What’s Actually Happening in AI Right Now?

Disclaimer: The views expressed in this post are my own and do not reflect the views of my employer or any organisation I’m affiliated with.

Stop me if this sounds familiar.

You open LinkedIn or TechCrunch, and it’s wall-to-wall AI.

Another funding round. Another startup promising “AI for X.”

Everyone’s got a take. Everyone’s building something.

Everyone’s talking — and almost none of it feels meaningful.

You’re not anti-AI. You’ve seen the potential. You work close to the action.

But something about this moment feels… off.

Valuations are skyrocketing.

Use cases feel thin.

Models still hallucinate.

Layoffs are happening in anticipation of AI that doesn’t exist yet.

And the sheer volume of wrapper tools, AI copilots, and pitch-deck startups? Exhausting.

If you’ve felt that low-level hum of scepticism — like you’re watching a hype cycle on steroids and wondering where the real value is — you’re not alone.

I’ve been living inside this wave too. And while I’m still bullish on the long-term potential, I think it’s worth pausing to name what’s actually happening, what’s driving the noise, and how to separate the substance from the spectacle.

In this post, you’ll get a grounded breakdown of:

• Why AI feels so noisy right now
• The seven dynamics creating confusion and fatigue
• Where this is all headed — and how to stay focused on value in the chaos

Let’s get into it.

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Why AI Feels So Noisy Right Now — And Why That’s Normal

Everywhere you look, there’s another AI story.

New models. New wrappers. New job losses. New valuations.

And the pace hasn’t slowed — it’s accelerated.

So if it feels overwhelming, that’s because it is.

But here’s the key: this kind of noise is normal in any emerging platform shift.

AI isn’t just a new tool — it’s a new layer of abstraction.

It’s changing how people build, how businesses operate, and how we think about knowledge work. And whenever there’s a shift this big, the early phase is always chaotic.

It’s the same story we’ve seen before:

• The dot-com boom gave us both Amazon and Pets.com
• The mobile app wave brought WhatsApp and a thousand flashlight apps
• The crypto rush produced both Ethereum and thousands of now-dead tokens

We’re in that same early-exuberance phase with AI.

And just like before, the market is flooded with half-baked ideas, gold rush builders, and a lot of noise that looks like progress — but doesn’t feel like value.

What makes this moment even louder:

• The speed of development is faster than ever before
• The barrier to launch is lower (thanks to APIs and open models)
• And the stakes feel higher, because every industry is being touched

That’s why it feels like there’s too much happening at once.

And why it’s so hard to tell what matters — and what doesn’t.

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7 Dynamics Creating Confusion and Hype Fatigue

There’s a reason AI feels both exciting and exhausting right now. It’s not just that the technology is moving fast — it’s that everything around it is moving too: money, headlines, decisions, expectations.

Here are the seven forces that are making this moment feel so chaotic — and why it’s worth stepping back to see them clearly.

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1. Valuations are outrunning value

AI companies — especially infrastructure and tooling layers — are being valued like they’ve already won. $500M+ raises. $2B pre-revenue valuations. Pre-product rounds closing overnight.

But in many cases, the business models aren’t proven. The moat is unclear. And the core product is built on the same foundation as dozens of others: OpenAI or Anthropic.

Hype cycles always attract capital. That’s not new. But the gap between current utility and future expectations has rarely been this wide — and it’s creating a fog around what’s real.

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2. Use cases sound great, but solve little

There’s no shortage of AI tools promising to:

• Save you 10 hours a week
• Replace your assistant
• Write your emails
• Summarise every meeting

But a lot of these are solving convenience problems, not core problems. They’re easy to try, hard to retain, and even harder to build a business around.

Value doesn’t come from novelty. It comes from stickiness — and a lot of these use cases don’t have it.

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3. Models are powerful, but still unreliable

We’ve seen massive leaps in model capabilities. Reasoning is improving. Multi-modal is here. But for most real-world use cases, hallucinations are still a problem. And consistency is far from guaranteed.

This matters. Because trust is everything.

When you’re building something on top of LLMs, reliability isn’t optional — it’s the foundation. And right now, most systems still need human-in-the-loop checks to work safely and at scale.

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4. Layoffs in anticipation of AI — but it’s still early

Some companies are restructuring based on the idea that AI will take over certain roles — from content to customer support to code.

In some cases, this is forward-thinking. In others, it’s premature.

The gap between what AI can technically do and what it can reliably do, at scale, inside a business process is still wide. Laying off people today based on tomorrow’s AI capabilities is a risky bet — especially when the tooling still needs humans to catch its blind spots.

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5. Wrapper companies are everywhere

It’s never been easier to build an AI tool. Take an API, wrap a clean UI around it, and ship.

The result? Thousands of startups doing variations of the same thing:

• “AI for email”
• “AI for marketing”
• “AI for sales enablement”
• “AI for product managers”

Some of these will find real traction. Most won’t.

The problem isn’t that wrappers are inherently bad — it’s that they’re easy to copy and hard to differentiate. Without a defensible edge — proprietary data, distribution, workflow lock-in — it’s a race to the bottom.

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6. GPU scarcity is real — and shaping innovation

There’s not enough compute to go around. And that’s affecting who can build, who can scale, and how fast things can move.

It’s also creating pressure:

• Some teams are forced to optimise too early
• Others can’t test new ideas at all
• Infra costs are dictating product choices

This kind of constraint can breed creativity — but it also means the landscape is shaped by access, not just innovation.

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7. Everyone wants in — which dilutes the signal

You’ve got builders. VCs. Enterprises. Creators. Agencies. Corporates. All jumping into AI — all with different levels of depth and different goals.

The result? A flood of content, products, pitches, and predictions — most of it surface-level.

And when everyone’s speaking at once, it’s harder than ever to tell who’s actually saying something useful.

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Why This Is the “Early Internet” Phase of AI

If you zoom out, what we’re living through right now isn’t new. It just feels new because the pace is faster, the noise is louder, and the stakes feel higher.

But structurally? This looks a lot like the early days of the internet.

In the late ’90s:

• Everyone was building a website
• Capital flooded the market
• Valuations skyrocketed
• Most products had novelty, not durability
• The infrastructure wasn’t mature yet
• And people started making bold predictions about how everything would change

Sound familiar?

Back then, it was domain names and dot-com startups. Today, it’s prompt engineering and wrappers around foundation models. Same story. Different stack.

And just like the internet, AI will go through phases:

1. The infrastructure buildout (happening now)
2. The wave of exploration and over-excitement (we’re in it)
3. The inevitable shakeout
4. The emergence of real, lasting value

It’s easy to get cynical in the noisy middle — when you see 20 AI pitch decks a day or get 5 product demos that all do the same thing.

But the signal is there. And history tells us this:

The real value shows up after the hype dies down.

And it’s usually built by the people who kept their heads down while everyone else was shouting.

That’s the phase we’re heading into next.

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What Actually Matters: Building Things That Are Useful, Durable, and Hard to Copy

In the middle of all the noise, there’s a simple question that cuts through everything:

What’s going to last?

Because AI won’t be won by the flashiest demo, the fastest release, or the loudest pitch. It’ll be won by the teams that build products people come back to — not because they’re novel, but because they solve real problems in a way that’s hard to replace.

Here’s what actually matters right now:

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1. Usefulness over novelty

The best AI products disappear into the workflow. They make people faster, better, more capable — not once, but repeatedly.

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2. Durability over speed

Speed is good, but staying relevant is better. Without a defensible edge — like proprietary data or deep integrations — you’re just a few weeks ahead of the next wrapper.

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3. Distribution over features

Getting your product into the hands of users at scale matters just as much as the model behind it. Build reach, not just features.

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4. Trust over automation

If users can’t trust it to work when it matters, they won’t stick around. Build systems that work reliably — and make failure modes clear.

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5. Focus over FOMO

Don’t try to ride every wave. Solve one meaningful problem for one group of users — and go deep.

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The Takeaway: Ignore the Noise, Focus on Value

The AI space isn’t broken — it’s just loud.

And like every wave of innovation before it, this one will sort itself out. The hype will fade. The noise will die down. Most of the shallow bets will disappear.

What will be left?

The builders who stayed focused.

The products that solved real problems.

The teams who didn’t chase the trend, but stuck to creating something useful, durable, and defensible.

If you’re working in AI — or trying to make sense of it — here’s the mindset that will carry you through:

• Don’t panic.
• Don’t chase.
• Don’t build for show.

Instead:

• Pick a real problem.
• Solve it well.
• And keep going when the hype moves on.

Because AI is real.

The potential is massive.

But the value? That still comes down to the basics: clear thinking, focused execution, and a product people can’t live without.