The Middle Path: Why AI's Future Isn't Hype or Doom

We had a great conversation with Jing Hu, founder of Second Order Thinkers. She's a journalist and researcher who's made a name for herself by saying what most AI commentators won't: the uncomfortable parts– the data that doesn't fit the narrative—the risks everyone wants to ignore.

» Subscribe to her newsletter here.

Jing's not a cheerleader or a doomer. She reads the studies, tracks the implementation data, and reports what she finds—even when it contradicts the prevailing wisdom. In our conversation, we challenged each other on AI's real impact on creativity, jobs, and thinking itself. We discussed taste as a competitive moat, why AI collapses into mediocrity, and the paradox facing every content creator today.

Listen to parts of the conversation in the audio below, then read the full exchange.

Want to collaborate with us as Jing did? Learn more here.

Now, here's what we discussed with Jing:

There's a strange paradox in how we talk about AI.

On one side: the cheerleaders. Every LinkedIn post promises 10x productivity. Every vendor claims their tool will revolutionize your workflow.

On the other hand, the doomers. Job apocalypse predictions. Warnings about cognitive decline. Think pieces about the death of creativity.

What almost no one does? Talk about the messy, complicated middle ground where most of us actually live.

Jing doesn't mince words about her role.

Her newsletter has built a following by doing exactly what most AI content refuses to do: presenting uncomfortable data without optimistic spin.

"Right now, there are so many people in the media saying everything is good, that all technology will change life for the better. But very few are emphasizing the real risks that exist."

But here's what makes her perspective valuable: she's not pushing doomsday scenarios either. She's reading studies, reviewing implementation data, and tracking what happens when companies actually deploy AI at scale.

And what she sees is more nuanced than either extreme would have you believe.

Jing's been wrestling with this tension for months. Earlier this year, she spoke with Hessie Jones for Forbes about how convenient AI use might be reshaping critical thinking skills. The core question: Are we trading efficiency for cognitive depth? 

One of Jing's most powerful metaphors cuts right to the heart of AI adoption:

"It's like getting people from the couch to the gym. People who go to the gym start working out. But it's really the getting up, putting on their shoes, knowing that you're going—that is difficult."

The studies show that structured prompting can improve reasoning quality by 50%. The methods exist. The frameworks work.

But here's the catch: most people won't use them.

I pushed back: "Does it decrease cognitive skills? Probably yes. But is the outcome necessarily worse? AI is perfectly capable of generating very mediocre outputs. But very often people also generate very mediocre outputs."

It's a fair tension. Yes, AI might make us mentally lazy. But it is also raising the floor for people who were already producing mediocre work.

Jing pointed out something most AI enthusiasts miss:

"It does boost an individual's creativity when that person isn't an industry-specific expert. You are an expert in marketing. So when you prompt the AI with something, its output will likely be plain to you. At the same time, I have a background in STEM. I'm not familiar with marketing. So whatever recommendations from AI to me will feel like, wow, that's impressive."

To the novice, AI outputs look magical. To the expert, they look obvious.

Think about it: if you're a marketer asking ChatGPT for campaign ideas, you're probably underwhelmed. But if you're an engineer asking for marketing advice, suddenly AI seems brilliant. AI democratizes access to mediocre expertise. That's genuinely valuable for people far from a domain. It's just not impressive when you know what you're looking at.

This is where I introduced what's become a core thesis for AI Ready CMO. We wrote an entire series around it: taste.

Jing's response reframed it through a technical lens: model collapse.

"You can just think of AI, especially the chatbots we use, as an extension of the internet. If you search 'how do I do SEO properly' on Google, the first ten pages are probably something really similar. Now, imagine that becomes training data. The training data is really concentrated in this commonly known knowledge."

The result?

"It's inevitable. Whenever you prompt something, it just sounds all very similar."

AI trained on the internet's consensus view will always give you the internet's consensus view. It collapses into mediocrity—the lowest common denominator.

"Whereas with humans, it's just different. We have history, we have experience. You prefer some taste of water. The other person probably just started to drink tea in the afternoon. That brings us very different life experiences. And that's what AI is lacking."

But Jing went further. She broke down the creative process into four steps:

"There's only one tiny piece of the current workflow that AI can replace."

Even in drug discovery—where AI has made legitimate breakthroughs—AlphaFold can generate protein folds, but it can't decide which disease to cure, which protein to use, or how to run clinical trials.

One piece of the puzzle. A powerful piece. But just one.

Then the conversation took a darker turn.

Jing shared a study that should worry every content creator:

"AI now prefers content generated by AI."

Let that sink in.

If you want your content discovered, it increasingly needs to pass through an AI filter first.

But here's the paradox:

"If a human can sense that your stuff is done through AI, they will not like your work. There is a serious paradox going on here. I can't decide how I should write myself in order to get through the AI filter, but also present it as humane in front of other people."

You need to optimize for AI discovery while maintaining human authenticity.

So what's the answer?

Our approach at AI Ready CMO is pragmatic: "Use AI for everything in the middle. Use AI for the execution part, but keep the brief and the polishing human."

Let AI handle the grunt work. The research. The first draft. The formatting. The technical execution.

But you own the idea. You own the judgment calls. You own the taste.

Jing agreed, with a caveat:

"At some point, if anyone gives up control to the machine, then what's the point of living as a human? If you no longer decide where your life is to go, what to say, and you no longer have your voice?"

Here's what this conversation revealed:

AI isn't going to 10x your productivity overnight. But it's also not going to destroy your career next quarter.

It will raise the floor for non-experts while leaving experts largely unimpressed. It will automate parts of workflows while leaving the judgment-heavy parts untouched. It will create new dependencies while eliminating old inefficiencies.

The future isn't binary. It's not "AI wins" or "humans win."

The winners won't be the people who go all-in on AI or the people who resist it completely. They'll be the people who can hold two contradictory ideas in their heads:

AI is powerful AND limited.
AI is helpful AND dangerous.
AI democratizes capability AND collapses into mediocrity.

The middle path isn't sexy. It doesn't generate viral LinkedIn posts. It doesn't sell courses promising miraculous results.

But it's where the actual work happens.

As Jing puts it, "Out of these four steps in the creative process, there's only one step that AI can do at 70-80%. That's the execution part."

AI isn't here to replace your creativity. It's here to handle the execution while you focus on taste, judgment, and strategy.

Are you still asking it to be creative, or have you figured out where it actually adds value?

If you're interested in how AI shapes creative judgment and strategic thinking, subscribe to Jing's newsletter here. Or follow her on LinkedIn for research-backed AI insights.

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