Why traders are abandoning NFTs for high-turnover memecoins

ambcryptoОпубліковано о 2025-12-29Востаннє оновлено о 2025-12-29

Анотація

NFT trading activity has significantly declined, with volume becoming dominated by just two major marketplaces, OpenSea and Blur, while smaller platforms fade. Traders have shifted their attention and capital to high-turnover memecoins, particularly on the Solana network. Memecoins like Dogecoin, Shiba Inu, and newer Solana-based tokens offer fast, low-cost trading, allowing for quick entries and exits—aligning with the current preference for speed and flexibility. This shift is driven by the desire for more engaging, accessible assets that don’t require large upfront investment or long holding periods, making memecoins the new focus for speculative activity.

NFTs don’t feel like they used to. Trading is slower, and a lot of people just... seem to have moved on. That doesn’t mean traders are done taking risks. The attention has gone back to memecoins, especially those built on Solana [SOL].

NFTs are so yesterday!

Over the past year, overall NFT activity has gradually decreased, with few spikes and a flat trading pattern across marketplaces. What’s interesting is where the remaining volume is going. NFT market share has become increasingly concentrated, with OpenSea and Blur accounting for the bulk of total trading volume, while smaller platforms lose relevance.

A handful of marketplaces dominate NFT trading, while most others contribute only a sliver of activity. Interest is shrinking, with traders sticking to the most liquid venues and avoiding risk.

NFTs are definitely losing pace, and they are no longer where the spotlight is right now.

Attention is flocking into memecoins instead

CoinMarketCap data shows consistent volume across the largest memecoins; big names like Dogecoin [DOGE], Shiba Inu [SHIB], Pepe [PEPE] are proof – even on tame days, their numbers are astounding. Newer entrants are seeing strong daily trading activity too. Many of the most actively traded tokens over the past week are either native to Solana or heavily traded on it.

This is where the network stands out. Several Solana-based memecoins post high volume despite smaller market caps; they’re frequently traded rather than held long-term. Low transaction fees and fast settlement make it easier for traders to enter and exit positions quickly. This keeps activity elevated.

This was perhaps what lead to the quick rotation from NFTs.

What traders are responding to

It’s not just about NFTs versus memecoins; it’s about how traders want to participate. Capital is moving toward fast, flexible and constantly engaging assets. Memecoins fit that need better right now than NFTs, which often require patience, higher upfront costs, and longer holding periods.

Another change is scale. Memecoins allow smaller traders to stay active without committing large amounts of money. That’s harder in both NFTs and large-cap crypto. There’s more participation, even if it is mindless fun. Traders are choosing markets that let them move quickly, and step away just as fast.

Final Thoughts

  • NFT trading volume is lesser and concentrated on just two marketplaces.
  • Memecoins, especially on Solana, are where it’s at now, with low fees and fast trades.

Пов'язані питання

QAccording to the article, why are traders moving from NFTs to memecoins?

ATraders are moving to memecoins because they offer fast, flexible, and constantly engaging trading with low fees and quick settlement, which better fits the current desire for quick entry and exit positions compared to NFTs that require more patience, higher upfront costs, and longer holding periods.

QWhich two NFT marketplaces currently account for the bulk of the total trading volume?

AOpenSea and Blur are the two marketplaces that account for the bulk of the total NFT trading volume.

QOn which blockchain are many of the new high-turnover memecoins being built?

AMany of the new high-turnover memecoins are being built on the Solana blockchain.

QWhat are two key technical features of the Solana network that support high memecoin trading activity?

AThe two key features of the Solana network that support high memecoin trading are its low transaction fees and fast settlement times.

QHow does the article describe the overall trend in NFT activity over the past year?

AThe overall NFT activity has gradually decreased over the past year, with few spikes and a flat trading pattern, indicating it is losing pace and is no longer in the spotlight.

Пов'язані матеріали

How Likely is it for TradeXYZ to Go Solo from Hyperliquid?

The article analyzes the possibility of TradeXYZ, a dominant project on Hyperliquid's HIP-3 real-world asset (RWA) perpetual contracts market, splitting off to build its own independent trading platform. With TradeXYZ commanding over 90% of HIP-3 volume and over 35% of Hyperliquid's total open interest, its leverage and influence are significant. The primary motive for a split is seen as capturing the underlying protocol fees, of which TradeXYZ currently receives only half. However, several factors strongly discourage such a move. First, replicating Hyperliquid's high-performance infrastructure would be challenging and could degrade TradeXYZ's product quality. Second, Hyperliquid serves as a crucial distribution channel, with most users accessing TradeXYZ's liquidity through its frontend. Third, the close, trusting relationship between the founders of both projects makes a hostile "betrayal" unlikely. The conclusion is that a split would likely result in a lose-lose scenario. Hyperliquid would lose its main growth narrative and see its valuation impacted, while TradeXYZ would face immense technical hurdles, lose its primary user channel, and damage its reputation. Instead of a full separation, the more probable path is for TradeXYZ to negotiate better terms while deepening integration, focusing on enhancing its own brand and user ownership.

marsbit18 хв тому

How Likely is it for TradeXYZ to Go Solo from Hyperliquid?

marsbit18 хв тому

"Teletubbies" Robot Cleaning Service, $30/Hour, Pure·Manual·Intelligence

Anthropomorphic "Teletubby" robot offers cleaning services in San Francisco at $30/hour, but it's entirely remote-controlled. The robot, created by startup Tau Robotics, can perform household tasks like washing hands, mopping floors, and taking out trash. While the initial demo videos appear impressive and are notably shown at normal speed (unlike many sped-up robot demos), the company reveals the actions are performed via human teleoperation, not autonomous AI. Tau Robotics, founded in 2024, argues this "cheat" is a strategic way to bridge the current capability gap, ensure task completion, and collect real-world home data to eventually train autonomous systems. Their service features three robot models: Chelsea for kitchens/bathrooms, Elon for regular tidying with memory, and Tony for deep cleaning. Priced at $30 per hour, it's cheaper than average human cleaners in the US. The article discusses the broader challenge of deploying humanoid robots in homes, comparing Tau's approach to others like China's Ziliang and the US's 1X Neo, which also use teleoperation. A key reason for choosing a humanoid form is to make remote control more intuitive for human operators. The piece also notes the potential "emotional value" of having a humanoid servant. The service is currently invite-only in San Francisco.

marsbit32 хв тому

"Teletubbies" Robot Cleaning Service, $30/Hour, Pure·Manual·Intelligence

marsbit32 хв тому

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit1 год тому

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit1 год тому

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit1 год тому

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit1 год тому

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit1 год тому

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit1 год тому

Торгівля

Спот
活动图片