Learn how to code? A standard engineering degree may not be enough.
In one the last post IN Harvard Business ReviewGoldman Sachs Chief Information Officer Marco Argenti said AI can now write high-quality code, sometimes bigger than humans, and the technology is only getting better.
So how does an engineer keep it? He told readers of the advice he gave his college-age daughter: “If you want to pursue a career in engineering, you should focus on learning philosophy in addition to traditional engineering subjects.”
Argenti is an engineer with one BACKGROUND in technology entrepreneurship and leadership at other major companies such as Amazon Web Services and Nokia.
Marco Argenti, CIO of Goldman Sachs, left, and Robert Kyncl, CEO of Warner Music Group, right. Credit: Patrick McMullan/PMC via Getty Images
He said that technology engineering courses are not enough as AI expands into complexity, because AI code can be technically correct but not do what it is supposed to do.
If an engineer doesn't get it right, AI will create code that can be somewhat correct in the best case scenario and completely incorrect in the worst case scenario. But philosophy-based engineers must have the reasoning and first-principle thinking skills to keep pace.
The question for engineers won't be, “Can you code?” but “Can you get better code from your AI by asking the right question?”
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Rapid AI engineering, or communicating with AI in the best words to get the desired result, is an emerging field. Jaime Teevan, Principal Scientist at Microsoft, has written in December that communicating with AI is different than telling a team member what to do. AI needs more context and may need rewording or examples before producing a workable result.
PricewaterhouseCoopers partner Robin Stein said about it CIO publication that PwC “realized very quickly that we needed to upskill our 75,000 people on the fundamentals of generative AI, how to apply general AI responsibly, and how to become a fast engineer.”
Argenti emphasized that in an era of AI hallucinationsit was even more important to ask the right questions and use a philosophical mindset.
“Having a clear mental model about a problem, being able to break it down into steps that are solvable, perfect first-principle thinking, sometimes being prepared (and able to) debate an AI stubbornness – these are the skills that will make an engineer successful in the future, and the same consideration likely applies to many job categories,” wrote Argenti.