u/Aromatic-Result-6091

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Weird thing I’ve been noticing lately, a lot of online content now feels optimized less for humans and more for AI systems and algorithms.

SEO pages, LinkedIn posts, newsletters, even comments sometimes feel engineered for discoverability rather than genuine communication.

And now with AI-generated content flooding the web, it feels like we’re entering a loop where:

AI generates content → platforms rank it → other AI models train on it → more AI content gets generated.

At some point, does the internet just become synthetic knowledge talking to itself?

Curious if others feel this shift too, or if this is just the natural evolution of the web.

A lot of people frame AI as this future replacement threat, but honestly it feels like the first thing it’s exposing is how much white-collar work was already template-driven.

Slide decks, reports, content, summaries, outreach, brainstorming, a huge chunk of “knowledge work” seems to collapse pretty quickly once language models get good enough.

Which makes me wonder whether the real value going forward shifts from execution → judgment, taste, and decision-making.

Feels like we’re entering a phase where being “good at doing tasks” matters less than being good at deciding what’s worth doing in the first place.

Curious if others think AI is actually replacing work, or just revealing how repetitive a lot of work already was.

If Claude Mythos can autonomously discover large numbers of zero-day vulnerabilities, then the real issue isn’t just capability, it’s distribution.

Right now, that level of capability sits behind closed doors, controlled by a small group.

On one hand, that’s safer.
On the other, it creates a massive concentration of power with very little external scrutiny.

There’s an argument that broader access (even controlled) could actually strengthen security ecosystems, because more people can identify and patch issues faster.

Curious where people land on this, is restriction the safer path, or does it just delay a bigger problem?

The whole Claude Mythos story is wild if you think about it seriously.

An internal model identifying thousands of zero-day vulnerabilities across systems like Linux, OpenBSD, and major browsers isn’t just a “cool capability”, it fundamentally changes the asymmetry between defenders and attackers.

At that point, the limiting factor isn’t intelligence anymore, it’s access.

Which raises a bigger question:
how far ahead are internal models compared to what’s publicly available?

Feels like the gap might be larger than most people are comfortable admitting.

One thing I’ve been noticing is that AI doesn’t necessarily make better decisions, but it shortens the feedback cycle significantly.

You can test more ideas, get signals faster, and iterate quicker but you still need a strong framework to interpret results.

In that sense, AI feels more like a feedback acceleration layer than a decision engine.

Curious how others are thinking about this, is AI actually improving decision quality, or just speeding up the learning process?

Most AI tools in marketing right now seem to optimize for execution speed: content generation, campaign setup, automation workflows.

But when it comes to actual performance metrics like CTR, conversion rate, or ROAS, the impact feels inconsistent.

It almost seems like AI has solved the “how fast can you execute” problem, but not the “what actually works” problem.

Curious if others are seeing real performance improvements from AI, or mainly efficiency gains.