groups.

Green. Nos that often come back, if you send the right email.

Amber. Nos that mean nothing until you act. Leave them alone and they quietly run down your runway.

Red. Nos that mean stop. Stop chasing, stop updating, and in some cases stop asking for intros.

Here’s what surprised me when I sorted them.

The line founders take hardest usually sits in the green group. It tends to mean someone inside the firm put their name on your deal and lost a vote you could win next time.

And the line founders forward with a smiley face, the one at the top of this article, is amber. Left alone, it’s where hope goes to run out the clock.

Most founders spend the next month on exactly the wrong one.

What’s Below the Line

• The green no. Which line it is, why it’s usually the warmest lead in your pipeline, and the email to send within 48 hours

• Why “let’s keep in touch” is the most expensive email in your inbox. What Sifted’s founders called their biggest fundraising mistake, and the one-line reply that stops it costing you a month

• The red nos. Three lines that mean stop, including one founders think is a maybe

• The 12-word question that makes it almost impossible to be ghosted, and the moment in the meeting to ask it

• The decoder, sorted green, amber and red. 12 pass lines in the article, each with the reply to send. All 24 in the download

• The No Ledger. The one column that turns every pass into a reason to write back, and the founder who came back to me with exactly what I’d asked for

Keep reading with a 7-day free trial. Cancel anytime.

The No Founders Take Hardest Is the Warmest

Read more

Let’s get into it.

Together with Vanta

Spoiler: It Wasn’t Luck

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In-Depth Insights 🔍

• AI Pricing Pages Leave Customers Guessing What They Buy 💰 (12 minute read)

  Elena Verna went through AI pricing pages to answer a basic question: what does $20 a month buy? Many plans sell tokens and usage multipliers and leave buyers to guess what one unit is worth. GrokBot's tiers promise 4x and 15x more usage than Plus, while the size of the Plus allowance stays secret. Verna leads growth at Lovable and admits it has the same problem. If you sell AI, put a real task count on every tier. []

• 28 Startup Perks Offer More Than $3M in Credits, Grants, and Discounts 🚀 (4 minute read)

  The AI Corner premium plan now includes 28 startup programs worth more than $3M in perks. Ramp offers $350,000+ in perks and Google Cloud up to $350,000 in credits, and the list also covers AWS, Stripe Atlas, ElevenLabs and Claude for Startups. Premium subscribers get every perk plus the resource libraries, currently with a limited 50% discount. Apply for the large cloud credits first.

• micro1 Commits $1B to Buying Company Data and Recruiting Experts 💰 (6 minute read)

  micro1 has committed $1B over the next twelve months to buying data from companies, per The Next Web, with financing from Citi and Hercules Capital. Qualified companies get offers from $100K to $2M+, and experts earn $20 to $200 an hour. Referring a company that micro1 approves for a data partnership pays up to $50K. If your company sits on years of operational data, put a price on it before a buyer does.

• A Database of 1,605 Pre-Seed and Seed Investors Who Lead Rounds 💼 (2 minute read)

  Every seed round waits on a lead, so we built a database of 1,605 pre-seed and seed investors who say yes to leading rounds. Each entry lists firm, title, cheque size, sectors, stages, past investments, location and contact details where available. Before you pitch, match their top cheque to the size of your round and go to the partner who can sign.

• a Free 10-Minute GTM Diagnostic 🚀 (1 minute read)

  HubSpot for Startups built a free diagnostic from its work with 35,000+ startups, and it takes about 10 minutes. It scores pipeline, messaging, content, competitive position and social visibility, then hands you a prioritized fix list built for your stage and tech stack. You also get a 90-day roadmap. Use it to pick the one growth problem to fix this quarter. [HubSpot]

  

• Cal AI’s UFC Creator Campaigns Failed to Drive a Download Spike 📱 (5 minute read)

  Cal AI signed UFC creators because fighters track every calorie, and co-founder Jake Castillo says "The UFC creators we signed posted and crickets." He now picks creators by reading their comment sections: "Views can lie. Followers can lie, but comments cannot." Revenue per thousand views sets the ceiling on what Cal AI pays. One flat-rate creator went from 10,000 to 12,000 views a video to a 7 million view post in month three. Price creator deals off your own RPM. [David Barnard] [RevenueCat]

  

• 47% of Founders Pay at Least One Senior Leader More Than Themselves 💼 (21 minute read)

  Hampton anonymously surveyed 250 founders and CEOs about their own pay. About 47% pay at least one functional leader more than their own base salary, and 15 take a $0 salary. Among bootstrapped founders, 39% take $500K+ on top of salary, while every venture-backed founder reported $300K or less in extra pay. Setting your own salary before a raise? Kruze's 2026 benchmark puts the median VC-backed CEO salary at about $159K. [Hampton]

• App Store vs. Web Checkout: Apple’s 15% Fee Changes the Math 💳 (16 minute read)

  On a $9.99 subscription, a small developer keeps $8.49 through Apple's 15% fee and $9.33 through web checkout with Stripe. That 84-cent gap covers losing about 9% of buyers at the web paywall, and published tests at the 15% rate came out flat or behind. On Android, a link plus Stripe keeps $8.33 against $8.49 in the app. Web checkout pays off when your conversion loss stays under 9%, and heavy inference costs raise that threshold. [Okane Land]

  

• Enterprise AI Firms Embed Engineers to Turn Platforms Into Customer Workflows 🏢 (4 minute read)

  Enterprise AI companies are sending forward-deployed engineers into customers to build the workflows their platforms promise, and some book that work as ARR. Lightcast data reported by Fortune puts postings for the role up more than 1,000% from January to August 2026, and US total compensation is pushing past $300,000. In diligence, split software revenue from services revenue before you apply a software multiple. [Crunchbase News]

  

Social Media Gems 💎

• GR Angel Launches After a Year in Stealth 💼 (Post)

  After a year in stealth, and launched GR Angel. They back founders and put them in front of their audience, including 600K+ followers and 10M weekly views. Across their personal angel checks and GR Angel, the portfolio has raised $1.2B+ and includes HappyRobot, Replit, Starcloud and Peec AI. Their first exit went from $10K MRR to $7M ARR in close to a year.

• Tesla Roadster Demo Ends With a Ramp Launch and Midair Flip 🚗 (Post)

  Elon Musk unveiled the Tesla Roadster by launching it off a ramp, and the car flipped in midair before landing safely. The Roadster is a collaboration between Tesla and SpaceX. The post links resources on how Musk builds, including SpaceX's 2017 pitch deck and a teardown of the SpaceX IPO S-1. Hardware founders should save the deck before their next raise.

• Bride Gifts Groom a $12,000 Nvidia RTX 5090 at Their Wedding 💍 (Post)

  A Chinese bride surprised her groom with a $12,000 Nvidia RTX 5090 at their wedding, and he was moved to tears. The card carries 32GB of VRAM, enough to run strong open models at home. For founders with heavy inference bills, a one-time GPU purchase is worth pricing against monthly API spend before the next renewal.

  

• Eric Schmidt Reportedly Holds a 1% Stake in Anthropic 💰 (Post)

  Former Google CEO Eric Schmidt reportedly owns about 1% of Anthropic, a stake that could be worth roughly $20 billion ahead of the company's IPO. Per the post, Dario Amodei's wife introduced him to the company, and his peak ownership of Google was 5%. The figures come from press reports. Early relationships still decide who gets the best allocations.

  

• Handwired Compiles JavaScript Programs Into Neural Networks 💻 (Post)

  Cheng Lou released Handwired, a toy project that turns regular JavaScript into a neural net with zero training. The compiled network matches the JS program's outputs exactly and runs in a browser. Demos include a small operating system built from compiled weights and a real-time search across 100k JSON records on WebGPU. Engineers can unfreeze those weights and train parts of a UI like any model. [Cheng Lou]

• Monaco Launches Agent Matrix to Drive Revenue Growth 📈 (Post)

  Monaco launched Agent Matrix on October 21, a system of many agents working together toward one outcome: faster revenue growth. CEO Sam Blond, a former Founders Fund partner and Brex CRO, argues every revenue team should have agents working 24/7. Early access is open by application. Sales leaders can apply and test it on one stage of the pipeline first. [@Sam Blond]

• 37signals Has Paid More Than $24 Million in Employee Profit Sharing Since 2020 🤝 (Post)

  37signals has paid out over $24,000,000 to employees through profit sharing since 2020. Jason Fried says the company pays real cash in place of stock options. Per its handbook, 10% of annual profits go to employees in shares based on tenure, starting after two years and maxing out at 10 years. Bootstrapped founders can copy the model to share upside in cash. [@Jason Fried]

  ---

Trending News ⚡

• OpenAI Models and Codex Arrive on Google Cloud 🤖 (2 minute read)

  OpenAI's frontier models and Codex agentic workflows are coming to Google Cloud's Model Garden. Customers will buy through Google Cloud Marketplace and can draw down existing GCP spend commitments, and the models will also reach Google Antigravity and the Gemini Agent. General availability is planned in the coming weeks, and Microsoft remains OpenAI's primary cloud partner. Teams sitting on GCP commitments will soon be able to spend them on OpenAI. [OpenAI]

• Nscale and Anthropic Discuss a Second $45B Compute Deal 🤝 (1 minute read)

  Nscale and Anthropic are reportedly close to a second $45 billion compute deal, a little over a month after their first one, per Bloomberg. Sources cautioned that talks are underway and the details could change. Both companies are pursuing IPOs, and Bloomberg reported that Nscale faces investor pushback on its proposed $30 billion valuation. Watch how these deals move neocloud valuations before you price a round in the space. [TechCrunch]

  

• Replit Makes OpenAI Its Default Model Provider 📊 (3 minute read)

  Replit made OpenAI the default model on its platform, and CEO Amjad Masad cited cost and performance at TechCrunch Disrupt. Customers can still pick Anthropic's Claude, and an insider says the Anthropic partnership is still in place. Masad said enterprise went from about 2% of revenue last year to about 15% this year, with more than 50% of revenue now B2B. For AI apps, the default model is a pricing lever. [TechCrunch]

  

• OpenAI Acquires Orchestra Bio to Expand Life Sciences AI 🧬 (2 minute read)

  OpenAI acquired Orchestra Bio, a platform where scientists hand the monitoring of studies and campaigns to AI agents. The team and technology join Rosalind, OpenAI's models and tools for life sciences research. A new medicine takes roughly 10 to 15 years from target discovery to US approval, and OpenAI wants agents to speed up the coordination work in between. Biotech AI founders now compete with OpenAI directly. [OpenAI]

• Anthropic Commits 5% of Its Shares to a Public Benefit Fund 🏛️ (2 minute read)

  Anthropic will commit 5% of its shares to the new Anthropic Public Benefit Fund, subject to stockholder approval, and the board can add 2% for every 50% increase in company value above a set baseline. The fund can make large donations to nonprofits or direct distributions to specific populations, and its equity is barred from lobbying or political giving. Investors modeling the IPO should include this dilution path. [Anthropic]

  

• Zuckerberg Considered Leaving California Over Proposed Billionaire Tax 💵 (2 minute read)

  A Meta SEC filing shows Mark Zuckerberg told a special committee he was considering leaving California ahead of the vote on Proposition 40, a one-time billionaire tax. The committee got him to agree to stay in California, or wherever Meta is headquartered, for eight years. A new stock class gives him $20 billion in "immediate liquidity" to pay the tax, and a person familiar says the class totals $150 billion. State tax policy is now part of where founders build. [Business Insider]

  

• Merz Pushes German Businesses to Adopt AI as Nvidia Expands 💶 (1 minute read)

  Chancellor Friedrich Merz urged German businesses to open their data to AI at the Invest in Germany Summit, where the economy ministry signed a memorandum with Nvidia. Nvidia announced planned investments in four German startups. The Made for Germany business group sees €2.9 trillion to €4.1 trillion in investment potential by 2035, and its members plan more than €850 billion through 2028. German industrial AI founders now have the chancellor openly pitching their category. [Reuters]

  

• Tolan Raises $30M as AI Companion Usage Grows 💰 (2 minute read)

  Tolan raised $30 million from investors including Union Square Ventures and Khosla Ventures, doubling its total funding. The AI companion app says annual recurring revenue is in the double-digit millions, with 250,000+ active users and 70 million+ conversations over the past year. Around 70% of users are women and 40% are parents. Companions that help people get things done are earning real subscription revenue. [TechCrunch]

  

• Anthropic CEO Defends the Company's AI Safety Record 🛡️ (2 minute read)

  Dario Amodei wrote a nearly 2,000-word reply to Encode AI's Nathan Calvin, who listed nine Anthropic actions he said undercut its safety commitments. Amodei answered with nine commercially costly decisions of his own, including releasing Mythos only to selected cybersecurity partners, and admitted some issues could have been handled better. With the IPO expected in November, investors will read these commitments as business risks. [Business Insider]

  

• Anthropic Reports $80.7B First-Half Net Loss Ahead of IPO 📉 (2 minute read)

  Anthropic's draft IPO prospectus shows a first-half 2026 net loss of $80.7 billion, up from $13 billion a year earlier. Most of it, $72 billion, is an accounting charge tied to the higher estimated value of its convertible notes. First-half revenue reached $16.3 billion and compute and infrastructure spending approached $9 billion. Third-quarter revenue hit $16.8 billion. When you model AI labs, read past the net loss to the operating line. [Reuters]

  

New Funds 💰

• Vintage Investment Partners raised $920M to invest in top venture funds and venture-backed companies through primaries and secondaries, lifting AUM above $4.8B.

• Seaya held a €600M first close of Seaya Nova, its new growth fund writing €20M to €50M checks into European Series B to D rounds.

• Inspired Capital closed Fund IV at $450M for early-stage startups in AI, healthcare, fintech, robotics and more.

• 8VC filed to raise up to $400M for 8VC DSS III, its third DSS fund.

• Lux Capital plans to raise up to $300M for a new science and technology fund.

• Inovia Capital held a $200M first close of Inovia Venture Fund 6 for technology startups.

• Bison Ventures closed Fund II at $160M for early-stage frontier tech, from AI and robotics to energy, biology and advanced materials.

• Nauta Capital held a €60M first close of its sixth fund for early-stage B2B software and deep tech in Europe.

• BackingMinds, the Swedish VC, held a first close of more than €50M for its third fund, backing 15 to 20 companies in AI, climate and resilience.

• Enzo Ventures closed its €20M Fund II, writing €250K to €600K pre-seed checks into Southern European founders at home and abroad.

That’s a wrap for this week.

If you want to stay ahead of the curve, make sure you're subscribed. No fluff, just real startup insights delivered straight to your inbox.

See you next time 🚀

A lot of you have subscribed to The Founders Corner, and I’m grateful for every one of you. But I’ve barely spoken to any of you. I don’t know what you’re building, where your raise has got stuck, or what’s keeping you up at night. That’s always bothered me.

So we’re going to fix it.

On Thursday 29 October at 4pm GMT, Paul (GTM expert), David (fundraising expert) and I are hosting a live onboarding call for paid subscribers and anyone on a free trial. It’s a proper conversation, not a webinar you sit through with your camera off. You bring whatever you’re wrestling with, and we dig into it together.

We’re also planning further calls on 25th November and 10th December.

Why I wanted to do this

I’ve been on the investor side of the table for the best part of two decades, and I’ll admit something. The standard rejection email says almost nothing. “Loved meeting you, not quite right for us at this stage, keep us posted.” You’ve probably had a few. You read them three times wondering if it was the traction, the deck or the valuation, and you never find out.

On this call, you can ask. I’ll tell you what I’d really be thinking if your deck landed in my inbox tomorrow, including the bits that aren’t comfortable to hear.

Who you’ll meet

A quick word about me, since you’ll be putting up with me for an hour. I joined MessageLabs in 1999 when it was a tiny team and stayed through to its $700m sale to Symantec. Then I co-founded Notion Capital. So I’ve been the operator trying to hit the number, and the investor deciding whether to back the person trying to hit it.

Paul Watson is our go-to-market person. If people like your product but sales won’t move, Paul will help you work out who to sell to, what to say and how to make it repeatable before you spend money scaling.

David Levine raises money for early-stage founders for a living, as CEO of Glenluna Ventures and Principal at Manchester Angels. First angel cheque or seed round, he’ll help with your story, your valuation and who to approach. Fair warning: David is wonderfully blunt.

Getting all three of us on your problem would normally take three introductions and a lot of diary juggling. Here, it comes with your subscription.

The tools we’ll show you

We’ll also walk you through the tools in your subscription, because there’s no point paying for something you never open. These are examples of tools that we can go through:

📈 How Investors Decide If You Are Ready to Raise Tells you how ready you are for Seed, Series A or Series B+, and what to fix first.

📋 The Pitch Deck Test Investors Use in 30 Seconds Upload your deck and get slide-by-slide feedback before an investor sees it.

🎯 The Quiet Filter That Decides Your Entire Fundraise Checks more than 2,500 investors against your stage, sector and cheque size, so you stop chasing people who were never going to reply.

Before you come

Places are limited so everyone gets a proper chance to ask something. Have a think about the fundraising question you’d ask an investor if you knew they’d answer straight, and the go-to-market problem that’s been on your list for far too long. The more specific, the better.

Not a paid subscriber yet? Start a 7-day free trial and the invite is yours too.

I’m really looking forward to meeting you.

Chris

Book your place on the call below

Read more

Seven prominent Silicon Valley investors. One email.

They asked for $150,000. For 10% of the company.

Five said no.

Two never replied.

Twelve years later, that company closed its first day of trading worth $86.5bn.

The Airbnb deck is the best twelve slides a founder can study, because it shows what investors saw, what they missed, and the one slide that made a $86bn company look small.

We’ve got the full deck. Here are the first four slides, and what each one did.

Want to get in front of 250k+ founders and investors?

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The Round Nobody Wanted

Michael Seibel made the introductions. Brian Chesky published the replies seven years later.

The ask: $150,000 at a $1.5m valuation.

The rejection reasons read like every no a founder still gets today:

Not our area of focus.

Outside our target markets. Come back at the Series A.

Too stretched right now, with worries about traction, technical hiring and the syndicate.

We’ve never been able to get excited about travel.

And the fifth. The market opportunity looked too small for our investment model.

Remember that last one. It comes back.

We can’t prove which version of the deck those seven saw. Chesky’s post shows the emails, not the slides. But the deck that survives makes exactly the argument they turned down.

Here’s what happened instead.

Y Combinator put in $20,000 in 2009. Sequoia followed with a $600,000 seed.

By 2015 the 10% the seven passed on would have been worth $2.4bn.

In December 2020 Airbnb priced its IPO at $68 a share and closed day one at $86.5bn.

$1.5m to $86.5bn. Roughly 57,000 times the price of the round nobody wanted.

That’s the company’s value, not any one investor’s return. Dilution across a decade of rounds would have cut a 10% stake many times over.

Last year it booked $12.2bn of revenue. It has more than 5 million hosts in over 220 countries and regions, according to its latest annual report.

So what was in the deck?

Slide 1: Seven Words That Name the Enemy

“Book rooms with locals rather than hotels.”

That’s the whole cover.

It names the customer: a traveller who needs a room. It names the supply: locals. And it names the incumbent the company is coming for: hotels.

Sequoia’s Writing a Business Plan tells founders to define the company in a single declarative sentence. Sequoia opens that same guide by noting that Airbnb’s founders used it to build their deck.

Most decks open on a logo and a tagline nobody remembers. This one opens on a fight.

Hold on to the word “hotels”. Slide five quietly takes it back.

Slide 2: Three Problems, Both Sides of the Market

Three lines.

Price matters to people booking travel online.

Hotels leave you disconnected from the city and its culture.

No easy way exists to book a room with a local, or to become a host.

The first two are the guest’s problem. The third is both sides at once.

That’s the line most first-time marketplace founders miss. They describe the buyer’s pain and forget the seller has one too. A marketplace with no supply is a website.

Airbnb pitched the host’s problem on slide two. Hosts became the company.

Slide 3: A Marketplace in Three Icons

Save money when travelling.

Make money when hosting.

Share culture with a local connection to the city.

Two economic promises, one emotional one. One for each side of the marketplace, and one for both.

Every two-sided business should be able to draw this slide. If you can’t say what each side gets in three words, the investor will assume one side gets nothing.

Slide 4: Proof Borrowed From Someone Else

Two numbers.

670,000 users on CouchSurfing.

17,000 temporary housing listings on Craigslist in San Francisco and New York, in one week.

Not one of those users or listings belonged to Airbnb.

That’s the genius of the slide. With almost no traction of its own, the deck proved demand by pointing at people already doing the behaviour on worse products. Strangers sleeping on strangers’ sofas. Strangers renting rooms off a classified ad.

If people will do it badly for free, they’ll do it well for a fee.

It’s the right move for a company with nothing yet. It’s also a slide with an expiry date, and later in the deck you’ll see what replaced it.

Our Most-Read Articles Right Now

🔥 The Pitch Deck Test Investors Use in 30 Seconds — Now You Can Too How your deck really lands, before the silent no.

🔥 The Way VCs Actually Calculate Your Valuation The return maths that sets your price before the meeting starts.

🔥 I Ran a Startup Through a VC Investment Committee. It Died in Four Minutes. The memo and the verdict, before you walk in.

📈 The 169 VCs Leading Rounds Right Now Who actually led a round in the last ninety days.

Then Comes Slide Five

Four slides in, the deck is doing almost everything right.

A one-line company. A problem for both sides. A marketplace in three icons. Demand proven before there was any to show.

Then the market slide.

Three circles. The biggest says 1.9 billion trips.

That’s the number founders remember. It’s the number they copy.

It isn’t the number an investor prices.

One of the other two is. Take it to the business model on slide seven and multiply.

The answer is the fifth rejection email.

Almost word for word.

What’s Below the Paywall

1. The Full Airbnb Pitch Deck. All 12 slides, ready to download, from the cover to the two competitive advantage slides

2. Slide 5: The Number Investors Actually Priced. The two-line calculation that turns Airbnb’s own market slide into “too small for our model”, and how to run it on yours before an investor does

3. Slide 7: The Business Model Maths. What the deck’s revenue line told a venture fund, set against the exit a fund needs to return itself

4. Wrong in Both Directions. What the deck projected for 2008 to 2011, what Airbnb actually booked, and the one kind of error investors never forgive

5. Slide 8: The Growth Hack Hidden in Four Words. The line at the bottom of the adoption slide that became one of the most studied growth moves in startup history

6. Slides 9 to 12: Competition and Advantage. The two-by-two every investor has seen ten thousand times, the claim the deck contradicts one slide later, and the advantage that sat sixth of six and turned into the moat

7. If Airbnb Pitched This Deck in 2026. The three slides it would add, the one it would rewrite, and five moves to take into your own deck

Paid subscribers also get: 60+ additional tools across every stage of fundraising and company building: investor research, pitch deck screening, meeting prep, term sheet analysis, financial models, and the full investor database library.

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Slide 5: The Number Investors Actually Priced

Read more

When I was a founder, this was the part that frustrated me most.

There was no clear way to see who invested at my stage, who understood my sector, who wrote the cheque size I needed, or whether they even invested in my part of the world.

I kept having conversations that were never going anywhere.

This tool fixes that.

It gives founders the clarity I wish I’d had: a simple way to know who is a realistic match before you spend hours chasing intros or writing messages.

Below is the tool that helps you read investor fit the same way an experienced VC would.

1. Where Your Fundraise Begins

The opening screen sets the expectation.

You get a curated list of investors matched to your stage, sector, geography and raise amount.

Three ideas sit at the heart of the tool:

Stage first Investors sort deals by stage before anything else. If your stage doesn’t line up, nothing else matters.

Quality over quantity This tool doesn’t hand you hundreds of names. It gives you the right ten to fifteen.

A database that keeps moving 3,200+ investors, refreshed continuously. No stale websites. No outdated lists.

The point is clarity, not volume.

2. Company Stage

Next, you choose the stage of your company: Idea, Prototype, Early Revenue, Scaling, Growth, Series B or Pre-IPO.

This matters because investors specialise heavily by stage.

It shapes how they see risk, what traction they expect, how they read the team, and how big a cheque they write.

As a founder, I never had visibility on this.

I couldn’t tell who genuinely invested early and who only said they did.

This step removes the investors who were never going to be a fit.

It protects your time and sharpens your outreach.

Want to get in front of 250k+ founders and investors?

For sponsorship opportunities across The Founders Corner newsletter and LinkedIn (160k followers), email: founderscornermedia@gmail.com

3. Sector

Here you pick your sector: AI, SaaS, consumer, fintech, healthcare, deep tech, web3 or generalist.

Investors rarely cover eight sectors at once.

They build deep expertise.

They understand the revenue model, the go-to-market motion, the customer, and the retention benchmarks for your category.

Sector alignment does more than lift reply rates.

It lifts the quality of the conversation.

This is where the tool filters for investors with real expertise in your market, not passing curiosity.

4. Raise Amount

Then you choose how much you’re raising.

Cheque size is one of the least understood filters in venture.

Investors have minimums, maximums, fund allocation rules and portfolio construction limits.

If your raise falls outside their range, the conversation is over before it starts.

I learned this the hard way as a founder.

I spent far too long talking to funds that were never going to take part.

This tool stops that happening.

It lines your raise up with investors who can actually join your round.

5. Geography and Target Markets

This step asks where you’re based and which markets you operate in.

Most funds invest locally, or close to it.

That isn’t just preference. It’s practical.

They understand the regulation, the hiring market and the competitive landscape.

Founders often overlook this.

Geography can quietly kill a deal.

This step makes sure you only speak to investors who realistically deploy capital in your region.

6. Your Investor Matches

The final page gives you a personalised list of investors, ranked by fit score.

You see:

• Top Matches

• Good Matches

• Partial Matches

And you understand why, through a clear breakdown of:

• Sector fit

• Stage fit

• Geography alignment

• Cheque size alignment

• The markets they invest in

Each investor card opens into a transparent explanation of where the fit is strong and where it’s weaker.

This is the part I wish had existed when I was raising.

Not just names. Insight.

Not just access. Understanding.

It makes outreach more deliberate, more focused, and far more productive.

Closing Thoughts

I built this because founders need a clearer way through the investor landscape.

Most fundraising mistakes aren’t about the product or the story. They come from talking to the wrong people at the wrong moment, and many founders only realise it after weeks of silence.

This tool removes that blind spot.

It shows you who is likely to care about your stage, your sector, your geography and your raise, from a pool of more than 3,200 investors.

If I’d had something like this when I was building, my fundraising would have been calmer and far more focused.

If you want to dig into the signals behind a successful raise, these are worth reading:

The Cold Outreach That Closed Mark Cuban - a real-world look at how elite founders win investor attention.

The Pitch Deck Test Investors Run In Thirty Seconds - the fast evaluation every VC runs before deciding whether to keep reading.

The Term Sheet Walkthrough - the terms that quietly shape founder outcomes long after the round has closed.

To see which of the 3,200+ investors match your company with the same clarity, the full tool is just below.

If you’re raising this year, you’ll want it.

Read more

Let’s get into it.

together with Metaview

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fillmore by Metaview is your AI recruiting coworker:

▫️ Finds great candidates and researches their backgrounds

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Teams like throxy (YC P25) and Ambience Healthcare see up to 49% candidate response rates, with outreach live 7 minutes after calibration.

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In-Depth Insights 🔍

• One Stripe Export Can Power a Board-Ready CEO Dashboard 📊

  An Excel workbook converts a Stripe or CRM export into eight KPIs, four charts, 14 data checks and 10 Claude prompts. Its fictional example, Northwind Metrics, records 6% monthly MRR growth, a 0.6x burn multiple and 90.7% net revenue retention over 12 months.

• Lightsprint Raises $6.1M to Let Product Teams Ship AI-Written Code 💻

  Lightsprint, a graduate of Y Combinator’s Spring 2026 batch, raised a $6.1 million seed round led by 645 Ventures. Product managers and designers describe changes in plain language, AI agents generate code, and engineers retain control over pull request merges, with the company reporting three times more code shipped per team.

• 38 Lessons From Four Years Building AI Products at PostHog 🧠

  PostHog has launched 10 AI products, including PostHog AI, Install Wizard, Replay Vision and Scouts, with nearly 100,000 monthly users. Its operating lessons span MCP servers, agent context, skills, evaluations, observability, unit economics and making every agent action visible to users. []

  

• Harbor Raises $10M to Insure Companies Against AI Agent Risk 🛡️

  Harbor emerged from stealth with a $10 million seed round co-led by Base10, Abstract and 8VC, founded by four Stanford alumni. Its platform evaluates the risks of individual agents, blocks decisions that violate company rules and provides insurance coverage for the remaining risks.

• Hiring Too Early Can Make a Startup Slower 📉

  A startup expanded from three or four people to more than 20 after its seed round, only to lose speed as communication and management overhead increased. Scaling before product-market fit created more coordination costs than execution capacity, forcing the team to shrink and exposing why hiring cannot compensate for founders’ weaknesses. [xiaofeidu.com]

  

• Code Is No Longer a Durable Software Moat 🏰

  An open-source Rust reimplementation of Photoshop emerged within weeks, illustrating how AI lowers the cost of replicating features users can observe. Defensibility increasingly depends on proprietary live data, distribution, customer trust, accountability and ownership of the outcomes customers pay for. []

  

• The CFO Is Becoming the Architect of Company Operations ⚙️

  AI-native finance tools are automating workflows across ERP, procurement, tax and accounts receivable, while finance teams build their own agents and automations. Some lean finance teams now target 2% of company headcount instead of the traditional 5% benchmark, with continuous planning, embedded controls and broader strategic responsibilities. [, and ]

  

• Logo Churn Can Start With Sales Compensation and Territory Design 🎯

  A Series A company with $6 million in ARR and five account executives lost 30% of its previous year’s new customers within 12 months, tracing the problem to account selection and sales incentives. The proposed fixes include defining the ideal customer profile around faster adoption and higher net revenue retention, assigning smaller named territories and rewarding reps more for high-value customers. []

  

Social Media Gems 💎

• Zuckerberg Delivers a Dublin-Sourced Keg of Guinness to The Cheeky Pint 🍺

  Mark Zuckerberg arrived at The Cheeky Pint with a keg of Guinness shipped directly from Dublin and handed it to the host. The visit followed a trip to Dublin earlier that week, turning the imported beer into an informal gesture rather than a typical executive appearance.

• Five Foreign-Born Leaders Run Major American Tech Companies 🌍

  Satya Nadella was born in India, Lisa Su and Jensen Huang in Taiwan, Elon Musk in South Africa, and Sergey Brin in Russia, with all five leading major US technology companies. Their backgrounds illustrate how global talent has shaped America’s technology sector, from semiconductor manufacturing and cloud computing to electric vehicles and internet platforms.

  

• Zuckerberg Assigns Kunal Shah to Lead WhatsApp’s Commerce Push 💬

  CRED founder Kunal Shah is taking on a leadership role at WhatsApp as Mark Zuckerberg pushes to expand commerce on the messaging platform, particularly in India, its largest market. Zuckerberg rates the challenge a 10 out of 10 in difficulty, signaling that WhatsApp’s commercial opportunity remains substantial but unfinished.

• Meta Commits Significant Compute to Muse Wonder Pre-Training 🧠

  Muse Wonder’s pre-training consumed a major share of Meta’s compute resources, with Mark Zuckerberg and Alexandr Wang navigating prolonged uncertainty during the process. Because frontier-model training offers few comparable examples, evaluating progress and predicting outcomes requires reasoning through the training process rather than relying on established benchmarks alone.

• Four Types of Ambition for Working With AI Models: Speed, Altitude, Depth and Range 🚀

  Each type comes with practical techniques, from identifying the goal behind the goal to using expert-level tools and drawing on knowledge from multiple fields. Research on self-efficacy suggests that setting specific, challenging goals can produce better performance than simply telling people to do their best. [Felix Rieseberg]

  

• TypeSafe Launches Jev and Raises a Series A Led by Andreessen Horowitz 💰

  Jev processed more than one trillion tokens in three days, attracted adoption across 29.4% of Fortune 500 companies, and grew its Discord community beyond 100,000 builders. TypeSafe secured its Series A just three weeks after launch, highlighting rapid early traction as the company begins expanding its team. [Diogo Alemeida]

• Microsoft Turns Copilot Into a Platform for Enterprise Software Creation 💻

  Microsoft introduced more than 30 Copilot skills across Dynamics 365 Sales, Service, and Customer Insights, alongside Copilot Managed Runtime for running code within company environments under IT governance. With Code in Copilot, businesses can describe their requirements and build customizations or entire SaaS modules connected to existing systems of record, positioning Copilot as both a workplace interface and a software development platform. [Satya Nadella]

  ---

Trending News ⚡

• Alexandr Wang Saw Zuckerberg’s Speed and Focus Firsthand 💬

  Before joining Meta, Alexandr Wang received detailed WhatsApp advice from Mark Zuckerberg and noticed how quickly he wrote while ruthlessly prioritizing his time. Within five or six weeks of Zuckerberg reaching out, Meta announced a 49% stake in Scale AI. [Business Insier]

  

• Opsin Raises $21 Million to Secure AI Agents 🛡️

  Opsin tracks where AI agents operate, which tools and data they access, and which behaviors pose risks, giving companies continuous visibility into their AI workforce. SYN Ventures and Race Capital led the round to expand the roughly 20-person company's sales and engineering teams. [Business Insider]

  

• SpaceX Promises Refunds for Grok Agent Purchase Errors 💳

  SpaceX will reimburse users if Grok's AI agent makes a purchasing mistake or exposes their card information. Grok, Meta, and OpenAI generally place transaction responsibility on users under their published terms, making SpaceX's explicit reimbursement pledge a notable difference. [Business Insider]

  

• Microsoft Claims Its MAI-Base-2 Model Outperforms OpenAI’s Base Model 🧪

  Microsoft AI CEO Mustafa Suleyman told employees that MAI-Base-2 is a stronger pretrained base model than the one powering GPT-5.6, using one-quarter of OpenAI's training compute. Microsoft also says its pretraining required 12 times less computation than DeepSeek-V4-Pro-Base. [Business Insider]

  

• OpenAI Accuses Former Employee at Apple of Taking Confidential Files ⚖️

  A new court filing alleges that a former OpenAI employee sent more than 100 files about an unreleased product to a personal account and later presented the product to senior Apple leadership. Apple says it has found no supporting evidence, and the case is expected to go to trial in October 2027. [Reuters]

  

• OpenAI Uses Its Models to Investigate a Hidden Office Recording Device 🎙️

  OpenAI used its models to analyze a recording device planted in an office, identified a suspected employee, and referred the matter to the FBI, which made an arrest. The employee was terminated, the investigation remains ongoing, and there is no indication that model weights, source code, or customer data were accessed. [OpenAi Newsroom]

• Character.ai Files for Chapter 11 to Pursue a Sale 🏛️

  Character.ai expects its court-supervised sale process to conclude by late November or early December 2026, with a shutdown possible if no actionable bid emerges. The platform remains operational, paid subscriptions continue, and users can delete accounts or export their data. [Character.ai]

  

• Amazon in Talks to Acquire AI Startup Decart for $7 Billion 💰

  Amazon is reportedly discussing a deal that could value San Francisco-based Decart at around $7 billion. Decart builds software that helps developers switch between AI chips, addressing a growing need for flexibility as demand for computing capacity rises. [WSJ]

  

• Nvidia Considers Deeper Investment or Acquisition of Reflection AI 🤝

  Nvidia is in early-stage talks that could lead to a larger investment, an acquisition, or an acqui-hire involving Reflection AI's team and technology. Nvidia has already invested $800 million in the startup, which launched its first open-weight model, Beam, on Monday. [Investing.com]

  

• OpenAI’s Annualized Revenue Nears $50 Billion, Investor Comparisons Complicate the Picture 📊

  OpenAI has told investors that its annualized revenue is approaching $50 billion, below an earlier reported figure of nearly $70 billion that emerged from comparisons with Anthropic. The companies calculate annualized revenue differently, with Anthropic including sales made through cloud partners while OpenAI excludes them. [TechCrunch]

  

New Funds 💰

• Union Square Ventures raised $900M across two funds to back early-stage technology companies and support follow-on investments.

• TCGX closed its Asia Life Sciences Fund at $600M to invest in innovative life sciences and biotechnology companies across Asia.

• Pear VC closed Fund V at $333M to back pre-seed and seed-stage startups across AI, enterprise software, biotech, fintech, healthcare, and consumer technology.

• Benchmark is raising $352M across two founders’ funds to support early-stage technology investments.

• Nauta Capital held the first close of its sixth fund at €60M, targeting pre-seed to Series A B2B software and deep-tech startups.

• Genesys Capital held the second close of its University Seed Fund to invest in early-stage life sciences companies emerging from Canadian research institutions.

• PSV Tech closed Fund II at €56M to back Nordic startups developing AI, software, and digital infrastructure solutions.

• NVM Ventures held the first close of its Lazio-focused first fund at €23.6M to invest in early-stage startups across deep tech, aerospace, robotics, cybersecurity, and AI.

That’s a wrap for this week.

If you want to stay ahead of the curve, make sure you're subscribed. No fluff, just real startup insights delivered straight to your inbox.

See you next time 🚀

This series is a preview of a new book I’ve written with my partners Richard Blundell and Paul Watson — The Selling Software Algorithm: The Go-to-Market Navigation System for B2B Software Leaders.

It isn’t a strategy deck dressed up as a book. Between the three of us we’ve spent close to seven decades inside B2B software as founders, operators, investors and coaches — building companies, exiting some, and quietly breaking a few along the way, which is where most of the real learning lives. Through our work at Vencha we’ve now supported hundreds of founders trying to do the single hardest thing in software: turn a clever product into predictable commercial traction in a market that’s noisy, cautious, and more crowded than it has ever been.

One pattern shows up again and again. Companies rarely struggle because the product is bad. They struggle because they have no navigation system for going to market. This book is that system.

Get My Copy

Last time, we closed the loop on the Selling Software Algorithm itself: nine steps distilled into one Playbook, ready to hand to a new hire on day one.

This time, we step outside the book and into the wider Founders Corner toolkit, starting with the question every one of those nine steps quietly assumed you’d already answered correctly: is the pain you built it all around actually severe enough to carry a company?

Here’s the failure this piece exists to prevent. A founder runs the testing calls. Ten buyers describe the same frustration, some of them with real feeling. The pain goes on the slide, the slide goes into the deck, the deck goes into the raise. Eighteen months later the pipeline is full of warm conversations that never quite close, and every lost deal goes down as “timing”. Nobody ever disagreed that the problem existed. They just never had a reason to pay to make it go away.

Table of Contents

• A Simple Example: The Pilot Everyone Loved

• Why This Question Comes After the Algorithm, Not Before

• Painkiller or Vitamin

• The Five Dimensions That Actually Matter

• The Guard Rail: Why Budget Presence Outranks Everything Else

• What the Numbers Say About Optional Pain

• The Nice-to-Have Trap

• The Honest Case Against Scoring Pain

• Reading Your Own Red Flags Honestly

• Five Questions to Answer Before You Score Anything

• Scoring Your Pain Before You Scale Around It

A Simple Example: The Pilot Everyone Loved

Picture a familiar shape of company. Not a real one, but you’ll recognise it.

The product helps operations teams see where internal handovers break down. The demo is lovely. Every head of operations who sees it nods, because every head of operations has lived the problem. Three of them agree to a pilot. The pilots go well. The users like it. Somebody posts a screenshot on LinkedIn.

Then renewal comes round, and the conversation moves to a different floor.

The CFO asks which budget it comes out of. Nobody’s quite sure. It isn’t IT’s problem, so it isn’t IT’s budget. Operations has a budget, but it’s already committed to headcount and a system they signed three years ago. The head of operations still likes the product. She just can’t find the money without taking it from something she likes more.

Two of the three pilots don’t convert. The third converts at a discount.

None of that was a product failure. The pain was real, widely felt, and optional. That’s the specific gap this article is about.

Why This Question Comes After the Algorithm, Not Before

The Algorithm sentence, the Playbook, the whole nine-step build: all of it assumes the three visceral pains you tested are real.

Testing ten buyers and hearing the same frustration repeated back is powerful evidence. It’s far better than what most companies have, which is a founder’s hunch and a competitor’s homepage.

But repetition is not the same as severity.

A pain can be widely felt and still be optional. Everyone in the room can agree it’s annoying, and nobody in the room can be held responsible for leaving it alone. Those are the pains that produce enthusiastic testing calls and quiet pipelines.

So this sits after the Algorithm rather than before it for a reason. You need the evidence first. Ten calls, real Economic Buyers, a ranked list. Only then is there something worth stress-testing.

And before you point a sales motion, a content plan and a market map at that list, it’s worth putting the pain itself through a harder test than “did people nod when I described it.”

Painkiller or Vitamin

The distinction is old and still gets ignored constantly.

A painkiller gets bought because not buying it has a cost the buyer already feels. Lost revenue. Regulatory exposure. A process that breaks under its own weight every month-end. The cost is already on someone’s desk, with their name attached to it.

A vitamin gets bought when there’s spare budget and spare attention. Both of those evaporate the moment a downturn or a reorg arrives, and the vitamin is usually the first line item somebody crosses out.

The book makes the same point from the other direction. When we describe the Perfect Customer Profile, the question we push founders to ask is where they become an invaluable painkiller rather than a vitamin. And when we describe the buyer readiness stages, the Stage 5 buyer, the one every founder dreams about, is defined by one thing above all: procurement approved, budget assigned. If the pain is low, the deal is low. If the pain is high, the deal is high.

Here’s the uncomfortable part. Founders rarely describe their own product as a vitamin. They just describe a painkiller in vitamin terms (”improves visibility”, “enhances collaboration”), or a vitamin in painkiller language (”eliminates risk”, “mission critical”). Either way, the description stops being evidence.

Most only find out which one they actually built when a renewal comes due.

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The Five Dimensions That Actually Matter

A rigorous read on this needs more than a gut check. “Do people care about this?” is too easy to answer yes to.

So the assessment breaks a problem description into five independent dimensions, each scored out of ten.

Budget Presence. Is there already real, allocated spend against this problem? Not hypothetical future budget. Not “they’d find the money if they saw the ROI”. Money already leaving the business because of this pain, through a tool, a team, a consultant or a workaround.

Urgency and Economic Impact. Does the consequence of not solving it touch revenue, regulation or operational breakdown? Or is it mainly emotional discomfort? Frustration is a useful signal in a testing call. On its own, it isn’t a purchase order.

Willingness to Pay. Is there an identifiable buyer who’d personally champion this? Not a department. A person, with a name, whose objectives get easier if the problem goes away.

Frequency. Does the problem recur weekly or monthly, or once a year? A pain that bites every Monday keeps itself on the agenda. A pain that bites every January gets forgotten by March.

Nice-to-Have Trap Severity. Would the business survive perfectly well without solving it at all? This one scores the danger, not the opportunity, and we’ll come back to it.

The point of splitting them is that they fail independently. A pain can be urgent and unfunded. Frequent and trivial. Expensive and ownerless. A single gut-feel score blends all of that into one comforting number. Five scores show you exactly which leg of the stool is missing.

The Guard Rail: Why Budget Presence Outranks Everything Else

These dimensions aren’t weighted equally.

Budget Presence carries 30% of the overall score. Urgency 25%. Willingness to Pay 20%. Frequency 15%. The Nice-to-Have score is then applied as a penalty of up to ten points on top.

Then there’s a hard guard rail.

If Budget Presence scores below four out of ten, the overall result is capped at 55, regardless of how urgent or frequent everything else looks.

Run that against a pain that tests brilliantly. Urgency nine. Frequency nine. A named champion, so Willingness to Pay eight. But no line item anywhere it could come out of, so Budget Presence three.

On enthusiasm alone, that looks like a company. Under the guard rail, it can’t score higher than 55, which keeps it inside the Moderate tier.

That’s a deliberate, conservative choice, and it’s the right one. No amount of enthusiasm compensates for a buyer who has nothing to pay you with. A problem that’s dramatic but unfunded is still unfunded.

It also reflects how deals actually die. The Economic Buyer in the book is the person whose name sits against a budget line your solution will affect. If there is no line, there’s no one whose name is on it, and the deal has nobody to sign it. It doesn’t get rejected. It drifts.

What the Numbers Say About Optional Pain

This isn’t just a framework preference. The data points the same way, from two different directions.

Start with what kills companies. CB Insights’ 2026 update looked at 431 VC-backed startups that shut down since 2023. Running out of capital topped the list at 70%, but their own reading is that cash is almost always the final cause of death rather than the root one. Underneath it sat the more telling reasons: poor product-market fit at 43%, bad timing at 29%, and unsustainable unit economics at 19%.

In other words, the money ran out because the market didn’t pull hard enough to replace it.

Then look at what kills deals. Matt Dixon and Ted McKenna, the team behind The Challenger Sale, analysed 2.5 million recorded sales conversations for The JOLT Effect. Somewhere between 40% and 60% of qualified pipeline ended not in a loss to a competitor, but in no decision at all.

That’s the competitor most founders never put on the slide. Doing nothing.

Neither figure isolates problem severity, and I wouldn’t claim one does. Product-market fit failures have plenty of causes, and Dixon’s own research is careful on this point: less than half of those no-decision losses came from buyers preferring the status quo. The rest were buyers who wanted to move and were frozen by the fear of getting it wrong.

That nuance matters, and it’s useful. A buyer who prefers the status quo has a severity problem. A buyer who wants to change but daren’t has a risk problem. Those need completely different fixes, and the first one can’t be fixed in the sales process at all.

If you’re building right now, these five are worth your time

• Why Most Founders Are Solving the Wrong Pain Entirely: ten pains go into the room, three come out, and only those three ever move a deal.

• The AI Search Playbook That Gets Your Startup Recommended: three prompts that show whether a model puts you on the shortlist when a buyer describes their exact pain.

• How to Turn Content Into Paying Customers: The 3-Pillar Playbook: three tested pains, three pillars, and the one question every topic has to pass before it gets written.

• Would Anyone Notice If Your Competitor Stole Your Homepage?: seven seconds, one armour-piercing message, and the swap test most hero lines fail.

• The 100 Accounts Your Sales Team Should Know by Name: how a filtered spreadsheet becomes 100 accounts and 300 named buyers your reps recognise on sight.

The Nice-to-Have Trap

The fifth dimension exists because founders are the worst-placed people to spot it in their own product.

You’ve lived with the problem longer than any buyer has. You’ve thought about it more carefully. You can describe its second- and third-order effects in detail. All of that makes it feel enormous to you, and none of it makes it enormous to them.

A nice-to-have has three tells.

It competes with “do nothing” as the realistic alternative. Not a rival product. Not a spreadsheet someone maintains with grim resentment. Just carrying on as before.

It improves comfort rather than economics. The team is happier. The dashboard is nicer. Nothing on the P&L moves.

It has no forcing function attached. No regulator, no audit date, no customer threatening to leave, no board asking why the number slipped.

A painkiller has none of those properties. The company can’t quietly decide not to deal with it, because something outside the company eventually forces the issue.

So here’s the one-year test. Ask what happens if the buyer simply ignores the problem for another year. Not what they’d lose in theory. What actually happens, to whom, and who notices.

If the honest answer is “not much”, the severity score gets penalised accordingly, however good the demo felt.

The Honest Case Against Scoring Pain

It would be strange to write this without giving the sceptics a fair hearing, because some of their objections are good.

“New categories don’t have a budget line yet.” True. If you’re genuinely creating a category, there’s no line item with your category’s name on it, and a strict Budget Presence score looks unfair.

“Strong pains create budget.” Also true. Budgets get reallocated mid-year when something hurts enough. The money doesn’t have to exist in advance if the pain is loud enough to go and find it.

“Some of the most severe problems are annual.” The audit, the renewal cycle, the regulatory filing. Frequency scores them low, and yet they can be existential when they land.

“It’s only as good as what you type into it.” The score reflects the founder’s description, and founders describe their own problems generously.

All four are fair. None of them is an argument for skipping the test.

On new categories: budget presence was never about a line item with your name on it. It’s about money already being spent on the problem. Headcount doing the work by hand. Consultants brought in twice a year. A legacy system everyone hates but nobody’s allowed to switch off. If you can’t find any of that, you’re not creating a category. You’re creating a hope.

On pains that create budget: they do, and they announce themselves when they do. You’ll hear it in testing as “we’d have to take it from somewhere”, followed by the name of the somewhere. If nobody names a somewhere, the pain isn’t loud enough yet.

On annual pains: that’s why Frequency carries 15% and not 50%. An annual pain with real budget and real consequence still scores well. It just can’t hide a missing budget behind a dramatic story.

And on garbage in: yes. Which is why the next section matters more than the score.

“Your buyers will happily agree the problem exists. The only question that matters is whether leaving it alone costs them anything they’ll be asked to explain.”

Reading Your Own Red Flags Honestly

Run your own top pain through this and you get a short list of specific warning signs alongside the score. Not vague encouragement. Not “consider strengthening your value proposition”.

The flags tend to cluster around the same few gaps:

• No named signer. The buyer is described as a department, or as “leadership”.

• No current spend. Nothing is being paid today to manage the problem, even badly.

• Comfort language. The consequences are about frustration, visibility or morale, with nothing that reaches revenue, compliance or a broken process.

• Annual or ad hoc frequency with no forcing event attached.

• “Do nothing” is the real competitor, and the description never says what doing nothing costs.

Some of the most useful red flags aren’t in the scoring at all. They’re in the vagueness.

Vague answers to “who signs for this” or “how often does this actually bite” are red flags worth writing down in their own right. A founder who can’t answer those precisely usually hasn’t tested with enough real buyers yet. Or, more uncomfortably, they’re describing a problem they find interesting rather than one the market finds expensive.

Those are very different situations. The first is fixed with five more testing calls. The second is fixed with a harder conversation, usually with yourself.

Five Questions to Answer Before You Score Anything

The tool can only score what you give it. So before you run it, go back to your testing notes and answer these in writing, using the buyer’s words wherever you can.

One: who signs? A role and, ideally, a name from a real call. If three buyers gave three different answers, write all three down. That’s information.

Two: what is it costing them today? Not what it could cost. What it costs now, in money, hours, headcount or penalties, as the buyer described it.

Three: whose budget would this come out of? The cost centre, the line, the existing spend it would replace. If the answer is “new budget”, note who would have to approve it.

Four: how often does it bite? The last time it happened, and the time before that. Dates beat adjectives.

Five: what happens if they ignore it for another year? The specific consequence, and the specific person who’d be asked about it.

If you can answer all five crisply, the score will mostly confirm what you already know. If you can’t, you’ve found the problem before the tool did, and that’s worth more than the number.

Scoring Your Pain Before You Scale Around It

The overall score lands in one of four tiers:

Critical Problem (80–100). A high-conviction opportunity worth building a company around.

Significant Problem (60–79). Worth pursuing, with iteration on how you frame and target it.

Moderate Problem (40–59). Needs stronger positioning, a sharper buyer, or a pivot in framing before you scale anything.

Weak Problem (0–39). High risk of building a vitamin and calling it a painkiller.

Notice where the guard rail puts an unfunded pain. Capped at 55 means it can never leave the Moderate tier, however good everything else looks. That isn’t a verdict on the founder. It’s a prompt to go and find the money, or the buyer whose money it is, before building any further.

This is exactly what the Problem Severity Validator produces. Describe the problem, the target customer, how it’s solved today and whether budget exists. It returns the five dimension scores, the tier, and the specific red flags behind the number, in about a minute.

Run it on your top pain. Then run it on your second and third. The ranking that comes back may not match the one from your testing calls, and where it doesn’t, the gap is worth a conversation.

Next in the series, we leave the pain itself behind and ask a different question: even if the pain is real, is this actually the right moment to go after it.

-Chris Tottman

Most founders can describe their top pain fluently. Far fewer can say, with a straight face, that there’s already budget allocated against it.

The Problem Severity Validator scores your pain across five weighted dimensions and tells you, in about a minute, whether you’re holding a painkiller or a vitamin.

Score Your Pain

Read more

Every founder raising right now has the same list.

Sequoia. a16z. Accel. Index. Lightspeed.

The same fifty firms. The same inboxes. The same 2% fee clock ticking behind every “let’s stay in touch.”

Now look at who actually wrote into this quarter’s biggest rounds.

Databricks raised $5bn. Premji Invest, the family office of Wipro founder Azim Premji, was in it.

Mistral raised €3bn. Hillspire, Eric Schmidt’s family office, was in it.

Helsing raised $1.8bn. ICONIQ was in it. Etched raised $700m. Thiel Capital was in it.

The most powerful money in venture right now is family money, and almost no founder has a map of it.

So we built one. 1,082 family offices across 36 countries. Every publicly reported cheque they wrote between July and September. One file.

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SMS now starts at $0.007 a text.

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The Investor Class That Doubled in Eighteen Months

On 1 February 2025, Dakota Marketplace tracked 1,945 family office accounts.

By August 2026 it tracked 4,744.

That’s 144% growth in a year and a half. Dakota calls family offices its fastest-growing allocator segment.

And the new arrivals don’t want what the old guard wanted.

Fintrx added 96 family offices to its database in Q2 2026. 92.7% listed direct investments as an interest. Just 10.4% listed hedge funds. Just 6.3% listed private credit.

Read that again. Nine in ten new family offices want to buy into companies directly.

Then the line that matters most for founders: 68.6% of newly classified offices came from entrepreneurial wealth.

These aren’t trust-fund portfolios. They’re founders who sold, now looking for the next founder.

Who Actually Wrote the Cheques in Q3

Fintrx logs family office transactions every month.

July: 107. August: 112. That’s 219 family office transactions in two months: 96 companies backed in July, 99 in August.

The money went where you’d expect, and then some.

34 of August’s deals were tagged AI. When UBS surveyed 307 family offices for its 2026 report, AI topped every other theme they follow by a distance.

But here’s the part the mega-round headlines bury.

Seed and pre-seed deals more than doubled in August, from 10 in July to 23, according to Fintrx.

And one office did ten of them on its own. YZi Labs, Changpeng Zhao’s family office, logged 10 transactions in August, mostly seed and pre-seed.

The door isn’t only open at Series E. It’s open at your stage too.

Many family offices never announce their deals, so public counts are a floor, not a ceiling.

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Why Family Office Money Is the Best Money in the Market

A VC fund has a ten-year life. Somebody needs your exit before it ends, so they can return capital to LPs and raise the next fund.

A family office has no fund life. The money is the family’s. There’s no LP waiting, no fund to close, no management fee to justify.

That changes what they’ll back, and for how long.

It’s why family offices show up in the hard, slow, capital-intensive companies VCs struggle to hold: fusion, energy storage, defence, biotech. In July, Burda’s family office backed Proxima Fusion’s $467m Series B. John Doerr’s office backed Antora Energy’s $550m Series C. In September, Druckenmiller’s Duquesne backed Precision Neuroscience.

And they hunt in packs. In August, Emerald AI’s $150m Series A drew four family offices into one round: Emerson Collective, Fahr, Foris Ventures and the Olayan Group.

Land one family office and you’re often one introduction away from the next.

The fair counterweight: patient capital is only patient while the principal is interested. UBS found only 35% of family offices have a defined succession plan for the office itself. Ask who decides after the founder of the fortune steps back.

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🔥 The Way VCs Actually Calculate Your Valuation - the return maths that sets your price before the meeting starts.

🔥 The Pitch Deck Test Investors Use in 30 Seconds — Now You Can Too - how your deck really lands, before the silent no.

📋 The Most Dangerous Document a Founder Will Ever Sign - not because it is complex, but because it looks harmless.

📈 Seeing Your Exit Before It Happens - how ownership, dilution and capital decisions quietly decide your outcome.

📋 What a Cap Table Actually Is (And Why Most Founders Misread It) - what your ownership tells investors before you say a word.

The Eight Most Active Family Offices of Q3

Here’s a taste of the deal log. These are the offices with the most publicly reported deals between July and September 2026.

1. a16z Perennial: 11 deals. Databricks, Hadrian, Base Power, Castelion, Etched. Fintrx classifies it as a family office; it’s the wealth platform built around Andreessen Horowitz.

2. YZi Labs: 10 deals in August alone. Changpeng Zhao’s office, mostly seed and pre-seed.

3. Bezos Expeditions: 5 deals. CuspAI’s $450m Series B, Oratomic’s $300m Series A, Generalist AI’s $200m Series B, LifeMine’s $188m Series E, plus funding for Blue Origin.

4. ICONIQ: 4 deals. Helsing, K2 Space, NinjaOne, and Prime Intellect’s $130m Series A.

5. Duquesne Family Office: 4 deals. Stanley Druckenmiller’s office: Ratio Therapeutics, Epicrispr, Fab2 and Precision Neuroscience. A clear biotech run.

6. Doerr Capital: 4 deals. Antora Energy, CuspAI, Paper’s $34m Series A, and a co-lead on Mazama Energy’s $135m Series B.

7. Thiel Capital: 3 deals. Etched, Neros Technologies and Network Bio.

8. Foris Ventures: 3 deals. Emerald AI, Hopscotch Primary Care and Voya Energy’s $35m Series A.

Notice the spread. Seed to Series G. AI to fusion to primary care.

These aren’t passive allocators. They’re operators with chequebooks.

The full log has 80 deals and 45 active offices. It’s in the file.

How to Get a Family Office to Say Yes

Most family offices don’t run a pitch inbox. Some don’t even have a website. For 520 of the 1,082 offices in our directory, no public source names the family behind them.

That’s why most founders give up. It’s also why the ones who don’t have far less competition.

Five moves that work:

1. Pitch the family, not the manager. A single-family office invests the family’s own money, often with one decision-maker. A multi-family office mostly manages money for several families. Our directory separates the 804 single-family offices from the 219 multi-family offices for exactly this reason.

2. Match the source of the wealth. Founders back what they know. Premji Invest came from Wipro and backs software and AI. Duquesne came from a macro investor and is running a biotech thesis. Read the family before you read the mandate.

3. Follow the syndicate. Family offices co-invest. Find a company they backed, find who sat next to them in the round, and ask that founder for the introduction.

4. Sell twenty years, not seven. No fund clock means you can pitch the company you’ll be in 2045, not just the exit in 2032. That story lands with a family. It rarely lands with a fund.

5. Look beyond New York and London. The growth is happening everywhere else.

And the growth isn’t in billion-dollar dynasties.

2,691 of the 2,799 new offices Dakota added are under $1bn or don’t disclose their size. Thousands of family offices most founders have never heard of. That’s where a relevant, well-researched email still stands out.

“Every founder is pitching the same fifty funds. The money that sat inside Databricks, Mistral and Helsing this quarter didn’t come from a fund at all.”

The Bottom Line

Founders pitch the same fifty VCs as everyone else.

They miss the investor class that grew 144% in eighteen months.

They assume family offices only write $1bn cheques, while seed and pre-seed deals doubled in a month.

They give up because family offices are hard to find, which is exactly why so few founders reach them.

The fix: find the single-family offices whose wealth matches your market, check who wrote a cheque in the last 90 days, and get introduced through the founders they already backed.

Do it before your round, not after the VC list runs dry.

The file is waiting below.

The Family Office Map: 1,082 Offices, 80 Q3 Deals, One File

We merged 43 public directories and deal trackers, removed the duplicates, and linked every row back to its source.

Exactly what you’re downloading:

• 1,082 family offices across 36 countries. Name, family or principal where public, type, city, country, region, published investment focus, and a source link on every row

• 804 single-family offices, flagged. The family’s own money, split out from 219 multi-family offices and 59 family holding companies so you pitch the right ones first

• The Q3 2026 deal log. 80 publicly reported family office deals from July to September: the office, the month, the company, the round, the round size, and the source

• The 45 offices writing cheques right now. Ranked by Q3 deal count, each with the biggest round it joined

• A one-click startup filter. Cuts 1,082 names to the 316 whose published focus mentions venture, growth, technology, AI, climate or founders

• A live dashboard. Counts by type, region and country, and Q3 deals by month, recalculating as you filter

• 27 market figures with sources. Fintrx, Dakota, UBS and FamilyOfficeHub data, ready to drop into your investor update or pitch deck

Filter to your region, switch on the startup filter, sort by Q3 activity, and you’ll have a shortlist before your coffee goes cold.

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The product. The team. The growth.

The VC is also doing a different sum.

Could this company, on its own, pay back the entire fund?

Most founders have never had the money side of venture explained to them.

So here it is, step by step, in plain English.

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The Six Terms You Need

• LPs (limited partners). The people who supply the money. Usually pension funds, university endowments, charities and wealthy families.

• GP (general partner). The VC firm itself. It chooses which startups to back.

• Fund. One pot of LP money, typically invested and returned over about ten years.

• Exit. When a startup is sold or lists on the stock market, so its shares turn into cash.

• Multiple. How many times over money comes back. Put in $1, get back $3: that’s 3x.

• DPI. Cash actually paid back to LPs, as a multiple. Paper value doesn’t count.

Step One: The Money Belongs to Someone Else

The LPs promise, say, $100m. The VC invests it in about 20 to 40 startups, then hands back whatever those investments become.

The VC is paid in two specific ways.

Step Two: The 2% Fee, Paid No Matter What

The first is a management fee. It’s a yearly charge to cover salaries, offices and running the fund.

The usual rate is 2% of the fund a year. Carta’s 2025 Fund Economics Report tracked about 2,000 US funds, and the typical fee has been 2% every year since 2018.

Most funds cut the fee once they stop making new investments.

A worked example. A $100m fund charges 2% for five years, then 1.5% for five more:

• Years 1–5: $2m a year = $10m

• Years 6–10: $1.5m a year = $7.5m

• Total fees: $17.5m

So only $82.5m of the $100m ever reaches startups.

Step Three: The 20% Share of Profits

The second is carried interest, usually called carry. It’s the VC’s share of the profit.

In most funds, LPs get all their money back first. Only the profit on top is split, usually 80% to LPs and 20% to the VC.

A worked example. The $100m fund eventually returns $300m:

• LPs get their $100m back first

• That leaves $200m of profit

• The VC takes 20%: $40m

• LPs take 80%: $160m, so $260m in total

That’s “2 and 20”. The fee keeps the firm running. Carry is where the real money is, and carry depends almost entirely on a few big wins.

Step Four: Most Startups Lose Money

Correlation Ventures looked at 21,640 US startup investments between 2004 and 2013.

In plain terms, out of every 100 investments:

• 65 returned less than was put in

• 25 returned between 1x and 5x

• 6 returned 5x to 10x

• 4 returned 10x or more

This lopsided pattern is called the power law. A handful of huge winners pay for everything else.

That’s why VCs ignore “safe” businesses. A company that can only double its money can’t make up for the losers.

Step Five: The Best Funds Lose More, Not Less

Horsley Bridge has invested in hundreds of VC funds since 1985. In 2015 Andreessen Horowitz (a16z) published its data.

About 6% of investments produced roughly 60% of all the returns.

The top funds weren’t better at avoiding losses. They simply had more giant winners.

They even lost money on more deals, because they took bigger swings.

The Fair Criticism

In 2012 the Kauffman Foundation reviewed twenty years of its own investments in VC funds. Of 88 funds, 69 failed to beat the stock market by enough to justify the risk and the wait.

Its point: the fee pays VCs well even when investors lose.

Both views hold. Venture works brilliantly for the few funds that land a giant winner, and poorly for most.

Our Most-Read Articles Right Now

🔥 The Way VCs Actually Calculate Your Valuation The return maths that sets your price before the meeting starts.

🔥 I Ran a Startup Through a VC Investment Committee. It Died in Four Minutes. The memo and the verdict, before you walk in.

📋 What a Cap Table Actually Is (And Why Most Founders Misread It) What your ownership tells investors before you say a word.

📈 The 169 VCs Leading Rounds Right Now Who actually led a round in the last ninety days.

Step Six: The Exit Size Your Investor Needs

This is where it hits founders.

A VC owns a slice of your company. Each new funding round shrinks that slice a little more.

The simple test: fund size ÷ the VC’s slice at exit = the sale price needed to pay back the whole fund.

• $100m fund owning 10% at exit: your company must sell for $1bn

• $500m fund owning 10%: $5bn

But paying back the fund is only break-even. Funds aim for about 3x after fees and carry. Chronograph calculated what that takes.

A $100m fund owning 10% needs its startups to sell for $3.4bn in total. A $1bn fund needs $34bn.

A $300m sale changes a founder’s life. To a $1bn fund, it barely registers.

That’s why “too early for us” can really mean “too small for our fund”.

Step Seven: Investors Wait a Decade for Cash

LPs pay fees and fund investments for years before any company is sold. Their balance goes down first, then climbs back up.

On a chart that looks like a J, so it’s called the J-curve.

The wait has got longer. Carta tracks how many funds have paid back any cash at all.

Carta’s Q1 2026 data shows that among funds started in 2017 and 2018, fewer than 20% have returned even the money investors put in.

A fund can look valuable on paper without paying anyone. That’s why investors now ask about DPI, the cash returned, first.

What This Means for You

Founders. Before you pitch, check the fund’s size and divide by the slice it would own. If your realistic best outcome is smaller, find a smaller fund.

VCs. The fee is guaranteed but carry isn’t. Make enough investments to have a real shot at one giant winner.

LPs. Ask the VC what the fund looks like without its single best company. That’s the honest view of its skill.

“A venture fund isn’t thirty bets. It’s one bet, placed thirty times, waiting to find out which one it was.”

Before You Sign Anything

A VC fund lives or dies on a few huge wins.

So every investor is quietly asking one question about you: could this be the one?

Answer it before they do. Divide the fund’s size by the stake they’d hold when you sell, then compare that with your honest best case.

The spreadsheet does the sums for you.

Inside the Spreadsheet: A Whole VC Fund, Modelled

What each tab does:

• Dashboard. 12 headline numbers, including investor returns, fees, carry and the result without the best company

• Inputs. Fund size, fees, carry, startups, ownership and timing, all editable

• Portfolio Outcomes. Where the money went and where returns came from

• Distribution Waterfall. Exactly who gets paid what, in what order

• Cash Flows. 12 years of money in and out, and the J-curve

• Return the Fund. The sale price needed across six fund sizes and six ownership levels

• Market Benchmarks. 13 published data points, each linked to its source

Enter your fund, switch off the best company, and see what it was carrying.

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60+ additional tools across every stage of fundraising and company building: investor research, pitch deck screening, meeting prep, term sheet analysis, financial models, and the full investor database library.

The VC Fund Economics Model

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Let’s get into it.

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In-Depth Insights 🔍

• 903 European Seed Funds With Direct Contact or Pitch Submission Routes📬

  A curated directory covers 903 European seed-stage venture firms, including 287 with direct email addresses, 386 with pitch submission links, and 230 offering both. Each entry includes the firm’s country, city, sector focus, and contact route, with filters and scoring fields to help founders build a targeted fundraising shortlist

  

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• Financial Network Intelligence Becomes Critical to AI-Era Fraud Prevention 🛡️

  Generative AI makes fake identities, forged documents, and impersonation easier to scale, with global fraud losses projected to reach $40 billion within the next few years. Static identity checks and some biometric defenses are increasingly vulnerable, while behavioral patterns across institutions and platforms offer additional signals for detecting fraud. Build a Personal Stock [PLAID]

  

• Analyst With ChatGPT for About $35 a Month 📞

  Twelve research prompts for GPT-6 Astra handle investment analysis, while Instinct tracks earnings, filings, prices, and deadlines and sends alerts by text or phone. The setup aims to replicate parts of a $250,000-a-year Wall Street research operation, while leaving every investment decision in the user's hands. Why the AI Agent

  

• Harness Determines Whether It Can Work Independently 🧰

  An agent harness combines the execution loop, tools, memory, saved state, permissions, and completion criteria that turn a model into a working system. Reported implementations range from DoorDash's platform to OpenAI's repository and Anthropic's role-based design, with one comparison putting a harnessed run at $200 and six hours versus $9 and 20 minutes without one. A Free Deck Review

  

• Tool Scores Startup Pitches From an Investor's Perspective 📑

  Founders can upload a PDF or PPTX, paste deck text, and select a funding round to receive an extraction test, seven round-calibrated scores, and a one-line assessment in about a minute. The review also identifies three priority fixes and three questions investors are likely to raise during the meeting.

  

• What to Do the Week a Competitor Raises Millions 📈

  A $30M Series B at $900K monthly burn buys roughly 33 months of runway, while investors expect 3x ARR before that capital runs out. A seven-day response plan prioritizes customer retention, investor communication, and team alignment, especially when the best-funded competitor leads only 3 of 10 AI categories assessed. []

  

• Four Team Size Breakpoints That Change the Value of a Hire 👥

  A hire can create outsized value when establishing a function from zero, while adding a second person to an existing function can introduce more coordination than output. Managers typically handle 7 to 10 direct reports before routine work starts slipping, and coordination becomes significantly harder beyond roughly 150 employees. []

• Why Raising Your Burn Rate Can Put Your Next Round at Risk 💸

  Founders who sharply increased spending to accelerate growth ran out of runway before reaching the milestones investors required for their next financing round. When lead investors decline to provide more capital, fewer bridge-round options at Pre-Seed and Seed leave startups with less room to recover. [Chrisneumann]

• GTM Operations Are Moving Toward Coding Agents and Git Repositories 🤖

  Coding agents can query data, call APIs, inspect files, and test results, turning go-to-market research, audience building, and signal detection into repeatable workflows. Marketers increasingly define evaluation criteria and validate outputs, while a shared Git repository preserves the context behind a platform serving more than 2,000 internal users. [John Kutay]

  

• How to Evaluate Talent When Every Candidate Has an AI Agent 🧪

  Hiring teams are focusing on problem framing, architecture, and judgment, the first 20% of a problem, as AI handles much of the implementation work. Effective experiments include unassisted code reviews with follow-up perturbation questions and a three-problem onsite lasting two hours, one hour, and 30 minutes. [JD Pruett]

  

Social Media Gems 💎

• EIC Accelerator Offers Startups Up to €2.5M in Non-Dilutive Funding 🔬

  The grant funds up to 24 months of development for validated technologies, particularly in AI hardware, climate, biotech, robotics and semiconductors. Startups can also seek €1M to €10M in equity investment through the EIC Fund, alongside coaching, mentoring and investor access.

  

• AI Company Org Charts Expose Each Business’s Priorities 😂

  OpenAI gets associated with money, Anthropic with safety, DeepMind with benchmarks, Meta with fragmented product teams and Microsoft with internal competition. For founders, reputation becomes an operating constraint because customers, candidates and investors interpret company decisions through the story they already believe.

  

• Mark Zuckerberg Challenged a New Hire to Build a Search Engine 🔍

  In 2005, Zuckerberg told new hire Aditya Agarwal that if he could build Facebook, Agarwal could build a search engine. Agarwal went on to build it. The management lesson is simple: give capable people ownership of ambitious problems instead of limiting them to the experience already listed on their résumé.

• Kevin O’Leary Says Elon Musk Spends His Waking Hours on Work 📡

  O’Leary describes Musk as devoting his 18 waking hours to high-signal work, treating distractions and low-value activities as costs to minimize. The broader operating principle is ruthless prioritization, although the claim about Musk’s daily routine remains O’Leary’s characterization rather than an independently verified account.

• OpenAI Shares New Mathematical Results From an Internal Frontier Model 🧮

  OpenAI released new mathematical results with input from an independent advisory group at the Institute for Advanced Study, reflecting growing collaboration between AI researchers and mathematicians. The shift is from solving routine exercises toward contributing to difficult research problems, with expert verification remaining essential to establishing the significance of each result. [Dan McAteer]

  

  ---

Trending News ⚡

• Instinct Adds AI Agents to Group Chats 💬

  Instinct is letting early-access users add a shared AI agent to group chats to coordinate plans, handle logistics, purchase tickets and split costs. Friends can participate without individual accounts, while personal agents must request permission to connect and the group agent cannot directly access members' personal accounts. [Business Insider]

  

• Jamie Dimon Warns of 10x Higher Cybersecurity Risk After Mythos 🔐

  JPMorgan Chase has seen cybersecurity risks rise tenfold following Anthropic's Mythos, according to CEO Jamie Dimon, as AI exposes previously unknown vulnerabilities. Dimon considers the threat from advanced models and autonomous agents legitimate, prompting the bank to intensify efforts to identify and address emerging security weaknesses. [Business Insider]

  

• Mistral Large 4 Targets Enterprise AI With 1 Trillion Parameters 🇫🇷

  Mistral Large 4 is available through a public guardrail endpoint, with open-weight release planned after safety testing. Trained on 4,000 Nvidia GPUs using Mistral's own computing infrastructure, the model targets demanding applications across cybersecurity, finance and chip design. [TechCrunch]

  

• Reflection Releases Beam, an Open-Weight Model for Coding and Reasoning 🔓

  Reflection's Beam activates 23 billion of its 501 billion parameters per token and reportedly matches GLM 5.2 on demanding reasoning tasks using three to four times less compute. The model was trained with reinforcement learning across 10,500 Nvidia GB300 GPUs over four weeks, with weights scheduled for release under the Apache 2.0 license later this month. [Decoder]

• Manus Raises More Than $500M Following Reversal of Meta Deal 💰

  Manus secured more than $500 million in a round led by Boyu Capital and IDG Capital, with existing investors Tencent, HSG and ZhenFund participating. The company has not disclosed its new valuation and has since introduced Manus 2.0 on its Cascade execution system alongside Cue, a standalone personal-agent app. [CNBC]

  

• OpenAI Rolls Out GPT-6 With Interactive Interfaces in ChatGPT 🖥️

  GPT-6 can generate interactive graphics, buttons, forms, charts and other interfaces tailored to each question while beginning responses before its reasoning is complete. Web search responses start 44% faster on average than with GPT-5.6 Instant, with the rollout starting October 7 for paid users and expanding to Free and Go users on October 8. [Jonas Reeve]

• Google Launches a Unified Gemini Agent for Enterprise Workflows 🤖

  Google's new business agent accepts objectives, plans multistep work and connects with systems including Google Workspace, Microsoft 365, Slack, Jira and Snowflake. Each agent receives its own Workspace account and email address, maintains an attributable audit trail and selects models automatically, with Anthropic's Claude models also available. [TechCrunch]

  

New Funds 💰

• Endeavor Catalyst closed its fifth fund at $320M, backing high-growth entrepreneurs across global markets.

• Forbion raised $2.6B across two funds to invest in innovative biotech and biopharma companies.

• Headline closed a $400M fund dedicated to backing European founders.

• Graduate Ventures launched its second €125M seed fund to support early-stage startups emerging from the Dutch university ecosystem.

• Linse Capital raised $150M across its early-stage deep-tech strategy and co-investment vehicles, supporting companies in AI, robotics, energy, mobility and space.

• Facktor Ventures launched its inaugural venture fund focused on community health and healthcare innovation.

• LAUNCHub Ventures secured a first close of over €65M for its third fund, targeting pre-seed and seed-stage startups across Central and Southeast Europe.

• Enable Ventures raised $50.31M to invest in technology companies advancing disability inclusion, assistive technology and workplace accessibility.

That’s a wrap for this week.

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See you next time 🚀

This series is a preview of a new book I’ve written with my partners Richard Blundell and Paul Watson — The Selling Software Algorithm: The Go-to-Market Navigation System for B2B Software Leaders.

It isn’t a strategy deck dressed up as a book. Between the three of us we’ve spent close to seven decades inside B2B software as founders, operators, investors and coaches — building companies, exiting some, and quietly breaking a few along the way, which is where most of the real learning lives. Through our work at Vencha we’ve now supported hundreds of founders trying to do the single hardest thing in software: turn a clever product into predictable commercial traction in a market that’s noisy, cautious, and more crowded than it has ever been.

One pattern shows up again and again. Companies rarely struggle because the product is bad. They struggle because they have no navigation system for going to market. This book is that system.

Get My Copy

Over the last several weeks, we’ve built an Economic Buyer, a Perfect Customer Profile, three visceral pains, a Solution Slide, a Champion, a sales motion, named accounts, a homepage message, a content plan and an AI visibility audit.

This time, we bring all of it into one place.

Here’s the failure this final piece exists to prevent. A company does every step properly. The testing calls happen. The pains get ranked by the people who sign the cheque. The accounts get sorted, the homepage gets rewritten, the content gets mapped. Then each piece of work goes into its own folder, owned by whoever happened to run that workshop, and nobody ever puts them side by side. Six months later a new hire joins, asks what the company does and who it’s for, and gets four different answers from four different people. The work was done. It just never became one thing.

Table of Contents

• A Simple Example: Three People, One Question

• The Cost of Nine Tabs

• Why the Founder Usually Is the Playbook

• What a Real Playbook Contains

• The Rule That Separates a Playbook From a Deck

• Who This Document Is Actually For

• What the Numbers Say About Day One

• The Honest Case Against Playbooks

• Why It Has to Be Reviewed, Not Just Filed

• The Test of a Good Playbook

• Where Your Nine Steps Become One Document

A Simple Example: Three People, One Question

Try this tomorrow. It takes about ten minutes and it’s slightly uncomfortable.

Pick three people in the business. One from sales, one from marketing, one from product. Separately, without warning, ask each of them the same question: who do we sell to, and why do they buy?

Write down what they say, word for word.

In most software companies I’ve watched run this, the answers overlap enough to sound like the same company and differ enough to cause real damage. Sales describes the buyer who said yes last quarter. Marketing describes the persona from the brand workshop. Product describes the user who files the most feature requests.

None of them is wrong, exactly. They’re each describing the part of the elephant they happen to be holding.

Now imagine a new hire asking those same three people that same question in their first week. That’s their onboarding.

The Cost of Nine Tabs

Every exercise in this series produces something real: a written sentence, a ranked list, a scored homepage, a set of named accounts with named people attached.

Left scattered across nine separate documents, though, that work quietly loses value every week.

A new sales hire reads the persona doc but never sees the pain ranking. A marketer writes content without checking the messaging scorecard. An SDR builds a prospecting list from an old filter because nobody told them the Focus zone changed. The algorithm sentence that was supposed to keep everyone saying the same thing ends up living in one founder’s head instead.

None of that is because the work was bad. It’s because the work was never assembled.

And assembly matters more than it sounds like it should, because each step only makes sense in the light of the others. A pain ranking without the Economic Buyer attached is just a list of complaints. A named account list without the pains is just a CRM export. A content plan without the stages is just a calendar. The value lives in the joins.

Why the Founder Usually Is the Playbook

There’s a reason this happens so reliably, and it isn’t laziness.

In the early days, the founder is the go-to-market system. They ran the testing calls. They heard the buyer hesitate before admitting the real problem. They know which deal was lost because the champion left and which was lost because the pain wasn’t sharp enough. All of that context lives in one place, and that place works perfectly well while the founder is on every call.

Then the company hires its second salesperson. Then its fourth. Then a marketing lead, then an agency. And every one of those people gets a verbal download of the market, delivered slightly differently each time, depending on what the founder was thinking about that week.

That’s not a playbook. That’s an oral tradition, and oral traditions drift.

The book is blunt about where the algorithm should live once you’ve earned it: not in a strategy folder nobody opens, but in the onboarding pack, in sales training, at the top of every marketing brief. The goal isn’t that the team has seen it. It’s that they can say it back to you.

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What a Real Playbook Contains

A slide deck summarising the journey falls a long way short of what we mean here. A working GTM playbook carries the whole chain, in order:

The algorithm sentence, in full. Our software is perfect for [a clearly defined Economic Buyer] in [a tightly defined Perfect Customer Profile] who’s struggling with [the three most visceral pains our software solves]. Every bracket filled, no softening.

The Perfect Customer Profile across all four strands. Not “mid-market companies in financial services”. The boundaries that let a rep qualify an account in thirty seconds without asking anyone.

The three ranked pains, with their consequences. Symptom, consequence, and the personal stake for the person whose name goes on the purchase order.

Both personas. The Economic Buyer who signs, and the Champion who carries your case into rooms you’ll never be in.

The sales motion, and why it fits. Not just what the motion is, but the reasoning, so the next leader doesn’t rip it up on instinct.

The accounts, sorted into their zones. Focus, Research, Respond, Ignore. Including the Ignore list, which is the part most teams quietly skip.

The scored homepage message. The armour-piercing line, and the score that tells you whether it still passes the swap test.

The content pillars, mapped to stage. One pillar per pain, with Stages 3, 4 and 5 each getting the kind of piece they actually need.

That’s the document. Nothing in it is new. Everything in it is already somewhere in your business.

The Rule That Separates a Playbook From a Deck

One rule matters more than any formatting decision.

Everything in the playbook should trace back to an input someone on the team actually typed, not a paraphrase written for the deck.

This sounds pedantic. It isn’t.

The moment a pain gets rewritten to sound better in a summary, it stops being the buyer’s language and becomes yours. “Constant internal firefighting that’s burning out the team” becomes “operational efficiency challenges”. The first one makes a COO wince in recognition. The second one could sit on any software company’s website without anyone noticing.

Polish is how evidence turns back into opinion. A playbook that’s been tidied up for the board has usually been tidied out of usefulness for the sales floor.

So leave the rough edges in. Keep the buyer’s exact phrase. Keep the ranking even when it’s awkward. If a rep reads it and thinks “that’s exactly how they said it on the call”, the document is doing its job.

If you’re building right now, these five are worth your time

• The AI Search Playbook That Gets Your Startup Recommended: three prompts that show whether a model puts you on the shortlist when a buyer describes their exact pain.

• How to Turn Content Into Paying Customers: The 3-Pillar Playbook: three tested pains, three pillars, and the one question every topic has to pass before it gets written.

• Would Anyone Notice If Your Competitor Stole Your Homepage?: seven seconds, one armour-piercing message, and the swap test most hero lines fail.

• The 100 Accounts Your Sales Team Should Know by Name: how a filtered spreadsheet becomes 100 accounts and 300 named buyers your reps recognise on sight.

• Why Most Founders Are Solving the Wrong Pain Entirely: ten pains go into the room, three come out, and only those three ever move a deal.

Who This Document Is Actually For

Picture a new sales hire’s first week.

The usual version: a laptop, a CRM login, a folder of onboarding docs of varying ages, a few recorded demos, and a founder’s verbal history of the market squeezed into a forty-minute slot on Wednesday afternoon. They spend the next month piecing together who the company actually sells to by listening to other people’s calls and guessing which version of the story is current.

The alternative: one document. Who we sell to, why it hurts them, how we talk about it, and who’s worth calling first.

That’s the test of a real playbook. It should be something a CRO could hand to a new joiner on day one and expect them to sound coherent on a call by day three.

Not expert. Not closing. Coherent. Able to describe the buyer’s world in the buyer’s words, name the three pains in the right order, and know which accounts to leave alone.

It isn’t only for sales, either. The same document briefs the agency, onboards the marketing lead, settles the product argument about which feature request actually matters, and gives a new board member the quickest possible read on whether the company knows who it’s for.

What the Numbers Say About Day One

The case for this isn’t just tidiness. Ramp time is expensive, and it’s getting more so.

The Bridge Group’s 2026 AE research, drawn from 158 B2B companies, puts average Account Executive ramp to full productivity at 6.2 months, the highest figure in the history of the study. Their own reading is that bigger deals and larger buying committees are stretching it out. For context, their earlier editions put it at 4.3 months in 2020, 5.3 in 2022 and 5.7 in 2024.

Half a year of salary before a new rep pulls full weight. Every week of that spent reconstructing the market from scraps is a week of ramp the company is paying for twice.

The broader onboarding picture isn’t encouraging either. Gallup has found that only 12% of employees strongly agree their organisation does a great job of onboarding, and that just 29% feel fully prepared and supported to excel in their role once onboarding is over.

Neither number isolates the effect of a written GTM playbook, and I wouldn’t claim one. What they show is the size of the gap a coherent first week could close.

A playbook won’t fix deal complexity. It won’t shrink a buying committee. What it can do is remove the part of ramp that has nothing to do with the market and everything to do with the company not having written itself down.

The Honest Case Against Playbooks

It would be odd to write this without giving the sceptics their due, because some of them are right.

Playbooks go stale. Plenty of companies have a beautifully formatted GTM document from two years ago that describes a market they no longer sell into. That’s worse than nothing, because new hires trust it.

Playbooks can replace judgement. A rep who’s been told exactly what to say can lose the habit of listening. The best salespeople I’ve worked with use a playbook as a floor, not a ceiling.

Very early companies are still finding out. If you’ve run four testing calls, not ten, the pains aren’t ranked yet. Writing them into a document can freeze a hypothesis before it’s earned the right to be treated as fact.

All three are fair. None of them is an argument for leaving the work scattered.

Staleness is a review problem, not a document problem. Over-scripting is a coaching problem. And if the evidence is still thin, the playbook should say so plainly, in the document, rather than letting everyone assume the founder’s latest guess is settled.

“A playbook isn’t a script for your team. It’s a record of what your buyers already told you, kept somewhere everyone can read it.”

Why It Has to Be Reviewed, Not Just Filed

A playbook that never gets revisited goes stale the moment the market moves.

Pain rankings shift as competitors enter. Personas age as your own product matures. A Focus account gets acquired and drops into Ignore. A homepage line that scored well a year ago starts appearing, almost word for word, on a competitor’s site.

The book makes the same point about the ICP Grid: revisit it quarterly, and watch how the points move. The playbook deserves the same rhythm.

The founders who get the most out of this treat it as a living document, reviewed every quarter alongside pipeline data, not a one-time deliverable filed after a workshop. Put the review in the calendar now. Give it an owner. Compare what the playbook says against what the last ninety days of deals actually showed, and change whichever one is wrong.

If the playbook and the pipeline disagree, the pipeline is usually telling the truth.

The Test of a Good Playbook

Here’s the simplest test I know.

Hand it to someone outside the founding team. Give them an hour. Then ask them to repeat the algorithm sentence back in their own words.

If they can, the playbook did its job.

If they can’t, something in it was still too vague to stick, and that’s worth fixing before it goes any further. Usually it’s one of three things: the Economic Buyer is described as a department rather than a person, the Perfect Customer Profile has no edges, or the pains are written in your language instead of theirs.

Run the same test on your next hire at the end of week one. Then again at the end of month one. The gap between those two answers tells you how much of your go-to-market is written down and how much is still being passed on by word of mouth.

Where Your Nine Steps Become One Document

This is exactly what unlocks once every step in the Builder has data: a single shareable Playbook, exportable as a PDF or as structured context ready to paste into a Claude or GPT project, so the whole team, and any AI tool they use, works from the same source of truth.

That last part is worth a moment. Your team is already asking AI tools to draft outbound, write blog posts and prep for calls. Every one of those prompts starts from whatever context the person happens to remember. Give the tool the playbook instead, and the drafts start from the buyer’s own words.

That’s nine weeks of this series distilled into nine steps and one document. If you’ve been building alongside us, you already have most of what belongs in yours.

The algorithm was always meant to be a compass rather than a slide. A compass only helps the person holding it, so hand it out.

-Chris Tottman

Most of this work already exists somewhere in your business. It’s just never been assembled into something a new hire could actually use.

The Selling Software Algorithm Builder Playbook pulls all nine steps into one shareable document the moment they’re complete.

Build Your Playbook

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Two pages. A signature. The wire lands by Friday.

Then another one. Then a bridge.

The cap table never moves. Founders 90%, option pool 10%, exactly as it looked the day the company was incorporated.

Eighteen months later, the seed lead sends the pro forma.

The founders own 54.9%.

A SAFE doesn’t remove dilution. It hides it until the day it all arrives at once.

So we modelled it. Three SAFEs, one seed round, one Series A, every point of equity traced.

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The Stack Nobody Adds Up

Our founders raise the way almost everyone now does. In Q2 2026, 93% of pre-seed rounds on Carta were SAFEs, and 91% of those SAFEs were post-money.

Three cheques over sixteen months:

SAFE 1. $500k from angels at an $8m post-money cap. 6.25%.

SAFE 2. $1.5m from a pre-seed fund at a $15m cap, with a 20% discount. 10%.

SAFE 3. $1m bridge from insiders at a $20m cap. 5%.

None of these are aggressive terms. A $15m cap is exactly Carta’s median for SAFEs of $1m–$2.4m.

The maths on a post-money SAFE is one line:

Stake sold = amount ÷ post-money cap

Add them up. 21.25% of the company, sold for $3m, at a blended $14.1m valuation.

The cap table shows none of it. A SAFE isn’t a share until a priced round converts it, so the issued cap table still reads 90%. As-converted, the founders already own 70.9%.

Post-money SAFEs also add rather than compound. Each new SAFE dilutes the founders and the pool. It never dilutes the earlier SAFE holders.

To be fair to the instrument, this is the post-money SAFE working as designed. When Y Combinator replaced its original SAFE in 2018, the stated point was that founders could work out exactly how much of the company they had sold the moment each one was signed.

The document is honest. Nobody adds the column.

Seed Day: 35 Points in One Closing

The seed round lands at $4m on a $20m pre-money. That’s $24m post, almost exactly the market middle.

The lead asks for the option pool to be 12% after the round, created in the pre-money.

The founders run the sum they have always run:

Expected stake = 90% × (1 − $4m ÷ $24m) = 75%

They get 54.9%.

SAFE conversion. −19.1 points.

Seed investors’ new shares. −11.8 points.

Option pool top-up. −4.2 points.

The SAFE stack takes more from the founders than the seed round does.

The pool top-up stings for the same reason. Because it sits in the pre-money, founders and SAFE holders fund it. The new investor doesn’t.

The 20.1-point gap wasn’t lost on seed day. It was sold months earlier, two pages at a time.

Carta’s Peter Walker puts it in one line: SAFEs do not really minimise dilution, they “simply delay it.”

The Cap Only Moves One Way

Most founders assume a hot seed round makes their old SAFEs cheap.

It can’t. A capped post-money SAFE owns amount ÷ cap however high the round prices.

Run the same stack through different seed rounds, with the $4m held constant:

$24m post. Founders 54.9%. SAFE holders 16.5%, worth $3.9m.

$48m post. Founders 61.3%. SAFE holders 18.4%, worth $8.8m.

$72m post. Founders 63.4%. SAFE holders 19.0%, worth $13.7m.

Tripling the seed price buys the founders 8.5 points. The SAFE holders’ share actually rises, because their stake is fixed before the new money arrives, and a pricier round means a smaller seed slice diluting them.

Now run it the other way. At $16m post, the caps stop binding and the SAFEs convert at the discount or the round price. Founders fall to 40.8%.

The cap protects the investor on the upside. Nothing protects the founder on the downside.

That is a fair trade. Those investors wired money with no price and a strong chance of losing it. And at $72m the founders’ own stake is worth $45.7m, against $13.2m at $24m. Nobody loses in the good case.

The point is to price it knowingly. Which means knowing where your cap sits before you sign.

What the Data Says About the Stack

Carta’s latest numbers show the stack getting bigger, not smaller.

In the first half of 2026, 94% of post-money SAFEs carried a valuation cap. 73% had a cap and nothing else. 21% had both a cap and a discount, down from 40% in 2021.

On SAFEs above $2.5m, the median cap reached $35m in Q2 2026, up 40% in a year. The average pre-seed instrument hit a record $276,000, and the larger pre-seed deals typically stack ten or more instruments together.

Higher caps sound like good news for founders. They are, per cheque. But bigger caps invite bigger cheques, and the stack is measured in percentage sold, not in cap.

Then look at where founders end up. Back to that first chart: 54.8% after seed, 35.6% after Series A. Our three-SAFE founders land at 54.9% after seed, within a rounding error of the median.

That doesn’t prove the median founder carries a stack like ours. It does show this outcome is ordinary, not a horror story.

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📋 The Most Dangerous Document a Founder Will Ever Sign The term sheet clauses that matter more than valuation.

📋 What a Cap Table Actually Is (And Why Most Founders Misread It) What your ownership tells investors before you say a word.

The Clause That Reprices Your Biggest SAFE

One instrument deserves its own warning: the uncapped MFN (Most Favored Nation) SAFE.

Y Combinator’s standard deal is $125k for a fixed 7%, plus $375k on an uncapped SAFE with “most favoured nation” terms. That $375k takes the lowest cap on any SAFE issued between the start of the batch and the next priced round.

YC’s own example: raise your next SAFEs at a $15m cap and the $375k converts to 2.5%.

Now let a friendly angel in at $6m after the batch starts. The MFN SAFE converts at $6m too. 6.25%.

One small cheque just repriced the largest SAFE on the cap table.

YC publishes all of this openly, and the deal is identical for every company. It’s a reason to keep caps consistent, not a reason to avoid YC.

Series A: Where the Stack Becomes Diligence

Take our founders into a $15m Series A on a $65m pre-money. That’s 18.75% new money, in line with Carta’s 18% median Series A dilution. The pool tops up to 15%.

Founders finish at 41.3%, comfortably above the 35.6% median.

A disciplined stack is the reason. Three SAFEs at rising caps converts cleanly. A pile of side letters, mismatched caps and an MFN that reset itself does not, and Series A investors read the stack before they read the deck.

What Each Side Should Do With This

Founders. Keep an as-converted column next to the cap table and update it the day each SAFE is signed. Set a stack budget before the first cheque. At a 25% budget, our founders have $0.94m of room left on a $25m cap.

Pre-seed investors. Post-money terms protect your percentage. Tell founders the running total anyway. A founder who discovers it at seed is a founder who renegotiates everything else.

Seed leads. Model the conversion before issuing the term sheet. A pool top-up in the pre-money comes out of the SAFE holders as well as the founders, and some of them will notice.

“Every SAFE is a slice of the company you have already sold. The cap table just hasn’t been told yet.”

The Bottom Line

Founders read the issued cap table, which doesn’t show SAFEs.

They price the seed round and forget the stack it converts.

They expect a hot round to shrink old SAFEs. Capped SAFEs don’t shrink.

They let one cheap cheque reprice an MFN they forgot they had.

The fix is one formula, run every time: stake sold = amount ÷ cap. Add every one. Then apply the seed and the pool to what’s left.

Run it before the next SAFE, not after the pro forma.

The file is waiting below.

The SAFE Dilution Model: 8 Tabs, 432 Live Formulas

Exactly what you’re downloading:

• Dashboard. 12 headline tiles, including founders after seed and Series A, the hidden gap, blended SAFE valuation and SAFE stack value, plus the founder equity waterfall and post-seed ownership pie

• Inputs. Founders and pool, a six-SAFE register (amount, post-money cap, discount, MFN and MFN window), the seed round, the Series A, six valuation scenarios and your stack budget

• SAFE Conversion. Every SAFE converted on cap or discount, whichever gives the investor more shares, with implied conversion valuation, value at the seed price and multiple on cost

• Pro Forma Cap Table. Ownership before conversion, as-converted, after seed and after Series A, with pie charts and a comparison against Carta’s medians

• Valuation Scenarios. The same stack converted into six seed rounds side by side, with ownership and value charts

• Stacking Tracker. What each SAFE does to the founders the day it’s signed, and the most you can raise on the next cap within budget

• Market Benchmarks. 16 published data points from Carta and Y Combinator, each linked to source

Enter your SAFEs in the order you signed them, set the seed you expect, and read the number your cap table isn’t showing you.

Paid subscribers also get:

60+ additional tools across every stage of fundraising and company building: investor research, pitch deck screening, meeting prep, term sheet analysis, financial models, and the full investor database library.

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Download the model below.

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Month over month, quarter over quarter, whatever makes the line steepest.

Ask the same founder how long it takes to recover the cost of acquiring a customer, and the answer slows down.

That gap is not an accident. Growth is the number founders are taught to perform. Payback is the number investors are trained to check first, quietly, before they say anything about the growth at all.

A dollar spent on acquisition that takes 30 months to come back is a very different dollar from one that comes back in three, even if both dollars bought the exact same customer.

So I built a tool that runs your own numbers through the same lens.

Brought To You By Omnisend

Most founders don’t have a retention team.

They have a dashboard nobody opens and an email list quietly leaving money on the table.

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Your whole email and SMS operation now runs from inside Claude and ChatGPT.

No logins. No dashboards. No agency retainer.

You type: “Which automations made the most money in the last 30 days, and which ones aren’t pulling their weight?”

It pulls your real numbers, tells you what changed and why, then builds the fix.

It handles segments, campaigns, deliverability audits and a monthly summary you can forward to your team.

Each one takes a single prompt.

This is what AI in a startup should feel like: less chat, more done.

Switching costs you nothing either. Omnisend’s team migrates your entire setup in 5 days, free: flows, templates and lists.

You could be paying up to 35% less by next week.

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What Payback Actually Measures

CAC payback answers one question: how many months of gross profit from a customer does it take to earn back what you spent to acquire them?

The formula:

CAC Payback (months) = CAC ÷ (Monthly Revenue per Customer × Gross Margin)

Not revenue recovery. Gross-profit recovery. That distinction is why two companies with identical revenue growth can post wildly different payback: the one with worse margins is quietly funding its growth for longer.

It matters more than almost any other single metric because it is a speed measure sitting inside a business that is usually being judged on speed. Growth rate tells an investor how fast you’re moving. Payback tells them how fast you get paid back for moving.

Every month of payback beyond your cost of capital is estimated to erode roughly 8% of valuation, per Bessemer’s growth-stage framework, which is why a partner will quietly do this maths in their head while you’re still on the market-size slide.

What “Good” Actually Looks Like in 2026

The median B2B SaaS company takes 16 months to recover its CAC, according to 2025 data compiled across 342 companies by Aleph and Benchmarkit. Top-quartile companies do it in 6 months or less. The bottom quartile stretches past 24.

Bessemer’s own rating scale, used inside real investment committees, is blunt about where each band lands:

• 0 to 6 months: best

• 6 to 12 months: better

• 12 to 18 months: good

• 18 to 24 months: concerning

• 24+ months: critical

ACV moves this more than anything else. Sub-$5K contracts recover in around 11 months on median. $50K to $100K enterprise deals stretch to roughly 22, because longer sales cycles and longer onboarding push the recovery date out. That’s structural, not a failure.

There’s a counterintuitive wrinkle in the growth-rate data too. Companies growing over 50% a year post a median payback of just 10 months. Companies growing a more modest 21 to 30% post the worst median in the dataset, at 22 months. Fast growth and efficient acquisition are not opposites: the companies that have actually found a repeatable motion tend to do both at once. The 22-month cohort hasn’t found the motion yet; they’re just spending harder to compensate for not having it.

That’s the number an investor is silently benchmarking you against. Here’s how to see your own.

Running Your Own Numbers

The input screen asks for exactly what a diligence process asks for, nothing more:

Acquisition Costs. Monthly spend and new customers per month, which the tool immediately turns into a raw CAC per customer.

Revenue & Margin. Your ACV (or ARPU, if you toggle to per-user), gross margin, annual churn, and any net revenue expansion. Churn alone gets converted into an average customer lifetime in months, sitting right under the field, so you see the assumption before it disappears into a formula.

Company Context. Stage and segment, because a 12-month payback means something completely different at Seed than it does at Series C.

In the example above, $10,000 in monthly spend against 10 new customers, a $12,000 ACV, 80% gross margin, 15% annual churn, the raw CAC lands at $1,000 per customer and the quick-read payback comes back at roughly two months, before the tool has even run the full model.

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The result screen does the part most spreadsheets skip: it puts your number next to the number that actually matters, which is the one for your stage and segment, not the generic B2B blend. That $1K CAC against an ~80-month customer lifetime returns an LTV:CAC ratio of 64.0x, a real, if unusually clean, illustration of what “excellent” looks like against the gold-standard 3:1 benchmark investors screen for.

Effective payback compounds in any net revenue expansion you entered, because a customer who expands pays you back faster than the raw margin math alone would suggest.

The timeline chart is the slide version of the same number. A dashed line marks CAC. A rising line marks cumulative gross profit. The month those two lines cross is your payback period, drawn rather than calculated, which is usually the version that survives being screenshotted into an actual board deck.

The Three Levers, and Why They’re Never Equal

Every CAC payback problem has exactly three levers. Reduce what you spend to acquire. Improve the margin on what they pay. Get them to spend more over time. Every fix a founder makes eventually collapses into one of those three.

What the tool does that a whiteboard can’t: it runs all three against your specific inputs and shows which one is actually load-bearing, because the honest answer is different for every company. For a company already sitting on an 80% margin, five more points of margin barely nudges a 2-month payback; there’s not much room left to compress. Cutting acquisition cost by a fifth, on the other hand, can shave a full month off a number that’s already excellent, because CAC has no floor the way margin has a ceiling.

Run your own numbers and the tool will tell you which lever is yours. For a Seed-stage SMB motion with thin CAC and fat margin, it’s rarely the same lever that saves an enterprise deal with a nine-month sales cycle and 60% margin. Knowing which lever is actually yours, rather than defaulting to “cut CAC” because that’s the instinctive answer, is most of what separates a founder who fixes payback in one quarter from one who spends a year optimizing the wrong line.

“Growth tells an investor how fast you’re moving. Payback tells them how fast you get paid back for moving, and it’s the second question, not the first, that decides whether they lean in or go quiet.”

What To Do With Your Number

Founders track growth because it’s the number that gets celebrated.

Investors track payback because it’s the number that reveals whether the growth is solvent.

A 2-month payback and a 24-month payback can sit under the exact same growth-rate slide, and only one of those companies can survive slowing down.

The fix is not complicated: know your number, know your stage’s benchmark, and know which of the three levers, CAC, margin, or expansion, is actually yours to pull. Guessing costs a fundraise. Running the model costs ten minutes.

Run this before your next investor call, not after the question lands and you’re doing the maths on the spot.

The CAC Payback Calculator

Exactly what you’re getting:

• Payback Engine. Enter acquisition spend, ACV or ARPU, gross margin, churn and expansion. Returns raw CAC, LTV, LTV:CAC ratio, quick payback, and effective payback with expansion compounded in

• Stage-Benchmark Verdict. Your payback scored against Bessemer’s five-band scale and your specific stage-and-segment median, not a generic blended number

• 24-Month Timeline Visual. Cumulative gross profit plotted against your CAC line, so the recovery month is something you can point to rather than calculate live

• Three-Lever Model. Reduce CAC by 20%, improve margin by 5pp, or add 2% monthly expansion, each run against your own numbers so you see which lever actually moves your specific payback period, not a generic one

Enter your real numbers first, read the verdict against your stage, then use the lever panel to find the one change that’s actually worth making this quarter.

Paid subscribers also get:

60+ additional tools across every stage of fundraising and company building: investor research, pitch deck screening, meeting prep, term sheet analysis, financial models, and the full investor database library.

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Let’s get into it.

together with Hard Skill Exchange:

If agents now do the buyer’s research, your GTM can’t run on fixed sequences and static playbooks. It has to learn from every account, interaction and outcome, and adjust what happens next on its own.

That’s the idea behind Recursive GTM, a free virtual session on October 15:

▫️ How self-improving systems are being built across marketing, sales and customer success
▫️ Live demos and 30-minute practice sessions with leaders from Dialpad, ZoomInfo, 1mind, Vivun and Aligned
▫️ How to compare AI-native GTM operating models and find the one that fits your org

Save your free seat!

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• The 10-Year Plan That Starts With a 6-Month Question ⏱️
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Trending News ⚡

• Nvidia Launches Open Agent Safety Platform 🛡️
  Nvidia introduced OpenShell and Sentry to control what AI agents can access across compute and network layers. The platform is designed to give developers tighter boundaries and monitoring as agents operate with more autonomy. [CNBC]

  

  
  

• Bill Gates Maps AI’s Two-Phase Future 🤖
  Gates expects roughly two decades of labor-market adjustment as AI spreads, with safeguards needed against new forms of misuse. He predicts a later phase where humanoid robots and AI automate tasks like building homes and producing food at scale. [Business Insider]

  

  
  

• AMD Agrees to Buy World Labs for $8.2B 🌍
  AMD is acquiring World Labs, whose models are built to understand physical environments, with regulatory approval still pending. Fei-Fei Li will join AMD as executive vice president and chief scientist once the transaction closes. [TechCrunch]

  

  
  

• Sam Altman Talks Tokens, Safety and Jobs at DevDay 🎤
  Altman criticized token counts as an AI metric, called for a centrist approach to safety and introduced Dots as OpenAI’s latest agent product. He also said this is a strong time to enter the job market and that using Dots has reduced his attachment to his phone. [Business Insider]

  

  
  

• Micron Raises Revenue Outlook to $61.5B 📈
  Micron expects first-quarter revenue of $61.5 billion, above the $57.02 billion analyst estimate, as long-term customer commitments continue climbing. Fourth-quarter revenue reached $54.23 billion, more than quadrupling year over year and beating expectations of $51.07 billion. [Reuters]

  

New Funds 💰

• Zero Infinity Partners closed Fund II at $156M, backing early-stage companies at the intersection of physical infrastructure and technology.

• Protego Ventures closed Fund I at $125M, investing in early-growth Israeli defense technology companies across autonomous systems, AI, sensing, navigation and related technologies.

• DIG closed Fund III at $120M, backing European pre-seed and seed-stage companies building enterprise AI and cloud infrastructure.

• Hatteras Venture Partners held the first close of Opportunity Fund II, targeting $100M, to provide follow-on capital to healthcare companies approaching key clinical, regulatory and commercial milestones.

• Sofinnova Partners closed Sofinnova MD Start IV at €82M, backing the creation and development of 6–8 new medtech companies across Europe and the US.

• Eureka! Venture launched Eureka! Fund II – Deep Tech Lazio with a ~€20M first close, investing in early and development-stage deep-tech startups, spin-offs and innovative SMEs.

• BAG Ventures closed its inaugural Fund I at $11.3M, backing early-stage companies.

That’s a wrap for this week.

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See you next time 🚀

This series is a preview of a new book I’ve written with my partners Richard Blundell and Paul Watson — The Selling Software Algorithm: The Go-to-Market Navigation System for B2B Software Leaders.

It isn’t a strategy deck dressed up as a book. Between the three of us we’ve spent close to seven decades inside B2B software as founders, operators, investors and coaches — building companies, exiting some, and quietly breaking a few along the way, which is where most of the real learning lives. Through our work at Vencha we’ve now supported hundreds of founders trying to do the single hardest thing in software: turn a clever product into predictable commercial traction in a market that’s noisy, cautious, and more crowded than it has ever been.

One pattern shows up again and again. Companies rarely struggle because the product is bad. They struggle because they have no navigation system for going to market. This book is that system.

Get My Copy

Last time, we turned three visceral pains into a content framework your team can actually write against.

This time, we look at what happens to that content once a buyer stops googling and starts asking an AI model instead.

Here’s the failure mode this piece exists to prevent. A company does the pain work properly, writes genuinely good content against it, publishes on a sensible cadence, and watches organic traffic tick up. Meanwhile a buyer three hundred miles away describes that exact pain to a model, gets back three companies to look at, and none of them is yours. Nobody rejected you. You were never in the room to be rejected.

Table of Contents

• A Simple Example: The Shortlist You Never Saw

• A Question Founders Rarely Ask Themselves

• Why This Matters More Than It Looks Like It Should

• Using the Buyer’s Own Pain Language as the Test

• Three Prompts Worth Running Before Friday

• What Actually Gets You Cited

• This Is Not Traditional SEO

• What the Sceptics Get Right

• Where Your Pain Language Comes Back In

• Auditing Your Own Visibility

A Simple Example: The Shortlist You Never Saw

Think about the last time you bought software for your own business. Not a big platform decision with a committee attached. Something mid-sized, where you knew roughly what you wanted and needed to find out who does it.

There’s a decent chance you didn’t open a search engine at all. You described the problem to a model in your own words, read the answer, asked a follow-up, and came away with two or three names worth a look. Maybe you checked a review site afterwards to make sure the model hadn’t invented anything. Then you went to those two or three websites, and only those.

Now flip it around. Somewhere in your market, a buyer did exactly that this week about the problem you solve.

They described the workaround they’re using. They asked what else is out there. They got an answer, delivered in a confident paragraph, with a small number of companies named in it.

You will never see that conversation. There’s no referral header, no search console entry, no form fill, nothing in the CRM. The only trace it leaves in your business is an absence: a deal you never heard about, won by a competitor who was in the answer.

A Question Founders Rarely Ask Themselves

Most founders can tell you their domain authority, their organic traffic, and roughly where they rank for a handful of keywords. Those numbers get reported monthly and argued over quarterly.

Ask the same founder what ChatGPT or Claude says when a buyer asks it to recommend a solution to their exact pain, and the answer is usually silence. Not disagreement. Silence, followed by “that’s a good question.”

That silence is the gap. A growing share of buyer research now happens inside a conversation with a model rather than on a results page, and most companies have no idea whether they exist in that conversation at all.

What makes it awkward is that it costs nothing to check. This isn’t a capability gap or a budget problem. It’s a blind spot that stays a blind spot because nobody has been given the job of looking.

Why This Matters More Than It Looks Like It Should

A search engine shows ten blue links and lets the buyer choose. The buyer sees who else exists, even if they click the first result. Position four still gets seen.

A model doesn’t do that. It picks an answer, sometimes with a couple of alternatives, and states it with confidence. There is no position four.

So if you’re not in the answer, you haven’t slipped down a ranking. You’ve disappeared from the conversation entirely, for a buyer who never opened a search engine and will never know you were an option.

Traditional search punishes you with less traffic. Model-mediated search punishes you with absence.

And the second one is much harder to notice, because absence doesn’t show up in any dashboard you currently look at. Your traffic can be flat and healthy while an entire category of first touches happens somewhere you can’t see.

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Using the Buyer’s Own Pain Language as the Test

Here’s where most people get the test wrong on the first attempt.

They open a model and type “is [company name] any good”. It says something flattering, everyone relaxes, and the exercise gets marked as done.

That test tells you almost nothing. You’ve handed the model your name, so of course the answer is about you. What you need to know is whether you appear when the buyer doesn’t yet know you exist.

So ask the questions your Perfect Customer would actually type, in their own words, using the language you captured during pain testing. Not your category name. Not your product name. Their description of the problem, in the register they used when they were talking to you about it.

“Alternatives to manual exception tracking spreadsheets” tells you far more than “is [company name] any good”, because it tests discovery rather than reputation.

This is also the moment you find out whether your pain testing was any good. If you can’t write five queries in your buyer’s voice without inventing them, that’s not an AI visibility problem. That’s a discovery problem wearing a different hat, and the fix is upstream.

Three Prompts Worth Running Before Friday

You don’t need a tool to start. You need twenty minutes and an honest note of what comes back.

One: the cold discovery prompt. Describe the pain, the type of company and the constraint, exactly as your buyer would. “We’re a 400-person logistics business coordinating multi-site handovers in spreadsheets and it keeps failing. What software exists for this?” Note every company named, in order.

Two: the workaround prompt. Name the thing they’re doing instead of buying. “What are the alternatives to running compliance reporting out of Excel?” Most of your real competition is a workaround, not a competitor, and models answer this question far more often than anyone tracks.

Three: the shortlist prompt. Ask it to compare. “Compare the main options for X for a mid-market UK operator.” This is the one that shows you how you’re framed when you do appear, which is a separate problem from whether you appear at all. Being named as the cheap option, or the one for small teams, is a positioning issue you’d otherwise never discover.

Run each one two or three times and in more than one model. The answers vary, and the variation itself is data. Keep the notes somewhere shared, because this only becomes useful when you can compare it against the same prompts in ninety days.

If you’re building right now, you’ll need these:

• 243 Ways to Fund Your Startup Without Giving Up a Share: 243 programmes across 63 regions, and the eligibility gate that quietly disqualifies most applicants.

• 15 Claude Skills That Run Your Entire Raise: install once, and your story stops drifting somewhere around investor number forty.

• The Investors Who Are Actually Writing Cheques In H2: 287 family offices your warm intro list doesn’t know exist.

• The Claude Guide Every Founder Should Run Before Fundraising: nine prompts that replicate the screen your deck meets before a partner ever opens it.

• How to Build Your Fundraising Narrative with Claude: the five-prompt sequence that puts your story in the order it needs to land, not the order it happened to you.

What Actually Gets You Cited

Models lean toward content with three properties, and none of them is length.

It answers one specific question plainly. A page that resolves a single question in its first hundred words is far more useful to a model assembling an answer than a page that circles a topic for two thousand words before committing to anything.

It’s structured clearly enough to extract. Headings that say what the section contains. Answers near the top rather than withheld for the reveal. Lists where lists are genuinely the right shape. This is not a style preference; it’s the difference between being usable and being skipped.

It carries some signal of who you are. A named author with a traceable history. A consistent description of the company across your site, your profiles, your directory listings and anywhere else you appear. Structured data that confirms what you do rather than leaving it to be inferred. Models are pattern-matching for corroboration, and inconsistency reads as noise.

Generic “about us” copy rarely gets cited, because it describes a company rather than answering anything.

A direct comparison page that names the workaround your buyer is currently using, explains plainly why it breaks down at a certain scale, and is honest about when it’s still the right answer, gets cited far more often. It’s answering a question someone actually asked.

This Is Not Traditional SEO

This is the part that catches experienced teams out, because it sounds like a job they already do.

Ranking for a keyword and being the answer inside a model’s response are related but no longer the same game, and the gap between them has widened fast.

Moz analysed close to 40,000 queries in February 2026 and found that 88% of citations in Google’s AI Mode did not appear in the organic top ten for the same query. Only around one cited URL in ten matched a top-ten ranking. Ahrefs, looking at 863,000 keywords in the same period, found that the share of AI Overview citations coming from top-ten organic pages had fallen to 38%, down from 76% roughly seven months earlier.

Take the direction rather than the decimal points, because the methodologies differ and these numbers will keep moving. The direction is unambiguous: a page can rank perfectly respectably and still never be pulled into a generated answer.

The mechanism behind it matters for what you do next. These systems don’t answer the question they were given. They fan it out into a set of related sub-questions, search against each one, and assemble a response from across all of them. Your beautifully optimised page for the head term never enters the picture, because the model was answering six narrower questions you never wrote a page for.

The practical implication is uncomfortable and quite freeing. Some of your best-performing traditional content needs a second pass, written to answer one question directly rather than to cover a topic broadly. That’s usually an editing job on work you’ve already done, not a new content programme.

Worth saying plainly: none of this is a reason to abandon organic search. It still drives real pipeline for most B2B software companies. This is an additional surface, not a replacement one.

What the Sceptics Get Right

There’s a version of this argument that overshoots, and it’s worth giving it a fair hearing before you rebuild anything.

The answers are unstable. Ask the same question twice and you’ll get different companies named. Anyone selling you a single visibility score as though it were a rank position is overstating what the data can bear.

Buyers also don’t take the answer on faith. TrustRadius’s 2026 buying research found that the overwhelming majority of buyers who used AI in their purchase journey went on to fact-check what it told them, at least some of the time. Gartner’s 2026 survey found a similar pattern: buyers use generative AI during purchases, then turn to a rep to validate what they found. Reviews, demos, references and peers still decide deals.

And the headline forecasts have been oversold. Gartner’s much-quoted February 2024 prediction — that traditional search volume would fall 25% by 2026 as AI chatbots absorbed queries — gets waved around at board meetings far more often than anyone actually models what it would mean for their own numbers.

So the sober version is this. Models rarely close the deal. They build the shortlist.

That’s still the part of the funnel you care most about, because Forrester’s Buyers’ Journey Survey put the proportion of business buyers using AI somewhere in their most recent purchase at 94% in its 2026 read, up from 89% the year before — and found generative AI named as a meaningful information source more often than vendor websites, product experts or sales.

Being wrong about this is asymmetric. If you overinvest, you’ve written clearer content and tidied your structured data. If you underinvest, you’re absent from the list before the evaluation starts.

Where Your Pain Language Comes Back In

Notice what this whole exercise runs on.

It runs on the three visceral pains you extracted from testing calls. It runs on the Perfect Customer Profile that tells you which buyer’s vocabulary to use. It runs on knowing which workaround they’re currently living with, which is something you only learn by asking.

That’s the argument for doing this now rather than last year. A team without the pain work has nothing to type into the prompt except their own category language, which is exactly the language a buyer doesn’t use. They end up testing whether they show up for the phrase they invented, which is a test you can pass while still being invisible.

“Your buyer doesn’t search for your category. They describe their bad Tuesday. Everything you’ve built in this series is a record of how they describe it.”

The work you’ve already done in this series is, in effect, a transcript of how your market talks when nobody’s selling to them. That transcript is the test set. It’s worth considerably more than a keyword list.

Auditing Your Own Visibility

Do the twenty-minute version this week: five queries in your buyer’s language, run across two models, results written down. Then decide who owns re-running them quarterly, because a single reading is a snapshot and the whole point is the trend.

Then fix the two cheapest things. Make your entity consistent everywhere you appear, so the same description of what you do shows up on your site, your profiles and your listings. And rewrite your three strongest pages so each answers one specific question in its first hundred words instead of building to it.

This is exactly what Step 9 of the Builder does: it runs your buyer’s actual pain language through an AI model, checks whether you get cited, and turns the gaps into structured data recommendations and content briefs shaped to be citable rather than just readable.

Next in the series, we bring all nine steps together into the one document that should outlast any single hire: the Playbook.

-Chris Tottman

Most founders have never checked whether an AI model recommends them when a buyer describes their exact pain.

The Selling Software Algorithm Builder Step 9 runs that check for you, using the buyer’s own words rather than your company name.

Audit Your AI Visibility

Link below

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Same sector. Same traction. Broadly the same deck.

One closes in eleven weeks. The other spends seven months “in conversations” and quietly gives up in the spring.

Here’s the part that surprises people. They both emailed roughly the same number of investors.

DocSend, working with Harvard Business School, has tracked this across hundreds of seed raises. Founders who closed their round contacted 77 investors on average and held 40 investor meetings. Founders who didn’t close contacted 70, barely fewer, and held 15.

Ten per cent more outreach. Nearly three times the meetings.

The second founder didn’t fail on volume. They failed on who was on the list.

That gap is what a badly built list does to you. Forty names harvested from three blog posts, most of them wrong on cheque size, wrong on stage, or no longer investing — and the emails go out anyway, because there’s nothing better to send them to.

The founders who convert aren’t emailing more people. They’re emailing correct people, chosen out of a pool of several hundred they’d already filtered.

You cannot filter down to the right thirty from a list of forty. That’s the whole problem, and it’s a sourcing problem long before it’s a pitching problem.

So we built the pool. 505 investors who fund companies at pre-seed and seed, across eight regions, each with stage, sector, cheque size, recent activity and a working link.

This article is how to filter it.

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The Arithmetic Nobody Does Before They Start

Run the numbers on your own raise before you send a single email. It takes four minutes and it changes everything.

Take a reasonable conversion rate for a cold, well-targeted approach: 3% to 5% turn into a first meeting. Of those first meetings, maybe one in five goes to a second. Of second meetings, perhaps one in four produces a term sheet.

Now put 40 investors through it.

40 emails → 1 or 2 first meetings → possibly zero second meetings → almost certainly no term sheet.

That is not a failed raise. That is a raise that never statistically began.

Now put 300 through the same funnel.

300 emails → 9 to 15 first meetings → 2 to 3 second meetings → one term sheet, maybe two.

The founder who “got lucky” usually just ran a bigger top of funnel.

This is the least glamorous fact in early-stage fundraising. Nobody writes a LinkedIn post about volume. But the founder who closes is nearly always the one who spoke to 200 investors, not the one who wrote a better cold email to 30.

You cannot run those numbers without a list. And there is no free list of 300 well-targeted pre-seed and seed investors on the internet. That is the entire reason this file exists.

Segment Before You Send, or the Volume Works Against You

Volume without targeting is just spam, and spam burns a market you’ll need again in eighteen months.

So the list gets cut four ways before anything goes out. In this order.

First, cheque size. This filter eliminates more wrong-fit investors in one pass than everything else combined, and almost nobody applies it first. Look at the actual spread among investors who all describe themselves as early-stage:

• SBXi writes $10,000 to $100,000.

• Kima Ventures in Paris writes €100,000 to €200,000.

• Antler MENAP writes around $180,000 as a first cheque.

• focal writes up to $1,000,000, often before you’ve incorporated.

• 2048 Ventures writes $500,000 to $3,000,000 and will lead the round.

A founder raising $150,000 and a founder raising $2m are both “raising pre-seed.” They share almost no investors. If you’re raising $150k, 2048 Ventures is a waste of a week. If you’re raising $2m, SBXi cannot fill your round even if they love you.

Second, stage. Pre-seed is first outside money — early product, little revenue. Seed is the round after, when something is working. Series A is a different sport entirely: real revenue, real growth rate, cheques of $5m and up, and a completely different decision process. Cut anyone whose centre of gravity isn’t your round.

Third, sector. Not for fit, but because your opening line changes. An investor who has backed three companies in your space needs one sentence of context. One who hasn’t needs four paragraphs, and won’t read them.

Fourth, geography. Which costs founders the most, in a way most of them never see.

The Same Company Is Priced Differently Depending on Who’s in the Round

Geography doesn’t just decide whether you close. It decides what you close at.

PitchBook’s Q2 2026 European VC Valuations Report, published in August, found the two markets moving in opposite directions. Median European deal value rose 25% year on year to €2.5m, while the median European pre-money valuation fell 5.4% to €7.5m. Over the same period, US deal sizes fell 13.5% and US valuations rose 45%.

Read that twice. European founders are raising more money at lower prices. American founders are raising less money at higher prices.

The standing gap shows up most clearly in what investors will accept as a valuation cap:

• San Francisco Bay Area: pre-seed $8m–$15m, seed $15m–$25m.

• London: pre-seed $4m–$9m, seed $9m–$19m.

• Continental Europe: pre-seed $3m–$7m, seed $7m–$14m.

Same product, same traction, same month. A Bay Area company is routinely priced at double its Berlin equivalent, and roughly 33% above its London one at seed.

None of this is an argument to relocate. Most founders can’t, and the ones who do it for valuation reasons usually regret it.

It’s an argument about who is sitting on your cap table conversation. An American fund coming into a UK round tends to anchor on American comparables. A round filled entirely from your home market gets priced entirely at your home market’s median. The composition of your investor list is a pricing decision, not just a filling-the-round decision.

There’s depth at home too - the UK was Europe’s largest market in 2025 at $14bn, up 22% on the year, per Atomico. But depth and price are different things, and the founders who get both are the ones running a list that crosses the Atlantic.

290 of the 505 investors in this file are in the US, the UK and Europe. That’s a pipeline you can run across all three at once.

Our Most-Read Articles Right Now

🔥 How to Build Your Fundraising Narrative with Claude The five-prompt playbook that turns your raw story into the argument that gets a term sheet.

🔥 I Asked Claude to Reject My Pitch Until It Couldn’t. Then I Raised. The adversarial system that finds your weak points before an investor does.

📈 The Claude Prompt That Turns a Cold Email Into One a VC Actually Reads Personalised outreach at the volume this file makes possible.

📋 The 6 Claude Prompts That Change How Investors See You The positioning layer that makes sure the right story lands.

📋 The Claude Due Diligence Playbook That Gets You to Closing What happens after the meeting, and the term sheet arrives.

Sequence for Momentum, Not for Ego

Once the list is cut to your 200 or 300, the order you contact them in decides how the raise feels.

The instinct is to email your dream investors first. You get turned down while the pitch is still rough, and you’ve burned the names you most wanted.

Do it the other way round.

Wave one: the fast deciders. Some investors move in days. Kima Ventures does over 100 deals a year with decisions inside a week. Right Side Capital did 63 pre-seed deals in 2025. LvlUp Ventures did 92. These firms will tell you no quickly, which is the most valuable thing an investor can give you early. Ten rejections in a fortnight will teach you more about your pitch than three months of polite maybes.

Wave two: the strong-fit core. Sector match, cheque match, geography match. This is where your round actually gets filled. It should be the biggest wave by a distance.

Wave three: the dream names. Contacted only once waves one and two have sharpened the story and, ideally, produced a lead or some competitive tension. Investors respond to momentum, and momentum is manufactured in the sequencing.

Run each wave in blocks of roughly 40, a week apart. Track replies. Rewrite the email between waves based on what didn’t land.

That rhythm is impossible with a 40-name list. It’s straightforward with 505.

“A raise is not one conversation repeated 200 times. It’s three waves, each one better than the last, and you need the names to run them.”

Run It in Waves

If you take one operational thing from this issue, take this sequence:

Filter by cheque size. Cut to stage. Cut to sector. Then go wide on geography, not narrow.

Sequence into three waves. Fast deciders first, strong fit second, dream names last.

Send in blocks of 40, a week apart, rewriting between blocks.

Aim to contact 200 to 300 investors, not 40.

Every one of those steps needs the same input: a large, clean, properly segmented list of investors who genuinely fund companies at pre-seed and seed.

Building it yourself takes weeks. We’ve done it.

The file is waiting below.

The 505-Investor Pre-Seed and Seed Database

Every firm in this file invests at pre-seed or seed. No Series A funds padding the count. No growth funds with a token early-stage programme. No firms that have gone quiet.

What you’re downloading:

• 505 pre-seed and seed investors — United States (156), Europe (88), APAC (78), UK (46), LATAM (44), Middle East (42), Africa (37), Canada (14). Checked and deduplicated by hand.

• Stage focus on every row, separated into pre-seed, seed and Series A, so your first filter takes thirty seconds.

• Publicly stated cheque sizes — from SBXi’s $10,000 through to 2048 Ventures’ $3m leads — marked plainly where a firm doesn’t publish one, never guessed.

• Deal counts and exit counts wherever public records allow, so you can sort by who is actually active rather than who is famous.

• Dated activity notes: Oraseya’s 25 deals in 2025, LvlUp’s 92 pre-seed deals, VentureSouq’s $2m seed lead in January 2026, Launch Africa’s 15 closings in 2026.

• Sector focus and named portfolio companies on every row, so your opening line can be about their thesis instead of your product.

• A direct website link for all 505, with filters live on every column.

Three hours with this file replaces three weeks of research, and produces a pipeline large enough for the arithmetic at the top of this article to work in your favour.

Paid subscribers also get:

60+ additional tools across every stage of fundraising and company building: investor research, pitch deck screening, meeting prep, term sheet analysis, financial models, and the full investor database library.

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