If you arrived looking for Samsung NEXT’s Synthetic Media Landscape, you have reached the domain where that resource was once promoted. The present site is an independent creative-technology journal. It is not a Samsung or Samsung NEXT publication, and it does not offer the original report for download.
The distinction matters. A familiar domain can make an old reference appear current even when the publisher and purpose have changed. Our job here is to preserve the context of that reference, point to the surviving evidence and explain what a reader can still usefully take from the idea of a landscape.
What the historical landing page said
A landing-page snapshot dated 10 December 2022 describes a Samsung NEXT guide to media generated or modified by AI. It advertises a landscape of 158 companies and a 98-page guide, directed at investors, creators and founders. These are claims made by that historical page, not counts of the present market or a description of a report hosted by this journal. The page also linked its download invitation to an anchor on the same landing page. View the archived landing page.
A 2020 VentureBeat column by Iskender Dirik linked this domain from his Samsung NEXT Europe affiliation. Original column.
We have not established an available, authorised copy of the original downloadable report for republication. The archive link is a historical reference, not a replacement download button. Availability of the external archive can vary.
A landscape is a set of boundaries
A grid of company logos looks like an inventory of a market. It is also an argument about what belongs together. Put voice synthesis beside avatars and the map emphasises generated performance. Put dubbing beside editing and it emphasises a production task. Neither grouping is automatically wrong, but they answer different questions.
For a working studio, we would begin with the task rather than the company. What enters the workflow? What leaves it? Who must approve the result? What happens when the first output is wrong? Those questions expose differences that a broad label such as “video AI” tends to conceal.
Read across the production chain
Source preparation covers the material a system receives: a script, a licensed image, a recorded voice or a reference performance. Creation and transformation covers what the system changes or produces. Editorial control covers revision, selection and assembly. Delivery covers the exported file, captions and destination requirements. Evidence covers permissions, versions and the records needed to explain the result.
This is our editorial framework, not a reconstruction of the old guide’s categories. A single product can appear in several stages. That is useful: it prevents a convenient interface from hiding the work that still has to happen elsewhere.
Consider a multilingual training film. A speech generator may solve narration production while leaving translation review, pronunciation, timing, screen text and accessibility unresolved. Calling the whole task “voice generation” understates the job. A map built around the deliverable makes those dependencies visible before a team commits to a tool.
Keep a date beside every volatile claim
A market map becomes unreliable when product availability, export features and business claims lose their dates. For each shortlisted service, record the documentation consulted, the plan or product tier, the relevant limitation and the date of review. Preserve the exact version of an approved output separately from a link to a changeable project.
Use a modest status vocabulary: documented, observed in a trial, not checked, or no longer available. A company’s description of a capability is useful evidence about its offer. It is not the same as an observed result on your material. Keeping those statuses separate makes a small map more informative than an enormous undated list.
The missing layer is often assurance
NIST’s report on synthetic-content transparency treats provenance, watermarking, detection, testing and auditing as related technical areas. That is a reminder that creating an asset and explaining an asset are separate jobs. NIST AI 100-4.
Our preferred modern landscape therefore ends with a decision, not a collection of logos: which stage needs help, what evidence would show that a tool helps, and who will check the final output? The old resource explains why readers associate this domain with synthetic media. Those three questions explain the independent journal’s direction now.