According to @timnitGebru, claims that a "messiah or devil" level AI is arriving imminently are not materializing today, emphasizing continued skepticism toward near-term AGI timelines; source: https://twitter.com/timnitGebru/status/1991963336284799022. She attributes the persistence of such forecasts to uncritical parroting by parts of the press and academia with financial incentives, pointing to incentive-driven hype rather than evidence; source: https://twitter.com/timnitGebru/status/1991963336284799022. The post is a narrative critique and includes no product announcements, technical results, timelines, regulatory updates, or asset references, providing no direct trading catalyst; source: https://twitter.com/timnitGebru/status/1991963336284799022. The post also references a related discussion by @DKokotajlo, indicating broader debate on AGI imminence rather than new data; source: https://x.com/DKokotajlo/status/1991564542103662729.

According to Andrew Ng, AGI remains decades away or longer, and frontier models will not eliminate most application-layer businesses without substantial customization, indicating a longer buildout cycle for tools and services rather than rapid displacement, source: Andrew Ng on Twitter dated Nov 13, 2025 and deeplearning.ai The Batch issue 327. Ng reports current LLMs are narrow versus humans, excel mainly at text, require heavy context engineering, and he would not trust a frontier model alone for calendar prioritization, resume screening, or lunch ordering, noting his team achieved a decent resume screening assistant only after significant customization, source: Andrew Ng on Twitter dated Nov 13, 2025. Ng adds that while thin wrappers may be replaced, many valuable applications will not be displaced for a long time despite rapid model progress, which counters fears that model providers will quickly wipe out app startups, source: Andrew Ng on Twitter dated Nov 13, 2025. For traders, this points to sustained demand for vertical AI integration, data pipelines, and domain-specific agents over commoditized chat fronts, aligning with Ng’s view of persistent limitations and customization needs, source: Andrew Ng on Twitter dated Nov 13, 2025 and deeplearning.ai The Batch issue 327. For crypto markets, AI-focused tokens tied to compute, data, and application ecosystems may find more durable narratives than generic LLM wrappers given customization and feedback bottlenecks highlighted by Ng, source: Andrew Ng on Twitter dated Nov 13, 2025. Ng encourages newcomers to learn to build with AI now, signaling ongoing demand for skilled builders over many years, source: Andrew Ng on Twitter dated Nov 13, 2025.

According to @karpathy, AGI is on roughly a 10-year horizon he describes as a decade of agents, citing major remaining work in integration, real-world sensors and actuators, societal alignment, and security, and noting his timeline is 5-10x more conservative than prevailing hype, source: @karpathy on X, Oct 18, 2025. He is long agentic interaction but skeptical of reinforcement learning due to poor signal-to-compute efficiency and noise, and he highlights alternative learning paradigms such as system prompt learning with early deployed examples like ChatGPT memory, source: @karpathy on X, Oct 18, 2025. He urges collaborative, verifiable LLM tooling over fully autonomous code-writing agents and warns that overshooting capability can accumulate slop and increase vulnerabilities and security breaches, source: @karpathy on X, Oct 18, 2025. He advocates building a cognitive core by reducing memorization to improve generalization and expects models to get larger before they can get smaller, source: @karpathy on X, Oct 18, 2025. He also contrasts LLMs as ghost-like entities prepackaged via next-token prediction with animals prewired by evolution, and suggests making models more animal-like over time, source: @karpathy on X, Oct 18, 2025. For crypto builders and traders, this points to prioritizing human-in-the-loop agent workflows, code verification, memory-enabled tooling, and security-first integrations over promises of fully autonomous AGI, especially where software defects and vulnerabilities carry on-chain risk, source: @karpathy on X, Oct 18, 2025.

According to Sam Altman on Twitter, the focus in AI discussions is shifting from predicting the arrival of AGI to the timeline for the first self-replicating spaceship. This pivot highlights the growing relevance of advanced AI and automation technologies, which could spur increased investment in blockchain and crypto infrastructure supporting autonomous systems. Recent trends show that AI-driven developments often lead to heightened trading volumes and bullish sentiment in crypto assets like Worldcoin and AI-linked tokens (source: Sam Altman Twitter, May 23, 2025).