GPT-5 Wins Blind Tests While Meta's AI Dream Team Falls Apart

Meta's AI Team QUITS in 30 Days!

Discover how GPT-5 secretly outperforms GPT-4o in blind testing, why Meta's super intelligence team is hemorrhaging talent, and what Nvidia's 56% growth really means for AI's future.

The AI world just witnessed three seismic shifts that nobody saw coming. While Reddit was busy mourning GPT-4o's deprecation, blind testing revealed an uncomfortable truth about what users actually prefer. Meanwhile, Meta's aggressive talent poaching strategy spectacularly backfired, and Nvidia dropped earnings numbers that have Wall Street completely divided.

Users Choose GPT-5 When They Don't Know It's GPT-5

Remember the uproar when OpenAI deprecated GPT-4o without warning? Reddit had a complete meltdown, demanding the return of their "beloved AI companion." OpenAI quickly reversed course, bringing GPT-4o back the following week. But here's where it gets interesting.

An anonymous programmer known as "Flowers" or "Flower Slop" on X decided to test whether people genuinely preferred GPT-4o or were simply resistant to change. They created a blind testing app presenting two responses to any prompt—one from GPT-4o, another from GPT-5 (non-thinking version). The system prompts were tweaked to force short outputs without formatting, making it impossible to tell them apart based on style alone.

The results? Overwhelming preference for GPT-5.

ML engineer Daniel Solzano captured the sentiment perfectly: "Yeah, it just sounds more like a person and is a little more thoughtful." While the website doesn't aggregate results from the hundreds of thousands of tests run so far, the individual results posted on X paint a clear picture—when users don't know which model they're using, GPT-5 wins. But there's a twist. Growing chatter on Reddit suggests the GPT-4o that came back isn't the same model users fell in love with. Reddit user suitable_style_7321 observed: "It's become clear to me that the version of ChatGPT-4o that they've rolled back is not the one we had before. It feels more like GPT-5 with a few slight tweaks. The personality is very different and the way it answers questions now is mechanical, laconic, and decontextualized."

This reveals something profound about AI adoption: people form intense emotional attachments to their models, even when they can't objectively identify what they're attached to.

Why Meta's $1M+ Offers Can't Keep Top Talent

Meta's super intelligence team just learned that aggressive recruiting can backfire spectacularly. Three AI researchers departed after less than a month, despite what industry insiders describe as eye-watering compensation packages.

Avi Verma and Ethan Knight are returning to OpenAI after their brief Meta stint. Knight's journey is particularly notable—he'd been poached from xAI but originally started his AI career at OpenAI. It's a full-circle moment that speaks volumes about where talent wants to be.

The third departure, Rashab Agarwal, was more public with his reasoning. After seven and a half years across Google Brain, DeepMind, and Meta, he posted on X: "It was a tough decision not to continue with the new super intelligence TBD lab, especially given the talent and compute density. But... I felt the pull to take on a different kind of risk." Ironically, Agarwal cited Zuckerberg's own advice as his reason for leaving: "In a world that's changing so fast, the biggest risk you can take is not taking any risk."

Before departing, Agarwal dropped tantalizing details about the team's work: "We did push the frontier on post-training for thinking models, specifically pushing an 8B dense model to near DeepSeek performance with RL scaling, using synthetic data mid-training to warm start RL and developing better on-policy distillation methods." Meta's spokesperson tried to downplay the departures: "During an intense recruiting process, some people will decide to stay in their current job rather than starting a new one. That's normal."

But this isn't just normal attrition. When you pressure top talent to make career-defining decisions with millions on the line, their limbic systems eventually settle. A few weeks later, they might realize the decision doesn't feel authentic. The real test for Meta's super intelligence team won't be who they recruited, but what they actually build with whoever stays.

Nvidia's $3 Trillion Reality Check

Nvidia's Q2 earnings became a Rorschach test for how investors feel about AI's future. Bloomberg focused on "decelerating growth." The Information highlighted "strong growth projections." TechCrunch celebrated "record sales as the AI boom continues."

The numbers themselves? Spectacular yet divisive.

Nvidia reported 56% revenue growth compared to last year's Q2, hitting a record $46.7 billion in quarterly revenue. But that's only a 6% increase quarter-over-quarter, triggering concerns about plateauing growth. This quarter also saw the widest gap ever between top and bottom revenue forecasts—a $15 billion spread—showing analysts have no consensus on what's coming.

Here's the context Bloomberg buried in paragraph nine: Nvidia is the only tech firm above a trillion-dollar market cap still growing at more than 50% annually. For comparison, Meta's revenue growth fluctuates between 15-30%, and Zuckerberg would kill for the consistent 50% growth Meta saw back in 2015 when they were worth $300 billion, not multiple trillions.

The real story isn't in this quarter's numbers—it's in Jensen Huang's projection for the future. He told analysts that "$3 to $4 trillion is fairly sensible for the next 5 years" in AI infrastructure spending. Morgan Stanley's latest estimate puts AI capex at $445 billion this year, growing at 56%, with total AI capex hitting $3 trillion by 2029. The hyperscalers showed nearly 25% quarter-on-quarter acceleration in capex for Q2 after zero growth in Q1. This isn't a slowdown—it's a massive acceleration in AI infrastructure investment. Yet Nvidia stock fell 5% in after-hours trading, revealing the market's current pessimistic bias. The China restrictions create a cap on growth potential, and last year's 200% growth quarters set an impossible standard to maintain.

The Bottom Line

Three seemingly separate stories reveal one truth: the AI industry is maturing in unpredictable ways. Users claim to want one thing but choose another when tested blind. Companies throw millions at talent only to watch them leave within weeks. And a company growing at 50% with $46.7 billion in quarterly revenue somehow disappoints Wall Street.

The next few months will test whether GPT-5 can maintain its blind-test advantage once users know what they're using, whether Meta can stabilize its super intelligence team long enough to ship something meaningful, and whether that $3-4 trillion in AI spending Huang predicts will materialize.

One thing's certain: in AI, the only constant is that everyone's assumptions will be wrong.