Inside The MIT AI Study Everyone Misunderstood (And What It Means For Startups)

**Garry** (0:00)
Engineering teams at these orgs are filled with people that themselves don't actually really believe in AI, don't use cogen tools, think it's all super overhyped, are really excited when an MIT study comes out saying that it's all like hype and retweeted, and really want because it's a narrative they want to believe. But the consequence of that for the companies is that they can't build the product. So if your engineers don't believe in this, then how are you going to build a product that actually works? The knock on effect for start-up centers, if you can actually build something that works, the enterprises will talk to you because they have no other options. You can't build it internally, you can't go to an established company, so the start-ups are actually getting the shot that they never had before.

**Harj** (0:35)
I guarantee you someone's watching this right now and you've just horribly triggered them. Welcome back to another episode of the Lightcone. One of the things that has been really pissing me off is these AI influencers. You see them on X, you see them on YouTube, and they're claiming that 95% of AI projects are failures, and that's proof that AI is a scam. What's the real story, Jared? You actually dug in to the MIT report that these people are grifting with. What does the report actually say?

**Jared** (1:17)
What really went viral was like tweets about this study, and I think the tweets are actually quite misleading. Diane and I were talking to a bunch of college students recently, and they had concluded just by reading like the tweet version of the study, that like, oh, all these AI startups that YC is talking about, like, must not be working because the study says that they all fail. But actually, the more I read the study, the more I realized it was actually confirming a lot of the things we've talked about here on this podcast about what AI agents are really like in the real world and what approaches and categories are working. And so I thought it'd be interesting for us to talk about what the study really says.

**Diana** (1:54)
Because it's a very different approach to the go-to-market for all these AI solutions. It's not just standard enterprise sales. I think one of the big things that we talk a lot about is this aspect of teams, startup and founders embedding themselves into the business processes and really groping a lot of the internal systems of record and going deep, deep, deep in the integration, which is not something that has been typically done in the SaaS world. SaaS was like very plug and play, which is different. But when you do succeed and plug into the systems of record, the pot of gold is actually quite big. But it does take a long time. We actually have a lot of examples and work with companies that have succeeded, which we can talk about later.

**Jared** (2:40)
You had a really great way of having a mental model of what typically happens when an enterprise tries to adopt AI, and why the failure rate is so high. Can you give me some intuition? Yeah.

**Harj** (2:51)
If you think about enterprises are trying to get something done, and they've got internal IT, or sometimes when internal IT doesn't do it, they go out and they get an Ernst & Young, or they get some much bigger consulting shop at Deloitte to come in.
And it turns out if anyone has ever used internal IT systems, generally, internal IT systems are bad. And then not only that, if you decide that you can't build it in house, and you have to go to consulting, well, now you've got two problems. The output of the study is no surprise to me, in that the majority of software that actually gets built in the world is very, very bad. To be fair to IT consultants, Apple is very bad at software. You know, my favorite example is Apple of the company that can have infinite access to capital and infinite access to the smartest people in the world. All of us use iPhones, and I use the calendar app. I think you guys do too. We use it many times per day due to our schedules. And even the calendar app is a piece of trash. You know, you probably run into some sort of weird bug in that, like, almost every single day. So Apple, a company with infinite resources and infinite access to the smartest people in the world, cannot make a good calendar app. So, you know, if that's true for Apple, how could any normal company, let alone an internal IT system, let alone, like, Deloitte or Ernst & Young, like, very well-meaning people, but, like, you know, most of the time, the output of something like that is bad.