Gabe Newell Anticipates Shift as AI Enables Beginners to Outperform Seasoned Programmers
He claims even pure users will benefit from using AI tools.
Gabe Newell, the co-founder of Valve Corporation, recently gave an interview to YouTuber Zalkar Saliev. The full interview is yet to surface but a few shorts from the conversation have been released, including one about Newell's daily routine ("get up, work, go scuba diving"), and his thoughts on why startups chasing VC money makes no sense.
Takes on AI
Newell is asked at one point about the hot topic of the moment: AI. It's a field rife with promises, where the ultimate white whale is the search for artificial general intelligence (AGI), and there's little agreement on the best use cases for the technology as it is now.
To take one example, you'll find people claiming that LLMs will soon replace human programmers, while others will rightly point out that LLM code is often filled with errors and needs so much human oversight it may be counter-productive.
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That's the question put to Newell by Saliev: should younger folk looking at this field be learning the technical side, or focusing purely on the best way to use the tools?
"I think it's both," says Newell. "I think the more you understand what underlies these current tools the more effective you are at taking advantage of them, but I think we'll be in this funny situation where people who don't know how to program who use AI to scaffold their programming abilities will become more effective developers of value than people who've been programming, y'know, for a decade."
Newell goes on to emphasise that this isn't either/or, and any user should be able to get something helpful from AI. It's just that, if you really want to get the best out of this technology, you'll need some understanding of what underlies them.
"Even if you're just a pure tool user you're going to find that the gains to utilising those tools are very, very high," says Newell. "But your ability to use those tools will continue to improve the more you understand the underlying methods and mindsets of people developing machine learning systems. So I think it's both, and I think they're highly complementary."
