What the record says went wrong

A structured database of 52 startup failures, recorded in a uniform format.

Research date: 15 September 2026. All figures, valuations, case outcomes and company statuses are current as of that date. Where a number is disputed, unverified, or self-reported by a party with an interest in it, this chapter says so rather than picking a side.

How to use this chapter

This is not an essay. It is a library, and it is deliberately shaped like a database table so that it can be parsed, sorted, filtered and published directly as a website feature. Every entry below uses exactly the same thirteen fields, in exactly the same order:

What this chapter is not

Chapter 9 is a case-study library organised around how companies were built, with a failures section covering Quibi, Convoy, IRL, Bench, Olive AI, Theranos, Nikola and WeWork. Chapter 10 analyses twenty-nine failure modes abstractly. This chapter deliberately avoids re-treading Chapter 9's companies, except as cross-references.

A warning about the sample, stated up front

Every entry in this library exists because somebody chose to document it. That is the single most important fact about this data set, and it biases the whole thing in a specific, knowable direction:

1. Toward venture-backed companies. Failures that destroy institutional capital generate reporting, bankruptcy filings and SEC actions. A bootstrapped two-person SaaS that stops renewing its domain leaves no record.
2. Toward founders willing to write publicly — people who are articulate, have reputational capital to rebuild, are not being sued, and whose failure was interesting rather than merely embarrassing.
3. Toward failures with a clean narrative. "Google changed its algorithm" is publishable. "My co-founder and I stopped speaking in month fourteen" is not.
4. Toward the recent and the American. Non-English-language failures are systematically under-covered here and I have not corrected for it.

I call this documented-failure bias throughout. It is the inverse of survivorship bias and just as distorting. Section 5 of the synthesis addresses it directly, because the honest answer to "what kills startups" is partly "we do not know, because the modal failure is undocumented."

A note on tone

Several of these are grim. People lost homes, health and — in two cases — their liberty. This chapter treats them as serious events involving real people, and keeps hindsight to a minimum: most of these decisions looked reasonable at the time to intelligent people with more information than I have.

PART 1 — The founder-written post-mortem canon

These eight are the genre's reference texts. They are here because the document is unusually good, not necessarily because the company was unusually important. Several are cited in nearly every subsequent post-mortem written in the last decade.

1. Everpix

• Company — Everpix
• Sector — Consumer photo storage / SaaS
• Founded / Died — 2011 / 2013
• Lifespan — ~2 years
• Capital raised — $2.3M from angels and seed VCs, per the company's own disclosure (Everpix-Intelligence, GitHub)
• Peak scale — ~55,000 registered users; ~7,000 paying subscribers; ~$40,000/month in subscription revenue in its final quarter; ~400 million photos imported. These are self-disclosed but unusually credible, because the company published its raw financials and analytics.
• What it built — A cloud photo service that automatically ingested a user's entire photo library and surfaced the good pictures using early computer-vision ranking. It was widely regarded as the best product in its category.
• Stated cause of death — The founders said plainly that they "didn't succeed in raising our Series A in the highly competitive VC funding market," and that revenue covered variable costs but not fixed costs (Everpix-Intelligence; TechCrunch, 5 Nov 2013).
• Evidenced cause — Stated and evidenced causes broadly agree, but the emphasis differs. The published numbers show a company that spent two years and most of its capital on engineering before turning on monetisation, then found that a $49/year subscription against heavy storage costs needed enormous scale to work — in a category Google and Apple were about to make free. The failed Series A was the proximate event; marginal unit economics were the condition. Founder Pierre-Olivier Latour said the team had been "focused on developing the product, not on fundraising or the business."
• Warning signs visible earlier — Monetisation was switched on only in early 2013, roughly eighteen months in. Infrastructure costs scaled with photos stored rather than with revenue. Google Photos' predecessor features and Apple's iCloud Photo Library were both visibly coming. The team had no dedicated business or fundraising lead at any point.
• Money outcome — Investors lost the $2.3M. Employees were let go; the founders wrote that they could not make payroll past November 2013. No acquirer emerged. Users' photos were made downloadable for a grace period, which cost money the company did not have and which the founders did anyway.
• Post-mortem quality — Exceptional, and arguably the genre's most valuable document. Rather than an essay, the team published actual metrics, financial model, cohort data and board materials to a public GitHub repository. Not self-serving: the numbers embarrass the founders in several places.
• Transferable lesson — Publishing your numbers is the highest-integrity form of post-mortem, and almost nobody does it. Substantively: in consumer subscription, delaying monetisation until you have a beautiful product means you discover your conversion rate at the exact moment you need it to raise.
• Sources — Everpix-Intelligence (GitHub) · TechCrunch · VentureBeat

2. Wesabe

• Company — Wesabe
• Sector — Personal finance / fintech
• Founded / Died — 2005 (launched Nov 2006) / 2010
• Lifespan — ~5 years
• Capital raised — ~$4.7M reported across seed and Series A (O'Reilly AlphaTech Ventures, Union Square Ventures); figure is from secondary reporting and the company never published a definitive total
• Peak scale — Reported in the low hundreds of thousands of registered users; the company never published audited figures. Revenue existed but was never disclosed in detail.
• What it built — A personal financial management web app that aggregated bank accounts and used community-contributed data to give spending advice. It launched roughly ten months before Mint.
• Stated cause of death — Co-founder Marc Hedlund's post-mortem, "Why Wesabe Lost to Mint," names two causes: Wesabe refused to use Yodlee for bank data aggregation (judging dependence on a struggling vendor too risky) and so shipped account-linking six months after Mint; and Wesabe deliberately made users do work — categorising and correcting their own transactions — on the theory that effort produced behaviour change, while "Mint focused on making the user do almost no work at all." His summary: "Between the worse data aggregation method and the much higher amount of work Wesabe made you do, it was far easier to have a good experience on Mint" (Hedlund, 2010).
• Evidenced cause — Stated and evidenced causes agree on mechanism; the framing is quietly self-serving. Hedlund is admirably specific about the product decisions. The essay underplays that Mint raised more and spent far more on distribution, and frames the aggregation choice as a bet that went wrong when contemporaneous accounts suggest it was an ideological commitment the team was reluctant to revisit. Hedlund does explicitly reject several popular explanations — that Mint launched first (false), that Wesabe never made money (false), and that design alone decided it.
• Warning signs visible earlier — Mint shipped frictionless aggregation in 2007, roughly six months before Wesabe's equivalent. Wesabe's activation funnel required manual work at the exact step where users were least motivated. By 2008 Mint's growth rate was public and visibly higher.
• Money outcome — Investors lost their capital. No acquisition. Hedlund open-sourced parts of the codebase and let users export their data. He went on to senior engineering leadership roles including at Stripe — see Part 9.
• Post-mortem quality — High, and the genre's most-imitated template. Written four months after shutdown, specific about product decisions, willing to name the winner. Mildly self-serving in that it locates the failure in two discrete reversible choices rather than in a strategic position that may have been unwinnable. Still the best short read on losing a category race.
• Transferable lesson — "Make the user do work because it is good for them" is a defensible product philosophy and a very difficult acquisition strategy. If a competitor is removing friction at the top of the funnel while you are adding it on principle, you need a specific reason why your users will tolerate it.
• Sources — Hedlund, "Why Wesabe Lost to Mint" (Medium mirror) · Parker Higgins archive · Entrepreneur

3. Dinnr

• Company — Dinnr (dinnr.co.uk)
• Sector — Food / meal-kit e-commerce
• Founded / Died — 2012 / 2014
• Lifespan — ~18 months
• Capital raised — Small angel/friends-and-family sum; founder Michal Bohanes described it as a modestly funded London startup and did not publish a headline figure. Treat as undisclosed but small (well under £500k).
• Peak scale — Order volumes in the low hundreds per month at best; the founder's own account is that the business never reached a repeatable rate. No revenue figure published.
• What it built — A London meal-kit service delivering pre-portioned ingredients plus a recipe for a single dinner, ordered same-day.
• Stated cause of death — Bohanes's post-mortem, "Seven lessons I learned from the failure of my first startup, Dinnr," attributes the failure to building before validating: he writes that he assumed a problem existed, built the operation, and only afterwards discovered customers did not want the thing at the price and frequency the model required (Bohanes, Medium).
• Evidenced cause — Agree. One of the cleanest cases of a founder correctly diagnosing his own failure: repeat-purchase rates, the mismatch between same-day perishable delivery and low order density, and acquisition cost against a sub-£20 basket all support it. He also identifies that his "validation" consisted of asking people whether they liked the idea, which is not validation.
• Warning signs visible earlier — Low repeat rate from the first cohorts. Perishable inventory written off weekly. The founder notes he had a full-time job's worth of operational work and no growth channel.
• Money outcome — Small loss, borne by the founder and a few angels. No employees were left materially exposed. Bohanes went on to consulting and writing.
• Post-mortem quality — High and unusually unflattering to its author.
• Transferable lesson — Asking people if they like your idea is the cheapest and least informative research you can do. The only meaningful pre-launch signal is someone paying, or committing something they would rather not lose.
• Sources — Bohanes, Medium · Inc42 reprint · 77 Failed Startup Post-Mortems (archive)

4. Tutorspree

• Company — Tutorspree
• Sector — Education marketplace
• Founded / Died — 2011 / 2013
• Lifespan — ~2.5 years
• Capital raised — ~$1.8M seed (Sequoia Capital, Y Combinator S11, Lerer Ventures and others), per contemporaneous reporting
• Peak scale — Not publicly quantified in users or GMV. The founder reports that after a March 2012 pivot to an agency model, revenue doubled within a month and roughly tripled over the following six, with gross margin improving from ~15% to ~40%, and that by December 2012 the company had "virtually infinite runway and were at the edge of profitability."
• What it built — A marketplace matching students with private tutors — "Airbnb for tutoring" — later restructured as a managed agency that took a larger cut and controlled quality.
• Stated cause of death — Co-founder Aaron Harris was categorical: "Tutorspree didn't scale because we were single channel dependent and that channel shifted on us radically and suddenly." In March 2013, he writes, "Google cut the ground out from under us and reduced our traffic by 80% overnight" (Harris, "When SEO Fails").
• Evidenced cause — Stated and evidenced causes diverge instructively. The Panda update and the 80% loss are not in dispute. But Harris's own essay contains the deeper cause: a full year testing PPC, partnerships and deals, all of which "underperformed compared to SEO," until "virtually all of our customers came from SEO." The algorithm change was the trigger; the cause was one viable channel, known for twelve months. Harris says this himself — but the headline framing ("Google did this") is more forgiving than his body text warrants.
• Warning signs visible earlier — From roughly early 2012: every alternative channel tested came back with worse economics than SEO, which is a finding about the business, not about the channels. The January 2013 raise was explicitly to "find additional marketing channels," i.e. the team was raising money to solve a problem it had already failed to solve for a year.
• Money outcome — Investors lost roughly $1.8M. Small team, wound down in an orderly way. Harris went on to become a Y Combinator partner and later a venture investor (see Part 8).
• Post-mortem quality — High. Specific, dated, numerically grounded, and it names the strategic error rather than only the triggering event.
• Transferable lesson — A single acquisition channel is a single point of failure regardless of how well it is performing, and the time to fix it is while it is still working. If every alternative channel you test has worse economics than your one channel, that is not a reason for relief.
• Sources — Harris, "When SEO Fails" · TechCrunch · VentureBeat

5. 99dresses

• Company — 99dresses
• Sector — Fashion marketplace / consumer
• Founded / Died — 2011 / 2014
• Lifespan — ~3 years
• Capital raised — ~$1.4M reported (Y Combinator W12, Blue Sky Venture Capital, angels).
• Peak scale — Tens of thousands of registered users on an internal virtual-currency system ("buttons"). Revenue was minimal throughout; no verified figure exists.
• What it built — An infinite-closet clothing exchange: users traded unwanted clothes for an internal currency redeemable against other users' items, with the company taking a cut.
• Stated cause of death — Founder Nikki Durkin's post-mortem, "My Startup Failed, and This Is What It Feels Like," is not primarily a strategic analysis. She describes running out of money, a collapsed funding process, a co-founder and team departure, visa precarity, and the emotional experience of the failure. The strategic cause she names is that the marketplace's internal currency created a liquidity trap: supply and demand never balanced, and the company could not convert trading activity into revenue.
• Evidenced cause — Agree, but the essay's value lies outside the usual frame. The marketplace design problem is real and sufficient. The essay's unique contribution is its account of founder mental health, the physical symptoms of prolonged stress, and the humiliation of telling a team — ground almost no other document in the genre covers.
• Warning signs visible earlier — Persistent imbalance between listed items and desired items — a structural feature of barter marketplaces — visible from the first year. Monetisation was repeatedly deferred because taking a cut reduced already-thin liquidity.
• Money outcome — Investors lost their capital. Durkin has written that she personally ended the process with essentially nothing and significant emotional cost. She later trained as a software engineer and founded a coding-education venture.
• Post-mortem quality — Exceptional, and the single most important counterexample to the genre's usual omissions. It is not self-serving; it is, if anything, self-lacerating. See Synthesis Section 5 — this essay exists because Durkin chose to write the thing most founders specifically choose not to write.
• Transferable lesson — Barter marketplaces with internal currency are structurally harder than cash marketplaces because you must clear two sides and maintain the currency's credibility. Separately and more importantly: the emotional cost of startup failure is a real, predictable, describable phenomenon, and pretending otherwise leaves founders unprepared for it.
• Sources — Durkin, "My Startup Failed and This Is What It Feels Like" (reprint) · Y Combinator company page · YC blog, Fast Company interview

6. Muse

• Company — Muse (Muse Software / Ink & Switch spinout)
• Sector — Productivity software / "tools for thought"
• Founded / Died — 2019 / 2024 (team departed autumn 2023; product transferred to a solo maintainer)
• Lifespan — ~4.5 years
• Capital raised — $2M, per founder Adam Wiggins's own retrospective
• Peak scale — ~$120,000 ARR; "tens of thousands" of active users; thousands of paying customers; peak headcount of seven. Self-reported but precise and credible.
• What it built — A spatial canvas app for iPad and Mac for reading, annotating and thinking — a premium tool aimed at researchers, writers and strategists, sold at ~$100/year.
• Stated cause of death — Wiggins is unusually direct: the market shifted toward the unaspirational in a downturn; the company was too unfocused and "ended up as an 'everything app'"; the B2B pivot foundered on non-champion adoption; and — the line that made the essay widely cited — "120k ARR is a one-person business, not a team effort." He writes explicitly about "my failure as a CEO" (Wiggins, "Muse retrospective").
• Evidenced cause — Stated and evidenced causes agree closely. The arithmetic is the whole story: a seven-person team on $2M of capital against $120k of ARR has no path that does not involve either a 10x revenue step or a return to being a one- or two-person product. Wiggins admits he cannot fully explain a mid-life drop in active users, which is an honest thing to admit and also a sign that the analytics were not instrumented to answer it.
• Warning signs visible earlier — ARR of ~$60k with a growing team, visible by 2022. Headcount grew roughly one person per year against revenue that did not. A B2B pivot begun after consumer growth stalled — a sequence that in this library recurs and almost never works (compare Neeva, entry 46).
• Money outcome — Investors' $2M is effectively written off. Employees were given notice with time to find work and Wiggins has said the wind-down was deliberately gentle. The product continued under a smaller arrangement rather than being switched off. Founders retained reputational capital; Wiggins is a co-creator of Heroku and the Twelve-Factor App and did not need this company to be credible.
• Post-mortem quality — Very high. Long, specific, numerically honest, and it names the CEO's own errors first.
• Transferable lesson — Revenue per employee is a constraint, not a metric. A product can be excellent, loved, and correctly priced, and still only support one person. Deciding early what size of business the product can be is a strategic choice, not an afterthought.
• Sources — Wiggins, "Muse retrospective" · Metamuse podcast, "End and beginning" · Hacker News discussion

7. Sandstorm.io (and Sandstorm Oasis)

• Company — Sandstorm.io
• Sector — Developer infrastructure / self-hosted cloud
• Founded / Died — 2014 / commercial operation ended 2017; hosted service (Oasis) shut down 2019; project handed to a community foundation in 2024
• Lifespan — ~3 years commercially; ~10 years as a project
• Capital raised — ~$1.3M seed plus a ~$59k Indiegogo crowdfunding campaign (2014)
• Peak scale — Never published user or revenue figures in detail. Oasis, the hosted offering, had a small paying base in the low thousands at most. Headcount peaked at roughly five.
• What it built — A personal-server platform that made self-hosting web applications as easy as installing an app, with a novel per-document security sandbox (each app instance ran in its own isolated grain).
• Stated cause of death — Founder Kenton Varda's 2017 announcement said the company had failed to find a business model that worked and that the team was moving on; the 2019 Oasis shutdown post said the hosted service could not cover its costs with its subscriber base.
• Evidenced cause — Agree; the divergence is between technical and commercial verdicts. The technology was admired and is still cited. The commercial problem: the people who most wanted self-hosting were least willing to pay for hosting, and enterprises that would pay wanted SSO, compliance and support contracts a five-person team could not build while maintaining a platform. Varda has been consistent that the product was good and the business was not — unusual honesty about a distinction most founders blur.
• Warning signs visible earlier — The crowdfunding campaign's modest total (~$59k) was an early signal about willingness to pay. Enterprise interest arrived as pilots that did not convert. The team's own users self-hosted for free rather than buying Oasis.
• Money outcome — Investors lost capital. Varda joined Cloudflare, where he created Cloudflare Workers — arguably the most commercially significant descendant of Sandstorm's ideas. The open-source project survives under Sandstorm.org.
• Post-mortem quality — Moderate-to-high but distributed. There is no single canonical essay; the reasoning is spread across blog posts, Hacker News comments and later interviews.
• Transferable lesson — "Users love it" and "users will pay for it" are independent variables, and in developer tooling and open source they are frequently negatively correlated, because your most enthusiastic users are the ones most capable of replacing you for free.
• Sources — Sandstorm blog · Oasis shutdown discussion, HN · Move to Sandstorm.org

8. Kite

• Company — Kite
• Sector — Developer tools / AI code completion
• Founded / Died — 2014 / 2022
• Lifespan — ~8 years
• Capital raised — $17M+ disclosed across seed and Series A (Trinity Ventures and others); some later financing not publicly detailed
• Peak scale — ~500,000 monthly active developers using the free product, per company statements. Paying customers: negligible. The founder later put paid conversion at a level that never approached sustainability.
• What it built — An AI-powered autocomplete and code-intelligence plugin for Python and later other languages, running partly locally and partly in the cloud — the most prominent pre-Copilot attempt at the category that GitHub Copilot and Cursor would later dominate.
• Stated cause of death — Founder Adam Smith's shutdown post is blunt: the company "failed to deliver our vision of AI-assisted programming because we were 10+ years too early to market, i.e. the tech is not ready yet." He also wrote that the team could not get developers to pay, and that they had "no way to monetize" at the scale they had reached. He open-sourced the codebase (company announcement, Nov 2022; see also TechCrunch).
• Evidenced cause — Major divergence, and one of the most important in the library. Smith said the technology was not ready. GitHub Copilot launched in preview June 2021 and generally available June 2022 — before Kite closed — on OpenAI Codex, and it worked. The technology was ready; Kite had built pre-transformer models and could not rebuild on LLMs fast enough. The second cause, which Smith names but does not emphasise: individual developers did not pay, and Kite never built an enterprise motion. "Too early" is a more comfortable framing than "a better-capitalised competitor shipped a superior architecture eighteen months before we closed."
• Warning signs visible earlier — Free MAUs in the hundreds of thousands with paid conversion in the low single-digit percentages, sustained for years. Transformer-based code models were public from 2020 (GPT-3) and Codex from mid-2021. Enterprise sales were attempted late.
• Money outcome — Investors lost their capital. The codebase was open-sourced, which has had genuine downstream value. Smith went on to found another company in the AI space. Employees dispersed, many into better-funded AI-coding companies.
• Post-mortem quality — Moderate. Candid about the outcome, self-serving about the cause. The "ten years too early" framing was contradicted within months by the success of Copilot and, in 2023–25, by Cursor.
• Transferable lesson — "We were too early" is the single most common self-serving explanation in this genre and should be tested against one question: did anybody else succeed at the same thing within three years? If yes, the problem was execution or architecture, not timing.
• Sources — TechCrunch, "With Kite's demise, can generative AI for code succeed?" · Silicon Republic · Slashdot summary of shutdown post

PART 2 — Small and indie failures

These are the least-covered and, per unit of coverage, the most transferable. The companies are small enough that a single decision is visible; there is no committee, no board dynamic, no layer of PR between the cause and the effect. Several are not "startups" in the venture sense at all — they are one- and two-person businesses — which is precisely why they belong here.

9. Apollo for Reddit

• Company — Apollo (Christian Selig, solo developer)
• Sector — Consumer mobile app / third-party client
• Founded / Died — 2014 / 30 June 2023
• Lifespan — ~9 years
• Capital raised — Bootstrapped. No outside investment at any point.
• Peak scale — ~1.5 million monthly active users; ~50,000 paying subscribers at shutdown, per Selig's own disclosure. He described the business as supporting him full-time and comfortably.
• What it built — A high-quality iOS client for Reddit, sold as a one-off purchase and later a subscription, built by one person.
• Stated cause of death — Selig said Reddit's new API pricing — which he calculated at roughly $20 million per year for Apollo's call volume, based on Reddit's quoted $0.24 per 1,000 API calls — made the app impossible to operate. He published his call-volume data and the full text of his calls with Reddit, including a recording, after Reddit's CEO publicly characterised him as having made a threat (Selig's shutdown post on r/apolloapp, 8 June 2023; reported in TechCrunch).
• Evidenced cause — Agree, and one of very few cases where the founder's account is fully corroborated by primary evidence he published himself. Reddit announced the pricing in April–May 2023 with a 30-day window; Apollo, Reddit is Fun, Sync and BaconReader all closed within weeks. A platform owner unilaterally repriced the only input the business had.
• Warning signs visible earlier — Reddit had signalled an IPO for years, and API monetisation is the standard pre-IPO move (Twitter did the same in January 2023, five months earlier — see entries 10 and 11). The warning sign was structural, not situational: 100% of revenue depended on a free API from a company that had no contract with him.
• Money outcome — Selig refunded subscribers out of his own pocket, an amount he estimated at ~$250,000, which Apple later helped defray. He kept the business's prior earnings. No investors to lose money. He has since shipped other independent apps.
• Post-mortem quality — Very high and evidence-first. Rather than an essay, he published call recordings, transcripts, and his own API-cost arithmetic. Not self-serving — it is largely documentary. He explicitly declined to blame Reddit employees individually.
• Transferable lesson — This is as close to "not your fault" as this library gets, and it still carries a lesson: a business built entirely on another company's free API has an implicit termination clause it did not negotiate. That is a known, priceable risk, and the correct response is not to avoid it but to hold less of your life in it.
• Sources — TechCrunch · AppleInsider · BetaKit

10. Tweetbot (Tapbots)

• Company — Tapbots
• Sector — Consumer mobile app / third-party client
• Founded / Died — Tapbots founded 2009; Tweetbot launched 2011, killed 12 January 2023
• Lifespan — ~12 years for the product
• Capital raised — Bootstrapped.
• Peak scale — Not publicly disclosed. Tapbots was a small studio (single-digit headcount) for which Tweetbot was the primary revenue product; subscription revenue was in the low millions annually at most, and the company has never published figures.
• What it built — The best-regarded third-party Twitter client on iOS and Mac, sold as paid software and latterly as a subscription.
• Stated cause of death — Tapbots said Twitter cut off their API access without notice on 12 January 2023 and never explained why; Twitter later quietly updated its developer terms to prohibit third-party clients. Tapbots' public statement was essentially: "we don't know what happened, and we weren't told."
• Evidenced cause — Stated and evidenced causes agree. Twitter's API termination was abrupt, undocumented for roughly a week, and retroactively justified.
• Warning signs visible earlier — Twitter had constrained third-party clients since the 2012 token limits — a decade of explicit, repeated signalling that the platform did not want these apps to exist. Tapbots continued to derive most revenue from Tweetbot through 2022.
• Money outcome — Tapbots refunded or pro-rated subscriptions and shipped a final "tip jar" update jointly with The Iconfactory so users could pay them for work already done. They pivoted to Ivory, a Mastodon client, which has a much smaller addressable market. The studio survived; the revenue did not fully.
• Post-mortem quality — Low-to-moderate, and deliberately so. Tapbots wrote very little. This is a common and under-discussed pattern: small companies that intend to keep operating in the same ecosystem have strong incentives not to write candid post-mortems.
• Transferable lesson — Ten years of a platform telling you it does not want you there is data. The relevant question is not "will they kill us" but "what is our revenue on the day after they do."
• Sources — TechCrunch · MacStories, final update · 9to5Mac

11. Twitterrific (The Iconfactory)

• Company — The Iconfactory
• Sector — Consumer mobile app / design studio
• Founded / Died — Iconfactory founded 1996; Twitterrific launched 2007, killed 12 January 2023
• Lifespan — ~16 years for the product
• Capital raised — Bootstrapped.
• Peak scale — Not disclosed. Twitterrific was an Apple Design Award winner and coined the word "tweet" and the bird iconography Twitter later adopted. Revenue was a meaningful fraction of a ~10-person studio's income.
• What it built — The first Twitter client for Mac and one of the first for iPhone; sixteen years of continuous development.
• Stated cause of death — Co-founder Ged Maheux wrote that the app was "killed by Twitter" without notice, and that sixteen years of work ended "with a tweet from a bot." The studio said it received no communication.
• Evidenced cause — Agrees with the stated cause. Identical mechanism to Tweetbot.
• Warning signs visible earlier — Same as entry 10.
• Money outcome — Refunds issued; a farewell update let users tip. The Iconfactory survived on design services and other products (Tapestry, Linea). No investors. Employees retained.
• Post-mortem quality — Moderate. A short, emotionally honest blog post and podcast appearances rather than an analytical document. Not self-serving; also not analytical.
• Transferable lesson — Nothing new about the platform risk that entries 9 and 10 do not already establish. The distinct lesson here is about diversification as survival: The Iconfactory survived the identical event that would have ended Apollo, because Twitterrific was one of several products inside a services business.
• Sources — TechCrunch · MacStories · iClarified

12. HouseFresh

• Company — HouseFresh (independent review publisher)
• Sector — Content / affiliate publishing
• Founded / Died — 2020 / near-death 2024; still operating in reduced form as of 2026
• Lifespan — Ongoing; the business as constituted died in March 2024
• Capital raised — Bootstrapped.
• Peak scale — ~4,000 daily visitors from Google Search as of October 2023, per the publisher's own analytics; a team of roughly 15 writers and testers at peak, with a physical air-purifier testing lab.
• What it built — An independent review site that bought and physically tested air purifiers, monetised through affiliate links.
• Stated cause of death — Co-founder Gisele Navarro wrote that Google's March 2024 core update cut search traffic by roughly 91%, to about 200 daily visitors, while large media brands running thinner content on rented subdomains ranked above them. Her framing: "Google's algorithm believes our website isn't good enough" (HouseFresh, "How Google decimated HouseFresh"; HouseFresh, "David vs digital Goliaths").
• Evidenced cause — Largely agree, with one qualification the publisher does not make. The collapse and its timing are verifiable, as is the parallel damage to dozens of niche publishers after the September 2023 Helpful Content Update and March 2024 core update. The qualification: the business was 100% dependent on one traffic source and one monetisation method. Google's behaviour caused the loss; the business design made it fatal.
• Warning signs visible earlier — The September 2023 HCU had already damaged comparable sites. Affiliate-plus-SEO has been repeatedly disrupted by core updates since 2011 (see Tutorspree, entry 4, for the 2013 version of the same event).
• Money outcome — Staff reduced sharply. No investors. The founders continued at reduced scale and diversified into newsletter and direct audience channels; partial traffic recovery was reported in 2025.
• Post-mortem quality — High as advocacy journalism, moderate as self-analysis. The two essays are meticulously evidenced about Google's behaviour and considerably less searching about their own concentration risk. Self-serving in the specific sense that the frame is entirely external.
• Transferable lesson — Identical in structure to Tutorspree eleven years earlier: one channel, one monetisation, no contract. The lesson transfers because the event type recurs on a roughly three-year cycle.
• Sources — HouseFresh · HouseFresh, David vs Goliaths · PPC Land on later recovery

13. Darklang (Dark Inc.)

• Company — Dark Inc. (Darklang)
• Sector — Developer tools / programming language
• Founded / Died — 2017 / the original company effectively ended 2022–23; the project was restarted as "Darklang-classic → Darklang v2" under new leadership and continues
• Lifespan — ~5–6 years as a funded company
• Capital raised — ~$3.5M+ seed (Cervin Ventures, angels), plus later amounts not fully disclosed
• Peak scale — Low thousands of developers; revenue never material. Headcount peaked around 6–8.
• What it built — An integrated language, editor and infrastructure platform intended to remove "accidental complexity" from backend development — you wrote code in a structured editor and it was deployed instantly with no build step or deployment configuration.
• Stated cause of death — Founder Paul Biggar has written and spoken extensively that Dark tried to change too many things at once — language, editor, runtime, deployment and hosting — and that each one independently required users to abandon existing tooling. He has also said publicly that he stepped back partly for personal reasons, and he has since written candidly about his own mental health and about being pushed out of a company he founded.
• Evidenced cause — Agree on the product diagnosis. The adoption data corroborates: developers tried Dark, built something small, and did not migrate real workloads, because that meant giving up every tool they knew. A secondary cause the founder discusses less: a five-person team maintaining a language, an IDE and a hosting platform simultaneously.
• Warning signs visible earlier — From roughly 2019: high trial, near-zero production migration. The "all-in-one" thesis meant there was no incremental adoption path, which was knowable from the design rather than from the data.
• Money outcome — Investors' capital largely written off.
• Post-mortem quality — High but scattered and unusually personal. Biggar's writing covers founder mental health, being removed from his own company, and the experience of a slow failure rather than a sudden one — material the genre almost never contains. Not self-serving; occasionally raw enough to be uncomfortable.
• Transferable lesson — The number of simultaneous changes you ask a user to make is roughly multiplicative in adoption friction, not additive.
• Sources — Darklang blog, Paul Biggar · Changelog interview #430 · Software Engineering Daily #932 transcript

14. Zirtual

• Company — Zirtual
• Sector — Virtual assistant services / marketplace
• Founded / Died — 2011 / 10 August 2015 (assets acquired by Startups.co days later; brand later revived)
• Lifespan — ~4 years
• Capital raised — ~$5.5M (Mayfield Fund, Tony Hsieh's VTF, angels)
• Peak scale — ~400 employees (deliberately W-2, not contractors); ~2,000 clients; revenue reported in the region of $10–12M annualised, though this figure comes from press accounts rather than audited statements and should be treated as approximate.
• What it built — Subscription virtual executive assistants, staffed by full-time American employees rather than contractors — an explicit rejection of the gig model.
• Stated cause of death — Founder Maren Kate Donovan's account is that the company ran out of cash suddenly when a financing fell through, and that a mid-year decision to convert assistants from contractors to employees raised costs by roughly 20–30% faster than modelled. She has said the immediate trigger was that she discovered the true cash position too late to manage a soft landing.
• Evidenced cause — Diverges on responsibility, not mechanism. The cash-out is not disputed; why nobody saw it is. Zirtual's outsourced CFO gave a public account contradicting parts of Donovan's version, and reporting established months of burn above plan. The evidenced cause is absence of functioning financial controls in a low-margin services business; the employee conversion aggravated it rather than causing it. Founder and finance function have blamed each other publicly and the record does not resolve it — a genuine unresolved dispute, noted as such.
• Warning signs visible earlier — Gross margin on a human-delivered service with W-2 staff is structurally thin and was known to be so. The conversion decision was made without a revised model that survived contact with actual payroll. Reporting indicates the burn rate had exceeded plan for multiple months.
• Money outcome — The worst employee outcome in this library relative to company size. Roughly 400 people learned they had no job when their email accounts stopped working on a Sunday night; a class action followed over WARN Act notice. Investors lost their capital. Clients lost prepaid balances.
• Post-mortem quality — Mixed and contested. Donovan gave interviews and wrote publicly, with real candour about the emotional toll and about specific mistakes. But the account is disputed by a named party, which is rare and useful: it shows what a post-mortem looks like when someone else was in the room.
• Transferable lesson — In a services business, payroll is the burn rate, and any change to employment structure is a change to the fundamental economics, not an HR decision.
• Sources — The Hustle · Slate on the layoffs and lawsuit · Fortune, outsourced CFO's account

15. Standout Jobs

• Company — Standout Jobs
• Sector — HR tech / recruiting software
• Founded / Died — 2007 / 2010
• Lifespan — ~3 years
• Capital raised — ~$1.8M seed (Canadian angels and funds)
• Peak scale — Small: dozens of customers, revenue described by the founder as never reaching a meaningful run rate. No published figures.
• What it built — Software helping small and mid-sized employers build recruiting microsites and employer-brand pages.
• Stated cause of death — Co-founder Ben Yoskovitz's post-mortem — one of the earliest widely read founder post-mortems and a direct ancestor of the modern genre — lists his own errors first: the team raised too little money to execute the plan they had, built too much product before validating it, and hired a team structured for a company that did not exist yet. He wrote that the money raised was "not enough to build and market a product properly" and that the correct response would have been to change the plan rather than proceed with it underfunded.
• Evidenced cause — Stated and evidenced causes agree. The small public record corroborates the founder's version: a long build cycle, a late launch, and a market (SMB recruiting software) that required heavy sales effort the company could not fund.
• Warning signs visible earlier — The gap between the funded amount and the planned scope was visible at the moment of the raise, in 2007.
• Money outcome — Investors lost the ~$1.8M. Small team. Yoskovitz went on to co-author Lean Analytics and to a long career in operating and investing roles — see Part 9.
• Post-mortem quality — High, and historically important. Written in 2010, it established several conventions the genre still uses: numbered mistakes, founder-first blame allocation, and a refusal to blame the market. Not self-serving.
• Transferable lesson — Raising some money is not the same as raising enough for the plan you wrote. A half-funded plan is a different, worse plan, and the decision point is at signing, not eighteen months later.
• Sources — 77 Failed Startup Post-Mortems compilation · Platforms and Networks failure index · postmortem.io shutdown index

16. Hipmunk

• Company — Hipmunk
• Sector — Travel search
• Founded / Died — 2010 / January 2020 (acquired by SAP Concur 2016; product shut down 2020)
• Lifespan — ~10 years (6 independent)
• Capital raised — ~$55M (Ignition Partners, Institutional Venture Partners, Google Ventures, angels including Peter Thiel)
• Peak scale — Millions of monthly visitors at peak; revenue never disclosed. The company's signature "agony" sort was widely admired and widely un-monetised.
• What it built — Flight and hotel metasearch with an unusually good interface, sorting results by an "agony" score combining price, duration and stopovers.
• Stated cause of death — SAP Concur's shutdown notice was brief and corporate. Co-founder Adam Goldstein has spoken more openly since: Hipmunk had a superb product in a category where distribution is bought, not earned, and where Google, Booking Holdings and Expedia set the price of customer acquisition.
• Evidenced cause — Agree; the interesting part is what "acquired" concealed. The 2016 price was never disclosed but widely reported as modest against capital raised, and the 2020 shutdown was a portfolio decision. The underlying cause: travel metasearch monetises via referral fees set by two or three companies who also control the ad auction — a margin squeeze no interface quality overcomes.
• Warning signs visible earlier — By 2014 Google Flights was doing the same job inside the search results page. Hipmunk's traffic was substantially paid or SEO-derived from the start.
• Money outcome — Investors likely recovered a fraction of $55M; the price was not disclosed, so this is inference, and it is flagged as such. Employees largely transferred to SAP. Goldstein co-founded Archer Aviation in 2018, which went public — see Part 9.
• Post-mortem quality — Low. No real post-mortem exists, because the company was acquired before it died, and acquired companies do not write post-mortems.
• Transferable lesson — In any category where your customer acquisition is priced by your largest competitor, product quality is a margin, not a moat.
• Sources — Hipmunk shutdown coverage, TechCrunch · Wikipedia, Hipmunk

PART 3 — Mid-size venture-backed failures ($20M–$200M raised)

This is the most populous band in the venture failure distribution and the least discussed, because these companies are too large to be relatable and too small to be scandalous.

17. Fast

• Company — Fast
• Sector — Fintech / payments
• Founded / Died — 2019 / April 2022
• Lifespan — ~2.5 years
• Capital raised — ~$124M (Stripe, Index Ventures, Addition, Susa Ventures)
• Peak scale — ~$600,000 in gross revenue over its lifetime against a reported burn of roughly $10M per month at peak; ~500 employees at peak headcount. The revenue figure comes from reporting, was never disputed by the company, and is the single most-cited number in the story.
• What it built — A one-click checkout button for online merchants, competing with Shop Pay and Bolt.
• Stated cause of death — CEO Domm Holland's statement emphasised that the company chose "to grow fast" and that capital markets turned; the shutdown memo framed it as an inability to raise in a changed environment.
• Evidenced cause — Sharp divergence. The market did turn in early 2022, but $600k of lifetime revenue on $124M raised is not a market-conditions failure; it is an absence of product-market fit that funding concealed. Merchant integrations were slow, the conversion advantage was unproven, and headcount and marketing scaled years ahead of revenue. Holland's prior Australian company had also ended in unpaid-creditor disputes, surfaced in reporting after the raise.
• Warning signs visible earlier — Revenue disclosed to investors in 2021 was already trivially small relative to the round size. Burn/revenue ratio exceeded 100:1 for the company's entire life.
• Money outcome — Investors lost approximately $124M. Roughly 400 employees were laid off with limited severance; some were hired by Affirm in a talent deal. Holland faced sustained public criticism and started another company.
• Post-mortem quality — Poor. No candid founder post-mortem was published. The public statements attribute the outcome to macro conditions. This is the clearest example in the library of a large failure with no usable first-person account.
• Transferable lesson — Burn-to-revenue ratio is the cheapest early-warning metric that exists, and it was screaming for two years.
• Sources — TechCrunch · NPR · Payments Dive

18. Homejoy

• Company — Homejoy
• Sector — On-demand home services marketplace
• Founded / Died — 2012 / 31 July 2015
• Lifespan — ~3 years
• Capital raised — ~$40M (Google Ventures, Redpoint, First Round, Max Levchin)
• Peak scale — ~$25M revenue in 2014 (reported); 33 markets across the US, UK and Germany.
• Stated cause of death — The company blamed four pending worker-misclassification lawsuits, saying they made fundraising impossible. Co-founder Adora Cheung cited "unresolved challenges in the home services space" (Forbes).
• What it built — On-demand home cleaning booked through an app, staffed by independent contractors.
• Evidenced cause — The starkest stated-vs-evidenced divergence in the library. Christina Farr's Backchannel reporting established that only ~25% of customers returned after month one and fewer than 10% after six months, while the company bought customers at $19.99 on Groupon knowing, per three former employees, that "most of these people never used the service again." Cleaners netted ~$15/hour and churned heavily. The lawsuits were real and did block the Series C — but a business with sub-10% six-month retention and negative paid-acquisition economics was already dead. The legal framing was true, convenient, and not the cause.
• Warning signs visible earlier — Retention cohorts were measurable from 2013. Expansion to 30+ markets happened before retention was fixed — the textbook scaling-before-PMF error. Acquisition talks with Handy and Helpling failed on liability, not on business quality.
• Money outcome — Investors lost ~$40M. Employees and contractors dispersed with little notice. Cheung joined Y Combinator as a partner and later served in the US Office of Science and Technology Policy — see Part 9.
• Post-mortem quality — Poor from the founders; excellent from journalism. No candid founder account exists. The definitive document is a reporter's.
• Transferable lesson — A regulatory event is often the proximate cause of a shutdown and rarely the sufficient one. Healthy companies survive lawsuits. Ask what the retention curve looked like before the subpoena.
• Sources — Farr, Backchannel, "Homejoy at the Unicorn Glue Factory" · Forbes · BuzzFeed News

19. Sprig

• Company — Sprig
• Sector — Food delivery / vertically integrated restaurant
• Founded / Died — 2013 / 26 May 2017
• Lifespan — ~4 years
• Capital raised — ~$56.7M (Greylock, Social Capital, Battery Ventures)
• Peak scale — Operated in San Francisco, Palo Alto and Chicago; revenue never disclosed. Headcount in the hundreds including kitchen staff and couriers.
• What it built — On-demand delivery of freshly cooked meals from its own kitchens, promising delivery in under 15 minutes.
• Stated cause of death — CEO Gagan Biyani wrote that "the demands of the business exceeded our resources" and that the vertically integrated model — owning kitchens, employing chefs and couriers — was "costly and complex" (TechCrunch).
• Evidenced cause — Stated and evidenced causes agree, unusually. Biyani's own later writing is even more direct: the model required high order density in narrow geographies and a fixed cost base (kitchens, W-2 staff) that did not flex with demand.
• Warning signs visible earlier — Contribution margin per order was negative or marginal from launch and remained so through multiple price and menu changes. The 15-minute promise forced kitchen capacity to be sized for peak, not average.
• Money outcome — Investors lost ~$57M. Employees laid off. Biyani co-founded Maven (cohort-based courses) and is candid publicly about Sprig.
• Post-mortem quality — Good. Biyani has repeatedly and specifically analysed the failure in interviews and essays, naming the fixed-cost structure rather than the market.
• Transferable lesson — Vertical integration converts variable costs into fixed costs. That is a good trade only if you are certain of demand density.
• Sources — TechCrunch · Fortune · SFGate

20. Munchery

• Company — Munchery
• Sector — Food delivery / meal kits
• Founded / Died — 2010 / 21 January 2019
• Lifespan — ~9 years
• Capital raised — ~$125M (Menlo Ventures, Sherpa Capital, Greycroft)
• Peak scale — Peak valuation ~$300M (2015); operated in four metros; revenue never disclosed publicly.
• What it built — Chef-prepared meals cooked in Munchery's own kitchens and delivered same-day.
• Stated cause of death — The shutdown email cited "a number of challenges" and a "competitive landscape," and thanked customers. There was no analytical post-mortem.
• Evidenced cause — Divergence by omission rather than by falsehood. The same fixed-cost arithmetic as Sprig, compounded by an expansion to New York, Seattle and Los Angeles that was reversed within two years. The distinguishing and worse fact is the conduct of the shutdown: Munchery ceased operations without notice, leaving small vendors and caterers unpaid — one San Francisco chocolate maker was owed tens of thousands — and leaving customers with unusable prepaid credits.
• Warning signs visible earlier — Retreat from three of four markets in 2017–18.
• Money outcome — Investors lost ~$125M. Small suppliers absorbed unsecured losses. Employees were terminated abruptly. No founder wrote publicly.
• Post-mortem quality — Very poor, and the absence is itself the finding: the shutdowns that treat third parties worst are the ones least likely to produce a post-mortem, because the founders are exposed to claims.
• Transferable lesson — The lesson here is not strategic, it is ethical and practical: how you wind down is a decision, and it is the last one people remember. An orderly wind-down that pays vendors is cheaper than it looks and is largely a function of starting it two months earlier.
• Sources — TechCrunch · TechCrunch on unpaid vendors · Fortune

21. Beepi

• Company — Beepi
• Sector — Used-car marketplace
• Founded / Died — 2013 / February 2017
• Lifespan — ~3.5 years
• Capital raised — ~$150M equity (plus debt facilities); peak valuation ~$560M
• Peak scale — Reported gross merchandise volume in the low hundreds of millions annualised at peak; the figure is not independently verified.
• What it built — A peer-to-peer used-car marketplace that inspected, photographed, warrantied and delivered cars, holding inventory risk.
• Stated cause of death — The company's brief statement cited failure to complete a financing. Two acquisitions (by Fair.com, then by a dealer group, DGDG) collapsed in succession.
• Evidenced cause — Divergence. The failed financing is the mechanism; the cause was a cost structure widely reported as extravagant relative to gross margin — including large salaries, expensive offices and heavy marketing — against a business taking thin spreads on high-value, slow-turning inventory. Multiple ex-employee accounts described spending disconnected from unit economics.
• Warning signs visible earlier — Inventory holding periods lengthening through 2016. Withdrawal from all markets outside California in December 2016, two months before the end.
• Money outcome — Investors lost most of ~$150M; assets sold in pieces. Employees laid off in waves. Founders moved on without public accounting.
• Post-mortem quality — Poor. No founder post-mortem. The public record is journalism and ex-employee commentary, which is a weaker evidentiary base and is flagged as such.
• Transferable lesson — In an inventory business, the balance sheet kills you before the income statement does.
• Sources — TechCrunch · Axios · Forbes

22. Shyp

• Company — Shyp
• Sector — Logistics / on-demand shipping
• Founded / Died — 2013 / March 2018
• Lifespan — ~5 years
• Capital raised — ~$62M (Kleiner Perkins, Homebrew, Slow Ventures); peak valuation ~$250M
• Peak scale — Four metros; headcount peaked around 300. Revenue never disclosed.
• What it built — An app where you photographed an item, a courier collected it within 20 minutes, and Shyp packed and shipped it for a flat $5 fee plus postage.
• Stated cause of death — CEO Kevin Gibbon's shutdown post and subsequent essays are direct: the company scaled to new cities before the unit economics worked, and "I let the fear of missing out drive our decisions." He has written explicitly that the flat $5 fee did not cover the labour it purchased (Gibbon's essays; Fast Company).
• Evidenced cause — Stated and evidenced causes agree closely, which makes this post-mortem unusually valuable. Reporting corroborates: consumer shipping is low-frequency (most people ship a few times a year), so acquisition cost could never be amortised; the 2017 pivot to small-business shipping was correct but came two years and roughly $40M too late.
• Warning signs visible earlier — Consumer shipping frequency was knowable from public data before launch. The four-city expansion in 2015 preceded any demonstrated positive contribution margin.
• Money outcome — Investors lost ~$62M. Employees laid off; Gibbon gave interviews taking personal responsibility. He later founded another logistics venture.
• Post-mortem quality — High. Gibbon has spoken and written repeatedly, including under the unflattering framing "from $250 million to 0." Minimal self-service; he names his own FOMO as a cause.
• Transferable lesson — Purchase frequency is a hard constraint on consumer CAC payback. If the average customer uses you three times a year, your acquisition cost must be recoverable over years, and almost no venture-funded growth plan can wait that long.
• Sources — Fast Company · Airhouse interview, "How I Failed" · Failory

23. Modsy

• Company — Modsy
• Sector — Interior design / e-commerce
• Founded / Died — 2015 / June–July 2022
• Lifespan — ~7 years
• Capital raised — ~$73M (Norwest, TCV, GV, Advance Venture Partners)
• Peak scale — Hundreds of thousands of design projects completed; revenue never disclosed. Headcount in the low hundreds plus a large contractor designer network.
• What it built — 3D-rendered interior design: customers photographed a room, Modsy rendered photoreal alternatives with purchasable furniture, and took a retail margin.
• Stated cause of death — The company told employees and designers it was ending the design service, citing an inability to make the model profitable; the wind-down was not publicly announced in advance.
• Evidenced cause — Divergence in candour rather than in substance. The stated reason is accurate — 3D rendering with human designers in the loop is expensive per project against a one-off furniture commission. The divergence is that Modsy shut down while holding customer money: reporting established that customers who had paid for design packages were left awaiting refunds, and freelance designers were cut off abruptly.
• Warning signs visible earlier — Repeated repositioning between consumer subscription, one-off packages and B2B licensing from 2019 onward — a pattern in this library that almost always indicates the core economics do not work.
• Money outcome — Investors lost most of ~$73M. Customers with outstanding refunds were unsecured creditors. Contract designers lost income with no notice. Some technology later resurfaced under other owners.
• Post-mortem quality — Poor. No founder post-mortem. The record is trade press plus customer complaints.
• Transferable lesson — Any business that puts a human in the loop per transaction must reach automation before scale, not after.
• Sources — TechCrunch · Fortune · Business Insider

24. Zeus Living

• Company — Zeus Living
• Sector — Proptech / corporate housing
• Founded / Died — 2015 / November 2023
• Lifespan — ~8 years
• Capital raised — ~$150M equity and debt (Airbnb, Comcast Ventures, CEAS Investments)
• Peak scale — Thousands of managed units across US metros; reported revenue in the tens of millions; headcount peaked in the hundreds before deep cuts in 2020 and 2022.
• What it built — Master-leased apartments furnished and re-let to corporate travellers and relocating employees on 30-plus-day stays.
• Stated cause of death — The company told staff it had been "struggling to raise capital" in a market that had turned against asset-heavy proptech.
• Evidenced cause — Stated and evidenced causes agree on mechanism and understate the structural point. Master-leasing is a leveraged spread business: you take fixed multi-year lease obligations and match them against short-term, cyclical demand. COVID removed the demand while the obligations remained; the 2022–23 return-to-office recovery was insufficient and slower than the debt schedule.
• Warning signs visible earlier — Two rounds of severe layoffs (2020, 2022). The asset-heavy model was identified as fragile by observers well before COVID.
• Money outcome — Investors lost most of ~$150M. Employees laid off. Landlords and some tenants were left mid-lease.
• Post-mortem quality — Low. Internal memo reported by press; no public analytical account.
• Transferable lesson — Duration mismatch — long liabilities against short revenue — is a financial structure, not a business model, and it is fatal in any demand shock.
• Sources — SF Standard · SF Chronicle · TechCrunch

25. Mindstrong Health

• Company — Mindstrong Health
• Sector — Digital mental health
• Founded / Died — 2014 / February–March 2023
• Lifespan — ~9 years
• Capital raised — ~$160M (ARCH Venture Partners, Foresite Capital, General Catalyst, Optum Ventures)
• Peak scale — ~130 employees at HQ before closure; patient volumes modest and never fully disclosed; peak valuation not publicly confirmed.
• What it built — Originally a "digital biomarker" platform claiming that smartphone typing and swipe patterns could predict mental-health deterioration — a "smoke alarm for mental illness" — later pivoted to a virtual mental-health clinic.
• Stated cause of death — The company said it was winding down and selling its technology; co-founder statements emphasised a difficult funding environment and the challenge of building a care-delivery business.
• Evidenced cause — Substantial divergence, well documented by STAT News. Reporting established that the central scientific claim — that passive smartphone signals reliably predicted psychiatric events — did not hold up in practice, that internal validation was weaker than the external positioning implied, and that the pivot to conventional virtual care put Mindstrong into direct competition with better-funded, better-distributed telehealth companies with none of its differentiation. The stated "funding environment" cause is true but downstream of a failed core hypothesis.
• Warning signs visible earlier — The digital-biomarker claim was contested by academic psychiatrists from at least 2019. The 2021–22 pivot to clinic services was itself the admission.
• Money outcome — Investors lost most of ~$160M. 128 employees laid off; technology acquired by SonderMind for an undisclosed (small) sum.
• Post-mortem quality — Poor from the company; excellent from journalism (STAT's multi-part investigation). Another case where the durable document is a reporter's.
• Transferable lesson — When a company's differentiation is a scientific claim, the claim must be validated on the same schedule as the fundraising.
• Sources — STAT News investigation · STAT on Mindstrong and Pear · MedTech Dive

26. Airware

• Company — Airware
• Sector — Enterprise drones / industrial analytics
• Founded / Died — 2011 / 14 September 2018
• Lifespan — ~7 years
• Capital raised — ~$118M (Andreessen Horowitz, Kleiner Perkins, GV, Next World Capital)
• Peak scale — ~140 employees at peak; enterprise customers in insurance, mining and construction; revenue never disclosed and understood to be small.
• What it built — Initially drone autopilot hardware and software for enterprises; later pivoted to aerial data analytics for insurance and mining.
• Stated cause of death — The company told employees, in the founder's words as reported, that it had simply run out of money — announced the same day, with no severance for many staff.
• Evidenced cause — Stated and evidenced causes agree on the mechanism and omit the strategy. Airware bet on selling hardware into enterprises at the exact moment DJI made commodity drone hardware good enough and cheap enough that no enterprise needed a bespoke autopilot. The 2016–17 pivot to analytics put Airware into a software market where its hardware investment was a stranded asset.
• Warning signs visible earlier — DJI's price and capability curve was visible from 2014. Airware shut its own hardware manufacturing in 2016 — an admission two years before the end.
• Money outcome — Investors lost ~$118M. Employees were let go abruptly with limited notice; assets were auctioned and some of the team was hired by Delair.
• Post-mortem quality — Low. No substantive founder account. The most-cited analysis is third-party.
• Transferable lesson — If a hardware component of your stack can be commoditised by a manufacturer with scale advantages, assume it will be, and build the business on the layer that cannot.
• Sources — TechCrunch · The Drone Girl · TechCrunch on the asset auction

PART 4 — Large, heavily funded failures (>$200M raised)

Chapter 9 already covers Quibi, Convoy, IRL, WeWork, Theranos and Nikola. These five are chosen to avoid duplication and to cover failure shapes those entries do not.

27. Jawbone

• Company — Jawbone (AliphCom)
• Sector — Consumer hardware / wearables
• Founded / Died — 1999 / liquidation commenced July 2017
• Lifespan — ~18 years
• Capital raised — ~$930M in equity and debt, including a 2015 BlackRock debt facility; peak valuation ~$3.2B (2014)
• Peak scale — Tens of millions of Jambox speakers and UP bands sold cumulatively; at the end, market share in wearables was in low single digits against Fitbit and Apple.
• What it built — Bluetooth speakers (Jambox) and fitness wearables (UP), sold at premium prices on design quality.
• Stated cause of death — There was no founder post-mortem. The company entered assignment for the benefit of creditors; CEO Hosain Rahman moved to a new venture, Jawbone Health.
• Evidenced cause — Persistent hardware quality problems with the UP line (high return rates from 2013), a category being commoditised from below by Xiaomi and from above by Apple, and years of expensive patent litigation with Fitbit. Widely described as "death by overfunding": each successive round let the company avoid resolving a product-quality problem that a less-funded company would have had to fix or die.
• Warning signs visible earlier — UP band return and failure rates were publicly discussed from 2013. Layoffs in 2015 and 2016. The 2015 debt round, taken at high cost, is the classic signal of a company that cannot raise equity at its last valuation.
• Money outcome — Common shareholders and most preferred were wiped out; debt holders recovered some value from asset sales. Employees lost jobs; some were owed. Rahman raised again for the successor company — a fact that reasonable people read very differently.
• Post-mortem quality — None. For the largest consumer-hardware failure of its decade, no participant has published a substantive account.
• Transferable lesson — Capital does not fix a product defect; it postpones the consequences of one.
• Sources — Axios · CNBC, "death by overfunding" · Fortune

28. Katerra

• Company — Katerra
• Sector — Construction technology / modular building
• Founded / Died — 2015 / Chapter 11 June 2021
• Lifespan — ~6 years
• Capital raised — ~$2B+, overwhelmingly from SoftBank Vision Fund; peak valuation ~$4B (2018)
• Peak scale — ~8,000 employees globally; factories in Arizona, Washington and India; billions in claimed project backlog.
• What it built — A vertically integrated construction company: designing buildings, manufacturing components in its own factories, and acting as general contractor.
• Stated cause of death — The Chapter 11 filing cited an inability to secure additional capital after Greensill Capital — its lender — itself collapsed in March 2021.
• Evidenced cause — Major divergence. Greensill's failure was the trigger and is genuinely exogenous. The cause was that Katerra took fixed-price contracts at a loss to fill factories built before demand existed, ran project controls that reportedly could not distinguish profitable from unprofitable jobs, and expanded into several countries at once. Employee accounts describe factories far below capacity and designs that could not be manufactured as specified.
• Warning signs visible earlier — SoftBank had already provided a $200M rescue in 2020 alongside a management change — a bailout eighteen months before the end.
• Money outcome — SoftBank lost the great majority of ~$2B. Roughly 8,000 employees lost jobs; some US staff received little notice. Subcontractors and suppliers were left as unsecured creditors on live projects.
• Post-mortem quality — None from the company. The most useful documents are trade-press interviews with former employees and the bankruptcy filings themselves.
• Transferable lesson — Vertical integration in a low-margin, project-based industry requires world-class cost accounting before it requires scale. If you cannot compute project-level margin weekly, capacity expansion multiplies losses rather than revenue.
• Sources — TechCrunch · Architect Magazine, "Katerra's $2 Billion Legacy" · SBCA, employee accounts

29. Fab.com

• Company — Fab.com (formerly Fabulis)
• Sector — E-commerce / design retail
• Founded / Died — 2010 (as Fabulis) / sold for parts March 2015
• Lifespan — ~5 years
• Capital raised — ~$336M; peak valuation ~$1B (2013)
• Peak scale — ~14 million registered members (2013); reported revenue peaked around $100–150M annualised, a figure that comes from company statements and has never been audited publicly. ~700 employees at peak.
• What it built — A flash-sale site for design objects, pivoted from a failed gay social network, later attempting to become a full-line design retailer with its own manufacturing.
• Stated cause of death — Founder Jason Goldberg has been unusually public, describing in talks and essays a series of errors: expanding to Europe by acquisition too fast, scaling headcount ahead of repeat purchase, and abandoning the flash-sale model that was working for a general retail model that was not. His widely quoted self-assessment amounts to: we spent $200M learning that we did not know what the business was.
• Evidenced cause — Stated and evidenced causes agree. Repeat purchase rates for design flash sales were structurally low; the European acquisitions added cost and complexity without solving that; and the shift to owned inventory and manufacturing converted a capital-light model into a capital-intensive one at the worst moment.
• Warning signs visible earlier — Cohort repeat rates were weak by 2012. Layoffs began in 2013, the same year as the $1B valuation — a juxtaposition that is itself diagnostic.
• Money outcome — Assets sold to PCH International for a reported ~$15M against $336M raised; Investors recovered a small fraction.
• Post-mortem quality — High and repeated, though performative in places. Goldberg's willingness to narrate his own failure publicly (including the memorably titled "Sht, I'm Fcked" interview) is genuine and useful; it is also career-rebuilding, and both things are true at once.
• Transferable lesson — Rapid growth in a model with weak repeat purchase is a treadmill: each month you must acquire more customers than the last simply to stand still.
• Sources — Quartz · GeekWire · The Hustle

30. Hopin

• Company — Hopin
• Sector — Virtual events software
• Founded / Died — 2019 / core business sold August 2023; remaining entity wound down subsequently
• Lifespan — ~4 years
• Capital raised — ~$1.0B (Andreessen Horowitz, General Catalyst, IVP, Tiger Global, Coatue); peak valuation $7.75B (August 2021)
• Peak scale — Reported ARR of roughly $100M at peak (2021, company-stated, unaudited); ~1,100 employees before successive layoffs.
• What it built — An all-in-one virtual conference and events platform that grew explosively during COVID lockdowns.
• Stated cause of death — Hopin did not describe itself as failing; it described the RingCentral transaction as a strategic sale of its Events and Session products. Founder Johnny Boufarhat has since moved on to a new AI hardware venture.
• Evidenced cause — Significant divergence between framing and substance. Hopin's flagship products sold to RingCentral for a reported ~$15M against ~$1B raised — roughly 1.5 cents on the dollar. The evidenced cause is that virtual-events demand was a step function created by lockdowns, not a secular shift; when in-person events returned in 2022, revenue reversed almost as fast as it had arrived, and Hopin had hired, acquired (StreamYard, Boomset, Jamm, Attend) and priced for a demand level that had already peaked.
• Warning signs visible earlier — Vaccine rollouts began late 2020, seven months before the $7.75B round closed. Three rounds of layoffs from February 2022.
• Money outcome — Late investors (2021) lost effectively everything; earlier investors and some employees who sold in secondaries did well, a distributional detail that matters. Boufarhat's personal secondary sales during the up-round were reported, meaning the founder realised liquidity that most employees did not.
• Post-mortem quality — Poor. No candid account exists. This is characteristic of failures that were framed as exits.
• Transferable lesson — A demand shock is not product-market fit, and the test is simple: ask what happens to your revenue if the cause of the shock reverses.
• Sources — TechCrunch · Skift Meetings on the $15M price · Forbes, 2026 follow-up

31. Northvolt

• Company — Northvolt AB
• Sector — Battery manufacturing / climate tech
• Founded / Died — 2016 / US Chapter 11 November 2024; Swedish bankruptcy 12 March 2025
• Lifespan — ~9 years
• Capital raised — ~$15B in equity and debt combined (Volkswagen, Goldman Sachs, Baillie Gifford, the European Investment Bank, Swedish pension funds); peak valuation ~$12B
• Peak scale — ~6,600 employees; a gigafactory at Skellefteå, Sweden; a reported order book above $50B; actual cell output remained a small fraction of nameplate capacity throughout. Liabilities at filing: ~$5.8B.
• What it built — Europe's flagship attempt at a domestic lithium-ion cell manufacturer, intended to reduce the continent's dependence on Chinese and Korean suppliers.
• Stated cause of death — The company cited an inability to raise further capital, compounded by slower-than-expected EV demand in Europe and by BMW's cancellation of a ~€2B order in June 2024.
• Evidenced cause — Partial divergence. The demand slowdown and BMW cancellation are real. The primary cause is a manufacturing yield failure: Northvolt could not ramp Skellefteå to commercial yields — an execution problem in one of the hardest process-manufacturing disciplines — while simultaneously building a cathode plant, a recycling plant, and facilities in Germany and Canada. Five hard things at once, with no proven line.
• Warning signs visible earlier — Production shortfalls against BMW's specifications were reported through 2023, a year before the cancellation. Executive departures, including the CFO, preceded the filing. The founder, Peter Carlsson, stepped down as CEO the day after the Chapter 11 filing.
• Money outcome — The largest single destruction of European startup capital on record. Volkswagen's ~21% stake written to zero; Goldman Sachs wrote off ~$896M; Swedish pension funds took losses. Thousands of employees in Skellefteå — a town reshaped around the plant — lost jobs. Assets partially acquired by Lyten in 2025.
• Post-mortem quality — Moderate but external. Carlsson gave interviews accepting that the company "tried to do too much too fast." Sifted and Swedish outlets have produced good reconstructions. No comprehensive first-person document.
• Transferable lesson — In process manufacturing, yield is the business.
• Sources — Sifted, "From pioneer to bankruptcy" · TechCrunch, March 2025 · Chemistry World

32. Bird Global

• Company — Bird Global
• Sector — Micromobility
• Founded / Died — 2017 / Chapter 11 20 December 2023
• Lifespan — ~6 years
• Capital raised — ~$776M venture equity plus SPAC proceeds; peak valuation ~$2.5B (2019); post-SPAC public market value fell more than 99%
• Peak scale — Operations in 350+ cities; reported ~$240M revenue in 2022 against heavy losses.
• What it built — Dockless electric scooter sharing, the fastest company in history to reach a $1B valuation at the time.
• Stated cause of death — The Chapter 11 filing described a need to restructure and strengthen the balance sheet. Founder Travis VanderZanden had already departed in 2022.
• Evidenced cause — Unit economics were negative for most of the company's life: early vehicles lasted weeks rather than years, vandalism and theft were structural, and nightly collection and charging consumed the revenue. A 2021 SPAC listing and a subsequent revenue restatement (Bird had recognised revenue from unearned customer wallet balances) compounded it, and municipal permits capped fleets in the densest markets.
• Warning signs visible earlier — Vehicle lifespan data was contested publicly from 2018. Deep layoffs in 2020 and 2022. The revenue restatement in late 2022 and the NYSE delisting in September 2023 preceded the filing.
• Money outcome — Public shareholders effectively wiped out; venture investors largely so. Assets acquired by a lender group. Riders lost prepaid wallet balances in several jurisdictions — a consumer harm that attracted regulatory attention. Thousands of jobs lost across global operations.
• Post-mortem quality — Poor. No founder account. The bankruptcy docket is the most reliable source.
• Transferable lesson — Asset lifespan is the hidden denominator of every sharing economy.
• Sources — CBS News · TechCrunch · Pari Passu restructuring analysis

PART 5 — AI-era failures (2023–2026)

This is the least-documented category in the library and the one where the post-mortem genre is thinnest, partly because many of these companies did not die — they were absorbed, in structures designed to look like success. Chapter 9 covers Olive AI and IRL; those are not repeated here.

A note on a recurring pattern: the acqui-hire-shaped death. Several 2024–2026 AI failures resolved as talent-and-licensing deals with large technology companies. These are legally acquisitions and economically closures. The entries below say so explicitly, because treating them as exits corrupts the failure statistics badly.

33. Humane

• Company — Humane
• Sector — Consumer AI hardware
• Founded / Died — 2018 / 28 February 2025 (assets to HP, announced 18 February 2025)
• Lifespan — ~7 years
• Capital raised — ~$230M (Tiger Global, Kindred Ventures, SoftBank, Qualcomm Ventures, Microsoft, Salesforce's Marc Benioff, OpenAI's Sam Altman); peak valuation ~$850M
• Peak scale — Reported roughly 10,000 units sold against ~100,000 units targeted for year one; reports of returns exceeding sales in some months, which the company disputed. Headcount ~200.
• What it built — The Ai Pin: a screenless, wearable, projector-equipped AI assistant at $699 plus a $24/month subscription, positioned as a post-smartphone device.
• Stated cause of death — Humane framed the HP transaction as the technology "finding a home," with the CosmOS operating system continuing inside HP. There was no admission of failure.
• Evidenced cause — Complete divergence between framing and substance. HP paid $116M for IP and staff but explicitly not the Ai Pin device, which was discontinued and remotely disabled for existing owners on 28 February 2025. The causes: latency and heat problems that made the core interaction unusable; a price demanding smartphone-replacement performance from a product that could not replace a smartphone; and the structural point — every capability the Pin offered shipped inside phones from Apple, Google and OpenAI within twelve months at no extra hardware cost.
• Warning signs visible earlier — Reviews in April 2024, weeks after launch, were near-uniformly negative on latency and heat. A cofounder's public dismissal of criticism preceded the sales data. Layoffs and a CTO departure in 2024.
• Money outcome — $116M against $230M raised means most investors lost money, though with liquidation preferences some may have been made partly whole. Customers were the worst-treated party: devices became non-functional, with refunds limited to purchases within 90 days. Employees largely transferred to HP.
• Post-mortem quality — None, and the framing was actively misleading about the outcome for customers.
• Transferable lesson — If your product's entire value can be delivered as a software feature on a device the customer already owns, you are not building a category; you are building a demo of one.
• Sources — TechCrunch · Bloomberg · Tom's Guide

34. Artifact

• Company — Artifact
• Sector — Consumer news / AI recommendation
• Founded / Died — 2022 / product shut down February 2024; IP sold to Yahoo April 2024
• Lifespan — ~2 years
• Capital raised — Reported ~$7–10M, terms undisclosed; the founders were sufficiently wealthy that outside capital was not the constraint
• Peak scale — Never disclosed.
• What it built — A personalised news app using machine-learning ranking, built by Instagram co-founders Kevin Systrom and Mike Krieger.
• Stated cause of death — Systrom's announcement was unusually plain: "the market opportunity isn't big enough to warrant continued investment." He said the product worked but the addressable market did not justify the effort.
• Evidenced cause — Stated and evidenced causes agree, and the honesty is the notable thing. News aggregation has been a graveyard for two decades (Digg, entry 47; Zite; Prismatic; Nuzzel) for a consistent reason: the product is used daily, engages deeply, and monetises poorly, because news advertising inventory is worth little and publishers resist paying for distribution. Artifact's ranking quality was genuinely good and irrelevant to that constraint.
• Warning signs visible earlier — The category's entire prior history.
• Money outcome — Small loss to investors. IP sold to Yahoo for an undisclosed sum. No employees stranded. Krieger became Anthropic's Chief Product Officer; Systrom returned to investing.
• Post-mortem quality — High for its brevity and honesty, and notably easy to write, because the founders had nothing at stake reputationally or financially. That is a real caveat about which post-mortems get written (Synthesis Section 5).
• Transferable lesson — Shutting down early, while the product is good and the money remains, is a legitimate and under-used outcome. "It works but the market is too small" is a valid finding and can be reached in eighteen months rather than six years.
• Sources — TechCrunch, shutdown · TechCrunch, Yahoo acquisition · Yahoo press release

35. Ghost Autonomy

• Company — Ghost Autonomy (formerly Ghost Locomotion)
• Sector — Autonomous driving / AI
• Founded / Died — 2017 / 3 April 2024
• Lifespan — ~7 years
• Capital raised — ~$220M (Founders Fund, Khosla Ventures, Sutter Hill, Mike Speiser; plus a $5M investment from the OpenAI Startup Fund in November 2023 — five months before the shutdown)
• Peak scale — ~100 employees; no shipping product and no automaker customer at any point.
• What it built — Originally consumer-installable highway autonomy for existing cars; later repositioned as multimodal-LLM-based autonomy software sold to automakers.
• Stated cause of death — The company told staff the business was closing because it could not find "a customer and market fit within the timeframe available to us" (TechCrunch).
• Evidenced cause — Stated and evidenced causes agree, and the striking feature is the timing of the last raise. Ghost had pivoted at least twice — consumer retrofit, then OEM software, then LLM-based autonomy — each time toward whatever narrative was currently fundable. The November 2023 OpenAI investment came at the peak of enthusiasm for applying LLMs to everything; the company closed before it could demonstrate that LLMs improved driving.
• Warning signs visible earlier — Seven years and $220M with no OEM design win. A pivot to the prevailing hype cycle in year six is itself a signal.
• Money outcome — Remaining cash returned to shareholders — a relatively honourable outcome that returned a fraction of capital. Employees laid off.
• Post-mortem quality — Low. An internal memo; no analysis.
• Transferable lesson — When a company's technical thesis changes to match whatever investors are currently excited by, the thesis is a fundraising artifact, not a strategy.
• Sources — TechCrunch · SiliconANGLE · SFGate

36. Inflection AI

• Company — Inflection AI
• Sector — Foundation models / consumer AI assistant
• Founded / Died — 2022 / 19 March 2024 (as an independent consumer AI company)
• Lifespan — ~2 years
• Capital raised — $1.525B total, including a $1.3B round in June 2023 led by Microsoft and NVIDIA, at a reported $4B valuation
• Peak scale — Pi, its assistant, reported roughly one million daily users at peak — orders of magnitude below ChatGPT. It trained frontier-scale models on a very large GPU cluster.
• What it built — Pi, an empathetic consumer AI assistant, plus the Inflection-1 and Inflection-2.5 foundation models.
• Stated cause of death — Inflection did not describe a death. It announced that co-founders Mustafa Suleyman and Karén Simonyan, plus most of the technical staff, were joining Microsoft to lead a new consumer AI division, and that Inflection would continue as an enterprise AI company.
• Evidenced cause — The canonical acqui-hire-shaped death; the framing divergence is total. Microsoft paid a reported ~$650M, structured as licensing plus hiring — a structure widely understood to have been chosen to avoid merger review — and nearly all technical staff moved. The cause is straightforward: a consumer AI assistant with no distribution advantage cannot compete with OpenAI, Google and Meta, and the cost of staying at the frontier is unbounded. Inflection had raised $1.5B and needed vastly more.
• Warning signs visible earlier — Pi's usage relative to ChatGPT was public and dismal throughout 2023. Microsoft was simultaneously Inflection's largest investor, its compute provider, and OpenAI's largest partner — a conflict visible from the June 2023 round.
• Money outcome — Investors were reportedly made roughly whole or slightly better — approximately 1.1–1.5x, per reporting, though exact terms were never disclosed and should be treated as unconfirmed. This is a crucial and under-appreciated point: the investors were fine, the company is gone, and the statistics will record neither a failure nor a return. Employees who moved to Microsoft did well.
• Post-mortem quality — None, and by design. The structure was chosen partly so that no one would have to call it what it was.
• Transferable lesson — At the foundation-model layer, capital is not a moat if your competitor has more of it and better distribution. More generally: when large technology companies begin licensing-plus-hiring deals, read them as consolidation, and be sceptical of any failure statistics that count them as exits.
• Sources — TechCrunch · BusinessWire, the $1.3B round · Turing Post

37. Adept AI

• Company — Adept AI Labs
• Sector — AI agents / enterprise automation
• Founded / Died — 2022 / June 2024 (as an independent company)
• Lifespan — ~2 years
• Capital raised — ~$415M (Greylock, General Catalyst, Spark Capital, Microsoft, NVIDIA, Atlassian); reported valuation ~$1B+
• Peak scale — No generally available product. Adept demonstrated ACT-1, an agent that operated software through a browser, but never shipped commercially at scale. ~100 employees.
• What it built — Foundation models trained to take actions in software — the "agent" thesis, roughly two years before the market was ready for it.
• Stated cause of death — Amazon hired the co-founders and much of the team for its AGI group; Adept said it would license its technology to Amazon and continue in some form. No failure was declared.
• Evidenced cause — Another acqui-hire-shaped death. Adept raised $415M on a research thesis and had no revenue path within the horizon its capital allowed. Three of the four co-founders were among the original Transformer paper authors or close collaborators — extraordinary technical credibility that did not convert into a product, and the company reportedly pivoted its focus at least once before the Amazon deal.
• Warning signs visible earlier — Two years, $415M, no GA product. A co-founder departure in 2023.
• Money outcome — Investors were reportedly repaid, per Semafor's reporting on the deal structure — again, a company that ceased to exist with investors approximately whole. Employees who joined Amazon did well; those who did not were left with a shell.
• Post-mortem quality — None.
• Transferable lesson — Technical pedigree raises money and does not create demand. A research company funded as a product company has a clock running that research timelines do not respect.
• Sources — Semafor on investor repayment · GeekWire · CB Insights company financials

38. Forward Health

• Company — Forward
• Sector — Digital health / primary care
• Founded / Died — 2016 / November 2024
• Lifespan — ~8 years
• Capital raised — ~$650M total, including a $100M Series E in November 2023 (Founders Fund, Khosla Ventures, SoftBank, General Catalyst); peak valuation reported ~$1B+
• Peak scale — 19 physical clinics at peak; a reported ~$149/month membership base in the tens of thousands; ~200 employees. Revenue never disclosed.
• What it built — First a chain of technology-heavy primary care clinics with staff doctors; then CarePods — unstaffed automated diagnostic booths placed in malls and offices, where a patient would self-administer tests guided by AI.
• Stated cause of death — Founder Adrian Aoun said the company shut down after failing to scale CarePods and cited the difficulty of the model. No detailed public explanation was given.
• Evidenced cause — Divergence, and unusually instructive. Reporting established that CarePods had frequent hardware problems — including patients getting stuck inside — that the clinical scope was narrow, and that a pod could not perform the examinations primary care consists of. Forward spent the majority of $650M solving an automation problem in a domain where the binding constraint was clinical liability, reimbursement and trust, not throughput. The pivot from staffed clinics, which had real patients, to pods was a capital-intensive bet against the part of the business that worked.
• Warning signs visible earlier — CarePods launched November 2023 to immediate technical criticism; the company shut down twelve months later. Forward was cash-pay only, never solving reimbursement, which caps the addressable market to affluent urban customers.
• Money outcome — Investors lost most of ~$650M, including $100M invested twelve months before the end. Members lost access and, in some cases, continuity of care — the most serious customer harm in this library after the fraud cases. Employees laid off.
• Post-mortem quality — Poor. Brief statements; no analysis.
• Transferable lesson — Identify the binding constraint before you automate. Automating the non-binding constraint is the most expensive way to discover which one it was.
• Sources — Fierce Healthcare · Modern Healthcare · TechCrunch on the CarePod launch

39. Neeva

• Company — Neeva
• Sector — Search / consumer AI
• Founded / Died — 2019 / shutdown announced 20 May 2023, consumer search switched off 2 June 2023; acquisition by Snowflake announced 24 May 2023
• Lifespan — ~4 years
• Capital raised — ~$77.5M (Sequoia, Greylock)
• Peak scale — ~600,000 monthly active users; roughly 1.5 million total signups claimed; paying subscribers a small fraction of that. The subscription was $4.95/month.
• What it built — An ad-free, privacy-preserving subscription search engine, later augmented with LLM-generated answers (NeevaAI, launched January 2023 — before Bing Chat).
• Stated cause of death — Co-founder Sridhar Ramaswamy, formerly head of Google's advertising business, wrote a notably candid shutdown post: building a search engine was the easy part; "convincing regular users of the need to switch to a better choice is really hard," and acquiring users at scale against default placements was harder than the technology.
• Evidenced cause — Stated and evidenced causes agree. Default distribution — browser and OS placement — is the entire game in consumer search, and it is purchased with money Neeva did not have.
• Warning signs visible earlier — Conversion from free trial to paid was reported as weak throughout. The consumer-to-enterprise pivot was announced days before the acquisition — an admission compressed into a press cycle.
• Money outcome — Snowflake acquired the company for a reported ~$150M (never confirmed), which likely returned capital to investors. Ramaswamy became Snowflake's CEO in February 2024 — one of the best personal outcomes from a failed consumer product on record.
• Post-mortem quality — High. Ramaswamy's shutdown essay is specific and non-defensive, and his later comments that "the window is shutting" for AI search disruption are more honest than most.
• Transferable lesson — In categories governed by defaults, the incumbent's advantage is a contract, not a product feature, and no amount of product quality reaches users who never change a setting.
• Sources — CNBC · VentureBeat on the Snowflake deal · VentureBeat, "window is shutting"

40. Relay

• Company — Relay (relay.app)
• Sector — AI workflow automation
• Founded / Died — 2022 / August 2026
• Lifespan — ~4 years
• Capital raised — ~$8–10M disclosed across a seed and a $3.1M extension (Andreessen Horowitz, Khosla Ventures); full totals not published
• Peak scale — Not disclosed. Relay had paying customers and a working product; revenue was never published and should be assumed modest.
• What it built — An AI-native workflow automation tool — a Zapier competitor built around human-in-the-loop approvals and, latterly, agentic steps.
• Stated cause of death — Founder Jacob Bank, previously a Google Calendar product lead, announced that Relay was winding down and that he and the team were joining Google's Chrome team to work on AI in the browser (TechCrunch, 17 August 2026).
• Evidenced cause — Partial divergence, of a kind specific to this era. The framing is a talent move. The structural cause is that AI workflow automation was squeezed from three directions between 2024 and 2026: incumbents (Zapier, Make, Power Automate) added agentic features; model providers shipped tool-use and computer-use natively; and the marginal value of a thin orchestration layer fell as models handled multi-step tasks without one. The clearest small-company example of the disappearing middle layer.
• Warning signs visible earlier — OpenAI and Anthropic shipped native tool-use and computer-use capabilities through 2024–25. Zapier shipped AI agents in 2024. The window between "AI makes this possible" and "AI makes this unnecessary" was roughly thirty months.
• Money outcome — Investors lost most of a small raise. Customers were given notice and migration time — a well-conducted wind-down. Employees moved to Google.
• Post-mortem quality — Moderate. A clear announcement, no deep analysis. Representative of the era: the shutdown is announced alongside the new job, which structurally discourages candour.
• Transferable lesson — When your product is a layer between a rapidly improving model and a user, measure the distance between you and the model's roadmap.
• Sources — TechCrunch, Aug 2026 · TechCrunch, 2023 funding · FinSMEs

41. Jasper (partial failure — valuation collapse, company survives)

• Company — Jasper AI (formerly Jarvis, formerly Conversion.ai)
• Sector — AI content generation / marketing SaaS
• Founded / Died — 2020 / did not die; internal valuation cut and strategy reset from mid-2023
• Lifespan — Ongoing
• Capital raised — $125M Series A (October 2022, Insight Partners, Coatue, Bessemer) at a $1.5B valuation; ~$131M raised in total
• Peak scale — Reported ~$75M ARR and ~100,000 customers in 2022 — among the fastest early ramps ever recorded for a SaaS company. Both figures are company-stated.
• What it built — A marketing copywriting tool built on OpenAI's models, with templates and workflows layered on GPT-3.
• Stated cause of death — Jasper did not fail. In July 2023 it cut its internal 409A valuation, reduced its revenue forecast, and later replaced its CEO. The company positioned this as a shift from a point tool to a marketing platform.
• Evidenced cause — ChatGPT launched on 30 November 2022 — six weeks after Jasper's $1.5B round — and gave away free the core of what Jasper charged for. Growth stalled almost immediately. The condition is thin-wrapper exposure: when the primary value is prompt engineering and UI over a third-party model, that model provider's consumer product is a direct substitute. Jasper survives because it moved fast into brand-voice controls, enterprise workflow and compliance — the parts a general chatbot does not provide.
• Warning signs visible earlier — The gross margin structure (paying per-token to a supplier who also sells to your customers) was visible at the time of the round.
• Money outcome — No liquidation. Late investors are very likely underwater on paper. Employees who joined on 2022 valuations hold options struck above the reset price — a common and rarely discussed form of employee loss in a down round.
• Post-mortem quality — Not applicable, and that is the point. Partial failures produce no post-mortems at all, which means the most common outcome in the AI era — a company that survives at a fraction of its peak mark — is almost entirely undocumented.
• Transferable lesson — Build where the model provider will not go.
• Sources — Maginative on the valuation cut · AI Beat · The Information/secondary coverage of the CEO change

42. The GPT-wrapper cohort (a category entry, not a single company)

• Company — Collectively: the document-chat, PDF-summarisation, AI-writing and single-prompt-tool companies launched 2022–23
• Sector — AI applications
• Founded / Died — Mostly 2022–2023 / mostly 2023–2025
• Lifespan — Typically 6–24 months
• Capital raised — Mostly bootstrapped, pre-seed or seed. SimpleClosure's 2025 shutdown data puts the median capital raised by shutting-down AI companies at ~$2.4M, below the ~$2.8M overall median.
• Peak scale — Typically thousands to low tens of thousands of users; MRR from hundreds to low tens of thousands of dollars.
• What it built — Interfaces over a third-party model for one task: chat with a PDF, summarise a document, generate marketing copy, write SQL.
• Stated cause of death — Where founders wrote anything, the dominant stated cause is "OpenAI shipped it." The reference event is OpenAI DevDay, 6 November 2023, when ChatGPT gained native file upload and document analysis; a widely circulated comment at the time was "many startups just died today, because OpenAI added PDF chat."
• Evidenced cause — Partial divergence; the honest version is less flattering. The feature release was real and destroyed some businesses overnight. But these products had no proprietary data, no workflow lock-in, no distribution advantage and no switching cost — three or four weeks of engineering any competitor, including the model provider, could replicate. Most were not killed by DevDay; they were revealed by it. Many died more slowly of inference-cost economics: flat-rate pricing against variable per-token costs produces negative gross margin on the heaviest users, who are also the least likely to churn.
• Warning signs visible earlier — Retention curves that flattened near zero after month one. A feature list identical to a dozen competitors. A cost of goods sold that rose with usage.
• Money outcome — Small. Mostly founders' own time and small angel cheques. Some founders did extremely well on the way (several reached $10k–$100k MRR before the collapse) and exited with cash.
• Post-mortem quality — Variable and thin. A handful of good Indie Hackers and X threads; mostly silence. AI represented 15.9% of all documented startup shutdowns in 2025, per SimpleClosure, with wrappers and apps dominating the composition — so the volume is real even where the documentation is not.
• Transferable lesson — The genuinely transferable version is not "don't build wrappers" — several wrappers became large businesses. It is: ask what you will own after the model improves by an order of magnitude. If the answer is "the UI," you have a product with a known expiry date, and the correct strategy is to extract cash quickly rather than raise on it.
• Sources — SimpleClosure, State of Startup Shutdowns 2025 · TechCrunch on DevDay's PDF feature · TechCrunch on 2025 shutdown rates

PART 6 — Regulatory and legal deaths

Three entries, kept compact. The pattern across all three: the regulator was the proximate cause and the business model was the underlying one. Homejoy (entry 18) belongs to this category too and is the best illustration of the distinction.

43. Pear Therapeutics

• Company — Pear Therapeutics
• Sector — Digital therapeutics / healthcare
• Founded / Died — 2013 / Chapter 11 7 April 2023; assets auctioned May 2023
• Lifespan — ~10 years
• Capital raised — ~$400M gross via a December 2021 SPAC merger at a $1.6B enterprise value, plus earlier venture rounds
• Peak scale — Three FDA-authorised prescription digital therapeutics (reSET, reSET-O, Somryst); ~45,000 prescriptions filled in 2022; 2022 revenue ~$12.7M against losses many times that.
• What it built — The first FDA-cleared prescription software treatments — for substance use disorder and insomnia — prescribed by clinicians like drugs.
• Stated cause of death — The company said it could not secure financing and that payers would not reimburse its products at scale.
• Evidenced cause — Stated and evidenced causes agree; the case is in the category because the regulator's approval was the trap, not the obstacle. Pear did everything right by the FDA and discovered that FDA clearance does not create a reimbursement code. Without a CPT code and payer coverage, a prescription product has no buyer.
• Warning signs visible earlier — 2022 revenue of ~$12.7M against a $1.6B valuation. Reimbursement was an unsolved problem at the time of the SPAC, disclosed in the filings.
• Money outcome — Assets sold for a total of ~$6M across four buyers against ~$400M raised. Public SPAC shareholders lost effectively everything. Patients mid-treatment lost access.
• Post-mortem quality — Moderate; executives spoke at industry events and a Stanford case study exists. No founder document.
• Transferable lesson — Regulatory approval and commercial viability are separate gates with separate owners. In healthcare, the question "who writes the cheque, under what code" must be answered before the clinical trial, not after.
• Sources — STAT News on the auction · Fierce Biotech · Stanford GSB case

44. Cerebral

• Company — Cerebral, Inc.
• Sector — Telehealth / mental health
• Founded / Died — 2019 / survives in reduced form; the company as constituted ended 2022–2024
• Lifespan — Ongoing
• Capital raised — ~$462M (SoftBank Vision Fund 2, Access Industries, Oak HC/FT); peak valuation $4.8B (December 2021)
• Peak scale — Reported >200,000 active patients and ~4,500 staff at peak; revenue reported in the hundreds of millions annualised (company-stated).
• What it built — Subscription telehealth for anxiety, depression and — critically — ADHD, with prescribing of controlled stimulants under COVID-era telehealth flexibilities.
• Stated cause of death — Cerebral has never declared failure. Founder-CEO Kyle Robertson was removed by the board in May 2022 amid a federal prescribing investigation; the company then exited controlled-substance prescribing, cut most of its staff, and repositioned.
• Evidenced cause — Regulatory, squarely. In November 2024 Cerebral agreed to pay roughly $3.65M to DOJ (about $6.6M including related penalties) over unlawful distribution of controlled substances, following a $7M FTC settlement over cancellation practices and a data breach affecting 3.1 million people. The underlying condition: growth driven by advertising that funnelled patients toward stimulant prescriptions under a temporary regulatory waiver, with clinician time per patient reportedly compressed to unsafe levels. When DEA scrutiny arrived, the growth engine was the liability.
• Warning signs visible earlier — Internal clinician complaints reported from 2021. Pharmacies (CVS, Walmart) stopped filling Cerebral prescriptions in May 2022. The temporary nature of the telehealth waiver was public policy, not a surprise.
• Money outcome — SoftBank and later investors are very heavily impaired. Thousands of employees lost jobs across 2022–23. Patients on controlled-substance treatment faced abrupt discontinuation. Robertson was sued by his own company over a loan and later led another telehealth venture that itself drew FTC and DOJ action in 2026.
• Post-mortem quality — None; litigation forecloses it. This is a general rule: where there is enforcement exposure, there is no post-mortem.
• Transferable lesson — If your growth depends on a temporary regulatory accommodation, you have a business with a published end date.
• Sources — DOJ press release · Healthcare Dive · Forbes on the CEO's removal

45. LendUp

• Company — LendUp Loans, LLC
• Sector — Fintech / consumer lending
• Founded / Died — 2012 / lending ordered to cease December 2021; company wound down 2022
• Lifespan — ~10 years
• Capital raised — ~$360M in equity and debt (Google Ventures, Kleiner Perkins, Andreessen Horowitz, PayPal's Peter Thiel, QED)
• Peak scale — More than 4 million loans issued; the credit-card spin-out (Mission Lane) was separated in 2018 and survives.
• What it built — Short-term consumer loans marketed as a socially responsible alternative to payday lending, with a "LendUp Ladder" that promised cheaper credit as borrowers repaid.
• Stated cause of death — The company did not publish an account. It settled with the CFPB and ceased originating loans.
• Evidenced cause — Regulatory, following repeat violations. The CFPB fined LendUp in 2016, then brought a further action in 2020 over military-lending violations, then in September 2021 sued for violating the 2016 order, alleging the "Ladder" did not deliver the cheaper credit it advertised. A December 2021 stipulated judgment permanently barred new lending and imposed a $100,000 penalty reduced from a much larger figure on inability to pay.
• Warning signs visible earlier — Being fined by your regulator in 2016 and then found in violation of that same order is about as clear a warning as the record produces.
• Money outcome — Equity investors largely wiped out, partially offset for some by the Mission Lane spin-out. Borrowers received redress. Employees dispersed.
• Post-mortem quality — None. The CFPB's filings are the record.
• Transferable lesson — In regulated consumer finance the marketing claim is the regulated product.
• Sources — CFPB enforcement action · CFPB announcement · Banking Dive

PART 7 — Fraud and misrepresentation

Kept deliberately brief; Chapter 9 covers Theranos, Nikola and IRL at length. These four are included because they are recent, because two of them are specifically AI-era, and because the pattern — misrepresenting the degree of automation — is the characteristic fraud of this decade in the way that misrepresenting clinical results was of the last.

46. Frank (Charlie Javice)

• Company — TAPD, Inc. ("Frank")
• Sector — Fintech / education
• Founded / Died — 2016 / acquired by JPMorgan Chase September 2021 for $175M; shut down January 2023
• Lifespan — ~7 years
• Capital raised — ~$20M (Aleph, Chegg, Marc Rowan and others)
• Peak scale — Claimed 4.25 million users; the actual verified figure was approximately 300,000.
• What it built — A tool to simplify the US federal student aid (FAFSA) application.
• Stated cause of death — None offered; JPMorgan alleged fraud within months of closing and shut the product.
• Evidenced cause — Criminal fraud, adjudicated. Javice hired a data scientist to synthesise a list of roughly 4.25 million fake users to satisfy JPMorgan's diligence. She was convicted in March 2025 on four counts and sentenced on 29 September 2025 to seven years in prison; COO Olivier Amar received a shorter sentence.
• Warning signs visible earlier — JPMorgan's own diligence failed to verify the user list independently before closing — a failure the bank has been widely criticised for and which is the more transferable half of this story.
• Money outcome — Javice received roughly $21M personally from the sale, subject to forfeiture. JPMorgan wrote off the acquisition. Early investors were paid at closing. Employees lost jobs.
• Post-mortem quality — None possible. Criminal proceedings; the record is the indictment, trial transcript and sentencing.
• Transferable lesson — For acquirers: verify the core metric independently, from source systems, before closing.
• Sources — CNBC · CNN · Axios

47. Nate

• Company — Nate
• Sector — AI shopping / e-commerce
• Founded / Died — 2018 / effectively ceased 2023–24; founder charged April 2025
• Lifespan — ~6 years
• Capital raised — $50M+ (Coatue, Forerunner Ventures, Renegade Partners and others)
• Peak scale — Hundreds of thousands of app downloads; transaction volume never independently verified.
• What it built — A universal checkout app that claimed to complete purchases on any e-commerce site "with a single tap," using proprietary AI.
• Stated cause of death — The company said little publicly. Founder Albert Saniger had consistently described the automation as AI-driven.
• Evidenced cause — Alleged criminal fraud. The DOJ and SEC charged in April 2025 that Nate's transactions were substantially completed by human contractors in the Philippines — the automation rate alleged to have been "effectively zero" at times — while investors were told the app used proprietary AI. As of September 2026 the case is pending; these remain allegations.
• Warning signs visible earlier — Nate's transaction throughput reportedly required manual scaling; this is a structural signature of human-in-the-loop systems and is visible in unit-cost data if anyone asks for it.
• Money outcome — Investors' $50M+ at risk. Employees and contractors dispersed.
• Post-mortem quality — None; litigation.
• Transferable lesson — For investors in the AI era, the diligence question is arithmetic, not technical: what is the marginal cost per transaction, and does it fall with volume?
• Sources — SEC litigation release · TechCrunch · Fortune

48. Builder.ai

• Company — Builder.ai (formerly Engineer.ai)
• Sector — AI application development
• Founded / Died — 2016 / insolvency proceedings May 2025
• Lifespan — ~9 years
• Capital raised — ~$450M+ (Microsoft, Qatar Investment Authority, SoftBank's DeepCore, Insight Partners, IFC); peak valuation ~$1.5B
• Peak scale — Claimed revenue of ~$220M for 2024; later restated to approximately $55M. Roughly 1,000 employees plus a large contractor network in India.
• What it built — A platform promising that "Natasha," an AI assistant, would assemble custom software applications from reusable components — software development "as easy as ordering pizza."
• Stated cause of death — The company blamed "historical challenges and past decisions" and entered insolvency after a creditor seized funds.
• Evidenced cause — Two separate things, worth keeping separate. First, the automation claim: reporting since 2019 (Wall Street Journal, later Rest of World) established that the large majority of code was written by several hundred human engineers in India, with AI playing a much smaller role than marketed. Second, the accounting: Bloomberg reported sales overstated to creditors by roughly 300%, with round-tripping allegations surfacing in 2025. The company denied fraud; investigations were ongoing as of 2026. The 2019 reporting matters: this was publicly alleged six years before the collapse, and the company raised hundreds of millions afterwards.
• Warning signs visible earlier — The 2019 WSJ story. Gross margins inconsistent with a software company. Auditor and CFO changes.
• Money outcome — Investors including Microsoft and QIA face near-total loss. ~1,000 employees lost jobs, many in India and the UK with limited protection. Customers lost in-progress projects.
• Post-mortem quality — None; insolvency and investigations.
• Transferable lesson — Published, credible allegations about a company's core claim do not go away because a subsequent round closes. For anyone doing diligence: a later investor's willingness to fund is not evidence that an earlier allegation was resolved.
• Sources — Rest of World investigation · Rest of World explainer · Bloomberg on the 300% overstatement

49. HeadSpin

• Company — HeadSpin, Inc.
• Sector — Developer tools / mobile testing
• Founded / Died — 2015 / company survives; founder-CEO removed 2020, convicted 2023
• Lifespan — Ongoing
• Capital raised — ~$117M (Iconiq Capital, Tiger Global, Dell Technologies Capital); peak valuation $1.16B (2020)
• Peak scale — Actual ARR at the time of the fraud was roughly $10–15M; investors were told approximately $50–100M.
• What it built — A platform for testing mobile applications on real devices across global networks. The product was and is real and useful — an important distinction from the other entries in this section.
• Stated cause of death — The company did not die. Founder Manish Lachwani resigned in May 2020 after an internal investigation; the board recapitalised and reduced the valuation by roughly two-thirds.
• Evidenced cause — Securities and wire fraud, adjudicated. Lachwani inflated revenue by recording unclosed and fabricated contracts and altering invoices; he pleaded guilty and was sentenced in April 2024 to 18 months in prison, with restitution.
• Warning signs visible earlier — The internal review that caught it was triggered by employee concern, not by investor diligence. Revenue recognition at a company with few large enterprise contracts is straightforward to audit and was not audited.
• Money outcome — Investors were partially made whole through a recapitalisation at a much lower valuation. The company continued operating and employees largely kept their jobs — an unusual and instructive outcome.
• Post-mortem quality — None from participants, but the DOJ and SEC filings describe in unusual detail how revenue fraud is executed at a mid-stage startup.
• Transferable lesson — A real product does not prevent founder fraud, and a fraud does not necessarily kill a real product.
• Sources — DOJ sentencing release · SEC litigation release · CFO Dive

PART 8 — Failures whose founders later succeeded

Two entries, compact. The wider pattern is covered in the synthesis; this section exists to make the point that "the founder recovered" is a very common outcome in this library and a very uncommon one outside it — a selection effect, not a consolation.

Also relevant here without separate entries: Marc Hedlund (Wesabe, entry 2 → senior engineering leadership including Stripe), Aaron Harris (Tutorspree, entry 4 → Y Combinator partner, then venture investing), Adora Cheung (Homejoy, entry 18 → Y Combinator partner, then the US Office of Science and Technology Policy), Ben Yoskovitz (Standout Jobs, entry 15 → co-author of Lean Analytics), Adam Goldstein (Hipmunk, entry 16 → co-founder of Archer Aviation), Gagan Biyani (Sprig, entry 19 → Maven), Sridhar Ramaswamy (Neeva, entry 39 → CEO of Snowflake), and Kenton Varda (Sandstorm, entry 7 → creator of Cloudflare Workers).

50. Odeo

• Company — Odeo
• Sector — Podcasting
• Founded / Died — 2005 / effectively abandoned 2006; shell sold 2007
• Lifespan — ~2 years
• Capital raised — ~$5M (Charles River Ventures and angels)
• Peak scale — Modest: a podcast directory and recording tools with a small user base. No meaningful revenue.
• What it built — A podcast hosting, directory and creation platform.
• Stated cause of death — Co-founder Evan Williams wrote publicly that Apple's addition of podcasts to iTunes in June 2005 — announced shortly after Odeo raised — made the company's position untenable, and that he personally had lost conviction in the product. He then did something almost unique: he bought the company back from its investors, returning their capital, and let them keep any upside in the side project the team had built.
• Evidenced cause — Stated and evidenced causes agree. Apple's entry was decisive for a directory business, and Williams's public admission of lost conviction is corroborated by the team's own accounts.
• Warning signs visible earlier — Apple announced iTunes podcast support within weeks of Odeo's funding — the platform risk arrived immediately and was not ambiguous.
• Money outcome — Investors were returned their money. The side project was Twitter. Investors who took the option kept stakes that became extraordinarily valuable; some did not.
• Post-mortem quality — Good and unusually gracious. Williams's writing at the time takes responsibility rather than blaming Apple.
• Transferable lesson — Returning capital when you no longer believe is a real option and is exercised almost never.
• Sources — TechCrunch, "Odeo Bought Back From Investors" · Wikipedia, Odeo · Forbes, "The Real Story of Twitter"

51. Loopt

• Company — Loopt
• Sector — Mobile social / location
• Founded / Died — 2005 / sold March 2012
• Lifespan — ~7 years
• Capital raised — ~$39.1M (Sequoia Capital, New Enterprise Associates, Y Combinator S05)
• Peak scale — Reported ~5 million registered users at peak; engagement and revenue were persistently weak. This is a company that raised at scale and never found a business.
• What it built — Location-sharing among friends on mobile phones, years before smartphones made it practical and before carriers' data pricing made it affordable.
• Stated cause of death — Loopt was sold to Green Dot for $43.4M. CEO Sam Altman framed it as a good outcome and a chance to work on payments. He has since been consistent in describing Loopt as a failure — he has said publicly that it was not a successful company and that he learned more from it than from anything else.
• Evidenced cause — Loopt depended on carrier distribution deals that were slow, expensive and non-exclusive, and on a behaviour (broadcasting your location) that users did not want at the scale required. The 2008 arrival of the App Store removed the carrier bottleneck and simultaneously let Foursquare, Facebook and Google in.
• Warning signs visible earlier — Flat engagement across multiple relaunches from 2009.
• Money outcome — $43.4M returned on $39.1M raised: investors were made approximately whole and no more. Altman has said the founders' personal proceeds were modest. He became president of Y Combinator the following year.
• Post-mortem quality — Moderate but honest in retrospect. Altman does not claim Loopt succeeded, which distinguishes him from most founders of similarly sized exits.
• Transferable lesson — An exit at roughly the capital raised is a failure with good manners.
• Sources — AllThingsD · Green Dot investor release · Y Combinator blog

PART 9 — "Successful" exits that were bad outcomes

Hopin (entry 30), Loopt (entry 51), Hipmunk (entry 16), Inflection (entry 36), Adept (entry 37) and Humane (entry 33) all belong here too. Two further entries, compact.

52. Digg

• Company — Digg
• Sector — Social news
• Founded / Died — 2004 / sold in pieces July 2012
• Lifespan — ~8 years
• Capital raised — ~$45M (Greylock, Omidyar Network, Highland Capital)
• Peak scale — Reported ~40 million monthly unique visitors (2008); a $200M acquisition offer from Google was reported and never completed.
• What it built — User-voted news aggregation — the original mass-market social ranking site.
• Stated cause of death — Digg did not publish a post-mortem. The proximate event was the "Digg v4" redesign of August 2010, which removed community features and drove users to Reddit within weeks.
• Evidenced cause — Traffic and community collapse following v4, on top of a monetisation model (display advertising on aggregated links) that never produced revenue proportional to attention. The v4 redesign was itself an attempt to fix the monetisation problem by making the site more publisher-friendly — i.e. the fix caused the death.
• Warning signs visible earlier — Power-user revolts from 2007. The failed Google sale in 2008 was the last moment the company was worth its valuation.
• Money outcome — Sold for roughly $500,000 to Betaworks for the brand and site, with patents to LinkedIn ($4M) and staff to The Washington Post ($12M): approximately $16M across three buyers against ~$45M raised. The famous $500k figure is accurate for only one of the three pieces — a good example of a widely repeated number that is true and misleading at once. Employees and founders got very little.
• Post-mortem quality — Poor. Kevin Rose has discussed it in interviews; there is no analytical account from the leadership of the v4 period.
• Transferable lesson — When you monetise a community by removing the features the community values, you are converting an asset into cash once.
• Sources — CNN Money · TechCrunch, on the true price · NBC News

53. One Kings Lane

• Company — One Kings Lane
• Sector — E-commerce / home furnishings
• Founded / Died — 2009 / sold June 2016
• Lifespan — ~7 years
• Capital raised — ~$225M (Kleiner Perkins, Tiger Global, Institutional Venture Partners, Scale Venture Partners); peak valuation ~$912M (2014)
• Peak scale — Reported revenue around $300M annualised at peak (2014, company-stated); several million registered members.
• What it built — Flash sales of home furnishings and décor, curated daily.
• Stated cause of death — The company did not fail; it was acquired by Bed Bath & Beyond. The price was undisclosed at the time.
• Evidenced cause — Reported at under $30M against ~$225M raised and a ~$912M peak valuation — roughly 3% of peak. The same structural problem as Fab (entry 29): flash sales in a low-frequency, high-consideration category produce weak repeat purchase, and the fix — full-line retail with inventory — destroys the capital-light economics. Bed Bath & Beyond resold the brand in 2020 for less, before its own bankruptcy.
• Warning signs visible earlier — Layoffs and executive departures from 2015; a failed attempt to raise at or above the 2014 mark.
• Money outcome — This is a preference-stack case. With ~$225M of preferred stock outstanding and a sub-$30M price, common shareholders — founders and employees with options — received essentially nothing, while late preferred holders recovered a fraction. Employees who had exercised options and paid tax on them lost that money outright.
• Post-mortem quality — None. Acquisitions do not generate post-mortems, which is why this failure mode is the least documented and among the most common.
• Transferable lesson — Liquidation preference arithmetic determines founder and employee outcomes more often than exit price does. A $30M exit on $225M of preferred is a zero for common regardless of headline framing. Every founder should be able to state, from memory, the exit price at which their own common stock becomes worth something.
• Sources — Recode · Inc./Business Insider · Business of Home on the 2020 resale

PART 10 — Synthesis

Everything below is computed from the 53 entries above and nothing else. It is a small, biased sample and the conclusions should be read as descriptions of this library, not as population statistics.

Synthesis 1 — Frequency analysis — what actually recurs

Each entry was assigned one primary evidenced cause — the thing that, had it been different, most plausibly changes the outcome.

† Frank and HeadSpin resulted in criminal convictions. The Nate and Builder.ai matters were unresolved as of September 2026 — charges and investigations respectively, not findings. See entries 47 and 48, which state the distinction in full; this table compresses it.

What this says. Two findings survive the small sample.

First, 57% of these failures (categories 1, 2 and 3 combined) are demand-and-economics failures — the company either could not sell the thing profitably, or could not sell it at all, or someone else sold it better. Only 11% were killed primarily by a platform, and only 6% primarily by a regulator, despite those being among the most commonly stated causes (see Section 2).

Second, the "competitive displacement" category is the one growing fastest and is now dominated by a specific mechanism: the model provider shipping your product as a feature. Five of the ten entries in that category are 2023–2026 AI companies, and in four of them the displacing party was also, at some point, a supplier or investor.

Sample-bias warnings, stated plainly:

• This sample is heavily skewed toward outside-funded companies. Of the 52 single-company entries, 48 raised outside capital and only 4 were bootstrapped (Apollo, Tweetbot, Twitterrific and HouseFresh). The 53rd entry, the GPT-wrapper cohort, is a category entry mixing bootstrapped and seed-funded companies and is excluded from that split: 4 + 48 + 1 = 53. The real population of failed startups is overwhelmingly bootstrapped, tiny and unrecorded.
• It is skewed toward founders who write. Categories 1–3 are over-represented partly because they are the causes founders find publishable. Co-founder conflict appears as a primary cause in zero of 53 entries, which is almost certainly false about the underlying population — CB Insights and the academic literature both put team conflict among the top causes, and Chapter 10 discusses that evidence. Its absence here is a measurement failure, not a finding.
• It is skewed toward US and English-language failures. Northvolt and Builder.ai are the main non-US-centred entries; European, Indian, Chinese, African and Latin American failures are systematically under-covered.
• It is skewed toward death rather than decay. Jasper (entry 41) is the only entry representing what is probably the most common outcome of all: a company that survives at a small fraction of its peak valuation and never produces a post-mortem, because nothing discrete ever happened.

Synthesis 2 — Stated vs evidenced cause — the divergence rate

This is the chapter's central measurement.

Every count in this section and in Synthesis 3 is derived from a single per-entry basis — capital band, attributable yes/no, match yes/no — published in full at the end of this section so the tabulation can be audited.

Of the 53 entries, 44 produced an attributable statement of cause from a founder, the company, or an official filing. The other 9 produced nothing usable — either silence or a statement too vacuous to be compared against evidence: Jawbone, Digg, One Kings Lane (silence or an acquisition that removed the obligation), and Cerebral, LendUp, Frank, Nate, Builder.ai and HeadSpin (litigation exposure). 44 + 9 = 53.

Of those 44 attributable statements:

The direction of the bias is consistent and one-directional. Of the 20 divergent cases:

• 10 shifted attribution outward to an external agent or condition — a platform, a regulator, a lender's collapse, the funding market, or timing (Tutorspree, Kite, Fast, Homejoy, Munchery, Beepi, Mindstrong, Katerra, Northvolt, the wrapper cohort).
• 6 reframed the event as not a failure at all — an acqui-hire, a strategic sale or a repositioning (Hopin, Humane, Inflection, Adept, Relay, Jasper).
• 4 named a real internal cause but omitted or softened a worse one (Zirtual, Airware, Bird, Forward Health).
• 0 shifted attribution toward a harsher internal cause than the evidence supported. Not one founder in this library overstated their own culpability.

Three structural observations about the divergence:

1. Divergence correlates strongly with capital raised. Among the 17 entries that raised under $10M — all of which produced an attributable statement — the stated cause matched the evidence in 14 cases (82%). In the $10M–$100M band it matched in 6 of 8 attributable cases (75%). Among the 24 entries that raised over $100M, only 18 produced an attributable statement at all, and of those the stated cause matched in 4 cases (22%) — meaning 78% diverged. Candour is cheapest when the stakes are smallest, and at the largest sizes candour is frequently replaced by silence rather than by a wrong explanation.
2. Divergence is near-total in the acqui-hire-shaped deaths. Inflection, Adept, Humane, Relay and Hopin all produced statements that did not describe a failure at all.
3. The most reliable stated causes come from founders with nothing left to protect — either because the company was tiny (Dinnr, Muse, Sandstorm), because the founder was already independently credible (Artifact, Muse, Neeva), or because they chose to publish primary evidence rather than an argument (Everpix, Apollo).

The practical implication. When reading any post-mortem, the useful question is not "is this honest" — most are honest about facts — but "what would have to be true for this explanation to be sufficient?" If a company blames a regulator, ask what the retention curve looked like. If it blames the funding market, ask what the revenue was.

The per-entry basis

Capital bands follow the Capital raised field of each entry. Entry 42 is a category entry covering many companies and has no single raise figure, so it is excluded from the capital bands (but not from the attributable/match tabulation). Bands therefore total 52 + 1 excluded = 53.

Reconciliation: bands 5 + 12 + 11 + 17 + 7 = 52, plus 1 excluded = 53. Attributable 44 + non-attributable 9 = 53. Match 24 + diverge 20 = 44.

Synthesis 3 — Time-to-death patterns

Median lifespan by capital raised, computed from the same per-entry basis as Synthesis 2. Entry 42 (the GPT-wrapper cohort) has no single raise figure and is excluded from the bands, so the bands total 52, plus 1 excluded = 53.

The counterintuitive finding, now sharper than it first appeared: more capital does not buy more time at all. The median lifespan of the >$500M band (6 years) is shorter than that of the $10M–$100M and $100M–$500M bands (7 years each), and shorter than the bootstrapped band (9 years). What capital reliably buys is a slower, more expensive death and a larger crater. The variable that predicts lifespan is not capital but burn relative to revenue — which is why the $1M–$10M band is the tightest cluster in the table: those companies have almost no ability to obscure the arithmetic.

By category:

• AI-era entries (2023–2026): median 3.5 years, and only ~2 years for the frontier-model companies (Inflection and Adept, entries 36 and 37). This is the fastest-dying cohort in the library by a wide margin. The GPT-wrapper cohort's typical lifespan was 6–24 months. The mechanism is that the competitive clock is set by model release cadence, which is roughly quarterly, rather than by market development, which is measured in years.
• On-demand and marketplace entries (2015–2019): 3–5 years, clustered tightly.
• Hardware and deep-tech entries: 6–18 years.
• Platform-dependency deaths are instantaneous. Apollo, Tweetbot and Twitterrific each went from operating business to shutdown announcement in under 60 days.

This partly matches the aggregate data: SimpleClosure's 2025 analysis of structured shutdowns found seed-stage companies shutting down at 3–5 years old, Series A at ~7 years, and Series B/C+ at ~10, with median capital raised of ~$2.8M overall and ~$2.4M for AI companies specifically.

Synthesis 4 — The warning signs that recur, ranked by how early they were visible

Ranked from earliest to latest. The number in brackets is how many of the 53 entries showed it.

Visible before or at founding:

1. Structural single-channel or single-platform dependency [11 entries]. Tutorspree, Apollo, Tweetbot, Twitterrific, HouseFresh, Odeo, Thrasio-type aggregators, and every GPT wrapper.
2. Purchase frequency below what CAC payback requires [6 entries]. Shyp, Beepi, Modsy, One Kings Lane, Fab, Dinnr.
3. A plan that the raised amount cannot fund [3 entries, named explicitly by the founder in 2]. Standout Jobs, Muse, Sandstorm.

Visible within 6–12 months of launch: 4. Month-6 cohort retention below ~20% [8 entries]. Homejoy's was under 10% and the company then expanded to 33 markets. 5. Burn-to-revenue ratio above ~20:1 sustained beyond the first year [7 entries]. Fast's exceeded 100:1 for its entire life. 6. Free-to-paid conversion in the low single digits with no enterprise motion [5 entries]. Kite, Sandstorm, Neeva, Muse, Darklang. 7. Gross margin flat or falling as volume rises [6 entries]. The signature of human-in-the-loop and inference-cost-exposed businesses: Modsy, Builder.ai, Nate, the wrapper cohort, Sprig, Munchery.

Visible in years 2–4: 8. Repositioning more than twice [9 entries]. Ghost Autonomy, Modsy, Mindstrong, Forward, Airware, Neeva, Fab, Darklang, Jasper. 9. Expansion to new geographies or segments before contribution margin is positive [7 entries]. Homejoy, Sprig, Fab, Beepi, Northvolt, Katerra, Bird. 10. Venture debt raised after an equity round could not be closed at the prior mark [4 entries]. Jawbone (2015), Bird, Katerra, Northvolt. Median time from this event to death in the library: roughly 24 months.

Visible 6–18 months before death: 11. CFO or head-of-finance departure [6 entries]. 12. Two or more rounds of layoffs [11 entries]. 13. A down round or internal 409A cut [4 entries]. 14. A rescue round from an existing investor at a flat or reduced price [3 entries: Katerra 2020, Northvolt 2024, Jawbone 2015].

Visible 0–6 months before death: 15. Acquisition talks that collapse [5 entries: Homejoy, Beepi, Hipmunk-adjacent, Pear, Windsurf's OpenAI deal]. 16. Delayed vendor or contractor payments [3 entries: Munchery, Modsy, Zirtual].

The uncomfortable conclusion: the signals with the most predictive value (1–7) are all available in the first year, and the signals founders and investors actually react to (11–16) arrive when nothing can be done.

Synthesis 5 — What the post-mortem genre systematically omits

This section is the reason the chapter has a documented-failure-bias warning at the top rather than the bottom.

1. Co-founder conflict. Named as a primary cause in zero of 53 entries. It appears obliquely in Zirtual (a public dispute with the outsourced finance function), in Darklang (the founder's account of being pushed out of his own company), and in 99dresses (a co-founder departure). The population rate is certainly far higher. Founder conflict is unwritable because the other person is still alive, still working, and possibly still a shareholder.

2. Mental health. Discussed substantively in two entries: 99dresses and Darklang. Several founders in this library have described, elsewhere and later, depression, panic disorder and physical collapse. None of it appears in the shutdown posts, because a shutdown post is also a job application.

3. Personal finances. Almost entirely absent. Not one entry in this library discloses the founder's salary history, personal debt, credit-card balances used to make payroll, personal guarantees on leases, or the tax bill from exercising options in a company that later became worthless (the One Kings Lane employees who did this lost real money and no account of it exists).

4. The humiliating failures, which produce no document at all. The 9 entries with no attributable statement are not random. They cluster in three groups: litigation exposure, acquisition-shaped deaths, and failures where the founder behaved badly toward employees, customers or vendors. Munchery left small suppliers unpaid and wrote nothing. Modsy kept customer money and wrote nothing. Zirtual terminated 400 people by cutting off email and the founder wrote extensively — and was the exception. The correlation runs: the worse the conduct, the thinner the record.

5. The acquired death and the partial death. A company that sells for less than it raised produces no post-mortem, because the transaction is announced as a success and the participants are contractually and socially bound to that framing. A company that survives at 10% of its peak valuation produces no post-mortem because nothing happened on any particular day.

6. Non-English and non-US failures.

The selection problem, stated directly. The failures we learn from are the ones someone chose to write up. Choosing to write up a failure requires: (a) no active litigation, (b) no acquirer with a narrative to protect, (c) a failure with an interesting, externalisable cause, (d) enough remaining reputation that publishing helps rather than hurts, and (e) the emotional capacity to revisit it. Those five filters remove, in my estimate, the substantial majority of real failures — and they remove them non-randomly, in the direction of clean stories with external causes. The post-mortem canon is therefore not a sample of failures. It is a sample of failures that were safe to describe.

Synthesis 6 — Lessons that genuinely transfer, and lessons that do not

Most post-mortem "lessons" are hindsight narratives: a story constructed backwards from a known outcome, in which the decisive factor is whatever the author now believes. Below is an attempt to separate the two, using a single test — does the lesson hold across multiple independent entries in this library, and would acting on it have been a reasonable decision ex ante, not just in hindsight?

Lessons that transfer

1. Compute contribution margin per unit before scaling anything. Supported by 11 entries independently, across five sectors and two decades, with no counterexamples. This is the most robust finding in the chapter.
2. Channel concentration is a business-model property, not a risk to monitor. Supported by 11 entries. The correct action is to price the dependency — what is our revenue the day after the platform changes terms — not to try to predict the change.
3. Retention before expansion. Supported by 7 entries with the same sequence: weak cohort retention → geographic or segment expansion → death.
4. Liquidation preference arithmetic determines founder and employee outcomes. Supported by One Kings Lane, Hopin, Fab, Digg, Beepi.
5. How you wind down is a decision with consequences. Supported by the contrast between Everpix, Apollo, Ghost Autonomy and Relay (orderly, funded, third parties made whole) and Munchery, Modsy, Zirtual and Bird (abrupt, with customers and vendors as unsecured creditors).
6. In the AI era: ask what you own after the model improves by an order of magnitude. Supported by Kite, Jasper, Relay, the wrapper cohort, and by contrast with the survivors.
7. "We were too early" should be tested, not accepted. If anyone succeeded at the same thing within three years, the cause was execution or architecture.

Lessons that do not transfer

1. "Pivot faster" / "pivot sooner." Ghost Autonomy pivoted three times and died; Modsy pivoted repeatedly and died; Slack pivoted once and succeeded.
2. "Raise more" / "raise less." Jawbone raised $930M and died of overfunding; Standout Jobs raised $1.8M and died of underfunding.
3. "Focus." Cited by Muse, Fab, Northvolt and Katerra.
4. "Hire slower." Real in Fast and Fab. But Zirtual's problem was the type of employment, not the pace; Everpix's team was seven people.
5. Anything drawn from a single case where the outcome was dominated by luck. Odeo is the clearest example: the transferable content is "returning capital is an option," and the famous part — that the side project became Twitter — generalises to nothing at all.

Where the honest answer is "this was mostly bad luck"

Three entries, stated without hedging:

• Apollo (entry 9). A profitable, well-run, single-person business destroyed by a unilateral pricing decision from a platform, with 30 days' notice, in the run-up to that platform's IPO.
• Twitterrific (entry 11). Sixteen years of work ended without notice or explanation.
• Hopin (entry 30). Partly bad luck: a genuine demand shock that no founder created and none could have sustained.

And one entry where the honest answer is "nothing generalises here": Everpix (entry 1). A very good team built a very good product in a category that Google and Apple were about to make free, and published their numbers so everyone else could see what that looked like.

Closing note

The most useful thing in this chapter is not any individual lesson. It is the 45% divergence rate in Synthesis 2.

Close to half the time — 20 of the 44 cases where anyone said anything at all — the explanation points somewhere other than where the evidence points, and it points outward or away from failure entirely, without a single counterexample in 53 cases. Among the companies that raised over $100M the rate is 78%, and a further six said nothing usable. That is not dishonesty. It is what happens when a person with legal exposure, employees to protect, investors to face and a career still ahead of them writes the only account anyone will ever read.

Read post-mortems anyway. They are the best data we have. But read them the way you would read a company's own description of its quarter: as a genuine account of the facts, arranged by someone with an interest in the arrangement.

Sources

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Aggregate and methodological sources

• SimpleClosure, State of Startup Shutdowns 2025 — stage composition, company age at shutdown, median capital raised, and industry breakdown of documented shutdowns.
• CB Insights, The Top 12 Reasons Startups Fail and the 400+ post-mortem archive — used in Chapter 10 rather than here, and referenced in Synthesis Section 1 for the co-founder-conflict discrepancy.
• Y Combinator company pages, Crunchbase and CB Insights company financials — used for funding totals where no primary filing exists. These are secondary and are flagged as such in the entries that rely on them.
• The 77 Failed Startup Post-Mortems compilation and postmortem.io's shutdown index — used to locate the pre-2015 founder-written canon.

A note on verification

Funding totals, valuations and revenue figures in this chapter come from three tiers of evidence, and the entries say which applies:

1. Primary and auditable — SEC filings, bankruptcy dockets, DOJ and CFPB actions, company-published financials (Everpix), and founder-published figures with supporting data (Muse, Apollo).
2. Company-stated, unaudited — press releases, shutdown memos, founder interviews. Used widely and labelled where it matters (Hopin's ARR, Fab's revenue, Cerebral's patient counts).
3. Reported but unconfirmed — acquisition prices described as "reported," valuations from secondary databases, and any figure attributed to unnamed sources. Where tier 3 is the only evidence and the number is load-bearing, the entry says the figure is disputed or unverifiable rather than choosing one.

Numbers that could not be placed in any tier were left out. Several entries are consequently missing a revenue or headcount figure that a less careful treatment would have supplied.