Why AI Skeptics and Boosters Might Both Be Wrong

For years, the debate over artificial intelligence has split into two loud camps: boosters predicting superintelligence and skeptics warning of overhyped snake oil. But science journalist Jacob Aron argues that both sides may be missing something important.

After spending a week experimenting with “vibe coding” — using AI tools to generate software through conversational instructions — Aron says his long-held skepticism has shifted. Not toward blind optimism, but toward a more nuanced middle ground.

What Is News

Journalist Jacob Aron re-evaluated his stance on AI after experimenting with “vibe coding.”

“Vibe coding” refers to guiding AI models to write software using natural language prompts.

Recent AI coding tools such as Claude Code and ChatGPT Codex have shown improved performance.

Aron concludes that both AI evangelists and critics are oversimplifying the technology’s impact.

What Is “Vibe Coding”?

The term was coined by AI researcher Andrej Karpathy to describe a new style of programming: instead of writing code manually, a developer instructs an AI model in plain language and refines its output iteratively.

Rather than acting as an autonomous engineer, the AI becomes a collaborative assistant.

Aron, who previously viewed large language models as impressive but fundamentally flawed, decided to test the claims that AI coding tools had become transformative.

He found something unexpected.

The tools were neither magical nor useless.

They were helpful — in ways that challenge both extremes of the debate.

What Is Analysis

The broader AI discourse often falls into binary thinking:

Boosters’ claim: AI is rapidly approaching general intelligence and will fundamentally replace knowledge workers.

Skeptics’ claim: AI systems are glorified autocomplete engines that hallucinate and overpromise.

Aron’s experiment suggests reality is more complicated.

1. AI as Cognitive Amplifier, Not Replacement

Vibe coding demonstrated that AI can dramatically accelerate certain tasks — especially structured, pattern-based work like writing boilerplate code or debugging simple issues.

But it still requires human judgment, context awareness, and verification.

The experience resembles collaboration more than automation.

This challenges the idea that AI is either replacing developers wholesale or incapable of meaningful contribution.

2. The Importance of Interaction Design

Much of AI’s perceived power depends on how it is used.

When treated as an oracle, it disappoints.

When treated as a conversational partner requiring guidance and correction, it becomes far more useful.

This reframes AI less as a finished product and more as a workflow layer.

3. Productivity vs. Understanding

A key tension remains: AI can generate correct outputs without “understanding” them in a human sense.

For skeptics, that’s proof it lacks intelligence.

For boosters, that distinction doesn’t matter if the output works.

Aron’s experience suggests the philosophical debate may be secondary to practical outcomes.

If a tool reliably accelerates work — even without consciousness — its utility is real.

Why Both Camps May Be Oversimplifying

Boosters often extrapolate rapid improvements into near-term superintelligence.

Skeptics often focus on errors and hallucinations to dismiss broader gains.

But the middle ground is this:

AI tools are uneven but increasingly capable collaborators.

They do not eliminate the need for expertise — but they can compress the time required to apply it

That is less dramatic than apocalypse or utopia — but potentially more economically significant.

A Shift in Perspective

Aron’s change of mind does not amount to AI worship. It reflects a recognition that dismissing the tools outright ignores their growing practical value.

The key shift is psychological.

Instead of asking:

“Is this superintelligent?”

or

“Is this worthless hype?”

The better question may be:

“How does this reshape how humans work?”

That question leads to more nuanced answers — and fewer absolutist conclusions.

Bottom Line

AI is neither the dawn of omniscient machines nor a hollow illusion.

Experiments like vibe coding reveal something subtler: AI is evolving into a capable, sometimes frustrating, but increasingly productive collaborator.

Both evangelists and critics may need to recalibrate.

The revolution may not look like replacement.

It may look like augmentation — uneven, imperfect, but undeniably real.