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Company profile · 4 min read

Absurd: an AI-video production canvas

Absurd gives filmmakers a shared canvas for AI video generation, creative direction, review and metered production spend.

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What it does

Absurd is an online whiteboard for filmmakers and creative teams working with AI video. The canvas holds scenes, references and generated variants; agents help turn a shot idea into prompts, keep track of creative rules and surface work that needs review. The current homepage shows the intended loop: describe a shot, let the agent generate and organize options, then keep the director in the decision path.

The buyer is a creative team producing launch films, social ads or narrative work that needs more continuity than a collection of one-off generation tools. Absurd is strongest for teams that already think in scenes, references and review passes. It is less obviously a fit for someone who only wants a single generated image and has no need for a shared production workspace.

Why I’d look closer

The rare advantage is that Absurd says it learned the product by using it as a production studio. Its careers page says the founders began by making AI short films and then produced commercials for Kalshi, Replit, Brex, Whop, Micro1, Gumloop and Reforge before building the tool. The YC profile reports that its Kalshi “Election Day” spot passed one million views and that its videos averaged 400K+ organic views; the homepage lists brand work and view counts. These are company-reported portfolio signals, not independent campaign measurement.

Pricing is public and usage-aware. The pricing page lists Free at $0/month, Individual at $60/month with $80 of included usage, Teams at $40/user/month with $55 per-seat usage, and Enterprise as custom. The page says generations are metered by model and size, and that a spend cap can pause work when a project reaches its limit. That transparency is useful, but a creative lead should model a normal month rather than treat the subscription as the total cost.

The team combines engineering and production context. The careers page describes Damian Chng as former Meta engineering and Daniel Kathein as having worked on agents at Nvidia, alongside filmmakers, editors and producers with commercial and film credits. That mix is a credible reason to look closer; it is not proof that the workflow will match a specific agency’s review process.

What I’d ask

Can a team export prompts, source references, model settings and approvals for a client archive? How are style rules preserved across a long project, and what happens when an agent’s continuity is wrong? I’d run one short campaign with a hard spend cap and inspect the final handoff, rights, model availability and revision history before moving production work into it.

My editorial take

Absurd is one of the more coherent AI-video propositions because the product is shaped around production memory, not only generation. The self-studio strategy is a real differentiator if the canvas reduces coordination cost. The proof to demand is not another viral clip; it is a repeatable project where a creative team can review, revise and deliver without losing control of spend or authorship.

Quick facts

Field Sourced detail
Buyer fit Filmmakers, agencies and creative teams producing AI video
Public pricing Free; Individual $60/month; Teams $40/user/month; Enterprise custom
Product boundary Agent-assisted canvas with human review and metered model usage
Proof signal Company-produced campaigns and reported view counts; not independent measurement

Sources checked

Checked 2026-09-19.

Source Used for
YC company profile Product, founders and company-reported portfolio traction
Absurd homepage Current canvas workflow and public portfolio surface
Pricing Plans, usage and spend-cap behavior
Careers Studio-origin story and public team context

Cohort context

Absurd is listed in Fall 2025. In our 2026-09-18 directory snapshot, 90 of 146 listed companies in that cohort have YC’s primary industry label B2B (61.6%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.

Public website snapshot

Observed 2026-09-19T16:14:50.821Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

Signal Homepage observation
Product description metadata Observed
Canonical link Observed
H1 or H2 heading Observed
Typed structured data Observed
Docs/developer link Not observed in this response
Pricing link Observed
llms.txt link Not observed in this response
Markdown alternate Not observed in this response

Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.

About the author

I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.

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