Letters from Andrew Ng

Personal messages to the AI community

Illustration of a programmer at a computer displaying PyTorch code, while a smiling colleague gives a thumbs-up in approval.

Learn the Language of Software: AI won’t kill programming. There has never been a better time to start coding.

Some people today are discouraging others from learning programming on the grounds AI will automate it.

Diagram of an RQ-Transformer speech system with Helium and Depth Transformers for audio processing.

Wait Your Turn! Conversation by Voice Versus Text: Text interactions require taking turns, but voices may interrupt or overlap. Here’s how AI is evolving for voice interactions.

Continuing our discussion on the Voice Stack, I’d like to explore an area that today’s voice-based systems mostly struggle with: Voice Activity Detection (VAD) and the turn-taking paradigm of communication.

Diagram comparing direct audio generation with a foundation model vs. a voice pipeline using STT, LLM, and TTS.

Technical Insights

What I’ve Learned Building Voice Applications: Best practices for building apps based on AI’s evolving voice-in, voice-out stack

The Voice Stack is improving rapidly. Systems that interact with users via speaking and listening will drive many new applications.

“Responsible AI” written on a wall, with “Safety” crossed out in blue paint.

Technical Insights

The Difference Between “AI Safety” and “Responsible AI”: Talk about “AI safety” obscures an important point; AI isn't inherently unsafe. Instead, let’s talk about “responsible AI.”

At the Artificial Intelligence Action Summit in Paris this week, U.S. Vice President J.D. Vance said, “I’m not here to talk about AI safety.

Technical Insights

Three Takeaways from DeepSeek’s Big Week: Innvations by China’s AI powerhouse DeepSeek highlight major shifts in the international scene

The buzz over DeepSeek this week crystallized, for many people, a few important trends that have been happening in plain sight.

Illustration of tech tools like OpenAI, MongoDB, Heroku, and Python with Andrew Ng working on a laptop

Technical Insights

My AI-Assisted Software Development Stack: The software development stack is evolving fast. Here are some things to consider as you choose components.

Using AI-assisted coding to build software prototypes is an important way to quickly explore many ideas and invent new things.

Andrew Ng celebrating and wishing a Happy New Year 2025 with sparklers.

Technical Insights

New Opportunities for the New Year: AI-assisted coding lets you prototype applications quickly and easily. Go forth and build!

Despite having worked on AI since I was a teenager, I’m now more excited than ever about what we can do with it, especially in building AI applications.

Graph showing cross-validation accuracy vs. number of features for raw and whitened inputs.

Technical Insights

Focus on the Future, Learn From the Past: 15 years ago, the idea of scaling up deep learning was controversial — but it was right. Keep your eyes open for such ideas in 2025.

I’m thrilled that former students and postdocs of mine won both of this year’s NeurIPS Test of Time Paper Awards.

Cartoon showing people stuck in wet concrete, with a person saying ‘You asked for a concrete idea!’

Technical Insights

Best Practices for AI Product Management: Generative AI is making it possible to build new kinds of applications in new ways. Here are emerging best practices for AI product managers.

AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications.

AI ecosystem layers: applications, orchestration, foundational models, cloud, and semiconductors.

Technical Insights

The Falling Cost of Building AI Applications: Big AI’s huge investments in foundation models enables developers to build AI applications at very low cost.

There’s a lingering misconception that building with generative AI is expensive.

Two people reading in bed, one with a book on library functions and a head labeled with AI layers.

Technical Insights

AI Is Part of Your Online Audience: Some webpages are written not for humans but for large language models to read. Developers can benefit by keeping the LLM audience in mind.

A small number of people are posting text online that’s intended for direct consumption not by humans, but by LLMs (large language models).

Man with tools says, “I optimized for tool use!” Woman at computer replies, “Should’ve optimized for computer use!”

Technical Insights

From Optimizing for People to Optimizing for Machines: Why large language models are increasingly fine-tuned to fit into agentic workflows

Large language models (LLMs) are typically optimized to answer peoples’ questions.

Two cheetahs in a savannah, with one saying ‘Move fast and be responsible!’ in a speech bubble.

Technical Insights

How to Get User Feedback to Your AI Products - Fast!: Your ability to prototype AI capabilities fast affects all parts of the product development cycle, starting with getting user feedback.

Startups live or die by their ability to execute at speed. For large companies, too, the speed with which an innovation team is able to iterate has a huge impact on its odds of success.

Comic where a robot is hiding in a closet during a game of hide-and-seek.

Technical Insights

Why Science-Fiction Scenarios of AI’s Emergent Behavior Are Likely to Remain Fictional: The sudden apparance of “emergent” AI capabilities may be an artifact of the metrics you study

Over the weekend, my two kids colluded in a hilariously bad attempt to mislead me to look in the wrong place during a game of hide-and-seek.

Technical Insights

Welcoming Diverse Approaches Keeps Machine Learning Strong: What technology counts as an “agent”? Instead of arguing, let's consider a spectrum along which various technologies are “agentic.”

One reason for machine learning’s success is that our field welcomes a wide range of work.

Letters from Andrew Ng

Learn the Language of Software: AI won’t kill programming. There has never been a better time to start coding.

Wait Your Turn! Conversation by Voice Versus Text: Text interactions require taking turns, but voices may interrupt or overlap. Here’s how AI is evolving for voice interactions.

What I’ve Learned Building Voice Applications: Best practices for building apps based on AI’s evolving voice-in, voice-out stack

The Difference Between “AI Safety” and “Responsible AI”: Talk about “AI safety” obscures an important point; AI isn't inherently unsafe. Instead, let’s talk about “responsible AI.”

Three Takeaways from DeepSeek’s Big Week: Innvations by China’s AI powerhouse DeepSeek highlight major shifts in the international scene

My AI-Assisted Software Development Stack: The software development stack is evolving fast. Here are some things to consider as you choose components.

New Opportunities for the New Year: AI-assisted coding lets you prototype applications quickly and easily. Go forth and build!

Focus on the Future, Learn From the Past: 15 years ago, the idea of scaling up deep learning was controversial — but it was right. Keep your eyes open for such ideas in 2025.

Best Practices for AI Product Management: Generative AI is making it possible to build new kinds of applications in new ways. Here are emerging best practices for AI product managers.

The Falling Cost of Building AI Applications: Big AI’s huge investments in foundation models enables developers to build AI applications at very low cost.

AI Is Part of Your Online Audience: Some webpages are written not for humans but for large language models to read. Developers can benefit by keeping the LLM audience in mind.

From Optimizing for People to Optimizing for Machines: Why large language models are increasingly fine-tuned to fit into agentic workflows

How to Get User Feedback to Your AI Products - Fast!: Your ability to prototype AI capabilities fast affects all parts of the product development cycle, starting with getting user feedback.

Why Science-Fiction Scenarios of AI’s Emergent Behavior Are Likely to Remain Fictional: The sudden apparance of “emergent” AI capabilities may be an artifact of the metrics you study

Welcoming Diverse Approaches Keeps Machine Learning Strong: What technology counts as an “agent”? Instead of arguing, let's consider a spectrum along which various technologies are “agentic.”

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