According to Richard Seroter, Google Cloud Run now supports NVIDIA RTX 6000 PRO Blackwell GPUs for AI workloads, enabling teams to serve 70B-plus parameter models without managing underlying infrastructure (source: Richard Seroter on X and Google Cloud blog). He highlights that drivers are pre-installed, no capacity reservations are required, and serverless instances offer 20 to 44 vCPUs with 80 to 176 GiB memory to streamline large language model inference and other high‑throughput tasks (source: Richard Seroter on X and Google Cloud Run documentation). This update allows on-demand scaling of LLM inference on Cloud Run while removing GPU infrastructure administration overhead for developers (source: Richard Seroter on X and Google Cloud blog).

According to @KobeissiLetter, Nvidia’s plan to invest $100 billion in OpenAI has stalled, with Jensen Huang cited as expressing concerns about competition from Google and Anthropic and a lack of discipline at OpenAI. @KobeissiLetter characterizes this as a major AI development and indicates the pause occurred abruptly, without providing additional terms or a new timeline.

According to @StockMKTNewz, citing The Wall Street Journal, Nvidia’s plan to invest up to 100 billion in OpenAI has reportedly stalled, and discussions have shifted to OpenAI’s latest funding round in which Nvidia may invest tens of billions. Per the same source, this indicates Nvidia could pursue exposure to OpenAI through the new financing round rather than a standalone mega commitment, a development relevant for NVDA stock positioning and AI deal flow.

According to @caprioleio, Nvidia called on Congress to quickly reauthorize the National Quantum Initiative, framing AI and quantum as converging pillars of next generation supercomputers, per the Nvidia corporate blog. Nvidia said a renewed NQI should explicitly support integrating AI, accelerated computing, and quantum processors to translate breakthroughs into secure technologies, skilled workforce, and long-term strategic advantage, per the Nvidia corporate blog. The author argues this policy push could drive capital into quantum computing equities tied to national security priorities, per @caprioleio. He also urges faster quantum readiness from the Bitcoin BTC community, flagging a potential security narrative for crypto that traders should monitor alongside NVDA and quantum names, per @caprioleio and the Nvidia corporate blog.

According to @thedaoofwei, NVIDIA’s edge stems from Jensen Huang’s early GPU-first vision, the 1990s fast execution cadence, and the 2006 CUDA bet that unlocked general purpose compute on GPUs, positioning the company ahead in AI accelerators and gaming while fueling ongoing data center demand for systems like H100 and the Blackwell generation (source: @thedaoofwei). The source highlights prior cyclicality from the crypto mining boom and bust tied to BTC and ETH, which drove outsized GPU demand followed by an inventory glut and a sharp stock drawdown, underscoring execution discipline and supply management as key risk controls for NVDA (source: @thedaoofwei). The thread also flags intensifying competition from AMD, Intel, Google, and Amazon in AI chips, while noting NVIDIA’s continued lead, reinforced by platform depth and management’s urgency-first culture, which is relevant for trading NVDA amid capacity constraints and hyperscaler capex cycles (source: @thedaoofwei).

According to @StockMKTNewz, CoreWeave (CRWV) is up more than 10 percent in pre market trading after Nvidia (NVDA) invested 2 billion dollars in CoreWeave Class A common stock at 87.20 dollars per share and announced an expanded partnership to accelerate the buildout of more than 5 gigawatts of AI factories by 2030. The source states the partnership expansion aims to advance AI adoption at global scale, which is driving the bullish pre market reaction in CRWV.

According to @StockMKTNewz, NVIDIA (NVDA) invested $2 billion in CoreWeave (CRWV) and the companies expanded their partnership to enable CoreWeave to accelerate the buildout of more than 5 gigawatts of AI factories by 2030 to advance AI adoption at global scale; source: @StockMKTNewz on X. For traders, the move highlights continued scaling of AI infrastructure and GPU cloud capacity, making NVDA and CRWV key tickers to watch for exposure to AI compute buildout; source: @StockMKTNewz on X.

According to @DeepLearningAI, Andrew Ng wrote in The Batch that leaders seeking real AI transformation should redesign entire workflows rather than automate isolated steps. According to @DeepLearningAI, OpenAI is testing ads inside ChatGPT, indicating the company is exploring monetization within the product. According to @DeepLearningAI, Nvidia introduced Alpamayo R1 as a reasoning model. According to @DeepLearningAI, these updates collectively emphasize workflow-level AI adoption, product monetization tests, and advances in reasoning models.

According to @FuSheng_0306, Jensen Huang said his only regret was selling Nvidia stock when the company was worth about $300 million to buy an ~$80,000 1999 Mercedes S-Class for his parents, which @FuSheng_0306 notes would be worth about $1.2 billion today if the shares had been held. According to @FuSheng_0306, while it was a warm family memory, it became an expensive decision from an investment perspective, underscoring a massive opportunity cost. According to @FuSheng_0306, traders can read this as a lesson in compounding and position management: prematurely cashing out of a long-term winner like NVDA can forfeit outsized future gains, a point to consider when planning entries, trims, and profit-taking.

According to @garyblack00, Tesla has begun removing safety monitors from its robotaxis in Austin, aligning with his prior view that TSLA would achieve unsupervised autonomy (source: @garyblack00 on X). According to @garyblack00, the core issue is TSLA’s valuation, highlighting a 2026 adjusted EPS P/E above 200x while five rivals tied to GOOG, BIDU, PONY, WRD, and AMZN are already delivering roughly 750k paid robotaxi rides per week without safety monitors (source: @garyblack00 on X). According to @garyblack00, NVIDIA is launching an unsupervised autonomous platform that could democratize autonomy for other OEMs, challenging TSLA’s exclusivity premium (source: @garyblack00 on X). According to @garyblack00, he exited TSLA at 358 in May 2025 due to valuation discipline despite liking the company and notes TSLA has lagged NDX and the Mag 7 year to date (source: @garyblack00 on X). According to @garyblack00, traders should remain cautious on TSLA at current multiples and monitor autonomy platform beneficiaries such as NVDA; the source does not cite any direct cryptocurrency linkages (source: @garyblack00 on X).

According to @StockMKTNewz, Nvidia CEO Jensen Huang held 3 million NVDA shares at IPO, valued at $59 million after the first trading day. According to @StockMKTNewz, that same stake would be worth $266 billion today, a stark illustration of long-term compounding in AI leaders that traders monitor.

According to Gary Black, Tesla has begun removing safety monitors from its robotaxis in Austin following a company announcement, marking progress toward unsupervised autonomy. According to Gary Black, the core debate is valuation, with TSLA trading at an implied 2026 P/E above 200x despite his claim that five rivals (Alphabet, Baidu, Pony.ai, WeRide, Amazon) already complete roughly 750,000 paid robotaxi rides per week without safety monitors. According to Gary Black, Nvidia is launching an unsupervised autonomous driving platform that could democratize autonomy for other automakers, intensifying competitive pressure on Tesla’s premium multiple. According to Gary Black, he exited TSLA at $358 in May 2025 on valuation discipline, still liking the company’s execution but not the stock at current levels, and he notes TSLA’s year-to-date performance has lagged the NDX and Mag 7 despite autonomy progress.

According to @StockMKTNewz, Nvidia (NVDA) has joined the investor group backing Harmonic, an AI startup founded by Robinhood co-founder Vlad Tenev that builds systems to solve mathematical problems (source: @StockMKTNewz; Axios). The post does not disclose funding amount, valuation, or round details, and no crypto assets were mentioned in the item, limiting immediate trading model inputs and crypto tie-ins (source: @StockMKTNewz).

According to CNBC (Jan 14, 2026 tweet), Nvidia (NVDA) shares are struggling and the outlet examines what’s going wrong and whether the AI juggernaut can break out of its funk (Source: CNBC tweet, Jan 14, 2026). According to CNBC (Jan 14, 2026 tweet), the analysis is framed for traders assessing near-term momentum and potential breakout scenarios in NVDA (Source: CNBC tweet, Jan 14, 2026).

According to @StockMKTNewz, Nvidia Senior Director of AI Infrastructure Dion Harris stated that Nvidia is the only AI company that works with every AI company (source: @StockMKTNewz on X, Jan 14, 2026). This on-record claim signals broad ecosystem engagement for Nvidia’s AI infrastructure, a data point traders use when assessing NVDA’s exposure to model-builder demand and platform standardization risk (source: @StockMKTNewz on X, Jan 14, 2026). For crypto, the breadth of Nvidia’s AI relationships, as reported here, is relevant to sentiment in the AI-related crypto segment that is sensitive to GPU-based compute narratives (source: @StockMKTNewz on X, Jan 14, 2026).

According to @StockMKTNewz, Nvidia NVDA accounted for 35% of all semiconductor industry growth in 2025, underscoring its outsized influence on sector performance. Source: @StockMKTNewz. For traders, this concentration means upcoming NVDA earnings, guidance, and data center GPU demand updates could disproportionately move semiconductor indices and peer stocks. Source: @StockMKTNewz. The source did not provide methodology or segment breakdowns, so position sizing and risk management should account for headline sensitivity around NVDA specific catalysts. Source: @StockMKTNewz. The source did not reference cryptocurrency markets; any crypto impact is not stated by the source. Source: @StockMKTNewz.

According to @StockMKTNewz, NVIDIA NVDA Senior Director of AI Infrastructure Dion Harris discusses data centers, why AI CAPEX spend is not slowing, and the longer-term outlook, offering direct context for NVDA traders tracking enterprise AI infrastructure demand; source: @StockMKTNewz on X, Jan 12, 2026. The interview video is accessible via a WOLF Financial post linked by @StockMKTNewz, providing first-hand commentary from NVIDIA on data center trends relevant to NVDA stock analysis; source: @StockMKTNewz on X, Jan 12, 2026. The post includes no cryptocurrency-specific content, indicating no direct crypto market guidance from this interview; source: @StockMKTNewz on X, Jan 12, 2026.

According to Stanford AI Lab, the team released End-to-End Test-Time Training (TTT-E2E), enabling LLMs to continue training during deployment by using live context as training data to update model weights (source: Stanford AI Lab on X, Jan 12, 2026). According to Stanford AI Lab, the announcement names NVIDIA AI and Astera Institute as collaborators and provides links to a project blog and an arXiv preprint for the full release (source: Stanford AI Lab on X, Jan 12, 2026). According to Stanford AI Lab, the release does not mention any cryptocurrencies, tokens, or blockchain integrations, indicating no direct on-chain changes for digital assets to track in this announcement (source: Stanford AI Lab on X, Jan 12, 2026). According to Stanford AI Lab, traders can reference the official blog and arXiv links to evaluate benchmarks and implementation details once reviewed, which can inform assessments of compute intensity and hardware dependencies relevant to AI-infrastructure exposure (source: Stanford AI Lab on X, Jan 12, 2026).

According to @StockMKTNewz, WOLF Financial posted a CES interview with NVIDIA VP of Generative AI Software Kari Briski covering AI, NVIDIA’s open-sourced Nemotron model catalog, and the importance of open-source models, with the full clip available on X. Source: https://x.com/StockMKTNewz/status/2010470721156206723 and https://x.com/WOLF_Financial/status/2010437680702837246. For trading relevance, the clip offers direct executive commentary on NVIDIA’s generative AI software priorities and open-source tooling, which NVDA-focused investors and AI-related crypto token traders can review for primary-source product roadmap signals. Source: https://x.com/WOLF_Financial/status/2010437680702837246.

According to @StockMKTNewz, Nvidia CEO Jensen Huang said in a video that when AI is open, it proliferates everywhere, highlighting the open-source AI theme that traders monitor when positioning in NVDA and AI-related crypto narratives; source: @StockMKTNewz on X, Jan 10, 2026. The clip provides no additional numbers or policy details, so near-term setups are sentiment-driven around open AI headlines across AI equities and digital assets; source: @StockMKTNewz on X, Jan 10, 2026.