Name: If an AI agent Makes a Mistake, Who is Responsible?
Uploaded: 2026-04-26T19:00:00Z
Duration: 3 min 18 s
Description: Box co-founder and CEO Aaron Levie explains the new corporate paradox facing companies deploying autonomous AI. From the impossibility of humans manually reviewing tens of thousands of lines of AI-generated code to the risks of going "full throttle" on automation. Levie explores why the push for unprecedented efficiency inevitably clashes with the need for human accountability.

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Box co-founder and CEO Aaron Levie explains the new corporate paradox facing companies deploying autonomous AI. From the impossibility of humans manually reviewing tens of thousands of lines of AI-generated code to the risks of going "full throttle" on automation. Levie explores why the push for unprecedented efficiency inevitably clashes with the need for human accountability. Photo: Nikki Ritcher for the WSJ Leadership Institute

https://m.wsj.net/video/20260420/cfe7af23-08ab-4c1a-83c4-2be3cea63617/1/hls/manifest-hd-wifi.m3u8

Box co-founder and CEO Aaron Levie explains the new corporate paradox facing companies deploying autonomous AI. From the impossibility of humans manually reviewing tens of thousands of lines of AI-generated code to the risks of going "full throttle" on automation. Levie explores why the push for unprecedented efficiency inevitably clashes with the need for human accountability.

If an AI agent Makes a Mistake, Who is Responsible?

This is an unresolved paradox of AI, which is as an agent does more and more work in the background, that's how you get productivity gains. If you're just going back and forth and you're constrained by your ability to go back and forth to the agent, then there's limited productivity upside. It's like, you know, you'll get like 20% productivity upside or something like that. If an agent can go off and actually do like real hours of work in the equivalent of hours of work in minutes, now you're talking could be 2x, 5x, 10x productivity gain. The challenge is it's gonna make a series of decisions that are gonna be harder and harder to kind of parse all the little tiny elements that it used across that decision. And so the human in the loop element, we have greater degrees of abstraction of what the human in the loop is able to really process. And so the most extreme case that's happening in Silicon Valley right now in engineering, you can kind of look to engineering as effectively the benchmark of like where AI is going. Now there's gonna be a longer gap as it's actually gonna be interesting. The faster you see things change in engineering, you'll actually see a wider gap between the rest of the world and what it's gonna take to get the rest of the world to be able to use agents like engineering. So it's not like these are gonna be fast follows in the rest of knowledge work. And we can get into why if you want, but if you look at engineering about a year ago, let's say, it was like completely the best practice. Everybody agreed you have to review the code that the agent did. And then we said, oh, actually, maybe what we'll do is we'll have an agent review the code that another agent wrote and then you'll just review the final review of that agent that did the review. And now at the real, real state of the art of like the fastest moving startups are kind of in the position of saying like, if the thing tests and is verified as tests complete, like we're gonna ship it, and the human... Because a human can no longer look through 50,000 lines of code.

And do you think they've gone too far or you think that's okay?

Depends on what kind of system you're working on. And this is actually gets now that this might get a little closer to-

The Pentagon thing.

Yeah. Like I actually wouldn't do that type of approach for missiles. Would I do that for an internal form where you had to build a quick application for some employee experience thing? A lot, probably, safer. A lot riskier, if it's a trading platform, if it's a healthcare system, if it's a government weapons system. Lot harder to do that.

And the truth is we probably make those kinds of decisions all the time. You know, how closely do you read that contract? How closely do you read that language?

But the big difference is I can still hold you accountable for not reading that contract.

If you didn't do it, you accountable.

And so this is so the fun paradox. This is where the tension's gonna get and we are gonna be, you all are gonna be like at the center of this, it's gonna be hilarious, so-

They're having a great time.

So we are going to be like full throttle, we have to go AI first and everything. At the exact same time, when AI takes down your website, you're gonna be like, "Well, like, who do we blame?" And eventually you're gonna find, you probably have to blame the engineer that used AI-

Went full throttle.

That went full throttle. But then they're gonna say, "You were the one that told me to go full throttle," and we're gonna have this loop for a while. And this is why, you know, there's no free luncheon in automation.

The WSJ Leadership Institute Presents: Leaders. A new podcast series bringing you revealing conversations with top executives shaping business today. Hosted by Alan Murray, president of the WSJ Leadership Institute, the series goes beyond headlines to explore how leadership really works in times of upheaval, transformation, and uncertainty. Our first episode was released Monday, Sept. 22, at 5:00 a.m. ET, with new episodes every other Monday.