TON Rebrands Native Token As Gram, Reviving Original White Paper Name

bitcoinistPublished on 2026-06-03Last updated on 2026-06-03

Abstract

The native token of the Toncoin (TON) network is being rebranded to 'Gram,' reviving its original name from the project's first white paper. This change, announced by Telegram co-founder and CEO Pavel Durov, is the fourth step in his "Make TON Great Again" (MTONGA) initiative. The transition period is expected to take about three weeks, and a new logo has been previewed. The rebrand follows Telegram's official return to the ecosystem in May as its largest validator, after a six-year absence that began with a 2020 legal dispute with the SEC. Durov stated that the move "returns to our roots" and paves the way for future developments, with three more steps remaining in the MTONGA roadmap. At the time of the announcement, Gram was trading around $2.02.

Toncoin’s native token has rebranded to ‘Gram’ as part of the latest step in Pavel Durov’s “Make TON Great Again” roadmap.

Toncoin’s Native Token Is Now Called Gram

In a new post on Telegram, Pavel Durov has shared details related to a rebranding of the native token of the Toncoin network. The asset is set to see a name change to Gram, with the transition period expected to take about three weeks.

Durov is the co-founder and CEO of Telegram, and one of the biggest backers of TON. In the blockchain’s early days, its full form even stood for the Telegram Open Network, with the Telegram team handling its development. Telegram’s official involvement with the token, however, ended back in 2020 following a legal dispute with the US Securities and Exchange Commission (SEC).

After Telegram pulled out, the ecosystem rebranded itself to The Open Network and development was handed off to independent contributors. While the messaging giant ended its involvement in the project, it didn’t break all ties. In 2023, Telegram integrated a wallet based on the blockchain to its official app.

Durov himself also remained a supporter of the project. This year, the Telegram CEO kickstarted the “Make TON Great Again” (MTONGA) initiative, which is going to have a total of seven steps. The first two steps of the roadmap went into action in April and provided upgrades to the network’s transaction speed and fees.

The third step, announced in early May, saw Telegram officially re-enter the picture after a six-year absence, replacing the TON Foundation as the driving force behind the ecosystem. The messaging company also became the network’s largest validator.

“Telegram becoming TON’s largest validator strengthens decentralization,” said Durov in an X post a day after announcing the move. “It lets other major players join the validator pool without centralizing the network — with Telegram as the counterbalance.”

Now, the Telegram co-founder has unveiled the rebrand to the name Gram as the fourth checkpoint in the MTONGA plan. This change, which only applies to the blockchain’s native token, will bring back the asset’s original name from its first white paper. The new website for the token provides a teaser of a fresh logo for the cryptocurrency.

The new logo and name for the cryptocurrency | Source: Gram.org

“We’re returning to our roots — and starting a new chapter,” noted the Telegram co-founder. “This rebranding will pave the way for what comes next.” There are three more steps left in the MTONGA roadmap, but it only remains to be seen what they will bring to the network.

Gram Price

At the time of writing, Gram is trading around $2.02, up over 5% in the last seven days.

The trend in the price of the coin over the last five days | Source: TONUSDT on TradingView

Related Questions

QWhat is the new name of Toncoin's native token and why was it chosen?

AThe new name is 'Gram'. It was chosen to revive the asset's original name from its first white paper, marking a return to the project's roots.

QWho announced the rebranding of the native token and what initiative is it part of?

APavel Durov, co-founder and CEO of Telegram, announced the rebranding. It is the fourth checkpoint in his 'Make TON Great Again' (MTONGA) roadmap initiative.

QWhat was the full form of 'TON' originally, and what is its status with Telegram now?

AOriginally, TON stood for the 'Telegram Open Network'. Telegram officially re-entered the TON ecosystem in early May 2024, after a six-year absence, replacing the TON Foundation as the driving force and becoming its largest validator.

QWhat was the reason for Telegram's initial withdrawal from the TON project in 2020?

ATelegram withdrew from the TON project in 2020 following a legal dispute with the US Securities and Exchange Commission (SEC).

QAccording to the article, what was the approximate price and weekly performance of Gram at the time of writing?

AAt the time of writing, Gram was trading around $2.02, up over 5% in the last seven days.

Related Reads

Gate Research Institute: The 'Wall Street-ization' Wave of Crypto Financial Products – Competition or Integration?

The article titled "Gate Research Institute: Are Crypto Financial Products Sparking a 'Wall Street' Wave—Competition or Convergence?" explores the evolving relationship between the crypto ecosystem and traditional finance (TradFi). The piece begins by reflecting on Bitcoin's original 2009 vision of decentralization, disintermediation, and moving away from banks. It then contrasts this with the 2024 landscape, where key crypto assets like Bitcoin are increasingly held through Wall Street products like ETFs issued by giants like BlackRock. The article questions whether this signifies that TradFi is systematically taking over the rights to issue, price, custody, and distribute crypto financial assets. The core argument is that this is not a zero-sum takeover but rather a bidirectional convergence where each side addresses the other's weaknesses. Crypto offers 24/7 global markets, programmable settlement, and open access but lacks compliant channels, institutional-grade custody, deep fiat liquidity, and mainstream distribution. TradFi possesses these but is constrained by legacy systems, limited operating hours, and slow settlement. Two primary convergence paths are highlighted: * **Path A (CEX to TradFi):** Exemplified by Gate, which has progressed from offering tokenized stocks and CFDs to providing direct, real stock trading (US, Hong Kong, South Korea) within its platform, using USDT. * **Path B (TradFi to Crypto):** Exemplified by Robinhood, which has integrated crypto trading, acquired exchanges like Bitstamp, and is moving traditional assets like stocks onto the blockchain via tokenization and its own Layer 2. Both paths are ultimately competing to become the next-generation, unified financial account—a "super account" where users can seamlessly trade cryptocurrencies, stocks, ETFs, RWA (Real World Assets), and tokenized treasury products in one interface. The growth of RWA and tokenized treasuries (e.g., BlackRock's BUIDL) is presented as the asset-layer fusion, providing stable, yield-bearing assets on-chain and acting as a bridge between the two worlds. In conclusion, the "Wall Street-ization" of crypto is framed as a mutual transformation. Decentralized ideals persist in the protocol layer, while at the application layer, a more efficient, global, and accessible unified capital market is emerging from this convergence. The future competition lies not between crypto exchanges and stockbrokers, but between platforms vying to offer the most comprehensive asset coverage, liquidity, and user experience within a single account.

marsbit4m ago

Gate Research Institute: The 'Wall Street-ization' Wave of Crypto Financial Products – Competition or Integration?

marsbit4m ago

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbit9m ago

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbit9m ago

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

Feeding "Noise" to AI Can Improve Performance: A Method Enables Positive Transfer from Noise This work, Semi-Supervised Noise Adaptation (SSNA), introduces a Noise Adaptation Framework (NAF) that challenges traditional transfer learning. Instead of requiring a labeled source domain of real data (e.g., images, text), NAF uses randomly generated Gaussian noise as the source. For a target task with C classes, it constructs C noise clusters by sampling from Gaussian distributions. Although this synthetic noise contains no semantic meaning, NAF trains it to form a discriminative class structure in a shared representation space—clustering same-class noise and separating different classes. The key is aligning this learned structure from the noise domain to the real, sparsely labeled target domain. A small number of target labels are still essential to establish the correspondence between noise clusters and actual classes. The training objective combines: 1) supervised loss on the few labeled target samples, 2) classification loss for the noise to build its structure, and 3) a distribution alignment loss (using Negative Domain Similarity) to minimize the gap between the noise and target domains in the shared space. Experiments show significant gains in few-label settings. With just 4 labels per class, NAF with a ResNet-18 backbone improves accuracy over a standard supervised baseline (ERM) by +12.35% on CIFAR-10, +7.61% on CIFAR-100, +4.38% on DTD-47, and +2.74% on Caltech-101. It also benefits fine-grained datasets and scales to ImageNet-1K (with 100 labels/class) and text classification (AG News). NAF can be integrated into existing semi-supervised methods like FixMatch for further gains. Ablation studies confirm the transferred benefit comes from the discriminative structure of the noise, not randomness itself. Collapsing all noise into a single point causes negative transfer, while increasing separation between noise cluster centers improves performance. The amount of noise per class is less critical once a basic structure forms. In conclusion, this work demonstrates that for positive transfer, the semantic content of source data may not be necessary. What can be effectively transferred is the *organizational structure* of categories within a representation space. This offers a promising alternative for scenarios where real source data is unavailable due to privacy, copyright, or procurement constraints.

marsbit10m ago

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

marsbit10m ago

Trading

Spot
活动图片