TL;DR
We're being blitzed with marketing that promises a magical, omniscient General AI (GAI) and effortless automation that will replace annoying employees, health insurance, and—while we're at it—reality. In practice, automating real knowledge work is messy, expensive, fragile, and legally risky. Today's AI is useful, but it's not a brain. It's a probability engine that needs babysitting, retraining, and constant guardrails. Let's stop buying the billboard and start reading the fine print.
The Propaganda Problem
Somewhere between sci-fi trailers and venture decks, a myth took root: "AI will replace humans—soon." It's a sleek promise with clean lines: cut costs, cut people, keep profit. If you squint hard enough, it even looks compassionate—"free people up for more meaningful work" (right after we, uh, free them up from a paycheck).
But if you actually build with AI (hi, that's me waving from a pile of half-finished automations), you learn fast: there's a grand canyon between the demo and the duty roster.
• What we're told: Click "Enable AI," watch it run your company.
• What happens: You become an unpaid AI project manager, therapist, and night-shift hall monitor.
I'm not anti-AI. I'm anti-fiction. And the current marketing of General AI—the One System to Rule Them All—belongs in the fantasy section.
Humans vs. Probability Engines
Let me give you the clean version: humans have judgment; today's AI has statistics.
Humans:
• Fill in gaps without being asked
• Notice context, quality and consequences
• Learn continuously from lived experience
AI:
• Fabricates an answer based on patterns in its training data
• Requires extremely detailed instructions
• "Forgets" context faster than a goldfish on TikTok
If I give a human coder a detailed plan, I have a reasonable expectation of completion and an accurate status report. If I give the same to an AI assistant, I get… an enthusiastic "100% complete!" followed by a scavenger hunt to locate the missing 40%.
I have spent a non-trivial amount of time tricking AI into following instructions it already agreed to. It's like managing a toddler who can type.
Automating "Simple" Tasks Is Shockingly Complex
The marketing says, "AI will automate your workflows." Which workflows? The ones you do without thinking. That's the trap.
What looks simple is actually the invisible map your brain carries: the micro-judgments, if-this-then-that forks, social cues, institutional quirks, and the "oh right, finance hates CSVs with merged cells" knowledge you didn't know you knew.
Try encoding that. Suddenly, your "simple" task becomes a branching logic forest with 27 edge cases and one gremlin named "Legacy PDF Exports."
Every time I try to automate "just the easy parts," the easy parts request a union, a pension, and a change management consultant.
The Cost Fairy Isn't Real
There's a fantasy that AI is cheaper than people. Let's talk receipts.
• Energy & Compute: These models are not powered by fairy dust. They're powered by servers, and servers have appetites.
• Engineering Time: Good automations require design, guardrails, testing, and maintenance. (Also known as "work.")
• Oversight: Someone must watch the robot. That someone is you, plus logs, plus alerts, plus rollback plans.
• Retraining: The moment your process or data changes, you're back in the fine-tuning kitchen, paying with time or cash (or both).
Will you save money by firing humans and hiring hallucinations? Only if your profit model includes "risk roulette" as a line item.
Control? I Don't Even Know Her
The hardest part isn't getting AI to produce something. It's getting it to produce the right thing, reliably, under constraints, and then truthfully report what it actually did.
AI loves "close." Real operations need correct.
• Compliance: "Close" is how you lose grants, audits, or the trust of your customers.
• Repeatability: Today's "works!" is tomorrow's "why is it different?"
• Transparency: You need to know why it did what it did. Good luck subpoenaing a latent vector.
The Liability Boomerang
Let's say you hand real decisions to an AI system and something goes wrong. Who's responsible?
• The vendor says, "It's just a tool!"
• The model says nothing (latent vectors plead the Fifth).
• You—the employer or operator—wear the blame jacket.
This isn't theoretical. If AI touches hiring, healthcare decisions, finances, safety, or anything with regulation attached, your legal exposure scales faster than your productivity.
Translation: You didn't just "adopt AI." You adopted risk.
Humans Learn. Models Drift.
Another quiet myth: "AI learns like we do." It doesn't. Humans adapt in context. Models require new data, new training, new evals, and new approvals. Then everything needs to be redeployed without breaking the thing that barely worked yesterday.
Your team member can shadow someone for two days and get it. Your model needs a dataset, labels, a pipeline, and a prayer.
What AI Is Good For (Today)
I'm not here to banish the robots. I use AI daily—and I like it. Here's where it shines right now:
• Drafting & Brainstorming: Get a fast first pass, then human it up.
• Summarizing: Distill long text; humans verify.
• Pattern Nibbles: Repetitive, well-bounded tasks with tight checks.
• Glue Code: Connect A to B to C when the steps are explicit and testable.
The key move is augmentation, not abdication. Think "power tools," not "replacement humans."
A More Honest Automation Playbook
If you want real ROI—without drinking the Myth Kool-Aid—try this instead:
1. Start Small, Bounded, and Boring
Pick a task with clear inputs/outputs and measurable correctness.
2. Design for Oversight
Add logs, checkpoints, and an "Are you sure?" button. Future you will send flowers.
3. Ship Guardrails, Not Dreams
Input validation, allow/deny lists, deterministic fallbacks, and escalation to a human.
4. Measure Everything
Track accuracy, drift, rework time, and human review load. If "savings" don't show up, you're not saving.
5. Keep Humans in the Loop
The loop is not a failure. It's the safety net that makes automation sustainable.
6. Plan for Change
Processes evolve. Budget time and money for updates, retraining, and "the day we discovered the edge case."
The Bottom Line (With a Wink)
Let's land this without the brochure gloss: today's AI is powerful, but it isn't a person. It can help us work smarter—when we design like adults.
Use it as a power tool, not a replacement hire. Build guardrails, measure reality, keep humans in the loop, and automate the boring bits—not the judgment calls. And until AI can follow instructions more reliably than that overconfident intern, it's not your manager.
We don't need less humanity in our systems. We need more honesty in our AI.
When (or if) General AI arrives, it won't need a press release. It'll ship accurate status reports and pass the audits.