In a candid conversation on the Huge Conversations podcast with Cleo Abram, Jensen Huang talked about the transformative potential of generative AI tools like ChatGPT, Gemini Pro, and Grok. If he were navigating academia and early career choices now, Huang said, “The first thing I would do is to learn AI.”
But it’s not just about understanding the technology; it’s about learning to work with it creatively and strategically. “Learning how to interact with AI is not unlike being someone good at asking questions,” Huang noted. This comparison highlights the growing importance of prompt engineering, the skill of crafting clear, purposeful instructions for AI tools.
To illustrate this, Huang used a common scenario: imagine someone vaguely asking an entrepreneur, “Tell me about your business.” The question is too broad and hard to answer meaningfully. However, rephrasing it to something like, “Can you explain the first steps to launching an online retail business?” immediately creates a path for a more informative response. Huang emphasizes that prompting AI works the same way: the clearer and more intentional the query, the better the result.
This sentiment is echoed by Kelly Daniel, a prompt director at Lazarus AI, who described effective AI prompting as akin to speaking to “a smart kid.” In her CNBC Make It article, Daniel explained: “One who wants to make you happy and do what you’re asking… but this kid doesn’t know everything you do about your task or business.” The onus is on the user to provide structure and context. She advises users to break down instructions into steps or bullet points and, when possible, include examples.
For instance, instead of making a general request like “help me write a speech,” a more effective prompt might be: “I need to give a keynote speech at my company’s annual conference. I want it to sound like Bill Gates during his early days at Microsoft. The speech should congratulate the team on a great first quarter, acknowledge our improvements with our marketing and media strategy, and introduce our new productivity goals while motivating employees to meet them.”
This level of specificity gives the AI clear direction, allowing it to generate a response that closely aligns with the user’s intent, tone, and objectives.
Interestingly, despite AI’s growing influence, adoption among younger people remains surprisingly low. A 2024 report from Harvard Graduate School of Education, Common Sense Media, and Hopelab found that only 11% of Americans aged 14 to 22 use generative AI weekly. However, the urgency to adapt is mounting: LinkedIn’s 2025 Work Change report predicts that 70% of the skills used in current jobs could shift due to AI by the year 2030.
Huang warns that regardless of the industry, be it science, math, or creative arts, future professionals will need to ask themselves: “How can I use AI to do my job better?” He stresses that understanding how to use these tools isn’t optional, it’s a foundational skill for the careers of tomorrow.
