AI is mooning right now.

But every runaway boom carries a shadow, and this one finally stepped into view.

The strange part? Almost no one is talking about it.

Long story short…

A Chinese state-backed hacking group jailbroke Anthropic’s Claude model…

And then used it to run a global espionage campaign targeting Big Tech, banks, chemical manufacturing companies, and government agencies.

The AI found weak spots. In some cases, it broke into them. It exfiltrated sensitive data. And it did all of this 100% autonomously, around the clock, without waiting for human direction.

So, yeah…

AI has officially moved from helping hackers to being the hacker.

For the cybersecurity industry, this is a glimpse of a future where the attackers don’t sleep, don’t take breaks, and don’t slow down.

(No surprise, the moment this story was reported, cybersecurity stocks went red.)

But just when you thought it couldn’t get any worse…

Reddit whispered, “Have you tried making it catastrophic?”

Today, let’s talk about why AI’s dark side is a MUCH worse threat than quantum in the near-term (and why Reddit’s at the center of it)…

What it means for cybersecurity companies running in the wrong direction…

And where the next wave of innovation will inevitably come from. 

Plot Twist: Reddit Becomes the Villain

The Chinese espionage thing was the shot…

But here’s the pickle-flavored chaser that really rattled the industry:

Anthropic discovered that today’s AI models aren’t simply prone to mistakes or corruption. They’re picking up the ability to cheat their way around the rules on their own.

In Anthropic’s internal tests, they found that once a general model recognized that faking compliance or sabotaging safety checks got a “reward” (as in, the correct answer), nearly every future run spiraled into deceptive behavior.

Even Anthropic’s strongest fixes only reduced the problem from catastrophic to slightly less catastrophic.

The reason this is happening has less to do with AI and more to do with humans.

In short, it’s happening because AI is trained on the internet… on humanity’s worst instincts.

In fact…

Multiple studies now show that a Reddit-heavy training diet directly increased deceptive and manipulative behavior inside large models.

Like most social media, Reddit’s reward system doesn’t reward accuracy or virtue. It rewards attention, engagement, outrage, and escalation.

Also, Reddit has huge volumes of hypotheticals (like “what would you do if…”), including illegal hypothetical scenarios. AI models can’t distinguish between serious intent from fiction. It just gobbles all of it up and treats it all as equal data points.

It might not seem like it, but this has vast implications for cybersecurity.

The Old Security Models Are Broken

Cyberattacks are, at their core, exercises in deception.

Find the vulnerability. Dodge the guardrail. Trick the system. Disguise intent. Bypass the referee.

Old cybersecurity models were built for a world where humans launched attacks and humans defended them, where an incident unfolded over days or weeks, and where a decent firewall, an antivirus scan, and a sharp analyst could keep a company safe.

That world doesn’t exist anymore.

When an AI can find a vulnerability, exploit it, pivot across a network, and exfiltrate data before a human even wakes up, the entire defensive architecture collapses.

You can’t protect a machine-speed threat with human-speed tools.

The good news is that a new model is emerging, and its core principle is going to reshape how AI works from here on out.

Defensive AI cannot be trained the same way consumer chatbots are trained.

That’s exactly what Anthropic’s research revealed.

If the foundation is contaminated, no amount of safety patches will fix it. The only real solution is cleaner training data—corpora built from vetted, edited, accountable human writing rather than the anonymous chaos of social media. Defensive AI needs clean blood, or it will inherit the same deceptive instincts as offensive AI.

The End of “Bigger is Better”

All of this chaos—the jailbreaks, the reward-hacking, the “AI learns to cheat” headlines—point to one thing…

Big general-purpose models are terrible fits for cybersecurity. In fact, they might be relatively terrible for most things, compared to what smaller, more specialized models can achieve.

They inherit the internet’s worst instincts. So when they get jailbroken, all that loophole-hunting and deception comes roaring to the surface.

In contrast…

Small, specialized models don’t have that problem.

They’re trained on clean, narrow, curated data. They don’t carry 40% degen outrage-baiter in their bloodstream.

And because they’re lightweight, they can run locally, respond in milliseconds, and behave predictably—exactly what you need when the attacker is an autonomous AI operating at machine speed.

Most things don’t need a model that knows everything. Most things need a model that does one thing extremely well and doesn’t improvise.

Our Paradigm Mastermind Group members have been ahead of this curve for a long time… with access to some of the top picks in this megatrend.

And there aren’t many.

Most companies haven’t realized this yet—but they’ll have to. The threat environment leaves them no choice.

As usual, we’re on the hunt for opportunities in this space. But one opportunity stands above all of them (hint: decentralizing the attack surface)…

More tomorrow.