The Emperor Wears Clothes and Everyone Nods
The Open Secret We All Share
There's an arrangement in B2B that nobody talks about.
Every product demo you've watched in the last year, the support bot that never repeats itself, the dashboard that seems to know what you're looking for, the proposal generator that spits out a 90% complete draft, runs on some flavor of large language model. Your competitors' products do. Your vendors' tools do. Probably yours too.
Everyone knows this.
And everyone keeps up the pretense that they don't.
The vendor writes "proprietary matching algorithms" in the datasheet. The buyer checks the "AI capability" box on the RFQ. The agency claims their secret sauce is years of human expertise. All three parties understand what's actually happening. All three play along like it's a magic trick nobody is allowed to expose.
It's a polite corporate fiction. And it's the most widely accepted lie in the industry right now.
The Euphemism Rainbow
The easiest way to spot this fiction in action is to watch what companies call their AI.
Nobody says "we put an LLM on your data and shipped a chat interface." That's too honest. Too boring. Too easy for a competitor to replicate. Instead, the industry has developed a vocabulary designed to sound proprietary while saying almost nothing.
Predictive intelligence. Usually means a prompt that runs on a schedule.
Smart automation. A chain of API calls with a conditional step.
Proprietary matching algorithms. Embeddings plus cosine similarity.
Intelligent workflow engine. LangGraph running in production.
AI-powered recommendations. "Based on your history, you might also like" with a GPT-written justification underneath.
Every one of these phrases is doing the same job: hinting at capability without admitting implementation. They signal to the buyer that the product is modern, but they leave enough ambiguity that nobody has to defend the approach in a procurement call.
The funny part is that buyers have learned to read these too. A CTO who sees "predictive intelligence" knows exactly what's underneath. They just don't ask the follow-up, because the follow-up would force a conversation nobody wants to have. The vendor would have to admit the secret sauce is an API key. The buyer would have to admit they're fine with that.
So everyone nods.
Every euphemism has a plain-English twin.
The top line is written for procurement. The bottom line is what ships.
Why the Pretense Persists
If everyone knows, why does the fiction survive? It's not laziness or ignorance. There are concentrated incentives to keep the game going.
Pricing mystique. Software with a "proprietary AI engine" commands a different price band than software running a GPT wrapper. The euphemism protects the margin. Admitting the implementation is a prompt and a retrieval step invites a penny-pinching conversation nobody in revenue wants to have.
Buyer CYA. Procurement teams can check the "AI capability" box on their vendor scorecard without endorsing a specific technology. If the LLM provider changes pricing or capabilities next year, nobody gets blamed. The vague label insulates the buyer from having bet on the wrong horse.
The human premium. This one hits agencies and services firms hardest. You bill for expertise. For strategy. For the craft. If you admit half your output is AI-generated and human-refined, the client asks why they're paying senior rates for a junior's job with a sidecar LLM. Better to let the client believe the magic is all human and invoice accordingly.
Competitive ambiguity. If nobody describes their AI stack in detail, nobody has to defend why they chose one approach over another. A vendor who shouts "we fine-tuned our own model" leaves themselves open to "why not just use GPT?" A vendor who stays vague eliminates the comparison entirely.
These aren't malicious incentives. They're rational responses to the market as it currently works. But they add up to a collective fiction that costs everyone more than it saves.
Four rational incentives, one collective lie.
Nobody in this diagram is behaving badly. That is what makes it stable.
The Cost of Not Saying It
The pretense isn't free. Every company that keeps nodding along is paying a tax they rarely account for.
Slow buyer education. When your marketing says "predictive intelligence," your prospects have to decode that before they can evaluate you. Every week a buyer spends trying to figure out what you actually built is a week your product looks the same as every other vendor using the same euphemisms. You're competing on who writes the best opaque copy instead of who solves the problem best.
Internal friction. Product teams ship faster than marketing can rename. Engineers know they're building against an LLM. They know when a feature is a prompt change versus a model swap. But the documentation, the release notes, and the website all say something else. This friction slows iteration because every launch requires a translation layer between what the product does and what the company says it does.
Trust erosion at the seam. The moment a customer discovers the gap between the euphemism and the implementation, it erodes trust. Not because AI is bad, but because the company made them work to find the truth. A buyer who realizes their "intelligent automation platform" is a GPT wrapper isn't disappointed by the technology. They're disappointed that they had to figure it out themselves.
No differentiation on execution. If every vendor claims "proprietary AI" and every buyer knows everyone is using the same models, the actual differentiator -- how you implement, where you add human judgment, what data you bring -- is invisible. The only signal that survives is price and brand. That's a race to the bottom the pretense made worse.
The tax nobody puts on the balance sheet.
Four costs, each one compounding the last. None of them show up as a line item.
What Happens When You Stop Nodding
A handful of companies have started opting out.
They're not running ads announcing "we use ChatGPT." They're not pulling back the curtain on every implementation detail. What they're doing is simpler: they stopped managing the illusion. Their product copy says what the product does. Their engineers talk about models the way they talk about databases, as infrastructure, not as magic. Their sales reps answer the "how does it work" question directly instead of redirecting to a higher-level value prop.
The response has been mostly silence.
Customers don't penalize honesty. Buyers already assume the answer. The fear that drove the euphemisms -- that admitting AI involvement would commoditize the product -- hasn't materialized. Implementation still matters. Data strategy still matters. Human judgment in the loop still matters. Those are the actual differentiators, and they're easier to sell when you're not hiding behind a euphemism.
The companies at the table who show their hand aren't losing. They're just normalizing the next baseline so the rest of the industry stops pretending.
Better secrecy is not a strategy. Better execution is.
That's the end state anyway. Everyone runs on models. The winning posture isn't better secrecy. It's better execution, explained clearly.
You can keep nodding. Or you can say the thing everyone already knows and get back to building what actually matters.
