# Andrew Ng: AI Engineering Skills Map: Shaping the Build > Source: > Published: 2026-09-12 11:05:23+00:00 When you’re skilled at AI Engineering, your best work won’t be merely implementing a product that someone else spec’ed out. Instead, you will actively shape the build. Before modern AI tools accelerated and expanded what a single developer could do, tech companies established the practice of having product managers (PMs) and designers specify what should be built and then developers build it. Perhaps a project manager additionally drives the timeline. However, these roles are blurring. A developer who is skilled at AI engineering not only builds software but participates in these other roles. (Similarly, product managers and designers are gaining AI Engineering skills and participating in building software.) This change is vastly accelerating software development. When you know how to shape the build, you can move faster without waiting for a PM to figure out what to do. The key skills for shaping the build are: Driving the build loop Making product decisions Communicating and leading High-agency ownership Driving the build loop. Most software is built via a loop in which you write some code, then get some feedback, and decide what to do next. As a skilled AI engineer, you play a key role in driving this loop, repeatedly deciding on the next step to move your project forward. You have a bias for action, and drive this loop at the high velocity that AI has made possible. For example, you might decide to build a quick prototype to test a technical concept or user feature, build an MVP (minimum viable product) to take to users to demonstrate value, add features, or invest in an enterprise-grade system. You frequently ship in small batches to keep up velocity. You know when to get feedback from users or other stakeholders, or when to run a technical experiment (such as train a model) to gather information to decide the next step. You make these decisions taking into account the product vision, stage of the project, technical feasibility, key risks, effort, and budget. For more mature projects, you know how to define key metrics and project-manage to drive improvements to those metrics. Making product decisions. Developers don’t have to become PMs, but you will make decisions the product spec doesn’t cover. If you are asked to build without a spec, you know how to develop one. You have product sense that enables you to pick a product direction that meets real user needs, without having to wait for a PM to make every decision. You also have at least a basic design sense, and can build things that aren’t just functional but pleasing to use. You also have some basic business sense, so you can think through issues like go-to-market, market size, unit economics, and profit and loss (P&L) and make tradeoffs that are economically sensible. Your ability to make product decisions is rooted in your user empathy. Further, you continually hone this empathy using a wide range of methods, such as quick informal interviews with 2-3 users, surveys of hundreds of users, large-scale A/B tests, or analyzing the behaviors of thousands or millions of users. You use the resulting input to improve your understanding of users. Communicating and leading. Your skills in AI Engineering enable you to participate in a broader scope of work than traditional software development allowed. I’ve written previously about how specialized developers (like frontend developers) are now likely to play a broader full-stack role. AI Engineering skills open the door to expanding your scope even beyond this: You might participate in other functions that affect your project like marketing, finance, legal, and so on. This makes your ability to communicate with these other functions more important than before — you can play a key role moving your project forward by aligning and coordinating among stakeholders. (Communication skills also form an important foundation for speaking with users to hone user empathy.) Additionally, because AI technology is rapidly evolving, many people outside of engineering are trying to understand the technology, its impact on their jobs, and the new practices and products it makes possible. Your technical skill in AI Engineering puts you ahead of the game and allows you to play a unique role in shaping these perspectives. For example, you can explain why certain initiatives may be technically feasible or not. This allows you to help lead your broader organization forward. High-agency ownership. AI engineering skills give you vast opportunities to make a difference. However, many people — including some executives — do not yet understand what AI can do and therefore do not know what are good project directions. This creates an opening for someone with technical skill to bridge this gap: You can spot problems, propose solutions, and execute on them — being respectful of the organization’s priorities and constraints, but without waiting for precise top-down direction. This skill requires a high degree of agency, in which you identify opportunities, prioritize what matters, and act on them. Additionally, you know how to own an initiative end-to-end, take accountability for issues that arise, act in the face of ambiguity, persist through setbacks, and measure your work not just by task completion, but according to the value you create. Finally, you invest in improving your skills. You track the technology frontier, pick up new tools, tune your workflows, and keep on learning — so you become better over time. The opportunity to not just build but to shape the build makes AI Engineering more exciting than traditional software development. You are more empowered, have broader scope, and make more decisions. But doing all this well requires a larger set of skills. DeepLearning.AI’s focus is to help you, if you wish, become skilled at AI Engineering. I look forward to the road ahead!