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What does GPT-3 mean for the future of the legal profession?

GPT-3 provides some new tools in a legal department’s arsenal and will be focused on assessing practical, impactful solutions, hopefully making better legal organizations in the process.
Rudy DeFelice Contributor Rudy is co-founder and CEO of Keesal Propulsion Labs, a digital transformation company serving the law departments of the Fortune 500. Rudy is an attorney, technology entrepreneur, TEDx speaker and best-selling author. He is a an alumnus of Harvard Business School and the University of Connecticut School of Law.

One doesn’t have to dig too deep into legal organizations to find people who are skeptical about artificial intelligence.

AI is getting tremendous attention and significant venture capital, but AI tools frequently underwhelm in the trenches. Here are a few reasons why that is and why I believe GPT-3, a beta version of which was recently released by the OpenAI Foundation, might be a game changer in legal and other knowledge-focused organizations.

GPT-3 is getting a lot of oxygen lately because of its size, scope and capabilities. However, it should be recognized that a significant amount of that attention is due to its association with Elon Musk. The OpenAI Foundation that created GPT-3 was founded by heavy hitters Musk and Sam Altman and is supported by Mark Benioff, Peter Thiel and Microsoft, among others. Arthur C. Clarke once observed that great innovations happen after everyone stops laughing.

Musk has made the world stop laughing in so many ambitious areas that the world is inclined to give a project in which he’s had a hand a second look. GPT-3 is getting the benefit of that spotlight. I suggest, however, that the attention might be warranted on its merits.

Why have some AI-based tools struggled in the legal profession, and how might GPT-3 be different?

1. Not every problem is a nail

It is said that when you’re a hammer, every problem is a nail. The networks and algorithms that power AI are quite good at drawing correlations across enormous datasets that would not be obvious to humans. One of my favorite examples of this is a loan-underwriting AI that determined that the charge level of the battery on your phone at the time of application is correlated to your underwriting risk. Who knows why that is? A human would not have surmised that connection. Those things are not rationally related, just statistically related.

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