Wanted PR Firm To Re-Brand AI......
- Sean Massey
- Oct 31
- 5 min read
Because "Artificial Intelligence" Is About to Get Artificially Uninvested
Recent warnings suggest AI stocks could be heading toward bubble territory, with some valuations reaching unsustainable levels and corporate AI projects struggling to deliver returns. Major AI companies have achieved price-to-sales ratios exceeding 40 times revenue, with some pushing close to 69 times sales — numbers that would make even the dot-com bubble blush.
The problem? We're selling the sizzle, not the steak. Or rather, we're calling everything "AI" when what we really have is a buffet of wildly different technologies that deserve their own identities. Imagine if we called cars, bicycles, and helicopters all "Auto-Transportation Devices." That's essentially what we're doing with AI.
So here's my (only slightly tongue in cheek) proposal: Let's fire "AI" and hire some new brand names that actually describe what these technologies do. Not only will this help investors understand what they're buying, but it might just save us from lumping brilliant innovations in with overhyped vapourware.
The Great Rebranding: What AI Really Does (And What We Should Call It)
1. Pattern Wizards (formerly: Machine Learning)
You know those systems that spot credit card fraud, recommend your next Netflix binge, or predict when your factory equipment will break? They're not "intelligent" — they're phenomenally good at finding needles in haystacks made of data needles.
Sample pitch: "Our Pattern Wizards detected 94% of fraudulent transactions before they cleared!"
Doesn't that sound more honest and somehow more impressive than "our AI solution"?
2. Conversation Engines (formerly: Large Language Models/Chatbots)
ChatGPT, Claude, and their cousins aren't trying to be intelligent. They're sophisticated text prediction machines that happen to be eerily good at seeming human. They're like that friend who can talk about anything at a cocktail party — charming, occasionally profound, but you wouldn't necessarily trust them to rewire your house.
Sample pitch: "Deploy our Conversation Engine to handle 80% of customer inquiries!"
See? No promises of sentience, just good old-fashioned useful communication.
3. Vision Processors (formerly: Computer Vision)
These systems look at images and tell you what's in them. They're not "seeing" the way humans do — they're running mathematical operations on pixel arrays. But they're fantastic at spotting tumours in X-rays, reading license plates, and identifying whether that's a cat or a muffin.
Sample pitch: "Our Vision Processors analyse 10,000 medical images per hour with 99% accuracy!"
Suddenly it's not about replacing radiologists — it's about giving them superpowers.
4. Response Predictors (formerly: Recommendation Systems)
Amazon knows what you want to buy. Spotify knows what you want to hear. These aren't mind readers — they're systems that have watched millions of people and noticed patterns. "People who bought weird kitchen gadgets also bought THIS weird kitchen gadget."
Sample pitch: "Response Predictors increased our cross-sell revenue by 34%!"
That's something a CFO can understand without wondering if Skynet is coming.
5. Decision Synthesizers (formerly: AI Decision Support)
These are the systems helping doctors diagnose diseases, helping lawyers review contracts, and helping executives analyse market trends. They don't make decisions — they chew through vastly more information than humans can and present options.
Sample pitch: "Our Decision Synthesizers review 50,000 pages of case law in minutes!"
Lawyers everywhere just perked up.
6. Generation Studios (formerly: Generative AI)
The image creators, video generators, and music composers. They're not "creative" in the human sense — they're remixing patterns from training data in novel ways. They're more like the world's most sophisticated collage artists.
Sample pitch: "Generation Studios produce custom marketing visuals in seconds!"
Nobody has to pretend computers have replaced creativity — they've just automated the boring parts.
7. Automation Orchestrators (formerly: Robotic Process Automation with AI)
These systems handle repetitive digital tasks — moving data between systems, filling out forms, processing invoices. They're not robots taking over the world; they're digital interns who never need coffee breaks.
Sample pitch: "Automation Orchestrators eliminated 200 hours of manual data entry per week!"
Every operations manager just felt their blood pressure drop.
Why This Matters (Besides Saving My Sanity)
Here's the thing: studies indicate that nearly 95% of corporate AI projects haven't delivered meaningful returns on investment. That's not because the technology is bad — it's because companies bought "AI" without understanding what specific problem they were solving.
When you buy a "Pattern Wizard," you know you need pattern-finding problems to solve. When you buy a "Conversation Engine," you know you're handling text-based communication. Suddenly, the due diligence gets easier, the ROI calculations get clearer, and investors can differentiate between companies solving real problems and companies just slapping "AI" on their pitch decks.
The Investment Thesis Gets Better, Too
Right now, investors are pricing "AI companies" like they're all going to be the next NVIDIA. But some AI stocks have valuations trading at over 64 times sales despite modest growth rates — disconnect that makes no sense when you squint at the fundamentals.
But imagine if analysts could say:
"This Conversation Engine company has a 40% margin and is growing at 30% annually in the customer service sector."
"This Vision Processor company dominates medical imaging with 75% market share and sticky enterprise contracts."
"This Pattern Wizard startup is burning cash trying to compete in a commoditised market."
Suddenly, you can invest like a grown-up instead of throwing darts at a board labelled "AI."
The Bottom Line
The AI bubble everyone's worried about isn't really about the technology failing. It's about the expectations being impossibly vague. When everything is "AI," nothing is special. When companies are valued based on having "AI capabilities" rather than solving specific problems profitably, you get dot-com-style irrationality.
So yes, I'm only half-joking about the PR firm. What we need is linguistic precision that matches the technical precision of these tools. We need investors who understand they're buying Pattern Wizards, not magic beans. We need executives who know whether they need a Decision Synthesizer or a Conversation Engine, not just "some AI."
And if we get that clarity? Maybe we won't need that market crash after all. The good companies will keep building useful tools with reasonable valuations. The mediocre ones will get appropriately priced. And we can all stop pretending that every startup with a Python script is going to change the world.
Now, if you'll excuse me, I need to go pitch my new startup: "Buzzword Eliminators" — it uses advanced semantic processing to remove marketing nonsense from corporate communications.
Wait, that's just Pattern Wizards applied to text.
See? This is already working.
What do you think? Are we overdue for some honest branding in tech, or am I just being a curmudgeon? Drop your thoughts below
Comments