Google’s Gemini is quietly testing a feature called “Import AI chats” that — if it ships as shown in leaks — would let users upload exported conversation archives from other chatbots (ChatGPT, Claude, Copilot and others) and continue those threads inside Gemini without losing prior context or media. Early screenshots and hands‑on reporting place the control in Gemini’s attachment/“+” menu as a beta option; the import flow appears to ask users to download a chat archive from the origin service and then upload it into Gemini for ingestion.
The friction of lost conversational history has become a real productivity tax for many AI users. Long-running chat threads accumulate clarifications, system prompts, iterative edits, code samples, images, and project history — essentially forming a working memory for a given assistant. Moving between assistants today typically means starting over, re‑teaching the new model months or years of context. Google’s reported import test attempts to addre making conversation portability simple and first‑party.
TestingCatalog first documented the interface elements and workflow in internal/beta builds; the find has since been picked up by multiple outlets, which corroborate the basic flow and the presence of other experimental UI entries (notably a “Likeness” control tied to video verification and new 2K/4K image download presets). Those independent reports strengthen the claim that this is active engineering work inside Google rather than pure rumor.
The leaks show a deliberate attempt to tackle a genuine UX problem — and they reveal Google engineering an import flow that could be transformational if handled responsibly. The core promise is simple and powerful: resume where you left off across assistants. The core challenge is equally simple: to do that without sacrificing privacy, provenance, or enterprise trust. The coming weeks — when Google either publishes documentation or rolls the beta more widely — will tell us whether the feature is a pragmatic usability milestone or an undercooked convenience that creates more questions than answers.
Source: Trak.in Import Your ChatGPT Chat Thread To Gemini AI, Sealessly - Trak.in - Indian Business of Tech, Mobile & Startups
Background / Overview
The friction of lost conversational history has become a real productivity tax for many AI users. Long-running chat threads accumulate clarifications, system prompts, iterative edits, code samples, images, and project history — essentially forming a working memory for a given assistant. Moving between assistants today typically means starting over, re‑teaching the new model months or years of context. Google’s reported import test attempts to addre making conversation portability simple and first‑party. TestingCatalog first documented the interface elements and workflow in internal/beta builds; the find has since been picked up by multiple outlets, which corroborate the basic flow and the presence of other experimental UI entries (notably a “Likeness” control tied to video verification and new 2K/4K image download presets). Those independent reports strengthen the claim that this is active engineering work inside Google rather than pure rumor.
Why portability matters: the cost of conversational inertia
For power users, teams, and creators, chat history is not just chat — it’s a record of decisions, small‑staged edits, and institutional memory. Losing that context has three practical consequences:- Re‑execution cost: hours or days lost re‑establishing the state of a project.
- Fragmentation: multiple assistants holding parts of a single project is inefficient.
- Behavioral lock‑in: users remain with the assistant that “knows” them because switching is expensive.
What the leaked flow looks like
Where the control appears and how the UI behaves
According to the leaked screenshots and testers’ hands‑on notes, the import control lives in the Gemini web client under the attachment or plus menu (beta).”** Triggering it opens a popup instructing users to export their conversation from another assistant (for example: request an export/ZIP from ChatGPT) and then upload that file into Gemini. The popup reportedly warns that uploaded content will be stored inside the user’s Gemini activity and may be used to improve Google’s models.The practical step sequence (as reported)
- Request or generate an export from the origin assistant (many services have export mechanisms that produce ZIPs containing JSON/HTML).
- Open Gemini and choose Import AI chats from the attachment menu.
- Upload the exported archive into Gemini.
- Gemini ingests and attempts to reconstruct the thread so the user can continue the conversation in place.
What’s plausible and what remains unknown
The basic engineering task — parsing exported conversation archives and mapping messages, timestamps, and attachments into Gemini’s conversation model — is technically feasible. Community tools already parse ChatGPT exports into searchable, structured formats, which demonstrates the core capability is achievable. But several critical unknowns determine whether imports will be genuinely useful:- Supported origins: The leaks show examples (ChatGPT, Claude, Copilot) but do not list a final, authoritative set of compatible platforms. Implementation will need to handle many exporters and schema differences.
- Accepted formats: ChatGPT’s export is a ZIP containing JSON/HTML; other platforms ship different structures and proprietary bundles. Does Gemini accept ZIPs with embedded blobs, or will it require a standardized schema?
- Fidelity guarantees: Will system prompts, hidden persona settings, plugin output, and branching/forked threads preserve their original semantics? The leaked flow suggests text and media are prioritized, but full fidelity is unconfirmed.
- Media handling: Many exports reference file names rather than embedding blobs. How will Gemini handle missing binaries, expired links, or attachments stored behind origin access controls?
- Scale and UX: Large archives (multi-GB) require resumable uploads, background ingestion, and progress reporting. The leaked UI shows a simple upload flow, but long‑running imports need robust client and server behavior to be reliable.
Technical challenges that will make or break the experience
1) Schema variability and parsing complexity
Exports from different assistants arrive in different shapes: nested JSON, HTML blobs, or proprietary archive formats. Building a tolerant importer requires either:- A library of parsers for each service’s export format, or
- A robust mapping layer that can interpret varied schemas into Gemini’s canonical conversation model.
2) Thread reconstruction and metadata fidelity
Reassembling chronology, nested replies, author metadata, and reaction/annotation markers is non‑trivial. If Gemini collapses threaded forks into linear chats, users will lose contextual cues that matter for debugging, editorial processes, or legal timelines. Preserving who said what, when is essential for trust and accuracy.3) Media, attachments, and external references
Images, audio files, PDFs, and code attachments are often stored separately from exported message text. A useful importer must either embed these blobs into the archive or handle graceful fallback (e.g., “attachment missing, original source requires login”). The user experience should surface these edge cases clearly.4) Computational cost, privacy, and model usage
Ingesting large archives likely involves processing for indexing, summarization, and attaching context to future prompts. The popup in leaked screenshots reportedly warns that uploaded data may be used to improve Google’s models. That raises policy questions for organizations and individuals concerned about intellectual property or regulated data. Any enterprise usage likely needs an export path that excludes training, or explicit contractual controls.Privacy, governance, and legal considerations
Moving chat archives between services is not purely technical — it’s a governance issue.- Sensitive content in chats: Conversations often contain names, contracts, client secrets, API keys, health data, or other regulated information. Uploading such content to a new provider can trigger compliance obligations and data‑processing limits.
- Model training and data use: The leaked import popup reportedly states that uploaded content may be used to improve Google models. That’s a live legal and privacy concern: organizations will want explicit opt-outs, and regulators may expect clear disclosures and record‑keeping.
- Provenance and audit trails: Good imports should preserve the origin label, timestamps, and hashable evidence so audits can demonstrate where content originated — crucial for legal defensibility and for tracking the lineage of generated artifacts.
- Cross‑border data flows: If a user exports data from a region with strict data residency rules and then uploads to a provider hosting data in another jurisdiction, organizations must ee and contractual safeguards.
Strategic implications for competition and user choice
If Gemini ships a robust import flow, it changes the competitive equation in two ways:- Lowering switching costs: By making migration inexpensive, Google removes a major friction that keeps users tethered to competing assistants. That could speed adoption for users already invested in other ecosystems.
- Platform leverage: Once conversations migrate, Google can offer integrated experiences (Search, Workspace, Chrome, Android) that are harder to replicate if the user started elsewhere. In short, portability can be a growth lever rather than simply a concession.
That said, portability also raises an interesting paradox: the easier you make it to bring corm, the more attractive that platform becomes — and the more important it is to provide robust privacy controls so users and organizations feel safe moving
Practical guidance for users who want to prepare now
If you’re thinking about switching assistants or preserving threads, here are concrete steps to take today:- Audit chat content: Identify threads containing sensitive data (client information, credentials, personal data) and remove or redact those items before exporting. Treat chat history like any other sensitive file.
- Use origin export tools: Most major assistants provide a data export mechanism (e.g., ChatGPT lets users request a downloadable ZIP containing conversations). Learn how to request and download that export now.
- Preserve provenance: Keep a local copy of the original export and store metadata (timestamps, export hashes) to prove origin if provenance matters later.
- Export modularly: When possible, export only the threads you need rather than entire archives. Smaller, focused uploads reduce risk and ease review.
- Check platform policies: Before uploading, read the target platform’s data usage and training policies; look for opt‑out controls or enterprise contract clauses that restrict training on your content.
Enterprise and compliance checklist
Organizations considering an assisted migration to Gemini should demand clarity on the following before any production transfers:- Does uploaded content become part of training data by default? Is there an explicit no‑training opt‑out for enterprise customers?
- Can imports be restricted to accounts under an enterprise organization policy and stored in regionally designated storage?
- Is there an audit log that preserves origin, timestamps, and a record of who imported what and when?
- How are attachments and linked external resources handled to avoid accidental exposoes the importer include a scrub or redaction tool to remove sensitive fields automatically before ingestion?
Safety and misuse vectors
Portability introduces new attack surfaces:- Exfiltration risk: Malicious insiders could export proprietary chat histories and upload them to another account or provider. Auditability and role‑based controls are essential.
- Data leakage: Attachments or third‑party links included in exports can contain hidden credentials or PII that become unintentionally more widely distributed during import.
- Model poisoning: If imported conversations are used to train production models without proper sanitation, adversaries could craft inputs that influence model behavior or leak confidential patterns.
What to watch for (timeline and signals)
Because the imports are currently labeled beta in internal builds, expect the following milestones and public artifacts before broad adoption:- Official documentation: A help page describing supported formats, limits, and privacy settings (this will be the single best indicator of production readiness).
- Enterprise controls: Contractual or admin‑level toggles that restrict training usage or enforce data residency.
- Progressive rollouts: A staged releasusers, then to mobile and enterprise consoles — matching the patterns seen with other Gemini feature rollouts.
- Community tooling: Third‑party import helpers and open‑source converters that bridge exporter formats into a standard schema. Early community work often precedes or accompanies official importer launches.
Bottom line: pragmatic optimism with guarded caution
The idea of a first‑party, user‑facing “Import AI chats” feature is a direct answer to one of the most persistent UX grievances of the AI era: losing conversational memory when switching platforms. In principle, it’s a good product idea that benefits users and lowers barriers to mctical value will hinge on execution. The important evaluation criteria are clear:- Fidelity — does the importer preserve thread structure, system prompts, and attachments?
- Transparency — are users told how the uploaded data will be used, and can they opt out of training?
- Safety and governance — are enterprise controls, audit trails, and redaction tools available before organizations attempt mass migrations?
Actionable takeaway for WindowsForum readers
- If you are an individual power user: experiment cautiously. Export threads you care about, examine the archive structure, and wait for official documentation before bulk transfers.
- If you manage IT or compliance: do not migrate enterprise conversations until Google provides firm contractual guarantees about training, data residency, and auditability. Draft a checklist for import readiness based on the questions in the Enterprise section above.
- If you are a developer or creator: consider exporting and locally archiving your most valuable threads now; community tools already parse ChatGPT exports and will help you prepare clean, portable bundles.
The leaks show a deliberate attempt to tackle a genuine UX problem — and they reveal Google engineering an import flow that could be transformational if handled responsibly. The core promise is simple and powerful: resume where you left off across assistants. The core challenge is equally simple: to do that without sacrificing privacy, provenance, or enterprise trust. The coming weeks — when Google either publishes documentation or rolls the beta more widely — will tell us whether the feature is a pragmatic usability milestone or an undercooked convenience that creates more questions than answers.
Source: Trak.in Import Your ChatGPT Chat Thread To Gemini AI, Sealessly - Trak.in - Indian Business of Tech, Mobile & Startups
