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Bitcoin Fuels Bear Market Fears as $49K Target Looms

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Bitcoin is trading sub-$50,000 ahead of Sunday’s weekly close, underscoring how bulls have struggled to reverse a slide that has kept the asset near ten-month lows. After a day in which BTC/USD declined more than 6% and failed to reclaim key levels, market participants remain cautious about the near-term trajectory. Traders are weighing whether a renewed downside pressure will give way to a relief rally, or if the current setup signals a deeper pullback as on-chain signals tilt toward risk-off postures and macro sentiment remains constrained.

Key takeaways

  • Price action points to sustained bearish pressure with the market testing support levels that have historically capped rallies.
  • Nearby CME futures gaps could act as short-term magnets, with the nearest notable gap near $84,000 potentially drawing bids back into the coming weeks.
  • On-chain indicators suggest a bearish regime, as price remains below the realized price for holders with 12–18 months of activity.
  • The market is watching a confluence of moving-average signals — notably the 21-week EMA crossing and its historical association with past bear markets — as a potential guide for the next leg.
  • Two measured downside liquidity targets cited by market observers sit at roughly $74,400 and $49,180, underscoring how fragility in the current phase could amplify if liquidity dries up.

Tickers mentioned: $BTC

Sentiment: Bearish

Price impact: Negative. BTC moved lower and failed to reclaim early-session gains, reinforcing downside pressure into the weekly close.

Trading idea (Not Financial Advice): Hold. With persistent bear factors and unfilled CME gaps, a cautious stance is warranted until a clear reclaim of key levels materializes.

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Market context: The move sits within a broader risk-off environment where liquidity remains constrained and traders are watching for catalysts that could shift sentiment, including macro developments and potential ETF-related flows.

Why it matters

From a longer-horizon view, the latest action reinforces a pattern that traders have observed in prior bear cycles. After a period of relative resilience, Bitcoin appears to be testing the strength of its support structure, with on-chain metrics highlighting the importance of realized price as a ceiling for rallies. In practice, realized price represents the aggregate cost basis for coins that last moved on-chain, and when price trades consistently below this line — particularly for addresses with 12 to 18 months of history — the market has historically faced more pronounced downside dynamics. The current configuration, with price below those thresholds and negative unrealized profitability, aligns with episodes that preceded extended declines rather than swift recoveries.

Market participants have also focused on the technical picture surrounding moving averages. The recent crossing of longer-term indicators — the 21-week and 50-week exponential moving averages — has, in past instances, preceded further downside. Traders like Rekt Capital have echoed the sentiment that a bearish continuation may unfold in the wake of such a cross, noting that momentum often lags price action before turning decisively. The discussion around these EMAs is not merely academic; it helps frame expectations for price behavior in the weeks ahead, especially if there is no sustained reclamation of the range lows that previously offered reprieve.

BTC/USDT perpetual contract one-month chart. Source: Cmt_trader/X

In the near term, the market is paying attention to structural gaps in CME’s Bitcoin futures market. The gaps, often described as price magnets on lower timeframes, have attracted speculation that price could be drawn toward the next target in the vicinity of $84,000 if buyers step in to fill the space. Market participants like Killa have highlighted the potential for BTC to gravitate toward that level in the coming weeks, especially if the market fails to reclaim the range lows that previously served as a baseline for bulls. The dynamic around CME gaps underscores how exchange-derived signals can influence price discovery, even if they do not guarantee a reversal.

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BTC/USDT one-week chart with 21-week, 50-week EMA. Source: Rekt Capital/X

Beyond the price action, the on-chain narrative remains largely risk-off. CryptoQuant’s Quicktake highlights that when spot price trades below the realized cost basis for the cohort of holders in the 12–18‑month bracket, the market tends to enter phased bear markets rather than brief pullbacks. Realized price itself appears to be stabilizing but remains above the current price in some cases, implying the overhead resistance could persist while sellers remain active. In this context, those watching for a deeper rebound may need to see a material shift in the balance of supply between newer buyers and longer-term holders who have yet to realize profits or cut losses.

Traders and analysts have pointed to a cluster of signals that could inform the next few weeks. A renewed push above the $80,000–$80,700 zone would be a notable development, as that level previously acted as a rough yardstick for the market’s health during the latest cycle. A sustained move back above this threshold would require not only robust buying interest but a re-emergence of liquidity that has been in shorter supply in recent sessions. Until such a move materializes, the balance of probabilities favors continuation of the current bearish texture, with occasional rallies failing to sustain momentum beyond a handful of sessions.

Bitcoin risks new “extended bearish phase”

Zooming out, on-chain research continues to frame the situation as a risk-off regime over longer horizons. The data points to a confluence of factors that have historically marked extended declines rather than fleeting pullbacks. The key observation is that price has drifted below the realized price for holders who entered the market in the mid-term window, a condition that CryptoQuant contributors describe as a potential indicator of structural bearishness. The underlying logic is that when price remains under a flat or rising realized cost, rallies tend to stall as market participants exit at breakeven levels or through concentrated selling pressure.

BTC/USD chart with one-year hodler realized price (screenshot). Source: CryptoQuant

In a broader sense, the combination of price under realized cost, negative unrealized profitability, and slowing balance growth has historically aligned with extended bearish phases. The narrative isn’t about a single catalyst but rather a suite of indicators that corroborate a cautionary stance for the immediate future. While some traders point to a potential near-term bounce anchored by the demand from CME-driven gaps, others warn that the structural headwinds — on-chain resistance, macro risk-off sentiment, and the absence of a decisive bullish trigger — could prolong weakness into the next quarter. The interplay between technicals and on-chain signals remains central to how the market calibrates risk once the weekend milestone passes.

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Risk & affiliate notice: Crypto assets are volatile and capital is at risk. This article may contain affiliate links. Read full disclosure

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Top Quantum Computing Stocks for 2026: IonQ, IBM, and Microsoft Lead the Charge

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IONQ Stock Card

Key Highlights

  • IonQ achieved a groundbreaking 99.99% fidelity world record and targets millions of qubits by 2030.
  • IBM earned a “Perfect 10” Smart Score rating on TipRanks with Moderate Buy consensus and analysts projecting 40.49% upside.
  • Microsoft’s Majorana 1 chip powers chemistry research applications and carries a Strong Buy rating with 56.62% potential upside.
  • Alphabet’s Google released research suggesting blockchain encryption could be compromised by quantum algorithms as early as 2029.
  • Industry analysts forecast the quantum computing sector will surge from $1.42 billion in 2024 to $4.24 billion by 2030.

Quantum computing has transitioned from theoretical research into tangible commercial applications at an accelerating pace. For investors monitoring this emerging sector, three companies emerge as particularly compelling: IonQ, IBM, and Microsoft.

The quantum computing industry reached a valuation of $1.42 billion in 2024. Market researchers anticipate this figure will climb to $4.24 billion by the decade’s end. Such explosive expansion is attracting enterprise clients, lucrative government partnerships, and substantial capital investments.

IonQ: Prioritizing Precision Over Speed

IonQ has established itself as the premier pure-play quantum computing enterprise. The company’s technology recently achieved an unprecedented 99.99% fidelity rating in industry-standard benchmarking tests—a global achievement.


IONQ Stock Card
IonQ, Inc., IONQ

Precision represents the fundamental obstacle preventing quantum computing’s mainstream adoption. Systems plagued by frequent computational errors cannot deliver reliable results for practical applications.

IonQ’s approach centers on trapped ion technology. This methodology prioritizes exceptional accuracy over raw processing velocity, contrasting sharply with the superconducting architectures favored by competitors.

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The organization’s 2026 roadmap includes deploying a 256-qubit architecture. Looking further ahead, IonQ aims to construct million-qubit systems by 2030. Successfully achieving these milestones while maintaining current accuracy standards could position the company as dominant in precision-dependent sectors.

IonQ’s quantum systems are accessible through partnerships with Amazon Web Services, Microsoft Azure, and Google Cloud. The company currently commands approximately $11 billion in market capitalization.

IBM: Bridging Quantum and Traditional Computing

IBM has charted a distinctive strategic course. Instead of solely pursuing qubit quantity, the tech giant emphasizes integrating quantum capabilities into established enterprise infrastructure.


IBM Stock Card
International Business Machines Corporation, IBM

IBM’s development strategy centers on hybrid architectures where conventional CPUs, GPUs, and quantum processors operate cohesively. Industry experts consider this integration model the most viable pathway toward immediate commercial viability.

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TipRanks analysts awarded IBM the platform’s maximum Smart Score of 10 out of 10. The stock maintains a Moderate Buy consensus rating, with Wall Street projecting 40.49% appreciation potential.

IBM leverages its extensive enterprise computing heritage and established client relationships, providing immediate market access for quantum services. The company’s development pipeline emphasizes enhanced qubit coherence and sophisticated error correction protocols.

Microsoft: Strategic Innovation with Transformative Potential

Microsoft has maintained a relatively understated public profile regarding quantum achievements compared to rivals like Google or IonQ. Nevertheless, its Majorana 1 quantum processor is delivering measurable outcomes.


MSFT Stock Card
Microsoft Corporation, MSFT

The processor currently facilitates advanced chemistry research, enabling quantum simulations of intricate molecular behaviors that exceed classical computing capabilities. CEO Satya Nadella has characterized quantum technology as the forthcoming catalyst for cloud computing evolution.

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Microsoft’s research concentrates on topological qubit architectures—a forward-looking methodology promising superior stability compared to existing quantum systems. The company’s Azure Quantum platform seamlessly embeds quantum capabilities into corporate computing environments.

Wall Street analysts assign Microsoft a Strong Buy recommendation with 56.62% upside potential. The stock holds a Smart Score of eight out of ten on TipRanks.

Alphabet’s Google division released 2025 research demonstrating an algorithm potentially capable of compromising contemporary blockchain encryption protocols in minutes—possibly operational by 2029. This revelation emphasizes the remarkable velocity of quantum computing advancement.

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AI’s Impact on Employment Clashes With C-suite Optimism

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AI’s Impact on Employment Clashes With C-suite Optimism

In March, the US jobs market recorded 178,000 new jobs, marking little change from the month before, according to the Bureau of Labor Statistics. 

The anemic growth in job listings comes amid volatile policy swings from the White House, increased energy prices due to the US and Israel’s war with Iran and, according to recent research, AI disruptions to the labor market. 

Proponents of AI and large language models have claimed that the tech will bring about an economic boom, thanks to the promise of efficiency breakthroughs. 

But as AI becomes more integrated into daily business operations, there is a widening gulf between that promise of growth and efficiency, and what is actually happening. 

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AI dampens employment growth

On March 6, venture capitalist and Netscape co-founder Marc Andreessen said on X that fears about AI job displacement were overblown. 

Source: Marc Andreessen

He also posted an article from Business Insider stating that, at least in tech, job openings are on the rise. Citing data from TrueUp, a tech jobs tracker, Business Insider said that job openings at tech companies have doubled to 67,000 since 2023.  

But openings don’t necessarily translate to hiring. According to the Bureau of Labor Statistics, most employment growth in March did not happen in the tech industry. Of the 178,000 new jobs added in March, healthcare employed 76,000, construction grew by 26,000, transportation and warehousing added 21,000 and employment in social assistance increased by 14,000.  

While the report doesn’t have a single section tracking the tech industry, related services like computing infrastructure providers and web search portals saw a 1,500 job decrease, or almost no change, respectively. Computer systems design and related services lost 13,000 jobs.

Related: Jack Dorsey’s Block to cut 4,000 jobs in AI-driven restructuring

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AI has actually axed 16,000 jobs per month over the past year, according to a recent report from Goldman Sachs, as cited by Fortune. In particular, AI has led to a collapse in hiring for entry-level roles. A 2025 study from SignalFire found that new grad hiring had dropped 50% compared to pre-COVID-19 pandemic levels. 

Source: SignalFire

“The door to tech once swung wide open for new grads. Today, it’s barely cracked. The industry’s obsession with hiring bright-eyed grads right out of college is colliding with new realities: smaller funding rounds, shrinking teams, fewer new grad programs, and the rise of AI,” the SignalFire study stated. 

This disruption could create ripples far into the future. According to Goldman Sachs, “AI-driven displacement could impose lasting costs on affected workers, worsening labor market outcomes for several years.”

“A key mechanism behind these worse outcomes is occupational downgrading. Workers displaced by technology are more likely to move into more routine occupations requiring fewer analytical and interpersonal skills, likely because the same technological shifts that eliminated their positions also eroded the value of their existing skills,” they continued

These job losses are justified by the theory that AI will, at the very least, make workplaces more productive. But even that isn’t a given.

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Reality of AI use clashes with C-suite expectations

Executives are still overwhelmingly supportive of AI. According to Harvard Business Review, 80% of leaders report weekly use of AI, with 74% reporting positive returns on early deployments. 

But workers don’t feel the same. A study from HR consulting firm Mercer found that, for 43% of workers, their job is more frustrating. 

One major issue is the number of mistakes churned out by generative AI. “For every 10 hours of efficiency gained through AI, nearly four hours are lost to fixing its output,” a Workday report stated. 

AI can also be used to offload labor onto coworkers in what researchers at the Harvard Business Review have called “workslop” i.e., “content that appears polished but lacks real substance, offloading cognitive labor onto coworkers.”

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They said that “41% of workers have encountered such AI-generated output, costing nearly two hours of rework per instance and creating downstream productivity, trust, and collaboration issues.”

According to Workday, only 14% of respondents to their survey said they “consistently achieve net-positive outcomes from AI use.”

Part of the gulf between executives’ understanding of AI and the reality at the productive level may be explained by the technology itself. 

Per the Harvard Business Review, “Senior leaders tend to use AI for high-level synthesis, strategic drafting, and decision support, tasks where the technology performs well, so the current capabilities tend to benefit their work.”

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For messier day-to-day operations like “workflows built over years, teams with uneven technical comfort, output that has to be consistently right, not just fast,” it doesn’t work so well. 

“When the tool works, both groups understand and reap the benefits. When it fails, typically only one of them has to cope with the aftermath.”

Many still don’t think that AI can handle complex tasks. Source: MIT

Brian Solis, the head of global innovation at enterprise AI firm ServiceNow, said that this divide has created an “AI tax,” i.e., “More checking. More rework. More anxiety. Faster pace. AI slop. Less trust.” 

Andreessen may not believe that the AI job-cut narratives are real, but OpenAI does. The AI company has acknowledged the impact the technology has on employment, and has even released a series of policy proposals to address it.

The list contains ideas that are “intentionally early and exploratory” that serve as a “a starting point for discussion that we invite others to build on.” It includes proposals to expand healthcare coverage, retirement savings and setting a new industrial policy agenda. 

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Far from Andreessen’s optimism, OpenAI’s proposal included a warning: “Unless policy keeps pace with technological change, the institutions and safety nets needed to navigate this transition could fall behind.”

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