I’ve lost count of how many times I’ve seen it.
A new AI tool drops, plastered all over X and Product Hunt. The landing page is slick. It’s got a cool name and promises to “revolutionize your workflow” with “next-gen AI synergy.” I sign up, play with it for ten minutes, and think, “Huh, kinda neat.”
Then I forget about it.
A month later, I see the same company in my inbox. But now… it’s a completely different product. They’ve pivoted. What was a text-to-video generator is now an AI meeting assistant. What was an AI website builder is now a data analysis tool. It’s dizzying, and honestly, a little frustrating.
You’re not crazy for feeling this way. The AI space is a whirlwind of chaos right now, and I just listened to something that put it all into perfect perspective.
The Verge’s Decoder podcast had Ellis Hamburger on, and it’s a must-listen. Ellis used to be a writer at The Verge back in the day, covering the mobile app boom. Now? He runs a marketing firm called Meaning that’s in the trenches with some of the buzziest AI startups out there, such as companies like Raycast, Readwise, and Daylight. He’s seeing the gold rush from the inside.
And his big takeaway?
“Everyone’s pivoting, then pivoting again.”
It’s a total frenzy.
🤑 The Great AI Pivot-pocalypse
So what’s actually happening here? It’s a classic gold rush scenario.
There’s this incredible new technology (Large Language Models, diffusion models, etc.), and venture capitalists are throwing mountains of cash at anyone who can build something with it. This creates immense pressure for founders to ship anything fast, slap an “AI” label on it, and hope it sticks.
But here’s the problem: many of these startups are building technology first and looking for a problem second. They have a powerful engine but no steering wheel and no destination. They launch, get a flicker of interest, but no real traction because they aren’t solving a burning-hot problem for a specific group of people.
So what do they do? They pivot. They take their AI engine and try to bolt it onto a different problem. Then another. And another. It’s a desperate search for a product-market fit, and it’s happening in public, right in front of our eyes. This is why the AI landscape feels so unstable and why it’s hard to get attached to any new tool.
🤔 Why Most AI Marketing is Just Noise
Ellis’s job is to help these companies find their voice, but it’s a massive challenge. Most AI startups are making the same fundamental mistake: they’re selling the what instead of the why.
They lead with the technology: “We use a 70-billion-parameter model with retrieval-augmented generation to power our chat interface.”
Who cares?!
That doesn’t mean anything to a normal user. It doesn’t solve a problem. It’s like a car company trying to sell you a car by talking about the specific alloys in the engine block. I just want to know if it’s fast, safe, and makes me feel cool driving it.
This is the core issue. The story is missing. Without a clear narrative about who the product is for, what problem it solves, and how it makes their life better, it’s just another cool tech demo destined to be forgotten.
✍️ How to Tell a Real Story (A Guide for Founders & Builders)
If you’re building in this space, you have a massive opportunity to stand out by simply being clear and human. Listening to the conversation with Ellis got me thinking about a playbook for doing just that. Here’s what I’d suggest:
- 📌 Start with the Human Problem. Don’t open your pitch with “AI-powered.” Open with the pain. “Hate spending hours summarizing meeting notes?” or “Tired of staring at a blank page?” Ground your product in a real, relatable frustration.
- 💡 Be Unapologetically Niche. Stop trying to build the “everything assistant.” That’s a recipe for being mediocre at everything. The most-loved tools do one thing exceptionally well for a specific audience. Are you for writers? For engineers? For project managers? Pick one and go deep.
- 🚀 Show, Don’t Just Tell. This is huge. Don’t say your tool is “powerful” or “intuitive.” Show me. A 15-second screen recording of your tool turning a chaotic transcript into a perfect summary is a thousand times more effective than a paragraph of buzzwords.
- ✨ Build a Narrative, Not a Feature List. What is the story of your product? The user should be the hero of that story. Your product is the magic wand or the wise guide that helps them conquer their challenge. Frame your entire message around that journey. Your features are just the tools that help them along the way.
- 🚫 Ban Buzzwords. Make a pact with your team. For one week, you are not allowed to use the words “AI,” “leverage,” “synergy,” “revolutionary,” or “game-changing.” Force yourself to describe your product using simple words that explain its benefit. You’ll be stunned at how much clearer your message becomes.
✅ How to Spot a Winner in the AI Chaos (A User’s Guide)
For the rest of us just trying to find good tools, this chaos makes it hard to know who to trust. How can you tell if a new AI tool is a future game-changer or just another pivot-in-progress?
Here’s my personal checklist:
- Does it solve a real problem for me? Right now. Not a hypothetical future problem. Does using this tool today save me time, make me more creative, or remove a frustration from my life? If it’s just a ‘cool demo,’ I move on.
- Is the messaging crystal clear? Can I explain what this tool does to a friend in one sentence? If I can’t, it’s a huge red flag that the founders themselves don’t have a clear vision.
- Who is the team? I look for teams that are obsessed with the problem space, not just the AI technology. An ex-designer building a tool for designers? A former researcher building a tool for academics? That’s a great sign. It shows they have deep empathy for the user.
- Is there a passionate community? Look for an active Discord, a subreddit, or a social media following where people are actually helping each other use the product. A real community is a sign of a product that’s delivering real value.
This whole topic is so fascinating, and the Decoder episode is the perfect deep dive. It’s an inside look at how the AI sausage gets made and marketed. If you’re building, investing in, or just trying to navigate the AI world, you need to listen to it. You’ll come away with a brand new filter for seeing through the hype.
- The current AI startup “frenzy” is defined by constant “pivoting” as founders search for viable business models. This indicates a market still in an early, experimental phase where the technology’s application is often not yet clear.
- In this crowded landscape, clear communication is a critical differentiator. Startups face the challenge of simplifying complex tech jargon into relatable stories that demonstrate real-world value, a necessary step for building consumer trust and securing investment.
- The current volatility is expected to lead to market consolidation, where many startups may fail or be acquired. Successful companies will likely be those that shift focus from the technology itself to solving specific problems, backed by a strong, clear narrative about their purpose.