Fact check: Is developing your own file format possible? Such as unique video, image, code language, or file formats for specific purpose?

1. Why engineers are reinventing the container: the data-format arms race

Recent work shows a concentrated push to design formats that solve contemporary big-data problems: columnar speed, schema evolution, transactional guarantees, and streaming compatibility. Projects like F3 aim to be a next-generation columnar format addressing perceived limits in incumbents such as Parquet, signaling that format-level innovation can target measurable performance and maintenance wins ^[1]. Open table formats—Iceberg, Delta Lake, Hudi, Paimon, DuckLake—exist because teams needed features not present in raw file formats: ACID semantics, time travel, and compatibility layers for analytic engines. These efforts demonstrate that new formats emerge when existing standards leave operational gaps ^[3].

2. Simpler protocols for schema evolution: a practical wedge for adoption

Format innovation is not only about storage layout but also about schema management and developer ergonomics. VBARE, positioned as a simpler alternative to Protobuf and Cap’n Proto, underlines that reducing complexity while preserving evolution guarantees is a credible path to adoption in serialization and on-disk layout. When a format lowers the cognitive and tooling burden for teams—fewer breaking changes, easier migration—it gains traction even without full ecosystem backing. This dynamic shows that practical gains (simplicity + stability) can outweigh theoretical completeness ^[2].

3. AI-driven representations: new media formats born from models

Research in generative models is producing bespoke internal representations that can serve as the basis for novel file types. BlobGEN-Vid and VideoPoet illustrate that video and high-dimensional media can be encoded as compact, model-friendly “blobs” or tokens, suitable for text-to-video workflows and compositional editing, while LogoMotion demonstrates code-like animation artifacts that double as a domain-specific format for motion. These projects indicate that formats tailored to ML workflows optimize for generation, editability, and compactness rather than human readability or legacy tooling ^{[4] [6] [5]}.

4. Trade-offs: technical possibility vs. real-world adoption

Creating a format is technically feasible; the harder question is ecosystem adoption. Proprietary or niche formats can deliver targeted benefits—performance, reduced complexity, or specialized features—but face barriers: lack of cross-platform tooling, vendor lock-in concerns, and interoperability costs. Articles comparing proprietary and open formats emphasize that many organizations prefer established ecosystems for reliability and support, which constrains how quickly new formats can displace incumbents despite technical merits ^{[7] [8] [9]}. Adoption usually follows when a community builds robust converters, libraries, and documentation.

5. Where custom formats make the most sense: niches and greenfield projects

The strongest cases for inventing a new format arise when existing standards cannot meet key requirements: extreme performance, novel representation for ML, or unique transactional semantics for analytic workloads. Open table formats and serialization experiments show that formats succeed when they solve operational pain points end-to-end—not merely as proofs of concept. Greenfield products, research prototypes, and internal systems with control over the full stack are prime places to deploy bespoke formats before wider standardization efforts take hold ^{[3] [2] [4]}.

6. Agenda and bias to watch: vendors vs. community-driven designs

Different stakeholders promote new formats for different reasons: startups and vendors may tout lock-in or product differentiation, while open-source communities emphasize interoperability and standards. The reporting on F3 and VBARE reflects advocacy for specific design philosophies—future-proofing and simplicity respectively—so readers should note that promotional narratives often underplay integration costs and retraining needs. Conversely, critiques from incumbents may emphasize transition risk; both perspectives are materially motivated ^{[1] [2] [8]}.

7. Practical checklist: if you build a format, what must you solve?

Successful format design must address implementation, tooling, and governance: libraries for multiple languages, converters to/from common standards, clear schema evolution rules, performance benchmarks, and community governance for updates. The Iceberg/Delta/Hudi family shows that format success correlates with ecosystem investments—connectors, compatibility layers, and adoption by analytic engines. ML-driven media formats must add model compatibility, compression strategies, and edit primitives to be useful beyond research ^{[3] [4] [5]}.

8. Bottom line: possible, useful, but adoption is the hard part

The collected evidence indicates that inventing a file format is both technically achievable and often advantageous in specialized contexts; projects across data engineering and AI actively demonstrate this. However, the decisive barrier is building or integrating an ecosystem of tools, documentation, and standards work that turns a useful format into a broadly adopted one. Expect continued fragmentation in the near term, with consolidation only once formats solve practical pains and attract cross-vendor support ^{[1] [2] [3] [4] [5] [6]}.