Dear friends,
The AI world has become incredibly noisy. Social media, traditional media, and an army of marketers produce a cacophony of hype and content that are often secretly sales pitches for their products. Good ideas are buried in the noise, but it is hard to figure out what’s worth your time to learn. I want to explain plainly how we decide what to teach at DeepLearning.AI.
We have a simple mantra that drives the order for whose interests we prioritize: “Learners first, Partners second, Ourselves last.” When you lead a team, you get to pick a small number of mantras to repeat ad infinitum, to the point where the whole team is sick of hearing them. For DeepLearning.AI, I decided to make this one of them.
When we are exploring what to teach, we explore a wide range of topics, gathering from a network of technical experts in companies and academia, senior technology and business leaders, writings by technical experts, our team’s practical experience, and objective metrics of different tools’ traction and/or performance. We use these to synthesize our view of the most important topics to learn if you want to become skilled at AI Engineering. We then look for partners who are experts on these topics to work with us.
And yes, we routinely tell partners up front that we prioritize their interests second, behind learners. Even though this might seem counterintuitive from a business point of view, I believe that working only with partners who are aligned with our “learners first” philosophy will result in better courses and better outcomes for everyone. Specifically, working with partners who want to teach technically deep content, rather than just put out marketing materials, helps our partners to attract a larger audience, which benefits them as well as learners.
We build courses with many leading AI companies like OpenAI, Anthropic, Google, Microsoft, Meta, and Amazon (disclosure: I serve on Amazon’s board — and, yes, if we have an additional relationship with a partner or course provider, we always try to disclose such relationships transparently) as well as startups with advanced offerings. If your goal is to learn to drive a car, you need a car to practice. But the skill is driving, not the car. After learning, what car you continue to drive should be up to you. For example, our courses on agentic frameworks, even if taught with one partner, leave you with fundamental skills applicable to building with any framework. Our courses on evals leave you able to use any evals framework (or no framework). Our courses on prompting help you to prompt any model.
AI Engineering has important fundamentals, such as (i) using coding agents well, (ii) key building blocks like evals, error analysis, agentic workflows, and guardrails, and (iii) adjacent skills such as making basic product decisions or iterating quickly to build 0-to-1 products. Mastering these — sometimes best illustrated through a specific vendor’s offerings — matters more than mastering any one vendor’s tools. (At the same time, mastering multiple vendors’ tools lets you use them efficiently and has value, too.)
We deeply respect our partners. We carefully select which companies to work with, since we want ones that have and are willing to share cutting-edge knowledge. That’s why we choose partners who have deep, hard-won expertise. However, to ensure editorial integrity — prioritizing learners first — we have never accepted payment from any partner to create a course.
Many companies reach out and sometimes offer payment to teach with them, and we do consider suggestions of course topics and partners. But we prioritize what courses to teach and who to work with based only on what we think is best for learners. The engineers who built a tool’s advanced capabilities are often the people most qualified to share how it works, so I am deeply grateful to the many partners who have joined us to serve learners together.
There is much to learn in AI, and DeepLearning.AI will continue to put learners first, partners second, and ourselves last.
Keep building!
Andrew