Modern engineering projects increasingly combine human judgment with machine capability. An engineer can define a problem, then ask an artificial intelligence tool to search relevant research and technical standards, generate design options, test them in simulation, and compare the results. The engineer can then bring the results to specialists who understand the parts AI cannot judge on its own, such as whether a design can be built safely, whether it makes sense in a particular setting, or how it might affect its users. The final decision about whether and how to move forward remains a human responsibility. AI can help engineers do early project steps that once required several people with different technical skills. The engineer’s role is therefore expanding from performing each technical step to deciding how people, machines, and specialists’ knowledge should work together on the project. The problem, not the title Engineering has always been divided into disciplines, and for good reasons. Electrical, mechanical, civil, software, and other engineering fields require deep knowledge that takes years to develop. AI does not replace that need. It simply changes how easily engineers can explore the parts of a problem that sit outside their own specialty. Consider a software engineer working on a medical device. AI can help the engineer review relevant biomedical research, understand unfamiliar terms, analyze data, and build an early model. That does not make the engineer a physician or biomedical expert. It allows the engineer to ask better questions before working with the experts. The same is true in energy, transportation, manufacturing, and many other fields. A problem might begin as a software issue and then quickly involve hardware, safety measures, economics, human behavior, and regulations. Engineers do not need to master every field; they need to recognize which expertise the problem requires and bring the right people and tools together. The importance of systems thinking Systems thinking thus becomes especially important. It requires looking at the whole problem: understanding how its parts affect one another, identifying constraints and failure points, and making trade-offs. AI adds a new element to the process. Engineers must decide not only how the technical parts fit together but also what work to give to AI, what a person must check, when a specialist should make a decision, and when the problem needs to be redefined. An engineer’s value is not in knowing everything. It is in knowing how to frame the problem, how to ask the right questions, how to test the answers, and when another expert must lead the project. This aspect is crucial because technical challenges in engineering are seldom static. As new insights emerge, objectives might shift, unseen limitations can surface, and the initial premise might prove flawed. Rather than rigidly following the starting specifications, an effective engineer adapts and refines the scope as a deeper understanding of the system develops. Thus, engineering extends beyond mastering technical concepts; it is an adaptable, structured approach to navigating evolving challenges. New evidence can change the goal, reveal a hidden constraint, or show that the original hypothesis was wrong. Effective engineers go beyond merely acting on the initial problem statement. They update it as the system becomes better understood. In that sense, engineering is not only a body of technical knowledge, it is also a disciplined way of thinking about a project. Human judgment is important AI can produce convincing answers even when its assumptions are weak. It can generate code that works in one test but fails in another. It can summarize research without understanding which evidence is strongest, or optimize a design for the wrong goal. Such limits make human expertise more important, not less. Machines are useful where speed, scale, search, pattern recognition, and data generation matter. People are essential where context, values, responsibility, and judgment matter. The challenge is to decide where each belongs. An AI system might generate several designs for a medical sensor, for example, and predict how each will perform. An engineer can test the predictions and reject designs that violate technical requirements. A clinician can determine whether the sensor can be realistically used. Patients and practitioners might reveal problems that neither the engineer nor the developer considered. The final solution emerges from combining the perspectives. The engineer is still a builder and specialist but increasingly also a coordinator of knowledge. An engineer’s value is not in knowing everything. It is in knowing how to frame the problem, how to ask the right questions, how to test the answers, and when another expert must lead the project. IEEE’s vital role The broader form of engineering creates an important role for IEEE. Its societies already bring together expertise in various technologies, engineering education, technical standards, and other fields. That makes IEEE well placed to help engineers work across specialties without weakening the value of specialization. IEEE could create more communities organized around problems that require expertise from several fields. Examples include trustworthy clinical AI, resilient infrastructure, energy reliability, scientific discovery, and safe autonomous systems. An engineer could begin in a home discipline and then work with specialists from other IEEE communities as a project requires. IEEE also can help set practical standards for human-machine collaboration. Engineers will need clear guidance on when AI-generated work must be checked, how uncertainty should be documented, when a specialist must make a decision, and who is responsible when a system uses both human judgment and machine output. Those questions are becoming part of everyday engineering. They should not be left to each company or project manager to answer independently. A professional organization can provide common expectations that follow engineers as they move between jobs, industries, and technical fields. A larger definition of the engineer AI does not make engineering disciplines less important; it makes the connections between them easier to use. Deep expertise gives engineers the foundation to judge what is sound. Systems thinking helps them understand how different parts of a problem fit together. AI can extend engineers’ ability to explore, test, and communicate across fields. Human judgment gives the work direction and responsibility. An engineer is not defined only by a job title or a fixed set of tasks. An engineer is someone trained to understand complex systems, define and redefine problems, bring together the knowledge and tools needed to solve them, test whether solutions work, and take responsibility for the outcome. That is what it means to be an “unbound” engineer. The boundaries between specialties become easier to cross, and machines might take on more of the technical work, but the core of engineering remains human: deciding what problem is worth solving, what evidence is good enough, how the pieces fit together, and what to do when the answer is uncertain.

A lesson in learning, summarization, and explaining via LLMs

What is “the true cost of a token”?

I’ll be speaking at the Bayesian Interest Group November 2026 Virtual Meeting. The topic is using Bayesian models to interactively pre-estimate the value of applying an expensive inspection or procedure to a large population. For example: estimating the value of using an LLM security service on a great number of […]

I started this blog a few years ago when I began my journey studying immunology, to have a place to write about what I was learning and about applications of AI to the field. My studies were a success: I completed a Masters in Microbiology-Immunology, explored interesting applications, and met some awesome people along the way. At the end of last year, I decided to return to my origins and re-joined fast.ai, which had grown into Answer.AI while I was away. Why now? AI is an increasingly hated field, and for good reason. I wrote about my decision: There are tons of false and over-hyped claims about what AI can do, but underneath those, it is a genuinely useful technology. I want to use AI in ways that don’t degrade my own critical thinking skills and that don’t pretend that resources are unlimited. The biggest companies have made their vision for the technology seem like the only option. It isn’t. OpenAI, Anthropic, Google, and xAI don’t own AI. Their values and decisions aren’t the only version of what AI technology can be. I understand why many people are anti-AI. My new job is rooted in the same objections. I returned to the field because I want to figure out how to use AI in ways that protect human creativity, autonomy, and problem-solving. You can read my full essay here. I am excited to continue exploring how to design AI tools that better encourage human understanding and human autonomy, particularly in the area of education. I have written several essays in the last year, posted at fast.ai and answer.ai, about how AI intersects with education, coding, and science: Breaking the Spell of Vibe Coding Stop Saying Boredom is Good for Kids I Don’t Want a Learning Dashboard for My Child Risks and Limitations of AI in the Life Sciences My friends all hate AI; I just joined an AI startup Going forward, you can find me at fast.ai, answer.ai, on twitter, and on LinkedIn. I look forward to reading your responses. Create a free GitHub account to comment below.