Fake it 'til you automate it: A guide on modern AI scams
Let’s be real: the only thing more inflated than tech CEO egos in 2025 is the phrase “AI-powered.” Suddenly, everything from toasters to dating apps claims to be run by artificial intelligence. But spoiler alert—not all of it is legit. In fact, we’re living through a golden age of AI fraud so good it could almost be... well, AI-generated.
From fake chatbots to human-powered “automations,” this blog dives into the dirty laundry of the tech world: AI washing, ghost-AI startups, deepfake scams, and the investors who throw millions at buzzwords. Whether you're a casual user or someone building real AI tools, this is the breakdown you didn’t know you needed—equal parts helpful and unhinged.
🪤 1. AI washing (The OG fraud)
Definition: Pretending to use AI or heavily exaggerating basic automation as “intelligent.”
Why it happens: Because “AI-powered” sounds way cooler than “we built a decision tree in Excel.” It attracts investors, media coverage, and gullible users.
Red Flags:
Buzzwords like “AI-enhanced” with no explanation
No mention of models, data, or architecture
No AI talent on the team
Everything looks suspiciously rule-based or manually operated
Example: A project management app says it uses AI to “predict team productivity.” Behind the scenes? It just flags when someone hasn’t updated a task in 3 days. Karen from HR could’ve built that in Airtable.
🎭 2. Fake AI products
Definition: Selling an “AI” tool that has no working AI—or any working parts at all.
Why it happens: It’s easier to build a sexy landing page than actual tech. Some scammers even pre-sell fake features and ghost users after collecting money or data.
Red Flags:
No working demo
Features suspiciously marked “coming soon” forever
FAQ section includes “What does AI mean?”
Example: A “voice-to-essay” tool that promises to turn your spoken thoughts into polished college essays. You try it and it spits out lorem ipsum... or worse, ChatGPT 3.5’s worst day.
👤 3. Human-in-the-loop deception (mechanical turk-ing)
Definition: Claiming your service is fully automated when it’s actually powered by humans behind the curtain.
Why it happens: Manual labor is faster (and cheaper) to stand up than building actual AI. It helps founders fake traction while buying time.
Red Flags:
“AI” that only works during business hours
Responses with human typos
Turnaround time suspiciously long for “real-time AI”
Example: An “AI therapist” app promises instant emotional support. You message it and 15 minutes later get a suspiciously human-sounding reply that includes, “Ugh, same girl.”
💸 4. Investor hype fraud
Definition: Using exaggerated AI claims to boost company valuation or win funding without real tech to back it up.
Why it happens: Investors love a buzzword—and FOMO is real. Founders toss around “transformative AI” and hope no one reads past slide 7.
Red Flags:
No technical team, just “visionaries”
Slides with words like “AI-native” or “cognitive computing synergy”
VC pitch decks heavier on vibes than metrics
Example: A startup says their AI will “revolutionize education.” They raise $8 million. Their product? A Google Form with a smiley face on it.
⛔️ 5. AI impersonation & scam tools
Definition: Using actual AI (deepfakes, voice clones, generative tools) for fraud or malicious impersonation.
Why it happens: Because AI can convincingly mimic real people—and scammers are fast learners. If it looks like Grandma and sounds like Grandma, people believe it’s Grandma.
Red Flags:
Sudden “urgent” requests involving money or passwords
Weird audio/video artifacts (blinking glitches, tinny voices)
Contextual inconsistencies (“I’m your granddaughter” but doesn’t know your name)
Example: You get a call from your “boss” asking you to urgently wire $10K for a “client emergency.” Sounds just like them. Turns out it was a voice clone built from YouTube interviews. Oof.
📊 6. Performance & benchmark fraud
Definition: Lying about your model’s performance, accuracy, or real-world success.
Why it happens: The more impressive the numbers, the more likely they’ll land deals, press, and funding. Most people won’t check if that “98.7% accuracy” was tested on 6 cherry-picked samples.
Red Flags:
No peer-reviewed testing or external audits
Benchmarks with missing context (e.g., “best on this one weird dataset”)
Claims that sound too good to be true (because they are)
Example: A facial recognition startup claims 99.9% accuracy across all skin tones. Turns out they only tested it on 10 white dudes and one stock photo of Beyoncé.
Final thoughts
If a product says it uses AI but can’t explain how, where, or why — be suspicious. Ask questions. Demand transparency. And maybe next time someone claims their spreadsheet is “AI-driven,” ask them to define “AI.” Just for fun. 😏
