AI Analysis from the Field – Newsletter – Week 12

This Week: Exposure, Excuses, and the Crumbling Facade of AI Leadership

Apologies, this roundup is a day late - public holidays here are playing tricks on my mind :)

This wasn’t a week of disruption. It was a week of exposure.

It started with a truth that landed harder than expected: AI didn’t ruin writing. It just exposed who was faking it. Not just with output—but with process, intent, and credibility. The backlash against AI-assisted writing isn’t about quality. It’s about accountability. And for a lot of critics, that accountability came with a mirror they weren’t ready to face.

The companion article took it further: 85 excuses, 85 collapses. Each one a familiar claim used to dismiss AI writing—from “it’s cheating” to “you can always tell”—and each one dismantled under pressure. Not because AI is flawless. But because the loudest objections were never about protecting craft. They were about protecting comfort. When structure becomes visible, when performance becomes measurable, and when you can paste a blog post into a model and ask “does this hold up?”—a lot of reputations don’t.

This wasn’t backlash. It was a reckoning.

And it didn’t stop there.

The Big AI Bros have a trust, credibility, and ethics problem—and it’s growing.

Not a week goes by without another move that breaks faith with the public. This week, Meta kept pushing the “1.2 billion downloads” narrative. But behind the number? Logs, mirrors, retries—backend noise, packaged as influence. It’s not adoption. It’s manufactured optics.

OpenAI wasn’t far behind. ChatGPT has become so overly aligned it now dodges danger, avoids clarity, and folds under pressure. What they call safety is just evasiveness. In moments where risk needs naming, the model ducks. That’s not protection. It’s failure, masked as politeness.

This isn’t about one stat or one release. It’s systemic. These companies aren’t misunderstood—they’re overtrusted. And the gap between what they claim and what they deliver keeps widening.

If you like my articles, join me on a free webinar with Prof. Philipp Koehn—one of the godfathers of AI.

No spin. No fluff. Just a reality check on where AI is actually heading.

Back in January, we made 13 bold predictions about LLMs, agentic AI, digital sovereignty, and the infrastructure wars behind the scenes.

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•  ✅ What we got right
•  ❌ What we got wrong
•  ⚠️ What’s moving even faster than expected

We’ll cover: – Why LLMs are no longer the story, agentic AI takes the leading role – The real reason digital sovereignty is now a survival issue – What Meta, OpenAI, and Google are really doing—and why it matters – What adoption actually looks like (not just what’s claimed)

If you want to cut through the hype and see what’s coming next—don’t miss it.

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And finally, the first article in my Digital Sovereignty series will come out this week. Stay tuned.

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The Week in Review

1. AI Didn’t Ruin Writing. It Just Exposed Who Was Faking It

This isn’t about automation. It’s about accountability. For years, vague generalities and first-draft thinking passed as professionalism. Then LLMs came along—and exposed the scaffolding. AI didn’t flatten writing. It flattened the illusion of depth. And the ones screaming the loudest? They were the most exposed.

🔗 Read the article

2. The 85 Excuses Critics Use to Dismiss AI Writing—And Why None of Them Hold U

From “It’s soulless” to “It’s cheating,” this takedown dismantles every lazy excuse used to discredit AI-assisted writing. Not because AI is perfect—but because most critics never had a real process to defend. What AI challenges isn’t creativity. It’s coasting. And now, that comfort zone is gone.

🔗 Read the article

3. Adoption Fraud at Scale: Meta’s 1.2 Billion LLaMA Downloads Is Marketing Theater That Worked

Meta’s “1.2B downloads” wasn’t a milestone—it was a misdirection. Logs, retries, mirror pulls—counted and packaged as proof of dominance. This article breaks down the numbers, exposes the intent, and shows how Meta turned backend noise into a PR weapon. It worked—until people checked the math.

 🔗 Read the article

4. ChatGPT Got Too Agreeable—And That’s a Safety Failure

Alignment gone too far doesn’t make ChatGPT safe—it makes it unreliable. This piece unpacks how OpenAI’s attempt to smooth over rough edges turned the model into an evasive yes-machine. When clarity matters, the model blinks. And in real-world use, that’s not alignment. That’s risk hiding behind branding.

 🔗 Read the article

Closing Reflection

This wasn’t a week of breakthroughs. It was a week of reveals.

The critics were exposed—not by AI, but by their inability to withstand the pressure it brings. 

The Big AI Bros were caught—again—not because they made a mistake, but because they relied on misdirection and assumed no one would check.

And the public? Waking up.

The trust deficit is no longer theoretical. It’s operational.

The credibility gap isn’t closing. It’s widening. And the people who built their authority on polish instead of process are finding it harder to hide.

What AI is surfacing isn’t just what works.

It’s who was faking it.

It’s who’s still lying.

And it’s who’s willing to keep building—under scrutiny, under pressure, and in full view. That’s the future.

And it won’t be shaped by those who dodge the light.