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Economic slowdown may force the Federal Reserve to adopt a dovish stance, but the policy shift is still constrained by inflation risks

ChainCatcher news, according to TheBlock, analysts pointed out that last Friday's disappointing U.S. February employment report reinforced expectations for a Federal Reserve rate cut, which could boost risk appetite and lift the stock market and crypto assets. However, ongoing inflation risks driven by tariffs and supply chain issues still constrain a policy shift. Last week, the U.S. Labor Department's seasonally adjusted data showed that non-farm employment added only 151,000 jobs from January to February, marking the weakest February growth since 2019 and falling short of the 170,000 expected by economists surveyed by Dow Jones. Government layoffs, federal funding cuts, tariff uncertainties, and tightening immigration policies will weigh on job growth in the coming months. These factors may lead to a slowdown in hiring, suppress economic momentum, and reinforce deflationary trends. The Federal Reserve is facing a complex policy environment: weak employment supports rate cuts, but supply-side constraints and inflation concerns from geopolitical risks make it cautious. Uncertainty may continue to suppress the crypto market.Wincent Senior Director Paul Howard stated that the disappointing employment report underscores the necessity of rate cuts to stimulate the economy, and reducing deficit costs may be a government priority, which would benefit risk assets like crypto; CoinPanel trading automation expert Kirill Kretov pointed out that rising unemployment could improve Bitcoin and DeFi liquidity by raising rate cut expectations. Slowing wage growth suggests easing inflation pressures, making it more likely for the Federal Reserve to pivot sooner. The CME FedWatch tool shows that 55.3% of rate traders believe the June FOMC meeting is the earliest point for a rate cut this year, while the Atlanta Fed's GDPNow model has downgraded U.S. first-quarter economic growth to a contraction of 2.4%, which, if realized, would mark the first deflation since the first quarter of 2022, intensifying recession fears.Analysts indicate that global economic uncertainty has prompted an increase in bearish positions in the derivatives market, with the risk reversal indicator over the past 24 hours leaning more towards put options, reflecting market concerns about increased selling pressure. Options flow suggests that bullish sentiment may need to wait until the third quarter. Although $80,000 remains a key short-term support level for Bitcoin, the upside potential is limited. Before a new narrative emerges, the correlation between Bitcoin and the stock market may strengthen. Tariff risks persist, and volatility may increase ahead of the release of U.S. CPI and PPI data this week.

Musk agrees with the view that AI training data has been exhausted and states that synthetic data will be the future direction

ChainCatcher news, according to TechCrunch, Elon Musk stated during a live conversation with Stagwell Chairman Mark Penn that the training of current AI models has largely exhausted real-world data, "We have exhausted the cumulative sum of human knowledge, which happened last year." Musk's views align with those of former OpenAI Chief Scientist Ilya Sutskever, who suggested at the NeurIPS machine learning conference that the AI industry has reached a "data peak," and that the way models are developed may need to change in the future.Musk believes that synthetic data will be a way to supplement real data, and AI will achieve self-learning through generating and self-evaluating data. This trend has been adopted by tech giants including Microsoft, Meta, OpenAI, and Anthropic, with models like Microsoft's Phi-4 and Google's Gemma combining real and synthetic data for training. Gartner predicts that by 2024, about 60% of data in AI and analytics projects will be synthetically generated.The advantages of synthetic data include cost savings; for example, the AI startup Writer spent only about $700,000 to develop its nearly entirely synthetic data-based Palmyra X 004 model, whereas the development cost for a similarly scaled OpenAI model is about $4.6 million. However, synthetic data also carries risks, including a decrease in model creativity, increased output bias, and potential model collapse, especially when the training data itself is biased, which can also affect the generated results.
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