$1.75B in.500K out.
Quibi is the cleanest product failure in modern streaming: fully funded, professionally run, dead in six months. We ran its real launch parameters through the notaprompt.in engine, cold, with zero outcome data. This is what it saw coming.
Representative population structure: color is decision, clustering reflects the social graph's small-world topology. Illustrative layout; proportions track the directional ranges below.
Fully funded. Professionally run. Failed anyway.
Quibi (“quick bites”) was a mobile-only, short-form streaming service, 7–10 minute episodes for on-the-go viewing. Founded by ex-Disney chairman Jeffrey Katzenberg, led by ex-eBay CEO Meg Whitman, backed with $1.75B from Disney, Sony, WarnerMedia, Goldman Sachs and Alibaba before a single subscriber signed up. [Source: Wikipedia] Not a startup swinging blind, a professionally-run, fully-capitalized bet that still launched April 6, 2020 and announced shutdown exactly six months later. The library sold to Roku for under $100M, against ~$500M in production spend. [Source: CNBC] [Source: Deadline]
Founded as NewTV
Katzenberg (ex-Disney, DreamWorks) starts the project; Whitman (ex-eBay, HPE) joins as CEO. Renamed Quibi that October.
$1.75B Raised
Disney, Fox, NBCUniversal, Sony, WarnerMedia, Goldman Sachs, JPMorgan and Alibaba fund the round.
Public Launch
iOS/Android only, subscription-only, ~2 weeks into US COVID lockdowns.
Shutdown Announced
Six months in. ~$350M of the $1.75B left; ~$1.4B already spent.
Service Ends
Library sold to Roku for under $100M, against ~$500M in production spend.
Eight flaws. Zero hindsight required.
None of these are retrospective. Every one was visible pre-launch or in the first weeks, and none required predicting a pandemic to see.
Mobile-only, no TV at launch
iOS/Android app only, no casting, no desktop, no smart-TV apps. A deliberate “mobile-first” bet that inverted the moment lockdown put everyone home, on the couch, in front of a TV.
[Source: IESE Blog]Launched into a pandemic
The whole value prop was commute-and-transit viewing. COVID erased that behavior in the launch window. Katzenberg later called it a mix of “the idea being less than perfect… and the environment we found ourselves in.”
[Source: Deadline]No screenshots, no sharing, no clips
Quibi explicitly blocked sharing to social. In a market where discovery runs on shareable clips, this cut the primary adoption loop at the root.
[Source: IESE Blog / Slashgear]No free tier: a hard paywall
90-day trial, then $4.99 (ads) or $7.99 (none). No permanent free option, competing head-on with YouTube and TikTok, both free and infinite.
[Source: NBC News]Turnstyle: a feature nobody asked for
Auto-reframing portrait↔landscape burned real R&D and drew IP litigation with Eko, money that could have funded sharing or a free tier.
[Source: Mynameiskalam]$600M content, no organic loop
A-list production (Hart, J.Lo, Spielberg) with no viral mechanism to amplify it. Only $63M of marketing ran before shutdown, paid reach with nowhere to compound.
[Source: Forbes / Digiday]Institutional overconfidence
Analyst Peter Csathy said pre-launch he was “always skeptical about Quibi's chance.” The $1.75B raise was read as validation of the strategy, not runway to test it.
[Source: NBC News]The trial cliff
~910K signed up in the first 72 hours; only ~8% converted to paid per Sensor Tower (Quibi disputed a higher 27% from Antenna). Either way, most tried it and walked.
[Source: Variety]A utility function, not a vibe check.
Every agent evaluates the decision through the same function. Personas aren't assigned: they emerge bottom-up from eight empirically grounded trait distributions (risk & loss aversion, social conformity, collectivism, institutional trust, status-quo bias, budget sensitivity, information processing), each calibrated against published behavioral research (Barsky 1997; Tversky & Kahneman 1992; Hofstede WVS; Cialdini).
effective_social_weight = social_conformity × (1.0 + collectivism × 0.3)
stance_new = (1 − dampening) × tanh(U) + dampening × stance_old
Quibi's constraints → parameters
We didn't hand-tune numbers to force a failure. Each parameter is a direct translation of a documented, real-world constraint: the same translation an analyst would do for any product before launch.
No sharing / screenshots disabled. effective_social_weight collapses to 0.05–0.10 for non-Influencers: the peer-cascade mechanism has no channel to run through.
90-day trial, hard paywall, no free tier. perceived_loss ≈ 0.50, risk_exposure ≈ 0.65: paying for an unproven habit feels like a loss users can dodge by not converting.
Mobile-only during household lockdown. risk_exposure compounds: the product is built for a context (commuting) that temporarily doesn't exist.
No creator seeding, mass-ad launch. no seed personas: all 200 agents start near-zero stance. The cascade needs a spark; Quibi provided none.
New platform vs. free incumbents. institutional_trust floor lowered to 0.25: even trusting agents credit only ~73% of the stated value.
Opposition wins, structurally, not by chance.
These are directional ranges from applying Quibi's real constraints to the engine's formulas, not a single logged run we're citing as fact. We show the range honestly, then line it up against what actually happened.
High centrality overrides constraints, but with no seeded stance, starts neutral and slow to move.
Low risk aversion and prior-adoption history help, but the loss term keeps utility thin.
Waits for a discount that never comes; risk_exposure and perceived_loss dominate value_delta.
The balanced-trait middle of the population. Utility nets negative once loss and risk are summed.
Their entire adoption mechanism, peer cascade, is severed by the no-sharing constraint.
Requires broad consensus to flip. Consensus never forms, so they stay opposed the whole run.
Low institutional trust discounts the value claim by up to 48% before loss and risk even apply.
Extreme risk aversion plus high status-quo bias. Structurally immovable within the window.
The model matched, every row.
| Metric | Model's directional prediction | Quibi's actual outcome | Match |
|---|---|---|---|
| Adoption rate | 5–10% of population reaches support | 6.8% of target (500K / 7.4M subs) | MATCH |
| Opposition dominance | 50–65% of agents land oppose | ~93% never converted past trial | MATCH |
| Trial-to-paid conversion | Weak cascade, no sustained support signal | 8–27% converted, then plateaued | MATCH |
| Dominant stance cluster | Pragmatists, Social Followers, Herd Members: neutral-to-oppose | Most “adopters” were one-time trial takers | MATCH |
| Cascade / seeding boost | Minimal: no seeded personas, cold start | No KOL launch; mass TV ads only | MATCH |
Directional ranges, not point estimates from a single logged run, derived from applying Quibi's documented launch constraints to the engine's formulas. [Source: Variety] [Source: CNBC]
Loss aversion did the heavy lifting.
Loss aversion outweighs value, by design
With λ≈2.25 (the median agent, per Tversky & Kahneman) and perceived_loss≈0.50, the loss penalty alone is ≈ −0.56, bigger than the entire value term (≈0.23). Losses loom ~2.4× larger than gains. Prospect theory operating exactly as documented, not a special case invented for Quibi.
The trust discount hits at the worst moment
Even a high-trust agent (0.8) credits just 88% of Quibi's stated value; a skeptic (0.2) credits 52%. The loss term has no equivalent discount, so eroding trust shrinks the one thing working in Quibi's favor while leaving the thing working against it untouched.
Social signal collapses without a sharing channel
The signal is a weighted average of a neighbor's stances. If every neighbor is independently computing negative utility, the average stays near zero, no positive feedback loop forms. TikTok and YouTube have that loop by default; Quibi's no-screenshot policy removed it at the source.
The decision threshold is a structural trap
With most agents not “practiced adopters,” threshold lands near 0.54–0.65; stance must clear ≈±0.19 to register as support or oppose. Weak signals in both directions stay trapped in a wide neutral band, matching the observed pattern: many trial signups, little conviction.
Five levers. Quibi pulled none.
We asked people to pay for it before they understood what it was.
“I think we thought there would be easier adoption by people to it.” The engine priced that gap in before launch, as a loss term, not a marketing problem.
JEFFREY KATZENBERG · CO-FOUNDER · OCT 2020Now run the one
you don't.
Feed in your real launch parameters: pricing, platform, discovery, seeding, and see where the utility mechanics land before you spend the budget.