New MIT study says most AI projects are doomed...

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Category: AI Trends

Tags: AIdevelopmentinvestmentsproductivitysoftware

Entities: Eric VaughnIgniteMark ZuckerbergMetaMITOpenAISam AltmanTupil

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Summary

    AI Industry Challenges
    • Mark Zuckerberg has put a freeze on AI hiring at Meta despite recent investments.
    • There is growing concern about an AI bubble, with 95% of AI-driven projects failing according to an MIT study.
    • Sam Altman suggests that investor excitement about AI might be overblown.
    AI in Software Development
    • AI coding tools have not significantly increased productivity for developers, with some feeling less efficient.
    • The MIT study highlights that failures in AI projects are often due to human errors and misalignment with operations.
    • Successful AI implementations require third-party tools rather than in-house development.
    Success Stories
    • Ignite CEO Eric Vaughn successfully replaced 80% of developers with AI, resulting in 75% profit margins.
    Actionable Takeaways
    • Evaluate the necessity and potential of AI before investing heavily.
    • Consider third-party AI tools instead of building in-house solutions.
    • Focus on aligning AI tools with existing workflows and operations.
    • Recognize the limitations of AI and maintain realistic expectations.
    • Explore remote collaboration tools like Tupil for enhanced team productivity.

    Transcript

    00:00

    Last week, Mark Zuckerberg put a freeze on all AI hiring at Meta, just weeks after spending billions of dollars poaching top talent from competitors like Open AI. Meanwhile, in a story few could have foreseen, everybody in Silicon Valley seems to be talking about an AI bubble.

    In part, because it was

    00:16

    recently revealed that 95% of AIdriven projects fail. And that's not just some random number I pulled out of nowhere.

    It's based on an MIT study that analyzed the results of companies using AI. It spooked investors who are relying on AI to maintain irrational exuberance in the

    00:31

    markets. And Sam Alman himself said this.

    Are we in a phase where investors as a whole are over excited about AI? In my opinion, yes.

    So, in today's video, we'll find out if the AI hype train is about to reach its terminus. It is August 25th, 2025, and you're watching the code report.

    I've been using AI

    00:47

    coding tools from the very beginning because, believe it or not, I actually hate writing code. I do like developing software, and the code is just a means to an end.

    After multiple years of using AI to write code, I still don't feel like a 10x developer. Sometimes I feel like a 2x developer, while other times I feel more like a 0.5x developer.

    And

    01:04

    apparently, I'm not the only one. This recent study from MIT analyzed 300 public deployments, interviewed 150 leaders, and surveyed 350 employees connected to recent AI integrations.

    We're talking about 30 to 40 billion in enterprise investment into generative

    01:19

    AI. And yet it was found that 95% of them failed to achieve the goal of rapid revenue acceleration.

    In fact, almost all of them experienced little to no measurable impact on the bottom line. In addition, the study found that companies that tried to roll out their own AI tooling had a much higher failure rate

    01:36

    because why pay for an AI tool when you can build a worse version yourself. Companies that paid a third party were better off.

    And I think the moral of the story here is that it's a great time to be an enterprise AI shovel salesman. But despite the high failure rate in the study, there are some great success stories out there.

    Like back in 2023,

    01:52

    enterprise software company Ignite CEO Eric Vaughn fired 80% of his developers and replaced them with AI. It's now 2 years later and he has no regrets and says the decision is now delivering 75% profit margins.

    And ultimately, the interpretation of the MIT study was that

    02:08

    it's not the fault of the AI models that the AI sucks at making money. The models are definitely smart enough.

    It's just the humans suck at using them. It's nothing but a skill issue.

    The AI integrations failed due to brittle workflows, lack of context, and misalignment with day-to-day operations.

    02:23

    Many have failed to realize that AI vibe coding is almost identical to crack. After the first hit, you feel invincible, like you could write a billion dollar piece of software in hours.

    But then 200 hits later, you've got nothing but errors in $100,000 cla bill, and you're still convinced that

    02:38

    the next prompt is going to be the one that fixes it all. With all the slop intensifying, it looks like programmers should still have a job writing code for the foreseeable future, which is why you need to check out Tupil, the sponsor of today's video.

    It's a remote pair programming app for Mac OS and Windows that's loved by teams at Shopify, Clerk,

    02:55

    and many more. That's because it gives you high-risisk screen sharing that lets you see the tiny text in each other's IDE, plus shared remote control with super low latency, so it feels like your team is working together on a single machine.

    It's like Figma and Zoom had a baby that was built specifically for

    03:10

    developers, but the entire app is written in C++, so it won't hog all your CPU cycles either. You can try Tupal for free at the link below, or use code fireship to get a special discount for your entire team.

    This has been the Code Report. Thanks for watching and I will see you in the next