According to Andrew Ng (@AndrewYNg), citing separate reports from Edelman and Pew Research, a significant portion of the U.S. and broader Western populations remain distrustful and unenthusiastic about AI adoption. Edelman’s survey found that 49% of Americans reject AI use while only 17% embrace it, contrasting sharply with China, where just 10% reject and 54% embrace AI. Pew’s data reinforces this trend, showing greater AI enthusiasm in many countries outside the U.S. This widespread skepticism poses concrete challenges for AI business growth: slow consumer adoption, local resistance to AI infrastructure projects (such as Google’s failed Indiana data center), and heightened risk of restrictive legislation fueled by public distrust. The main barrier cited by U.S. respondents for not using AI is lack of trust (70%), outweighing access or motivation concerns. Ng stresses that the AI industry must focus on transparent communication, responsible development, and broad-based benefits—including upskilling and practical applications—to rebuild trust and unlock market opportunities. Excessive hype and sensationalism, especially from within the AI community and media, have fueled public fears and must be addressed to prevent further erosion of trust. (Sources: Edelman, Pew Research, Andrew Ng via deeplearning.ai, Twitter)

According to Andrew Ng (@AndrewYNg), a new course titled 'Building Coding Agents with Tool Execution' is now available, taught by @tereza_tizkova and @FraZuppichini of @e2b. The course focuses on equipping AI developers with practical skills to build advanced coding agents that move beyond fixed function calls, enabling them to autonomously write and execute code, manage files, and handle errors through feedback loops (source: Andrew Ng, https://twitter.com/AndrewYNg/status/1996250415244235013). A key highlight is the use of E2B's cloud-based sandbox environments, allowing agent-generated code to run securely, mitigating risks of harmful operations. The curriculum emphasizes real-world applications such as data analysis with Pandas and full-stack web development using Next.js, providing immediate business value for enterprises seeking to automate complex workflows with AI agents. This reflects a growing trend toward robust, safe agentic AI solutions, unlocking new market opportunities for scalable automation in data science and software engineering.

According to Andrew Ng, the Agentic Reviewer AI system, launched just last week, has already surpassed the 21,575 paper submissions received by NeurIPS this year in both papers submitted and reviewed. This milestone highlights the rapid adoption and scalability of agentic AI in automating academic peer review processes. The widespread implementation of agentic reviewing technologies signals a shift toward more efficient and accessible scientific evaluation, opening significant business opportunities for AI platforms in academic publishing, research management, and knowledge dissemination (source: x.com/AndrewYNg/status/1995633795027079495).

According to Andrew Ng (@AndrewYNg), while the influx of capital into AI infrastructure, including OpenAI's $1.4 trillion plan and Nvidia's $5 trillion market cap, has raised concerns about an AI investment bubble, the reality is nuanced across different industry segments. Ng emphasizes that the AI application layer is currently underinvested, with substantial untapped business potential, citing a hesitancy among venture capitalists to back AI applications due to perceived difficulty in selecting winners (source: deeplearning.ai/the-batch/issue-329). In contrast, AI infrastructure for inference requires further investment to meet surging demand for token generation, particularly as agentic coding tools like Claude Code, OpenAI Codex, and Gemini 3 drive new use cases. Although supply constraints exist, overbuilding could result in low returns but benefit application builders. The riskiest segment is model training infrastructure, where rapid algorithm and hardware improvements—as well as the rise of open-source models—may erode competitive moats and threaten returns on massive investments. Ng warns that overinvestment in any one segment, especially training infrastructure, could trigger negative market sentiment, impacting the broader AI sector despite strong long-term fundamentals. He concludes that while short-term valuation swings are driven by sentiment, the long-term outlook for AI remains robust, with significant opportunities for business innovation and infrastructure scaling (source: deeplearning.ai/the-batch/issue-329).

According to Andrew Ng, the release of a new AI-powered 'Agentic Reviewer' for research papers demonstrates near-human-level performance, with Spearman correlation scores of 0.42 between AI and human reviewers compared to 0.41 between two human reviewers when tested on ICLR 2025 reviews (source: Andrew Ng, Twitter). This agentic workflow automates paper feedback using arXiv searches, enabling researchers to iterate much faster than traditional peer review cycles. The tool's ability to provide grounded, rapid feedback creates significant opportunities for AI-driven productivity platforms in academic publishing, scholarly communication, and research acceleration, particularly in fields with open-access literature (source: Andrew Ng, Twitter).

According to Andrew Ng on the 20VC podcast hosted by Harry Stebbings, significant bottlenecks remain in the widespread adoption of artificial intelligence, particularly in real-world business deployments (source: x.com/HarryStebbings/status/1990472838914945442). Ng emphasized that while AI technology has made rapid progress, many organizations face challenges in integrating AI into existing workflows, often due to data quality issues, lack of skilled talent, and operational inertia. The discussion also explored US-China geopolitics, highlighting how global dynamics influence AI market expansion and partnership opportunities. Ng stressed that there are still numerous untapped business opportunities for entrepreneurs and enterprises to build AI-driven solutions, especially in sectors like healthcare, manufacturing, and education. These insights underscore the ongoing need for practical AI implementation strategies and the vast market potential for innovative startups and established companies alike.

According to Andrew Ng, the recent AI Dev x NYC conference brought together a vibrant community of AI developers, emphasizing practical discussions on agentic AI, context engineering, governance, and scaling AI applications for startups and enterprises (Source: Andrew Ng, Twitter, Nov 20, 2025). Despite skepticism around AI ROI, particularly referencing a widely quoted but methodologically flawed MIT study, the event showcased teams achieving real business impact and increased ROI with AI deployments. Multiple exhibitors praised the conference for its technical depth and direct engagement with developers, highlighting a strong demand for advanced AI solutions and a bullish outlook on AI's future in business. The conference underscored the importance of in-person collaboration for sparking new ventures and deepening expertise, pointing to expanding opportunities in agentic AI and AI governance as key drivers for the next wave of enterprise adoption (Source: Andrew Ng, deeplearning.ai, Issue 328).

According to Andrew Ng, Agentic Document Extraction, powered by the document pre-trained transformer (DPT) model, successfully extracted key financial metrics such as NVIDIA's $57.01B quarterly revenue from the latest 10-Q earnings report released just an hour ago (source: Andrew Ng on Twitter). The AI-driven extraction tool demonstrated high accuracy by comparing the original PDF directly to the structured output, showcasing immediate and reliable document data extraction capabilities for financial analysis and compliance. This advancement in document AI highlights business opportunities for automated financial reporting, due diligence, and enterprise workflow automation, enabling faster and more precise insights for AI-powered enterprise solutions (source: Andrew Ng on Twitter).

According to Andrew Ng (@AndrewYNg), Redisinc experts @tchutch94 and @ilzhechev have launched a new course on semantic caching for AI agents. This course demonstrates how semantic caching technology can dramatically lower inference costs and reduce response latency for AI applications by recognizing and reusing semantically similar queries, such as refund requests phrased differently. The practical implications include greater scalability for AI-driven customer support, improved user experience, and significant operational cost savings for businesses deploying large language models (LLMs). Semantic caching is rapidly gaining traction as a critical optimization for enterprise AI workflows, especially in high-traffic environments (source: Andrew Ng on Twitter).

According to Andrew Ng on Twitter, the DeepLearningAI engineering team leveraged advanced AI coding tools to quickly develop and deploy a clone of basic Cloudflare functionalities after a major Cloudflare outage. This innovative use of AI for rapid infrastructure replacement enabled DeepLearningAI’s website to resume operations significantly ahead of other major sites impacted by the downtime. The event demonstrates the practical application of AI in crisis-driven DevOps scenarios, highlighting new business opportunities for AI in automated disaster recovery and web infrastructure resilience (Source: Andrew Ng, Twitter).

According to Andrew Ng (@AndrewYNg), concerns about AI making human contributions obsolete are largely fueled by hype, not reality. Ng emphasizes that despite rapid advancements in AI, current large language models (LLMs) are still highly specialized, require significant customization, and remain limited compared to humans in many business contexts (source: deeplearning.ai/the-batch/issue-327). He highlights that while AI tools are improving, they cannot fully automate complex tasks like resume screening or decision-making without extensive engineering. Ng points out that fears of AGI (Artificial General Intelligence) displacing all jobs are overstated, and that there are significant business opportunities in building AI applications tailored to specific problems. He encourages young professionals and students to learn AI and software development, as the industry will need skilled talent for decades, especially for creating, customizing, and deploying practical AI solutions in diverse markets.

According to @AndrewYNg, a new course titled 'Design, Develop, and Deploy Multi-Agent Systems with CrewAI' is now available, taught by @joaomdmoura, Co-founder and CEO of CrewAIInc (source: Andrew Ng on Twitter). This course provides hands-on training in building practical AI multi-agent systems that can automate complex workflows by mimicking human team collaboration. Participants will learn to use CrewAI’s open-source framework to create, coordinate, and deploy AI teams, focusing on agent design, task assignment, and system deployment. The curriculum covers building reliable AI agents with tools and guardrails, developing agent teams for planning and coordination, and deploying production-ready AI systems with monitoring and evaluation. This offering addresses growing industry demand for scalable AI workflow automation and equips professionals with in-demand skills for enterprise AI integration (source: deeplearning.ai course page).

According to Andrew Ng (@AndrewYNg), AI agents are rapidly improving their ability to analyze diverse business data sources, leading to substantial new value by connecting information that was previously siloed. Ng emphasizes that the increasing capabilities of AI, such as spotting correlations between email clicks and online purchases across platforms, make it more critical than ever for businesses to control their own data. He points out that many SaaS vendors intentionally create data silos by limiting data access or charging exorbitant fees for API access, which impedes the deployment of effective AI-driven workflows. Ng advises businesses to prioritize software solutions that ensure data ownership and accessibility, enabling flexible integration with both human and AI processes. He highlights the growing importance of organizing both structured and unstructured data, referencing tools like LandingAI’s Agentic Document Extraction for document processing and Obsidian for personal note management. This trend presents significant opportunities for AI-driven business optimization and underlines the competitive advantage of data interoperability (source: Andrew Ng, deeplearning.ai The Batch).

According to Andrew Ng, Roelof Botha's decision to step down as leader at Sequoia marks a pivotal moment for the AI investment landscape. Botha's leadership has shaped how top investors evaluate and support artificial intelligence startups, influencing capital flows and strategic priorities across the global AI industry (source: Andrew Ng, Twitter). This transition signifies the end of an era at Sequoia and introduces new opportunities for emerging AI-focused investors and entrepreneurs to shape the next wave of innovation. The AI industry is expected to see shifts in investment strategies, with increased attention on generative AI, enterprise automation, and vertical-specific AI solutions as investors seek new growth areas in 2025.

According to Andrew Ng (@AndrewYNg), Jupyter AI, developed by the Jupyter team and unveiled at JupyterCon, now offers integrated AI coding assistance directly within Jupyter notebooks. Unlike generic AI coding tools, Jupyter AI is tailored specifically for the notebook environment, enabling users to generate and debug code through a chat interface, attach relevant context like API documentation, and leverage features such as dragging notebook cells into the chat for more precise code generation (source: Andrew Ng on Twitter, Nov 3, 2025). The integration with the DeepLearningAI platform allows both experienced and new users to access AI-powered notebook development immediately, presenting significant business opportunities for platforms seeking to enhance developer productivity and streamline data science workflows. The open-source nature of Jupyter AI further expands its potential for enterprise adoption and custom AI workflow automation.

According to Andrew Ng's recent response to Chris Manning's tweet, top AI leaders are increasingly participating in public social media discussions that blend humor with artificial intelligence topics. This trend, highlighted on X (formerly Twitter), demonstrates how influential figures are using relatable content to foster broader engagement in the AI community (source: Andrew Ng, X, Nov 2, 2025). Such interactions help demystify AI, making it more accessible to a wider audience and promoting positive sentiment. For AI businesses, leveraging social media engagement and thought leadership can drive brand awareness, attract talent, and build trust with stakeholders, as shown by the active presence of Andrew Ng and Chris Manning.

According to Andrew Ng (@AndrewYNg), DeepLearning.AI Pro is now generally available, offering a comprehensive membership designed to accelerate AI application development for individuals and teams (source: https://twitter.com/AndrewYNg/status/1983946706564563171). The Pro membership provides full access to over 150 programs, including the new Agentic AI course and recently released Post-Training and PyTorch courses. This initiative responds to the rapid reduction in time-to-market for AI solutions, enabling solo developers to build projects that previously required large teams and months of work. Business opportunities arise from the program’s hands-on labs, practice questions, and certificates, which are aimed at upskilling professionals and fostering innovation in AI product development. Pro members receive early access to new tools, enhancing their ability to create and deploy advanced AI applications, which directly addresses the growing demand for practical AI expertise and accelerates career advancement in the industry (source: DeepLearning.AI official announcement).

According to Andrew Ng on Twitter, the strategic focus on GPUs was a pivotal decision for advancing artificial intelligence, enabling breakthroughs in deep learning and large-scale AI training (source: Andrew Ng, x.com/lefttailguy/status/1983601740462354937). The early recognition of GPUs’ parallel processing capabilities allowed for dramatic improvements in AI model performance and efficiency, especially in computer vision, natural language processing, and generative AI applications. This hardware focus has led to new business opportunities in AI infrastructure, cloud computing, and hardware optimization, shaping the competitive landscape for AI startups and enterprises (source: Andrew Ng, x.com/lefttailguy/status/1983601740462354937).

According to Andrew Ng (@AndrewYNg), DeepLearning.AI has launched the PyTorch for Deep Learning Professional Certificate taught by Laurence Moroney (@lmoroney). This three-course program covers core PyTorch fundamentals, advanced neural architectures, and deployment strategies, making it an essential resource for AI professionals and researchers. The curriculum starts with PyTorch basics like tensors and neural network training, advances to hyperparameter tuning and transfer learning with TorchVision and Hugging Face, and culminates with deployment techniques using ONNX and MLflow. The certificate addresses practical business needs by teaching skills such as model optimization, transformer implementation, and diffusion model development, which are critical for building scalable AI systems and custom solutions. This specialization meets high industry demand for PyTorch expertise, offering actionable knowledge for deploying efficient AI models in production environments (Source: @AndrewYNg, DeepLearning.AI, 2025-10-29).

According to @AndrewYNg, DeepLearning.AI has launched a new course titled 'Fine-tuning and Reinforcement Learning for LLMs: Intro to Post-training,' taught by @realSharonZhou, VP of AI at AMD (source: Andrew Ng, Twitter, Oct 28, 2025). The course addresses a critical industry need: post-training techniques that transform base LLMs from generic text predictors into reliable, instruction-following assistants. Through five modules, participants learn hands-on methods such as supervised fine-tuning, reward modeling, RLHF, PPO, GRPO, and efficient training with LoRA. Real-world use cases demonstrate how post-training elevates demo models to production-ready systems, improving reliability and user alignment. The curriculum also covers synthetic data generation, LLM pipeline management, and evaluation design. The availability of these advanced techniques, previously restricted to leading AI labs, now empowers startups and enterprises to create robust AI solutions, expanding practical and commercial opportunities in the generative AI space (source: Andrew Ng, Twitter, Oct 28, 2025).