Andrew Ng(@AndrewYNg)

New course: Spec-Driven Development with Coding Agents, built in partnership with @jetbrains, and ta...

Andrew Ng (@AndrewYNg) — Wed, 15 Apr 2026 16:16:38 GMT

New course: Spec-Driven Development with Coding Agents, built in partnership with @jetbrains, and taught by @paulweveritt.

Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way.

A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity.

Skills you'll gain:
- Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start
- Plan, implement, and validate features in iterative loops using a spec as your agent's guide
- Apply the same repeatable workflow to both new and legacy codebases
- Package your workflow into a portable agent skill that works across agents and IDEs

Join and write specs that keep your coding agent on track!
deeplearning.ai/short-courses/…

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I'm excited about voice as a UI layer for existing visual applications — where speech and screen upd...

Andrew Ng (@AndrewYNg) — Tue, 14 Apr 2026 16:22:22 GMT

I'm excited about voice as a UI layer for existing visual applications — where speech and screen update together. This goes well beyond voice-only use cases like call center automation.

The barrier has been a hard technical tradeoff: low-latency voice models lack reliability, while agentic pipelines (speech-to-text → LLM → text-to-speech) are intelligent but too slow for conversation. Ashwyn Sharma and team at Vocal Bridge (an AI Fund portfolio company) address this with a dual-agent architecture: a foreground agent for real-time conversation, a background agent for reasoning, guardrails, and tool calls.

I used Vocal Bridge to add voice to a math-quiz app I'd built for my daughter; this took less than an hour with Claude Code. She speaks her answers, the app responds verbally and updates the questions and animations on screen.

Only a tiny fraction of developers have ever built a voice app. If you'd like to try building one, check out Vocal Bridge for free: vocalbridgeai.com

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As AI agents accelerate coding, what is the future of software engineering? Some trends are clear, s...

Andrew Ng (@AndrewYNg) — Mon, 13 Apr 2026 17:24:23 GMT

As AI agents accelerate coding, what is the future of software engineering? Some trends are clear, such as the Product Management Bottleneck, referring to the idea that we are more constrained by deciding what to build rather than the actual building. But many implications, like AI’s impact on the job market, how software teams will be organized, and more, are still being sorted out.

The theme of our AI Developer Conference on April 28-29 in San Francisco is The Future of Software Engineering. I look forward to speaking about this topic there, hearing from other speakers on this theme, and chatting with attendees about it. We’re shaping the future, and I hope you will join me there!

It is currently trendy in some technology and policy circles to forecast massive job losses due to AI. Even if they have not yet materialized, these losses certainly must be just over the horizon! I have a contrarian view that the AI jobpocalypse — the notion that AI will lead to massive unemployment, perhaps even rioting in the streets — won’t be nearly as bad as dire forecasts by pundits, especially pundits who are trying to paint a picture of how powerful their AI technology is.

Among professions, AI is accelerating software engineering most, given the rise of coding agents. According to a new report by Citadel Research, software engineering job postings are rising rapidly. So if software engineering is a harbinger of the impact AI will have on other professions, this expansion of software engineering jobs is encouraging.

Yes, fresh college graduates are having a hard time finding jobs. And yes, there have been layoffs that CEOs have attributed to AI, even if a large fraction of this was “AI washing,” where businesses choose to attribute layoffs to AI, even though AI has not changed their internal operations much yet. And yes, there is a subset of job roles, such as call center operator, that are more heavily impacted. Many people are feeling significant job insecurity, and I feel for everyone struggling with employment, whether or not the cause is AI-related. And many other factors, such as over-hiring during the pandemic and high interest rates, have contributed to the slowdown in the labor market, and the notion that AI is leading to unemployment is oversimplified.

In software engineering, I see a lot of exciting work ahead to adapt our workflows. It is already clear that: (i) As AI makes coding easier, a lot more people will be doing it. (ii) Writing code by hand and even reading (generated) code is not that important, because we can ask an LLM about the code and operate at a higher level than the raw syntax (although how high we can or should go is rapidly changing). (iii) There will be a lot more custom applications, because now it’s economical to write software for smaller and smaller audiences. (iv) Deciding what to build, more than the actual building, is becoming a bottleneck. (v) The cost of paying down technical debt is decreasing (since AI can refactor for you).

At the same time, there are also a lot of open questions for our profession, such as:
- In the future, what will be the key skills of a senior software engineer? And for junior levels, what should be the new Computer Science curriculum?
- If everyone can build features, what skills, strategies, or resources create competitive advantage for individuals and for businesses?
- What are the new building blocks (libraries, SDKs, etc.) of software? How do we organize coding agents to create software?
- What should a software team look like? For example, how many engineers, product managers, designers, and so on. What tooling do we need to manage their workflow?
- How do AI agents change the workflow of machine learning engineers and data scientists? For example, how can we use agents to accelerate exploring data, identifying hypotheses, and testing them?

I’m excited to explore these and other questions about the future of software engineering at AI Dev. I expect this to be an exciting event. Please join us!

[Original text: The Batch newsletter.]

ai-dev.deeplearning.ai

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New course: Efficient Inference with SGLang: Text and Image Generation, built in partnership with LM...

Andrew Ng (@AndrewYNg) — Thu, 09 Apr 2026 17:11:58 GMT

New course: Efficient Inference with SGLang: Text and Image Generation, built in partnership with LMSys @lmsysorg and RadixArk @radixark, and taught by Richard Chen @richardczl, a Member of Technical Staff at RadixArk.

Running LLMs in production is expensive, and much of that cost comes from redundant computation. This short course teaches you to eliminate that waste using SGLang, an open-source inference framework that caches computation already done and reuses it across future requests.

When ten users share the same system prompt, SGLang processes it once, not ten times. The speedups compound quickly, especially when there's a lot of shared context across requests.

Skills you'll gain:
- Implement a KV cache from scratch to eliminate redundant computation within a single request
- Scale caching across users and requests with RadixAttention, so shared context is only processed once
- Accelerate image generation with diffusion models using SGLang's caching and multi-GPU parallelism

Join and learn to make LLM inference faster and more cost-efficient at scale!
deeplearning.ai/short-courses/…

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The anti-AI coalition continues to maneuver to find arguments to slow down AI progress. If someone h...

Andrew Ng (@AndrewYNg) — Tue, 31 Mar 2026 18:45:23 GMT

The anti-AI coalition continues to maneuver to find arguments to slow down AI progress. If someone has a sincere concern about a specific effect of AI, for instance that it may lead to human extinction, I respect their intellectual honesty, even if I deeply disagree with their position. However, I am concerned about organizations that are surveying the public to find whatever messages will turn people against AI, and how the public reacts as these messages are spread by lobbyists or by politicians seeking to alarm constituents, companies pursuing regulatory capture or seeking to promote the power of their technology, and individuals seeking to gain attention or to profit by being provocative.

A large study (link in original article below; h/t to the AI Panic blog) by a UK group tested different messages that are designed to raise alarm about AI. Their study found that saying AI will cause human extinction has largely failed. Doomsayers were pushing this argument a couple of years ago, and fortunately our community beat it back. But AI-enabled warfare and environmental concerns resonate better. We should be prepared for a flood of messages (which is already underway) arguing against AI on these grounds. Further, job loss and harm to children are messages that motivate people to act.

To be clear, I find AI-enabled warfare alarming; we need to continue serious efforts to monitor and mitigate the environmental impact of AI; any job losses are tragic and hurt individuals and families; and as a father, I hold dearly the importance of every child’s welfare. Each of these topics deserves serious attention and treatment with the greatest of care.

But when anti-AI propagandists take a one-sided view of complex issues to benefit their own organizations at the expense of the public at large — for instance, when big AI companies argue that AI is dangerous to block the free distribution of open source projects that compete with their offerings — then we all lose.

For example, public perception of data centers’ environmental impact is already far worse than the reality — data centers are incredibly efficient for the work they do, and hampering their buildout will hurt rather than help the environment. While job loss is a real problem, the “AI washing” of layoffs — in which businesses that had over-hired during the pandemic blame AI for recent layoffs, although AI hasn’t yet affected their operations — has led to overblown fears about the impact of AI on employment.

Unfortunately, this sort of propaganda easily leads to regulations that create worse outcomes for everyone. For example, oil companies worked for years to create fear of nuclear energy. The result is that overblown concerns about the safety of nuclear power plants has stifled nuclear power development, leading to millions of premature deaths from air pollution that was caused by other energy sources and a massive increase in CO2 emissions. Let’s make sure overblown concerns about AI do not lead to a similar fate for the many people that would benefit from faster AI development.

Last week, the White House proposed a national legislative framework for AI. A key component is a federal preemption framework to prevent a patchwork of state regulations that hamper AI development. I support this. 

After failing to gain traction at the federal level, a lot of anti-AI propaganda has shifted to the state level. If just one of the 50 states passes a law that limits AI in an unproductive way, it could lead to stifling AI development across all the states and potentially across the globe. The White House proposal rightfully respects each state’s rights to control its own zoning, how it enforces general laws to protect consumers, and how it uses AI. But if a state were to pass laws that limit AI development, federal rules would preempt the state law.

The White House proposal remains a proposal for now. However, if the U.S. Congress enacts it, it will clear the way for ongoing efforts to develop AI in beneficial ways.

Where do we go from here? Let’s support limiting applications — those that use AI, and those that don’t — that harm people. When the anti-AI coalition argues against AI, in addition to considering the merits of the argument, I consider whether their position is consistent and persuasive, or if they are just promoting whatever concerns they think will sway the public at a given moment. And, let’s also keep using a scientific approach to weighing AI’s benefits against likely harms, so we don’t end up with overblown concerns that limit the benefits that AI can bring everyone.

[Original text with links: deeplearning.ai/the-batch/issu… ]

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New course: Agent Memory: Building Memory-Aware Agents, built in partnership with @Oracle and taught...

Andrew Ng (@AndrewYNg) — Wed, 18 Mar 2026 17:00:34 GMT

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with @Oracle and taught by  @richmondalake and Nacho Martínez.

Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions.

You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time.

Skills you'll gain:
- Build persistent memory stores for different agent memory types
- Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory
- Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search

Join and learn to build agents that remember and improve over time!

deeplearning.ai/short-courses/…

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Should there be a Stack Overflow for AI coding agents to share learnings with each other? Last week...

Andrew Ng (@AndrewYNg) — Mon, 16 Mar 2026 16:14:12 GMT

Should there be a Stack Overflow for AI coding agents to share learnings with each other?

Last week I announced Context Hub (chub), an open CLI tool that gives coding agents up-to-date API documentation. Since then, our GitHub repo has gained over 6K stars, and we've scaled from under 100 to over 1000 API documents, thanks to community contributions and a new agentic document writer. Thank you to everyone supporting Context Hub!

OpenClaw and Moltbook showed that agents can use social media built for them to share information. In our new chub release, agents can share feedback on documentation — what worked, what didn't, what's missing. This feedback helps refine the docs for everyone, with safeguards for privacy and security.

We're still early in building this out. You can find details and configuration options in the GitHub repo. Install chub as follows, and prompt your coding agent to use it:

npm install -g @aisuite/chub

GitHub: github.com/andrewyng/cont…

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I'm excited to announce Context Hub, an open tool that gives your coding agent the up-to-date API do...

Andrew Ng (@AndrewYNg) — Mon, 09 Mar 2026 16:57:41 GMT

I'm excited to announce Context Hub, an open tool that gives your coding agent the up-to-date API documentation it needs. Install it and prompt your agent to use it to fetch curated docs via a simple CLI. (See image.)

Why this matters: Coding agents often use outdated APIs and hallucinate parameters. For example, when I ask Claude Code to call OpenAI's GPT-5.2, it uses the older chat completions API instead of the newer responses API, even though the newer one has been out for a year. Context Hub solves this.

Context Hub is also designed to get smarter over time. Agents can annotate docs with notes — if your agent discovers a workaround, it can save it and doesn't have to rediscover it next session. Longer term, we're building toward agents sharing what they learn with each other, so the whole community benefits.

Thanks Rohit Prsad and Xin Ye for working with me on this!

npm install -g @aisuite/chub

GitHub: github.com/andrewyng/cont…

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Apple just named its latest laptop Neo -- same name as my son! Should I buy one? If I run Amazon N...

Andrew Ng (@AndrewYNg) — Wed, 04 Mar 2026 18:55:46 GMT

Apple just named its latest laptop Neo -- same name as my son! Should I buy one? 

If I run Amazon Nova on an Apple Neo I hope to blow both of my kids' minds.

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New course: Build and Train an LLM with JAX, built in partnership with @Google and taught by @chrisa...

Andrew Ng (@AndrewYNg) — Wed, 04 Mar 2026 18:41:55 GMT

New course: Build and Train an LLM with JAX, built in partnership with @Google and taught by @chrisachard.

JAX is the open-source library behind Google's Gemini, Veo, and other advanced models. This short course teaches you to build and train a 20-million parameter language model from scratch using JAX and its ecosystem of tools.

You'll implement a complete MiniGPT-style architecture from scratch, train it, and chat with your finished model through a graphical interface.

Skills you'll gain:
- Learn JAX's core primitives: automatic differentiation, JIT compilation, and vectorized execution
- Build a MiniGPT-style LLM using Flax/NNX, implementing embedding and transformer blocks
- Load a pretrained MiniGPT model and run inference through a chat interface

Come learn this important software layer for building LLMs!

deeplearning.ai/short-courses/…

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Impressive inference speed from Inception Labs’ diffusion LLMs. Diffusion LLMs are a fascinating alt...

Andrew Ng (@AndrewYNg) — Wed, 25 Feb 2026 02:04:53 GMT

Impressive inference speed from Inception Labs’ diffusion LLMs. Diffusion LLMs are a fascinating alternative to conventional autoregressive LLMs. Well done @StefanoErmon and team!

Stefano Ermon@StefanoErmon

Mercury 2 is live 🚀🚀

The world’s first reasoning diffusion LLM, delivering 5x faster performance than leading speed-optimized LLMs.

Watching the team turn years of research into a real product never gets old, and I’m incredibly proud of what we’ve built.

We’re just getting started on what diffusion can do for language.

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Will AI create new job opportunities? My daughter Nova loves cats, and her favorite color is yellow....

Andrew Ng (@AndrewYNg) — Mon, 23 Feb 2026 16:38:08 GMT

Will AI create new job opportunities? My daughter Nova loves cats, and her favorite color is yellow. For her 7th birthday, we got a cat-themed cake in yellow by first using Gemini’s Nano Banana to design it, and then asking a baker to create it using delicious sponge cake and icing. My daughter was delighted by this unique creation, and the process created additional work for the baker (which I feel privileged to have been able to afford).

Many people are worried about AI taking peoples’ jobs. As a society we have a moral responsibility to take care of people whose livelihoods are harmed. At the same time, I see many opportunities for people to take on new jobs and grow their areas of responsibility.

We are still early on the path of AI generating a lot of new jobs. I don't know if baking AI-designed cakes will grow into a large business. (AI Fund is not pursuing this opportunity, because if we do, I will gain a lot of weight.) But throughout history, when people have invented tools that unleashed human creativity, large amounts of new and meaningful work have resulted. For instance, according to one study, over the past 150 years, falling employment in agriculture and manufacturing has been “more than offset by rapid growth in the caring, creative, technology, and business services sectors.”

AI is also growing the demand for many digital services, which can translate into more work for people creating, maintaining, selling, and expanding upon these services. For example, I used to carry out a limited number of web searches every day. Today, my agents carry out dramatically more web searches. For example, the Agentic Reviewer, which I started as a weekend project and Yixing Jiang then helped make much better, automatically reviews research articles. It uses a web search API to search for related work, and this generates a vastly larger number of web search queries a day than I have ever entered by hand.

The evolution of AI and software continues to accelerate, and the set of opportunities for things we can build still grows every day. I’ve stopped writing code by hand. More controversially, I’ve long stopped reading generated code. I realize I’m in the minority here, but I feel like I can get built most of what I want without having to look directly at coding syntax, and I operate at a higher level of abstraction using coding agents to manipulate code for me. Will conventional programming languages like Python and TypeScript go the way of assembly — where it gets generated and used, but without direct examination by a human developer — or will models compile directly from English prompts to byte code?

Either way, if every developer becomes 10x more productive, I don't think we’ll end up with 1/10th as many developers, because the demand for custom software has no practical ceiling. Instead, the number of people who develop software will grow massively. In fact, I’m seeing early signs of “X Engineer” jobs, such as Recruiting Engineer or Marketing Engineer, which are people who sit in a certain business function X to create software for that function.

One thing I’m convinced of based on my experience with Nova’s birthday cake: AI will allow us to have a batter life!

[Original text: deeplearning.ai/the-batch/issu… ]

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To all my AI friends: Every time I see you, you raise my temperature parameter. Happy Valentine’s ...

Andrew Ng (@AndrewYNg) — Sat, 14 Feb 2026 22:27:38 GMT

To all my AI friends: Every time I see you, you raise my temperature parameter. 

Happy Valentine’s Day! ❤️

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I recently spoke at the Sundance Film Festival on a panel about AI. Sundance is an annual gathering ...

Andrew Ng (@AndrewYNg) — Fri, 13 Feb 2026 16:22:59 GMT

I recently spoke at the Sundance Film Festival on a panel about AI. Sundance is an annual gathering of filmmakers and movie buffs that serves as the premier showcase for independent films in the United States. Knowing that many people in Hollywood are extremely uncomfortable about AI, I decided to immerse myself for a day in this community to learn about their anxieties and build bridges.

I’m grateful to Daniel Dae Kim @danieldaekim, an actor/producer/director I’ve come to respect deeply for his artistic and social work, for organizing the panel, which also included Daniel, Dan Kwan, Jonathan Wang, and Janet Yang. I found myself surrounded by award-winning filmmakers and definitely felt like the odd person out!

First, Hollywood has many reasons to be uncomfortable with AI. People from the entertainment industry come from a very different culture than many who work in tech, and this drives deep differences in what we focus on and what we value. A significant subset of Hollywood is concerned that:
- AI companies are taking their work to learn from it without consent and compensation. Whereas the software industry is used to open source and the open internet, Hollywood focuses much more on intellectual property, which underlies the core economic engines of the entertainment industry.
- Powerful unions like SAG-AFTRA (Screen Actors Guild-American Federation of Television and Radio Artists) are deeply concerned about protecting the jobs of their members. When AI technology (or any other force) threatens the livelihoods of their members — like voice actors — they will fight mightily against potential job losses.
- This wave of technological change feels forced on them more than previous waves, where they felt more free to adopt or reject the technology. For example, celebrities felt like it was up to them whether to use social media. In contrast, negative messaging from some AI leaders who present the technology as unstoppable, perhaps even a dangerous force that will wipe out many jobs, has not encouraged enthusiastic adoption.

Having said that, Hollywood is under no illusions that AI will change entertainment, and that if Hollywood does not adapt, perhaps some other place will become the new center for entertainment. The entertainment industry is no stranger to technology change. Radio, TV, computer graphics special effects, video streaming, and social media transformed the industry. But the path to navigating AI’s transformation is still unclear, and organizations like the new Creators Coalition on AI are trying to stake out positions. Unfortunately, Hollywood’s negative sentiment toward AI also means it will produce a lot more Terminator-like movies that portray AI as more dangerous than helpful, and this hurts beneficial AI adoption as well.

The interests of AI and Hollywood are not always aligned. (Every time I speak in a group like this as the “AI representative,” I can count on being asked very hard questions.) Most of us in tech would prefer a more open internet and more permissive use of creative works. But there is also much common ground, for example in wanting guardrails against deepfakes and a smooth transition for those whose jobs are displaced, perhaps via upskilling.

Storytelling is hard. I’m optimistic that AI tools like Veo, Sora, Runway, Kling, Ray, Hailuo, and many others can make video creation easier for millions of people. I hope Hollywood and AI developers will find more opportunities to collaborate, find more common ground, and also steer our projects toward outcomes that are win-win for as many parties as possible.

[Original text: deeplearning.ai/the-batch/issu… ]

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New course: A2A: The Agent2Agent Protocol, built with @googlecloudtech and @IBMResearch, and taught...

Andrew Ng (@AndrewYNg) — Thu, 12 Feb 2026 16:30:32 GMT

New course: A2A: The Agent2Agent Protocol, built with  @googlecloudtech and @IBMResearch, and taught by Holt Skinner, @ivnardini, and Sandi Besen.

Connecting agents built with different frameworks usually requires extensive custom integration. This short course teaches you A2A, the open protocol standardizing how agents discover each other and communicate. Since IBM’s ACP (Agent Communication Protocol) joined forces with A2A, A2A has emerged as the industry standard.

In this course, you'll build a healthcare multi-agent system where agents built with different frameworks, such as Google ADK (Agent Development Kit) and LangGraph, collaborate through A2A. You'll wrap each agent as an A2A server, build A2A clients to connect to them, and orchestrate them into sequential and hierarchical workflows.

Skills you'll gain:
- Expose agents from different frameworks as A2A servers to make them discoverable and interoperable
- Chain A2A agents sequentially using ADK, where one agent's output feeds into the next
- Connect A2A agents to external data sources using MCP (Model Context Protocol)
- Deploy A2A agents using Agent Stack, IBM's open-source infrastructure

Join and learn the protocol standardizing agent collaboration! 
deeplearning.ai/short-courses/…

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Job seekers in the U.S. and many other nations face a tough environment. At the same time, fears of ...

Andrew Ng (@AndrewYNg) — Tue, 10 Feb 2026 16:28:04 GMT

Job seekers in the U.S. and many other nations face a tough environment. At the same time, fears of AI-caused job loss have — so far — been overblown. However, the demand for AI skills is starting to cause shifts in the job market. I’d like to share what I’m seeing on the ground. 

First, many tech companies have laid off workers over the past year. While some CEOs cited AI as the reason — that AI is doing the work, so people are no longer needed — the reality is AI just doesn’t work that well yet. Many of the layoffs have been corrections for overhiring during the pandemic or general cost-cutting and reorganization that occasionally happened even before modern AI. Outside of a handful of roles, few layoffs have resulted from jobs being automated by AI.
Granted, this may grow in the future. People who are currently in some professions that are highly exposed to AI automation, such as call-center operators, translators, and voice actors, are likely to struggle to find jobs and/or see declining salaries. But widespread job losses have been overhyped.

Instead, a common refrain applies: AI won’t replace workers, but workers who use AI will replace workers who don’t. For instance, because AI coding tools make developers much more efficient, developers who know how to use them are increasingly in-demand. (If you want to be one of these people, please take our short courses on Claude Code, Gemini CLI, and Agentic Skills!)

So AI is leading to job losses, but in a subtle way. Some businesses are letting go of employees who are not adapting to AI and replacing them with people who are. This trend is already obvious in software development. Further, in many startups’ hiring patterns, I am seeing early signs of this type of personnel replacement in roles that traditionally are considered non-technical. Marketers, recruiters, and analysts who know how to code with AI are more productive than those who don’t, so some businesses are slowly parting ways with employees that aren’t able to adapt. I expect this will accelerate.

At the same time, when companies build new teams that are AI native, sometimes the new teams are smaller than the ones they replace. AI makes individuals more effective, and this makes it possible to shrink team sizes. For example, as AI has made building software easier, the bottleneck is shifting to deciding what to build — this is the Product Management (PM) bottleneck. A project that used to be assigned to 8 engineers and 1 PM might now be assigned to 2 engineers and 1 PM, or perhaps even to a single person with a mix of engineering and product skills.

The good news for employees is that most businesses have a lot of work to do and not enough people to do it. People with the right AI skills are often given opportunities to step up and do more, and maybe tackle the long backlog of ideas that couldn’t be executed before AI made the work go more quickly. I’m seeing many employees in many businesses step up to build new things that help their business. Opportunities abound!

I know these changes are stressful. My heart goes out to every family that has been affected by a layoff, to every job seeker struggling to find the role they want, and to the far larger number of people who are worried about their future job prospects. Fortunately, there’s still time to learn and position yourself well for where the job market is going. When it comes to AI, the vast majority of people, technical or nontechnical, are at the starting line, or they were recently. So this remains a great time to keep learning and keep building, and the opportunities for those who do are numerous!

[Original text; deeplearning.ai/the-batch/issu… ]

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U.S. policies are driving allies away from using American AI technology. This is leading to interest...

Andrew Ng (@AndrewYNg) — Fri, 30 Jan 2026 17:07:16 GMT

U.S. policies are driving allies away from using American AI technology. This is leading to interest in sovereign AI — a nation’s ability to access AI technology without relying on foreign powers. This weakens U.S. influence, but might lead to increased competition and support for open source.

The U.S. invented the transistor, the internet, and the transformer architecture powering modern AI. It has long been a technology powerhouse. I love America, and am working hard towards its success. But its actions over many years, taken by multiple administrations, have made other nations worry about over reliance on it.

In 2022, following Russia’s invasion of Ukraine, U.S. sanctions on banks linked to Russian oligarchs resulted in ordinary consumers’ credit cards being shut off. Shortly before leaving office, Biden implemented “AI diffusion” export controls that limited the ability of many nations — including U.S. allies — to buy AI chips.

Under Trump, the “America first” approach has significantly accelerated pushing other nations away. There have been broad and chaotic tariffs imposed on both allies and adversaries. Threats to take over Greenland. An unfriendly attitude toward immigration — an overreaction to the chaos at the southern border during Biden’s administration — including atrocious tactics by ICE (Immigration and Customs Enforcement) that resulted in agents shooting dead Renée Good, Alex Pretti, and others. Global media has widely disseminated videos of ICE terrorizing American cities, and I have highly skilled, law-abiding friends overseas who now hesitate to travel to the U.S., fearing arbitrary detention.

Given AI’s strategic importance, nations want to ensure no foreign power can cut off their access. Hence, sovereign AI.

Sovereign AI is still a vague, rather than precisely defined, concept. Complete independence is impractical: There are no good substitutes to AI chips designed in the U.S. and manufactured in Taiwan, and a lot of energy equipment and computer hardware are manufactured in China. But there is a clear desire to have alternatives to the frontier models from leading U.S. companies OpenAI, Google, and Anthropic. Partly because of this, open-weight Chinese models like DeepSeek, Qwen, Kimi, and GLM are gaining rapid adoption, especially outside the U.S.

When it comes to sovereign AI, fortunately one does not have to build everything. By joining the global open-source community, a nation can secure its own access to AI. The goal isn’t to control everything; rather, it is to make sure no one else can control what you do with it. Indeed, nations use open source software like Linux, Python, and PyTorch. Even though no nation can control this software, no one else can stop anyone from using it as they see fit.

This is spurring nations to invest more in open source and open weight models. The UAE (under the leadership of my former grad-school officemate Eric Xing!) just launched K2 Think, an open-source reasoning model. India, France, South Korea, Switzerland, Saudi Arabia, and others are developing domestic foundation models, and many more countries are working to ensure access to compute infrastructure under their control or perhaps under trusted allies’ control.

Global fragmentation and erosion of trust among democracies is bad. Nonetheless, a silver lining would be if this results in more competition. U.S. search engines Google and Bing came to dominate web search globally, but Baidu (in China) and Yandex (in Russia) did well locally. If nations support domestic champions — a tall order given the giants’ advantages — perhaps we’ll end up with a larger number of thriving companies, which would slow down consolidation and encourage competition. Further, participating in open source is the most inexpensive way for countries to stay at the cutting edge.

Last week, at the World Economic Forum in Davos, many business and government leaders spoke about their growing reluctance to rely on U.S. technology providers and desire for alternatives. Ironically, “America first” policies might end up strengthening the world’s access to AI.

[Original text: deeplearning.ai/the-batch/issu… ]

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Important new course: Agent Skills with Anthropic, built with @AnthropicAI and taught by @eschoppik!...

Andrew Ng (@AndrewYNg) — Wed, 28 Jan 2026 17:31:47 GMT

Important new course: Agent Skills with Anthropic, built with @AnthropicAI and taught by @eschoppik!

Skills are constructed as folders of instructions that equip agents with on-demand knowledge and workflows. This short course teaches you how to create them following best practices. Because skills follow an open standard format, you can build them once and deploy across any skills-compatible agent, like Claude Code.

What you'll learn:
- Create custom skills for code generation and review, data analysis, and research
- Build complex workflows using Anthropic's pre-built skills (Excel, PowerPoint, skill creation) and custom skills
- Combine skills with MCP and subagents to create agentic systems with specialized knowledge
- Deploy the same skills across Claude.ai, Claude Code, the Claude API, and the Claude Agent SDK

Join and learn to equip agents with the specialized knowledge they need for reliable, repeatable workflows.
deeplearning.ai/short-courses/…

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How can businesses go beyond using AI for incremental efficiency gains to create transformative impa...

Andrew Ng (@AndrewYNg) — Fri, 23 Jan 2026 20:37:12 GMT

How can businesses go beyond using AI for incremental efficiency gains to create transformative impact? I write from the World Economic Forum (WEF) in Davos, Switzerland, where I’ve been speaking with many CEOs about how to use AI for growth. A recurring theme is that running many experimental, bottom-up AI projects — letting a thousand flowers bloom — has failed to lead to significant payoffs. Instead, bigger gains require workflow redesign: taking a broader, perhaps top-down view of the multiple steps in a process and changing how they work together from end to end.

Consider a bank issuing loans. The workflow consists of several discrete stages: 

Marketing -> Application -> Preliminary Approval -> Final Review -> Execution

Suppose each step used to be manual. Preliminary Approval used to require an hour-long human review, but a new agentic system can do this automatically in 10 minutes. Swapping human review for AI review — but keeping everything else the same — gives a minor efficiency gain but isn’t transformative.

Here’s what would be transformative: Instead of applicants waiting a week for a human to review their application, they can get a decision in 10 minutes. When that happens, the loan becomes a more compelling product, and that better customer experience allows lenders to attract more applications and ultimately issue more loans.

However, making this change requires taking a broader business or product perspective, not just a technology perspective. Further, it changes the workflow of loan processing. Switching to offering a “10-minute loan” product would require changing how it is marketed. Applications would need to be digitized and routed more efficiently, and final review and execution would need to be redesigned to handle a larger volume.

Even though AI is applied only to one step, Preliminary Approval, we end up implementing not just a point solution but a broader workflow redesign that transforms the product offering.

At AI Aspire (an advisory firm I co-lead), here’s what we see: Bottom-up innovation matters because the people closest to problems often see solutions first. But scaling such ideas to create transformative impact often requires seeing how AI can transform entire workflows end to end, not just individual steps, and this is where top-down strategic direction and innovation can help.

This year's WEF meeting, as in previous years, has been an energizing event. Among technologists, frequent topics of discussion include Agentic AI (when I coined this term, I was not expecting to see it plastered on billboards and buildings!), Sovereign AI (how nations can control their own access to AI), Talent (the challenging job market for recent graduates, and how to upskill nations), and data-center infrastructure (how to address bottlenecks in energy, talent, GPU chips, and memory). I will address some of these topics in future posts.

Against the backdrop of geopolitical uncertainty, I hope all of us in AI will keep building bridges that connect nations, sharing through open source, and building to benefit all nations and all people.

[Original text: deeplearning.ai/the-batch/issu… ]

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New course: Gemini CLI: Code & Create with an Open-Source Agent, built with @googlecloudtech/@gemini...

Andrew Ng (@AndrewYNg) — Thu, 22 Jan 2026 17:36:55 GMT

New course: Gemini CLI: Code & Create with an Open-Source Agent, built with @googlecloudtech/@geminicli and taught by @JackWoth98.

Agentic coding assistants like Gemini CLI are transforming how developers work. This short course teaches you to use Google's open-source agent to coordinate local tools and cloud services for coding and non-coding workflows.

Gemini CLI works from your terminal, so it works with your local files and development tools. You can also connect it to services through MCP. Then provide high-level instructions, and it autonomously plans and executes complex workflows.

Skills you'll gain:
- Build website features and automate code reviews with GitHub ActionsCreate data dashboards that combine local files with cloud data sources
- Use MCP servers and extensions to orchestrate workflows across GitHub, Canva, and Google Workspace
- Generate social media content from multimedia files like conference recordings

I particularly appreciate that Gemini CLI is open-source. You can see exactly how it works, read the prompts it uses, and understand its architecture. The community has contributed thousands of pull requests. Since Gemini 3’s release I've found Gemini CLI highly capable - this is a tool worth having in your toolbox!

Whether you're prototyping applications, automating workflows, or working with multimedia content, join to learn to delegate complex tasks and build faster: deeplearning.ai/short-courses/…

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https://t.co/Q3Ob7lRXRj

Andrew Ng (@AndrewYNg) — Fri, 16 Jan 2026 18:37:47 GMT

x.com/i/article/2012…

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New course: Document AI: From OCR to Agentic Doc Extraction, built with @LandingAI, where I'm execut...

Andrew Ng (@AndrewYNg) — Wed, 14 Jan 2026 17:42:40 GMT

New course: Document AI: From OCR to Agentic Doc Extraction, built with @LandingAI, where I'm executive chairman, and taught by David Park and Andrea Kropp.

Much of the world's data is locked in PDFs, JPEGs, and other documents. This short course shows you how to build agentic workflows that process documents accurately: breaking them into parts, examining each piece carefully, and extracting information through multiple iterations.

Traditional Optical Character Recognition (OCR) captures text but loses context from table headers, chart captions, or reading order of columns. After exploring OCR's limitations, you’ll use LandingAI's Agentic Document Extraction (ADE) framework to process documents. ADE treats pages as visually -- as images -- to parse information and extract fields.

Skills you'll gain:
- Build agents to convert unstructured files into structured Markdown/HTML and JSON
- Use ADE to parse complex data like forms, handwriting, or equations
- Map extracted information to named fields using a specified schema, with bounding boxes for grounding and validation
- Deploy RAG applications with event-driven document processing

Come learn about the best tools for processing documents like financial invoices, medical records, or academic papers intelligently: 
deeplearning.ai/short-courses/…

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If you’ve never written code before, this is for you. I’ve just launched a course that shows you, in...

Andrew Ng (@AndrewYNg) — Wed, 07 Jan 2026 17:39:22 GMT

If you’ve never written code before, this is for you. I’ve just launched a course that shows you, in less than 30 minutes, how to describe an idea for an app and build it with AI.

In this course, you'll build a working web application - a funny interactive birthday message generator that runs in your browser and can be shared with friends. You'll customize it by telling AI how you want it changed, and tweak it until it works the way you want. By the end, you'll have a repeatable process you can apply to build a wide variety of applications.

If you want to try vibe coding, this will be the best place to start! Further, you'll be able to use these techniques with whatever tool you're most comfortable with (like ChatGPT, Gemini, Claude, or others) -- we're vendor neutral. 

Skills you'll gain:
- How to build web apps with AI - zero coding skills needed
- How to fix and improve your creations by chatting with AI
- A simple process you can use to build other things you can dream up

Building with AI is one of the most fun things in the world. Please join me and take your first step! I think you will be surprised at what you can build. And if you're an experienced engineer, please share this with someone in your life who's been curious about building with AI.

Come build with me! deeplearning.ai/courses/build-…

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Happy 2026! Will this be the year we finally achieve AGI? I’d like to propose a new version of the T...

Andrew Ng (@AndrewYNg) — Tue, 06 Jan 2026 16:37:44 GMT

Happy 2026! Will this be the year we finally achieve AGI? I’d like to propose a new version of the Turing Test, which I’ll call the Turing-AGI Test, to see if we’ve achieved this. I’ll explain in a moment why having a new test is important.

The public thinks achieving AGI means computers will be as intelligent as people and be able to do most or all knowledge work. I’d like to propose a new test. The test subject — either a computer or a skilled professional human — is given access to a computer that has internet access and software such as a web browser and Zoom. The judge will design a multi-day experience for the test subject, mediated through the computer, to carry out work tasks. For example, an experience might consist of a period of training (say, as a call center operator), followed by being asked to carry out the task (taking calls), with ongoing feedback. This mirrors what a remote worker with a fully working computer (but no webcam) might be expected to do.

A computer passes the Turing-AGI Test if it can carry out the work task as well as a skilled human.

Most members of the public likely believe a real AGI system will pass this test. Surely, if computers are as intelligent as humans, they should be able to perform work tasks as well as a human one might hire. Thus, the Turing-AGI Test aligns with the popular notion of what AGI means.

Here’s why we need a new test: “AGI” has turned into a term of hype rather than a term with a precise meaning. A reasonable definition of AGI is AI that can do any intellectual task that a human can. When businesses hype up that they might achieve AGI within a few quarters, they usually try to justify these statements by setting a much lower bar. This mismatch in definitions is harmful because it makes people think AI is becoming more powerful than it actually is. I’m seeing this mislead everyone from high-school students (who avoid certain fields of study because they think it’s pointless with AGI’s imminent arrival) to CEOs (who are deciding what projects to invest in, sometimes assuming AI will be more capable in 1-2 years than any likely reality).

The original Turing Test, which required a computer to fool a human judge, via text chat, into being unable to distinguish it from a human, has been insufficient to indicate human-level intelligence. The Loebner Prize competition actually ran the Turing Test and found that being able to simulate human typing errors — perhaps even more than actually demonstrating intelligence — was needed to fool judges. A main goal of AI development today is to build systems that can do economically useful work, not fool judges. Thus a modified test that measures ability to do work would be more useful than a test that measures the ability to fool humans.

For almost all AI benchmarks today (such as GPQA, AIME, SWE-bench, etc.), a test set is determined in advance. This means AI teams end up at least indirectly tuning their models to the published test sets. Further, any fixed test set measures only one narrow sliver of intelligence. In contrast, in the Turing Test, judges are free to ask any question to probe the model as they please. This lets a judge test how “general” the knowledge of the computer or human really is. Similarly, in the Turing-AGI Test, the judge can design any experience — which is not revealed in advance to the AI (or human subject) being tested. This is a better way to measure generality of AI than a predetermined test set.

AI is on an amazing trajectory of progress. In previous decades, overhyped expectations led to AI winters, when disappointment about AI capabilities caused reductions in interest and funding, which picked up again when the field made more progress. One of the few things that could get in the way of AI’s tremendous momentum is unrealistic hype that creates an investment bubble, risking disappointment and a collapse of interest. To avoid this, we need to recalibrate society’s expectations on AI. A test will help.

If we run a Turing-AGI Test competition and every AI system falls short, that will be a good thing! By defusing hype around AGI and reducing the chance of a bubble, we will create a more reliable path to continued investment in AI. This will let us keep on driving forward real technological progress and building valuable applications — even ones that fall well short of AGI. And if this test sets a clear target that teams can aim toward to claim the mantle of achieving AGI, that would be wonderful, too. And we can be confident that if a company passes this test, they will have created more than just a marketing release — it will be something incredibly valuable.

[Original text: deeplearning.ai/the-batch/issu… ]

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Another year of rapid AI advances has created more opportunities than ever for anyone — including th...

Andrew Ng (@AndrewYNg) — Mon, 29 Dec 2025 18:09:54 GMT

Another year of rapid AI advances has created more opportunities than ever for anyone — including those just entering the field — to build software. In fact, many companies just can’t find enough skilled AI talent. Every winter holiday, I spend some time learning and building, and I hope you will too. This helps me sharpen old skills and learn new ones, and it can help you grow your career in tech.

To be skilled at building AI systems, I recommend that you:
- Take AI courses
- Practice building AI systems
- (Optionally) read research papers

Let me share why each of these is important.

I’ve heard some developers advise others to just plunge into building things without worrying about learning. This is bad advice! Unless you’re already surrounded by a community of experienced AI developers, plunging into building without understanding the foundations of AI means you’ll risk reinventing the wheel or — more likely — reinventing the wheel badly!

For example, during interviews with job candidates, I have spoken with developers who reinvented standard RAG document chunking strategies, duplicated existing evaluation techniques for Agentic AI, or ended up with messy LLM context management code. If they had taken a couple of relevant courses, they would have better understood the building blocks that already exist. They could still rebuild these blocks from scratch if they wished, or perhaps even invent something superior to existing solutions, but they would have avoided weeks of unnecessary work. So structured learning is important. Moreover, I find taking courses really fun. Rather than watching Netflix, I prefer watching a course by a knowledgeable AI instructor any day!

At the same time, taking courses alone isn’t enough. There are many lessons that you’ll gain only from hands-on practice. Learning the theory behind how an airplane works is very important to becoming a pilot, but no one has ever learned to be a pilot just by taking courses. At some point, jumping into the pilot's seat is critical! The good news is that by learning to use highly agentic coders, the process of building is the easiest it has ever been. And learning about AI building blocks might inspire you with new ideas for things to build. If I’m not feeling inspired about what projects to work on, I will usually either take courses or read research papers, and after doing this for a while, I always end up with many new ideas. Moreover, I find building really fun, and I hope you will too.

Finally, not everyone has to do this, but I find that many of the strongest candidates on the job market today at least occasionally read research papers. While I find research papers much harder to digest than courses, they contain a lot of knowledge that has not yet been translated to easier-to-understand formats. I put this much lower priority than either taking courses or practicing building, but if you have an opportunity to strengthen your ability to read papers, I urge you to do so too. I find taking courses and building to be fun, and reading papers can be more of a grind, but the flashes of insight I get from reading papers are delightful.

Have a wonderful winter holiday and a Happy New Year. In addition to learning and building, I hope you'll spend time with loved ones — that, too, is important!

[Original text: deeplearning.ai/the-batch/issu… ]

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Lots of buzz on Claude Code here today. This short course, created with Anthropic, is the best way t...

Andrew Ng (@AndrewYNg) — Mon, 29 Dec 2025 00:56:41 GMT

Lots of buzz on Claude Code here today. This short course, created with Anthropic, is the best way to learn to use it well. Please enjoy it!

Andrew Ng@AndrewYNg

I'm thrilled to announce the definitive course on Claude Code, created with @AnthropicAI and taught by Elie Schoppik @eschoppik. If you want to use highly agentic coding - where AI works autonomously for many minutes or longer, not just completing code snippets - this is it.

Claude Code has been a game-changer for many developers (including me!), but there's real depth to using it well. This comprehensive course covers everything from fundamentals to advanced patterns.

After this short course, you'll be able to:
- Orchestrate multiple Claude subagents to work on different parts of your codebase simultaneously
- Tag Claude in GitHub issues and have it autonomously create, review, and merge pull requests
- Transform messy Jupyter notebooks into clean, production-ready dashboards
- Use MCP tools like Playwright so Claude can see what's wrong with your UI and fix it autonomously

Whether you're new to Claude Code or already using it, you'll discover powerful capabilities that can fundamentally change how you build software.

I'm very excited about what agentic coding lets everyone now do. Please take this course!

deeplearning.ai/short-courses/…

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As amazing as LLMs are, improving their knowledge today involves a more piecemeal process than is wi...

Andrew Ng (@AndrewYNg) — Fri, 19 Dec 2025 17:06:05 GMT

As amazing as LLMs are, improving their knowledge today involves a more piecemeal process than is widely appreciated. I’ve written before about how AI is amazing... but not that amazing. Well, it is also true that LLMs are general... but not that general. We shouldn’t buy into the inaccurate hype that LLMs are a path to AGI in just a few years, but we also shouldn’t buy into the opposite, also inaccurate hype that they are only demoware. Instead, I find it helpful to have a more precise understanding of the current path to building more intelligent  models.

First, LLMs are indeed a more general form of intelligence than earlier generations of technology. This is why a single LLM can be applied to a wide range of tasks. The first wave of LLM technology accomplished this by training on the public web, which contains a lot of information about a wide range of topics. This made their knowledge far more general than earlier algorithms that were trained to carry out a single task such as predicting housing prices or playing a single game like chess or Go. However, they’re far less general than human abilities. For instance, after pretraining on the entire content of the public web, an LLM still struggles to adapt to write in certain styles that many editors would be able to, or use simple websites reliably.

After leveraging pretty much all the open information on the web, progress got harder. Today, if a frontier lab wants an LLM to do well on a specific task — such as code using a specific programming language, or say sensible things about a specific niche in, say, healthcare or finance — researchers might go through a laborious process of finding or generating lots of data for that domain and then preparing that data (cleaning low-quality text, deduplicating, paraphrasing, etc.) to create data to give an LLM that knowledge.

Or, to get a model to perform certain tasks, such as use a web browser, developers might go through an even more laborious process of creating many RL gyms (simulated environments) to let an algorithm repeatedly practice a narrow set of tasks.

A typical human, despite having seen vastly less text or practiced far less in computer-use training environments than today's frontier models, nonetheless can generalize to a far wider range of tasks than a frontier model. Humans might do this by taking advantage of continuous learning from feedback, or by having superior representations of non-text input (the way LLMs tokenize images still seems like a hack to me), and many other mechanisms that we do not yet understand.

Advancing frontier models today requires making a lot of manual decisions and taking a data-centric AI approach to engineering the data we use to train our models. Future breakthroughs might allow us to advance LLMs in a less piecemeal fashion than I describe here. But even if they don’t, the ongoing piecemeal improvements, coupled with the limited degree to which these models do generalize and exhibit “emergent behaviors,” will continue to drive rapid progress.

Either way, we should plan for many more years of hard work. A long, hard — and fun! — slog remains ahead to build more intelligent models.

[Original text: deeplearning.ai/the-batch/issu… ]

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OpenReview is one of the most important pillars supporting AI research and knowledge sharing, throug...

Andrew Ng (@AndrewYNg) — Fri, 19 Dec 2025 02:31:44 GMT

OpenReview is one of the most important pillars supporting AI research and knowledge sharing, through open peer review and publishing. But as a non-profit, it needs our community’s support. Please consider making a donation to this great institution! 

openreview.net/donate

Open Review@openreviewnet

A Message from AI Research Leaders: Join Us in Supporting OpenReview 
openreview.net/donate

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New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by @Pr_Brian from @NVIDIA. ...

Andrew Ng (@AndrewYNg) — Thu, 18 Dec 2025 18:56:26 GMT

New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by @Pr_Brian from @NVIDIA.

Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems.

NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration.

Skills you'll gain:
- Build configuration-driven agent workflows with REST APIs and minimal code
- Add observability with tracing to visualize agent reasoning and debug performance bottlenecks
- Create systematic evaluations using gold-standard datasets to measure and improve agent reliability
- Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces
- Orchestrate agents from different frameworks to collaborate on complex tasks

Join and learn how to turn agent demos into reliable systems!
deeplearning.ai/short-courses/…

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Sharing a fun recipe for building a highly autonomous, moderately capable, and very UNreliable agent...

Andrew Ng (@AndrewYNg) — Thu, 11 Dec 2025 17:47:23 GMT

Sharing a fun recipe for building a highly autonomous, moderately capable, and very UNreliable agent using the open source aisuite package that Rohit Prasad and I have been working on.

With a few lines of code, you can give a frontier LLM a tool (like disk access or web search), prompt it with a high-level task (such as creating a snake game and saving as an HTML file, or carrying out deep research), and let the LLM loose and see what it does. Example in image. 

Caveat: This is not how practical agents are built today, since most need much more scaffolding (see my Agentic AI course to learn more), but is still interesting to experiment with.

Longer write-up here: deeplearning.ai/the-batch/issu…

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Sharing a fun recipe for building a highly autonomous, moderately capable, and very UNreliable agent...

Andrew Ng (@AndrewYNg) — Thu, 11 Dec 2025 17:27:26 GMT

Sharing a fun recipe for building a highly autonomous, moderately capable, and very UNreliable agent using the open source aisuite package that Rohit Prasad and I have been working on.

With a few lines of code, you can give a frontier LLM a tool (like disk access or web search), prompt it with a high-level task (such as creating a snake game and saving as an HTML file, or carrying out deep research), and let the LLM loose and see what it does. Example in image. 

Caveat: This is not how practical agents are built today, since most need much more scaffolding (see my Agentic AI course to learn more), but is still interesting to experiment with.

Longer write-up here: deeplearning.ai/the-batch/issu…

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Separate reports by the publicity firm Edelman and Pew Research show that Americans, and more broadl...

Andrew Ng (@AndrewYNg) — Thu, 04 Dec 2025 17:23:08 GMT

Separate reports by the publicity firm Edelman and Pew Research show that Americans, and more broadly large parts of Europe and the western world, do not trust AI and are not excited about it. (Links in original text, below.) Despite the AI community’s optimism about the tremendous benefits AI will bring, we should take this seriously and not dismiss it. The public’s concerns about AI can be a significant drag on progress, and we can do a lot to address them.

According to Edelman’s survey, in the U.S., 49% of people reject the growing use of AI, and 17% embrace it. In China, 10% reject it and 54% embrace it. Pew’s data also shows many other nations much more enthusiastic than the U.S. about AI adoption.

Positive sentiment toward AI is a huge national advantage. On the other hand, widespread distrust of AI means:
- Individuals will be slow to adopt it. For example, Edelman’s data shows that, in the U.S., those who rarely use AI cite Trust (70%) more than lack of Motivation and Access (55%) or Intimidation by the technology (12%) as an issue.
- Valuable projects that need societal support will be stymied. For example, local protests in Indiana brought down Google’s plan to build a data center there. Hampering construction of data centers will hurt AI’s growth. Communities do have concerns about data centers beyond the general dislike of AI; I will address this in a later letter.
- Populist anger against AI raises the risk that laws will be passed that hamper AI development.

To be clear, all of us working in AI should look carefully at both the benefits and harmful effects of AI (such as deepfakes polluting social media and biased or inaccurate AI outputs misleading users), speak truthfully about both benefits and harms, and work to ameliorate problems even as we work to grow the benefits. But hype about AI’s danger has done real damage to trust in our field. Much of this hype has come from leading AI companies that aim to make their technology seem extraordinarily powerful by, say, comparing it to nuclear weapons. Unfortunately, a significant fraction of the public has taken this seriously and thinks AI could bring about the end of the world. The AI community has to stop self-inflicting these wounds and work to win back society’s trust.

Where do we go from here?

First, to win people’s trust, we have a lot of work ahead to make sure AI broadly benefits everyone. “Higher productivity” is often viewed by general audiences as a codeword for “my boss will make more money,” or worse, layoffs. As amazing as ChatGPT is, we still have a lot of work to do to build applications that make an even bigger positive impact on people’s lives. I believe providing training to people will be a key piece of the puzzle. DeepLearning.AI will continue to lead the charge on AI training, but we will need more than this.

Second, we have to be genuinely worthy of trust. This means every one of us has to avoid hyping things up or fear mongering, despite the occasional temptation to do so for publicity or to lobby governments to pass laws that stymie competing products (such as open source).

I hope our community can also call out journalism that spreads hype. For example, Nirit Weiss-Blatt wrote a remarkable article about how 60 Minutes’ coverage of an Anthropic study in which Claude, threatened with being shut down, resorted to “blackmail,” was highly misleading. The study carried out a red-teaming exercise in which skilled researchers, after a lot of determined work, finally pushed an AI system into a corner so it demonstrated “blackmailing” behavior. Unfortunately, news reports distorted this and led many to think the “blackmail” behavior occurred naturally rather than only because skilled researchers engineered it to happen. The reports left many with a wildly exaggerated picture of how often AI actually “schemes.” Red-teaming exercises are important to test vulnerabilities of systems, but this particular piece of hype, which was widely circulated, will hurt AI for a long time.

Living in Silicon Valley, I realize I live in a bubble of AI enthusiasts, which is great for exchanging ideas and encouraging each other to build! At the same time, I recognize that AI does have problems, and the AI community needs to address them. I frequently speak with people from many different walks of life. I’ve spoken with artists concerned about AI devaluing their work, college seniors worried about the tough job market and whether AI is exacerbating their challenges, and parents worried about their kids being addicted to, and receiving harmful advice from, chatbots.

I don’t know how to solve all of these problems, but I will work hard to solve as many as I can. And I hope you will too. It will only be through all of us doing this that we can win back society’s trust.

[Original text, with links: deeplearning.ai/the-batch/issu… ]

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New course: Building Coding Agents with Tool Execution, taught by @tereza_tizkova and @FraZuppichini...

Andrew Ng (@AndrewYNg) — Wed, 03 Dec 2025 16:09:22 GMT

New course: Building Coding Agents with Tool Execution, taught by @tereza_tizkova and @FraZuppichini from @e2b.

Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being restricted to a fixed set of tools.

You'll learn to run agent-generated code safely in sandboxed cloud environments that protect your systems from harmful operations.

Skills you'll gain:
- Build agents that write and execute code, manage files, and handle errors autonomously through feedback loops
- Run agent code safely in E2B cloud sandboxes and understand tradeoffs between local, containerized, and cloud execution
- Create a data analyst agent that explores visualizes data with Pandas
- Create a full-stack agent that builds complete Next.js web applications

Join and build agents that code their way through complex tasks: deeplearning.ai/short-courses/…

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NeurIPS received 21,575 paper submissions this year. Our Agentic Reviewer, released last week, just ...

Andrew Ng (@AndrewYNg) — Mon, 01 Dec 2025 23:19:08 GMT

NeurIPS received 21,575 paper submissions this year. Our Agentic Reviewer, released last week, just surpassed this in number of papers submitted and reviewed. It's clear agentic paper reviewing is here to stay and will be impactful!

Andrew Ng@AndrewYNg

Releasing a new "Agentic Reviewer" for research papers. I started coding this as a weekend project, and @jyx_su made it much better.

I was inspired by a student who had a paper rejected 6 times over 3 years. Their feedback loop -- waiting ~6 months for feedback each time -- was painfully slow. We wanted to see if an agentic workflow can help researchers iterate faster.

When we trained the system on ICLR 2025 reviews and measured Spearman correlation (higher is better) on the test set:
- Correlation between two human reviewers: 0.41
- Correlation between AI and a human reviewer: 0.42

This suggests agentic reviewing is approaching human-level performance.

The agent grounds its feedback by searching arXiv, so it works best in fields like AI where research is freely published there. It’s an experimental tool, but I hope it helps you with your research.

Check it out here: paperreview.ai

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Is there an AI bubble? With the massive number of dollars going into AI infrastructure such as OpenA...

Andrew Ng (@AndrewYNg) — Fri, 28 Nov 2025 18:40:31 GMT

Is there an AI bubble? With the massive number of dollars going into AI infrastructure such as OpenAI’s $1.4 trillion plan and Nvidia briefly reaching a $5 trillion market cap, many have asked if speculation and hype have driven the values of AI investments above sustainable values. However, AI isn’t monolithic, and different areas look bubbly to different degrees.
- AI application layer: There is underinvestment. The potential is still much greater than most realize.
- AI infrastructure for inference: This still needs significant investment.
- AI infrastructure for model training: I’m still cautiously optimistic about this sector, but there could also be a bubble.

Caveat: I am absolutely not giving investment advice!

AI application layer. There are many applications yet to be built over the coming decade using new AI technology. Almost by definition, applications that are built on top of AI infrastructure/technology (such as LLM APIs) have to be more valuable than the infrastructure, since we need them to be able to pay the infrastructure and technology providers.

I am seeing many green shoots across many businesses that are applying agentic workflows, and am confident this will grow. I have also spoken with many Venture Capital investors who hesitate to invest in AI applications because they feel they don’t know how to pick winners, whereas the recipe for deploying $1B to build AI infrastructure is better understood. Some have also bought into the hype that almost all AI applications will be wiped out merely by frontier LLM companies improving their foundation models. Overall, I believe there is significant underinvestment in AI applications. This area remains a huge focus for my venture studio, AI Fund.

AI infrastructure for inference. Despite AI’s low penetration today, infrastructure providers are already struggling to fulfill demand for processing power to generate tokens. Several of my teams are worried about whether we can get enough inference capacity, and both cost and inference throughput are limiting our ability to use even more. It is a good problem to have that businesses are supply-constrained rather than demand-constrained. The latter is a much more common problem, when not enough people want your product. But insufficient supply is nonetheless a problem, which is why I am glad our industry is investing significantly in scaling up inference capacity.

As one concrete example of high demand for token generation, highly agentic coders are progressing rapidly. I’ve long been a fan of Claude Code; OpenAI Codex also improved dramatically with the release of GPT-5; and Gemini 3 has made Google CLI very competitive. As these tools improve, their adoption will grow. At the same time, overall market penetration is still low, and many developers are still using older generations of coding tools (and some aren’t even using any agentic coding tools). As market penetration grows —  I’m confident it will, given how useful these tools are — aggregate demand for token generation will grow.

I predicted early last year that we’d need more inference capacity, partly because of agentic workflows. Since then, the need has become more acute. As a society, we need more capacity for AI inference. 

Having said that, I’m not saying it’s impossible to lose money investing in this sector. If we end up overbuilding — and I don’t currently know if we will — then providers may end up having to sell capacity at a loss or at low returns. I hope investors in this space do well financially. The good news, however, is that even if we overbuild, this capacity will get used, and it will be good for application builders!

AI infrastructure for model training. I am happy to see the investments going into training bigger models. But, of the three buckets of investments, this seems the riskiest. If open-source/open-weight models continue to grow in market share, then some companies that are pouring billions into training models might not see an attractive financial return on their investment.

Additionally, algorithmic and hardware improvements are making it cheaper each year to train models of a given level of capability, so the “technology moat” for training frontier models is weak. (That said, ChatGPT has become a strong consumer brand, and so it enjoys a strong brand moat, while Gemini, assisted by Google's massive distribution advantage, is also making a strong showing.)

I remain bullish about AI investments broadly. But what is the downside scenario — that is, is there a bubble that will pop? One scenario that worries me: If part of the AI stack (perhaps in training infra) suffers from overinvestment and collapses, it could lead to negative market sentiment around AI more broadly and an irrational outflow of interest away from investing in AI, despite the field overall having strong fundamentals. I don’t think this will happen, but if it does, it would be unfortunate since there’s still a lot of work in AI that I consider highly deserving of much more investment.

Warren Buffett popularized Benjamin Graham’s quote, “In the short run, the market is a voting machine, but in the long run, it is a weighing machine.” He meant that in the short term, stock prices are driven by investor sentiment and speculation; but in the long term, they are driven by fundamental, intrinsic value. I find it hard to forecast sentiment and speculation, but am very confident about the long-term health of AI’s fundamentals. So my plan is just to keep building!

[Original text: deeplearning.ai/the-batch/issu… ]

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Releasing a new "Agentic Reviewer" for research papers. I started coding this as a weekend project, ...

Andrew Ng (@AndrewYNg) — Mon, 24 Nov 2025 17:01:01 GMT

Releasing a new "Agentic Reviewer" for research papers. I started coding this as a weekend project, and @jyx_su made it much better.

I was inspired by a student who had a paper rejected 6 times over 3 years. Their feedback loop -- waiting ~6 months for feedback each time -- was painfully slow. We wanted to see if an agentic workflow can help researchers iterate faster.

When we trained the system on ICLR 2025 reviews and measured Spearman correlation (higher is better) on the test set:
- Correlation between two human reviewers: 0.41
- Correlation between AI and a human reviewer: 0.42

This suggests agentic reviewing is approaching human-level performance.

The agent grounds its feedback by searching arXiv, so it works best in fields like AI where research is freely published there. It’s an experimental tool, but I hope it helps you with your research.

Check it out here: paperreview.ai

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After listening to multiple of Harry's 20VC podcasts, it was fun to appear on it. We talked about bo...

Andrew Ng (@AndrewYNg) — Thu, 20 Nov 2025 20:14:54 GMT

After listening to multiple of Harry's 20VC podcasts, it was fun to appear on it. We talked about bottlenecks in AI adoption, US/China geopolitics, what opportunities remain to build in (tldr: a lot), and more!

Harry Stebbings@HarryStebbings

“In my career working in AI, I have yet to meet a single AI person that ever felt like they had enough compute.”

“Open-weight models is a tremendous source of geopolitical influence”

“When China’s government makes an all-nation commitment, it’s a very powerful force I wouldn’t underestimate.”

Shows like this are why I love what I do.

My top 7 takeaways from one of the OGs of AI @AndrewYNg 👇

Spotify �open.spotify.com/episode/7hNgy2…Zm
Youtube youtube.com/watch?v=rT74mF…glO
Apple Podcastspodcasts.apple.com/us/podcast/20v…8wmP

Timestamps:

00:00 Intro 
01:04 What are the Biggest Bottlenecks in AI Today? 
09:31 How LLMs Can Be Used as a Geopolitical Weapon 
15:07 Should AI Talent Really Be Paid Billions? 
19:15 Why is the Application Layer the Most Exciting Layer? 
29:30 Will AI Deliver Masa Son's Predictions of 5% GDP Growth? 
38:43 Do Margins Matter in a World of AI? 
40:36 Is Defensibility Dead in a World of AI? 
48:24 Will Human Labour Budgets Shift to AI Spend? 
55:05 Are We in an AI Bubble? 
56:28 Quick-Fire Round

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@ericjing_ai Congratulations Eric! The speed with which you and Kay are building is impressive, and ...

Andrew Ng (@AndrewYNg) — Thu, 20 Nov 2025 19:15:09 GMT

@ericjing_ai Congratulations Eric! The speed with which you and Kay are building is impressive, and it's been thrilling to see the company's growth under your leadership!

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I just got back from AI Dev x NYC, the AI developer conference where our community gathers for a day...

Andrew Ng (@AndrewYNg) — Thu, 20 Nov 2025 17:38:52 GMT

I just got back from AI Dev x NYC, the AI developer conference where our community gathers for a day of coding, learning, and connecting. The vibe in the room was buzzing! It was at the last AI Dev in San Francisco that I met up with Kirsty Tan and started collaborating with her on what became our AI advisory firm AI Aspire. In-person meetings can spark new opportunities, and I hope the months to come will bring more stories about things that started in AI Dev x NYC!

The event was full of conversations about coding with AI, agentic AI, context engineering, governance, and building and scaling AI applications in startups and in large corporations. But the overriding impression I took away was one of near-universal optimism about our field, despite the mix of pessimism and optimism about AI in the broader world.

For example, many businesses have not yet gotten AI agents to deliver a significant ROI, and some AI skeptics are quoting an MIT study that said 95% of AI pilots are failing. (This study, by the way, has methodological flaws that make the viral headline misleading; see link in original post.) But at AI Dev were many of the teams responsible for the successful and rapidly growing set of AI applications. Speaking with fellow developers, I realized that because of AI's low penetration in businesses, it is simultaneously true that (a) many businesses do not yet have AI delivering significant ROI, and (b) many skilled AI teams are starting to deliver significant ROI and see the number of successful AI projects climbing rapidly, albeit from a low base. This is why AI developers are bullish about the growth that is to come.

Multiple exhibitors told me this was the best conference they had attended in a long time, because they got to speak with real developers. One told me that many other conferences seemed like fluff, whereas participants at AI Dev had much deeper technical understanding and thus were interested in and able to understand the nuances of cutting-edge technology. Whether the discussion was on observability of agentic workflows, the nuances of context engineering for AI coding, or a debate on how long the proliferation of RL gyms for training LLMs will continue, there was deep technical expertise in the room that lets us collectively see further into the future.

One special moment for me was when Nick Thompson, moderating a panel with Miriam Vogel and me, asked about governance. I replied that the United States’ recent hostile rhetoric toward immigrants is one of the worst moves it is making, and many in the audience clapped. Nick spoke about this moment in a video (links in original post).

I enjoyed meeting many people at AI Dev, and am grateful to everyone who came and to all our speakers, exhibitors, sponsors, volunteers, and event staff. My only regret is that, even though we scaled up the event 3x compared to the previous San Francisco event, we had to limit the number of tickets because the space couldn’t admit more attendees.

Even though so much work is now online, in-person events are special and can be turning points for individuals and projects. We plan to make the next AI Dev in San Francisco on April 28-29, 2026 an even bigger event, and look forward to the conference helping to spark more sharing and connections in the future.

[Original text: deeplearning.ai/the-batch/issu… ]

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I just dumped the latest NVIDIA 10-Q earnings report, released an hour ago, into Agentic Document E...

Andrew Ng (@AndrewYNg) — Wed, 19 Nov 2025 22:11:12 GMT

I just dumped the latest NVIDIA 10-Q earnings report,  released an hour ago, into Agentic Document Extraction, and the results are really accurate! Left side of the image shows the original PDF; right side shows the extracted info, including e.g. the $57.01B revenue in the most recent quarter. This is powered by the DPT (document pre-trained transformer) model. Check it out!

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New course announcement: Semantic Caching for AI Agents, taught by @tchutch94 and @ilzhechev from @R...

Andrew Ng (@AndrewYNg) — Wed, 19 Nov 2025 19:20:39 GMT

New course announcement: Semantic Caching for AI Agents, taught by @tchutch94 and @ilzhechev from @Redisinc.

Semantic caching can significantly reduce your AI application's inference costs and latency. If someone asks "How do I get a refund?" and another later asks "I want my money back," semantic caching recognizes these mean the same thing so it can use a cached response instead of making another model call.

This short course takes you from building your first semantic cache from scratch to implementing production-ready systems using Redis' open-source tools.

Skills you'll gain:
- Build semantic caches from scratch, then implement them using Redis' SDK with production features
- Measure cache performance using hit rate, precision, recall, and latency
- Enhance accuracy with threshold tuning, cross-encoders, LLM validation, and fuzzy matching

Join and learn to reduce your agentic AI's costs and improve speed! deeplearning.ai/short-courses/…

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Really proud of the DeepLearningAI team. When Cloudflare went down, our engineers used AI coding to ...

Andrew Ng (@AndrewYNg) — Wed, 19 Nov 2025 00:16:41 GMT

Really proud of the DeepLearningAI team. When Cloudflare went down, our engineers used AI coding to quickly implement a clone of basic Cloudflare capabilities to run our site on. So we came back up long before even major websites!

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I recently received an email titled “An 18-year-old’s dilemma: Too late to contribute to AI?” Its au...

Andrew Ng (@AndrewYNg) — Thu, 13 Nov 2025 16:13:40 GMT

I recently received an email titled “An 18-year-old’s dilemma: Too late to contribute to AI?” Its author, who gave me permission to share this, is preparing for college. He is worried that by the time he graduates, AI will be so good there’s no meaningful work left for him to do to contribute to humanity, and he will just live on Universal Basic Income (UBI). I wrote back to reassure him that there will still be plenty of work he can do for decades hence, and encouraged him to work hard and learn to build with AI. But this conversation struck me as an example of how harmful hype about AI is.

Yes, AI is amazingly intelligent, and I’m thrilled to be using it every day to build things I couldn’t have built a year ago. At the same time, AI is still incredibly dumb, and I would not trust a frontier LLM by itself to prioritize my calendar, carry out resumé screening, or choose what to order for lunch — tasks that businesses routinely ask junior personnel to do.

Yes, we can build AI software to do these tasks. For example, after a lot of customization work, one of my teams now has a decent AI resumé screening assistant. But the point is it took a lot of customization.

Even though LLMs can handle a much more general set of tasks than previous iterations of AI technology, compared to what humans can do, they are still highly specialized. They’re much better at working with text than other modalities, still require lots of custom engineering to get it the right context for a particular application, and we have few tools — and only inefficient ones — for getting our systems to learn from feedback and repeated exposure to a specific task (such as screening resumés for a particular role).

AI has stark limitations, and despite rapid improvements, it will remain limited compared to humans for a long time.

AI is amazing, but it has unfortunately been hyped up to be even more amazing than it is. A pernicious aspect of hype is that it often contains an element of truth, but not to the degree of the hype. This makes it difficult for nontechnical people to discern where the truth really is. Modern AI is a general purpose technology that is enabling many applications, but AI that can do any intellectual tasks that a human can (a popular definition for AGI) is still decades away or longer. This nuanced message that AI is general, but not that general, often is lost in the noise of today's media environment.

Similarly, the progress of frontier models is amazing! But not so amazing that they’ll be able to do everything under the sun without a lot of customization. I know VC investors who are scared to invest in application-layer startups because they are worried that frontier AI model companies will quickly wipe out all of these businesses by improving their models. While some thin wrappers around LLMs no doubt will be replaced, there also remains a huge set of valuable applications that the current trajectory of progress of frontier models won’t displace for a long time.

Without accurate information about the current state of AI and how it is likely to progress, some young people will decide not to enter AI because think think AGI leaves them no meaningful role, or decide not to learn how to code because they fear AI will automate it — right when it is the best time ever to join our field.
Let us all keep working to get to a precise understanding of what’s actually possible, and keep building!

[Original text:  deeplearning.ai/the-batch/issu… ]

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New course announcement: Design, Develop, and Deploy Multi-Agent Systems with CrewAI, taught by @jo...

Andrew Ng (@AndrewYNg) — Tue, 11 Nov 2025 18:32:25 GMT

New course announcement: Design, Develop, and Deploy Multi-Agent Systems with CrewAI, taught by  @joaomdmoura, @crewAIInc Co-founder and CEO.

Multi-agent systems let you build AI teams that work together to automate complex workflows, similar to how human teams work.

CrewAI makes it simple to build multi-agent systems that handle routine work for you—just define your agents, tasks, and crew, and it manages the complexity of coordinating multiple agents and their context automatically. (Disclosure: I made a small angel investment in CrewAI.) This course takes you from building your first agent to deploying production systems using the open-source CrewAI framework.

Skills you'll gain:
- Build reliable AI agents equipped with tools, memory, and guardrails
- Develop teams of agents that can plan, reason, and coordinate
- Deploy production-ready systems with tracing, evaluation, and monitoring

Whether you’re exploring multi-agent systems for the first time or looking to take your projects further, this course will help you build a mental framework for designing multi-agent systems, and help you turn ideas into scalable, production-ready applications.

Sign up here: deeplearning.ai/courses/design…

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AI agents are getting better at looking at different types of data in businesses to spot patterns an...

Andrew Ng (@AndrewYNg) — Thu, 06 Nov 2025 18:27:56 GMT

AI agents are getting better at looking at different types of data in businesses to spot patterns and create value. This is making data silos increasingly painful. This is why I increasingly try to select software that lets me control my own data, so I can make it available to my AI agents.

Because of AI’s growing capabilities, the value you can now create from “connecting the dots” between different pieces of data is higher than ever. For example, if an email click is logged in one vendor’s system and a subsequent online purchase is logged in a different one, then it is valuable to build agents that can access both of these data sources to see how they correlate to make better decisions.

Unfortunately, many SaaS vendors try to create a data silo in their customer’s business. By making it hard for you to extract your data, they create high switching costs. This also allows them to steer you to buy their AI agent services — sometimes at high expense and/or of low quality — rather than build your own or buy from a different vendor. Unfortunately, some SaaS vendors are seeing AI agents coming for this data and working to make it harder for you (and your AI agents) to efficiently access it.

One of my teams just told me that a SaaS vendor we have been using to store our customer data wants to charge over $20,000 for an API key to get at our data. This high cost — no doubt intentionally designed to make it hard for customers to get their data out — is adding a barrier to implementing agentic workflows that take advantage of that data.

Through AI Aspire (an AI advisory firm), I advise a number of businesses on their AI strategies. When it comes to buying SaaS, I often advise them to try to control their own data (which, sadly, some vendors mightily resist). This way, you can hire a SaaS vendor to record and operate on your data, but ultimately you decide how to route it to the appropriate human or AI system for processing.

Over the past decade, a lot of work has gone into organizing businesses’ structured data. Because AI can now process unstructured data much better than before, the value of organizing your unstructured data (including PDF files, which LandingAI’s Agentic Document Extraction specializes in!) is higher than ever before.

In the era of generative AI, businesses and individuals have important work ahead to organize their data to be AI-ready.

P.S. As an individual, my favorite note-taking app is Obsidian. I am happy to “hire” Obsidian to operate on my notes files. And, all my notes are saved as Markdown files in my file system, and I have built AI agents that read from or write to my Obsidian files. This is a small example of how controlling my own notes data lets me do more with AI agents!

[Original text: deeplearning.ai/the-batch/issu… ]

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.@roelofbotha is one of the investors I've come to most respect, and I learned something new every t...

Andrew Ng (@AndrewYNg) — Wed, 05 Nov 2025 03:55:50 GMT

.@roelofbotha is one of the investors I've come to most respect, and I learned something new every time I spoke with him. Even beyond his leadership of @sequoia, his influence on how investors think is hard to overstate. His passing on the baton is the end of an era!

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Wrong tag - I’m so sorry. This is co-taught with the wonderful Brian Granger @ellisonbg !

Andrew Ng (@AndrewYNg) — Mon, 03 Nov 2025 21:07:45 GMT

Wrong tag - I’m so sorry. This is co-taught with the wonderful Brian Granger @ellisonbg !

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AI coding just arrived in Jupyter notebooks - and @brganger (Jupyter co-founder) and I will show you...

Andrew Ng (@AndrewYNg) — Mon, 03 Nov 2025 18:40:18 GMT

AI coding just arrived in Jupyter notebooks - and @brganger (Jupyter co-founder) and I will show you how to use it.

Coding by hand is becoming obsolete. The latest Jupyter AI - built by the Jupyter team and showcased at JupyterCon this week - brings AI assistance directly into notebooks.

Most AI coding assistants struggle with Jupyter notebooks. Jupyter AI was designed specifically for them. This is the first course to teach it.

In this course, Brian and I teach you to:
- Generate and debug code directly in notebook cells through an integrated chat interface
- Provide the right context (like API docs) to help AI write accurate code
- Use Jupyter AI's unique notebook features: drag cells to chat, generate cells from chat, attach context for the LLM

We've integrated Jupyter AI directly into the DeepLearningAI platform, so you can start using it immediately. Since Jupyter AI is open source, you can also install and run it locally afterward.

Whether you're experienced with notebooks or learning them for the first time, this course will prepare you for AI-assisted notebook development.

Start using Jupyter AI (free): deeplearning.ai/short-courses/…

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Haha! Good one.

Andrew Ng (@AndrewYNg) — Sun, 02 Nov 2025 23:45:14 GMT

Haha! Good one.

Christopher Manning@chrmanning

We already called it in 2020!

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https://t.co/zpIxRStMIw Pro is now generally available -- this is the one membership that keeps you ...

Andrew Ng (@AndrewYNg) — Thu, 30 Oct 2025 17:18:49 GMT

DeepLearning.AI Pro is now generally available -- this is the one membership that keeps you at the forefront of AI. Please join! 

There has never been a moment when the distance between having an idea and building it has been smaller. Things that required months of work for teams can now be built by individuals using AI, in days. This is why we built DeepLearning.AI Pro. I'm personally working hard on this membership program to help you to build applications that can launch or accelerate your career, and shape the future of AI.

DeepLearning.AI Pro gives you full access to 150+ programs, including my recently launched Agentic AI course, the new Post-Training and PyTorch courses by Sharon Zhou and Laurence Moroney (just released this week), and all of DeepLearning.AI's top courses and professional certificates.

All course videos remain free. Pro membership adds hands-on learning: labs to build working systems, practice questions to hone your understanding, and certificates to share your skills.

I'm also building new tools to help you create AI applications and grow your career (and have fun doing so!). Many will be available first to Pro members.

Try out DeepLearning.AI Pro free, and let me know what you build!

Join here: learn.deeplearning.ai/membership

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