Memecoins will rise from the dead, but in a new form: Crypto exec

cointelegraphPubblicato 2025-12-14Pubblicato ultima volta 2025-12-14

Introduzione

According to MoonPay president Keith A. Grossman, memecoins are not dead despite the current market downturn and fading interest. He argues that their real innovation lies in the tokenization of attention through blockchain, which democratizes access to the attention economy—previously dominated by platforms and a few influencers. Although the memecoin sector saw significant decline in 2025 following high-profile collapses and criticism over their lack of intrinsic value, Grossman compares this phase to the early failures of social media before its eventual resurgence. Examples include the crash of Donald Trump’s memecoin (down over 90%) and the collapse of Javier Milei’s endorsed token, LIBRA, which led to investor losses and legal repercussions. Despite setbacks, Grossman believes memecoins will return in a new form.

Memecoins are not dead because the market is down and the narrative has faded, according to president of payment infrastructure company MoonPay, Keith A. Grossman, who said that memecoins will be back but in a different form.

The real innovation of memecoins is that attention can be tokenized easily and at low costs through blockchain technology, democratizing access to the attention economy, Grossman said. He continued:

“Before crypto, attention could only be monetized by platforms, brands and a small group of influencers. Everyone else generated value and gave it away for free. Likes, trends, inside jokes and communities created massive economic value.”
The memecoin sector declined significantly in 2025. Source: CoinMarketCap

However, that value did not flow back to participants and mostly remained trapped by large, centralized platforms, he added.

Grossman compared the dismal memecoin outlook among analysts to forecasts of the demise of social media after the first generation of social platforms failed in the early 2000s, before the rise of a latter cohort of companies that turned the niche sector into a cultural phenomenon.

Memecoins were one of the best-performing crypto asset sectors in 2024 and were the top narrative that year among crypto investors, according to crypto market data platform CoinGecko.

However, sharp criticisms that memecoins and other social tokens have no value and several high-profile token implosions eventually caused the market to crater and investors to move on from the narrative.

Related: Bubblemaps challenges PEPE’s fair launch, alleges 30% of genesis supply bundled

Presidential antics and the downfall of the memecoin sector

The memecoin market collapsed in Q1 2025 following several high-profile token collapses and significant drawdowns that were characterized as “rug pulls.”

United States President Donald Trump launched a memecoin ahead of the January 2025 inauguration, which reached a peak of $75 before collapsing by over 90% to about $5.42 at the time of this writing, according to CoinMarketCap.

The Official Trump memecoin from Donald Trump, president of the United States, has declined by over 90% since launch. Source: CoinMarketCap

Javier Milei, the president of Argentina, endorsed a social token called Libra in February, which also crashed, leaving 86% of LIBRA holders with realized losses of $1,000 or more.

The token had reached a market cap of $107 million before its collapse and was characterized as a rug pull by the crypto community.

Although Milei attempted to distance himself from the token launch, a government probe was launched into Milei’s involvement, which culminated in lawsuits from retail investors and calls for impeachment from Argentine lawmakers.

Magazine: Proton Mail exposing activist’s info showed the limits of encryption

Crypto di tendenza

Letture associate

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.

marsbit5 min fa

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

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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.

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

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