2026-04-17 Sexta

Centro de Notícias - Página 463

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Ads During the American Super Bowl Look Like Scams

The 2026 Super Bowl, often called the "American Super Bowl," was a spectacle of sports, entertainment, and high-stakes marketing. This year’s event featured three notable incidents that highlight the intersection of prediction markets, insider information, and viral marketing. First, a newly created account on the prediction market Polymarket placed nearly $80,000 in bets—with 17 out of 19 wagers correctly predicting details of the halftime show, including appearances by Lady Gaga and the absence of Travis Scott. The account’s near-perfect accuracy led to suspicions of insider trading, possibly linked to the event’s production team. Second, a trader named Alex Gonzalez ran onto the field during the game with promotional messages painted on his body. Reports indicate he had previously bet on such a field invasion occurring, after accounting for legal fees and bail, netted around $70,000. His actions blurred the line between predicting and creating events for profit. Finally, a viral “leaked” video showed influencer Logan Paul apparently betting $1 million on Polymarket during the game. It was later revealed to be a marketing stunt orchestrated by Polymarket itself, in which Paul has investment ties. Together, these events illustrate how prediction markets can be manipulated through insider knowledge, performative acts, and staged publicity—raising questions about authenticity in high-profile events.

marsbit02/12 05:55

Ads During the American Super Bowl Look Like Scams

marsbit02/12 05:55

The Next Earthquake in AI: Why the Real Danger Isn't the SaaS Killer, But the Computing Power Revolution?

The next seismic shift in AI isn't about SaaS disruption but a fundamental revolution in computing power. While many focus on AI applications like Claude Cowork replacing traditional software, the real transformation is happening beneath the surface: a dual revolution in algorithms and hardware that threatens NVIDIA’s dominance. First, algorithmic efficiency is advancing through architectures like MoE (Mixture of Experts), which activates only a fraction of a model’s parameters during computation. DeepSeek-V2, for example, uses just 9% of its 236 billion parameters to match GPT-4’s performance, decoupling AI capability from compute consumption and slashing training costs by up to 90%. Second, specialized inference hardware from companies like Cerebras and Groq is replacing GPUs for AI deployment. These chips integrate memory directly onto the processor, eliminating latency and drastically reducing inference costs. OpenAI’s $10 billion deal with Cerebras and NVIDIA’s acquisition of Groq signal this shift. Together, these trends could collapse the total cost of developing and running state-of-the-art AI to 10-15% of current GPU-based approaches. This paradigm shift undermines NVIDIA’s monopoly narrative and its valuation, which relies on the assumption that AI growth depends solely on its hardware. The real black swan event may not be an AI application breakthrough but a quiet technical report confirming the decline of GPU-centric compute.

marsbit02/12 04:38

The Next Earthquake in AI: Why the Real Danger Isn't the SaaS Killer, But the Computing Power Revolution?

marsbit02/12 04:38

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