Crypto market holds $2T after U.S. jobs unexpectedly fall by 92K

ambcryptoОпубліковано о 2026-03-06Востаннє оновлено о 2026-03-06

Анотація

The cryptocurrency market held steady near the $2 trillion mark following the release of weaker-than-expected U.S. labor data, which showed nonfarm payrolls unexpectedly falling by 92,000 jobs in February. This has reinforced expectations that the Federal Reserve may adopt a more accommodative policy stance later this year, potentially supporting risk assets like cryptocurrencies. Total crypto market capitalization excluding stablecoins hovered around $2.04 trillion, showing signs of stabilization after a sharp decline in February that erased approximately $1 trillion in value. Technical indicators, such as the Relative Strength Index (RSI), rebounded from oversold levels, suggesting easing selling pressure. Analysts see the $2 trillion level as a key psychological support, with the potential for accumulation if it holds. However, the market awaits further macroeconomic signals and Fed guidance before a broader recovery can materialize.

The cryptocurrency market held steady near the $2 trillion mark on Friday after new U.S. labor data showed an unexpected decline in job growth. It reinforced expectations that the Federal Reserve could shift toward a more accommodative policy stance later this year.

According to the latest Employment Situation report released by the U.S. Bureau of Labor Statistics, nonfarm payrolls fell by 92,000 jobs in February, while the unemployment rate remained unchanged at 4.4%.

The weaker-than-expected data signaled that the U.S. labor market may be cooling — a development closely watched by investors. Slower economic activity can increase the likelihood of interest-rate cuts, which typically support risk assets such as cryptocurrencies.

While traditional markets digested the macro signals, the crypto market remained broadly stable, with total capitalization excluding stablecoins hovering near $2.04 trillion.

Crypto market consolidates near key $2T level

Data from TradingView shows the total cryptocurrency market capitalization excluding stablecoins hovering around $2.04 trillion at the time of writing.

The market has been attempting to stabilize following a sharp decline in February that wiped roughly $1 trillion from total market value. This sent capitalization from nearly $3 trillion to around $2 trillion.

Despite the recent stabilization, the broader market structure still reflects the pullback seen earlier in the year. Crypto assets have formed a series of lower highs since January, suggesting the correction phase has not yet fully reversed.

However, the latest price action suggests the market may be attempting to establish a base around the psychologically significant $2 trillion level.

Momentum shows early signs of recovery

Technical indicators also point to a potential stabilization phase.

The Relative Strength Index [RSI] on the daily chart has recovered to around 46, rebounding from deeply oversold conditions near 20 recorded during February’s sell-off.

While the indicator remains below the neutral 50 level, the rebound suggests selling pressure has eased after the earlier correction.

Trading volumes also spiked during the February downturn, a pattern often associated with capitulation events, in which large amounts of selling occur before markets begin to stabilize.

If the $2 trillion level holds, analysts may view the recent consolidation as a potential accumulation phase following the sharp drawdown.

Macro signals may shape the next move

For now, macroeconomic signals remain a key driver of market sentiment.

Cooling labor data could strengthen the case for the Federal Reserve to adopt a more accommodative stance later in 2026. Lower interest rates typically support risk assets, including cryptocurrencies, by improving liquidity conditions.

However, the crypto market is waiting for clearer confirmation from upcoming economic data and Federal Reserve guidance before attempting a broader recovery.


Final Summary

  • The total crypto market cap, excluding stablecoins, has stabilized around $2.04 trillion since February’s sharp correction.
  • Weak payroll data suggests a cooling economy, which could increase expectations of Federal Reserve rate cuts that historically benefit risk assets like cryptocurrencies.

Пов'язані питання

QWhat was the key level that the cryptocurrency market held near after the U.S. jobs data release?

AThe cryptocurrency market held steady near the $2 trillion mark.

QHow many nonfarm payroll jobs were unexpectedly lost in February according to the U.S. Bureau of Labor Statistics report?

ANonfarm payrolls fell by 92,000 jobs in February.

QWhat does the weaker-than-expected jobs data signal about the U.S. labor market and its potential effect on Federal Reserve policy?

AThe data signaled that the U.S. labor market may be cooling, which reinforces expectations that the Federal Reserve could shift toward a more accommodative policy stance, such as interest-rate cuts, later this year.

QWhat technical indicator is mentioned as showing early signs of recovery, and what was its reading?

AThe Relative Strength Index (RSI) on the daily chart is mentioned, and it had recovered to around 46, rebounding from deeply oversold conditions near 20.

QFrom what level did the total cryptocurrency market capitalization fall to around $2 trillion during February's decline?

AThe total market capitalization fell from nearly $3 trillion to around $2 trillion during February's sharp decline.

Пов'язані матеріали

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.

marsbit47 хв тому

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

marsbit47 хв 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.

marsbit51 хв тому

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

marsbit51 хв 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.

marsbit51 хв тому

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

marsbit51 хв тому

Торгівля

Спот
活动图片