For the past three years, AI has promised a revolution. Autonomous agents. Superhuman copilots. Instant content and code generation. But as the dust settles, we're seeing a different story:
- AI isn't scaling.
- It's not solving meaningful problems at scale.
- And the cost — in money, compute, carbon, and credibility — is often far too high.
1. AI Is Too Expensive to Scale
Running large language models (LLMs) isn't cheap — not even for small workloads. Even with tiny models like LLaMA 3 8B or Mistral 7B, the infrastructure cost to serve just 10 users concurrently can exceed 3GB of RAM, plus expensive GPUs or quantization tricks. Once you move to high-end models like GPT-4 or Claude Opus, you're talking 30–60 GB per instance, per batch of users.
$0.10 per query isn't sustainable when users burn through 1000+ queries/month doing things like writing, debugging, or content generation. That's $100/month per user. Who's paying that?
2. AI Agents Are a Dead End (For Now)
The hype around AI agents — self-running, goal-seeking LLMs that plan and act autonomously — has mostly fizzled out. Tools like AutoGPT, BabyAGI, Devin, and LangChain agents make for flashy demos but collapse in production.
Why?
- They hallucinate tools and API calls
- They can't reason about real-world constraints
- They loop endlessly or freeze on edge cases
- They misinterpret vague goals like "book a trip under $2,000"
Giving AI agency doesn't make it smart. It just makes it faster at making mistakes.
Trying to build reliable autonomous agents on top of a stochastic text predictor is like training cats to code — amusing, chaotic, and entirely unproductive.
3. Copilots Create as Many Problems as They Solve
GitHub Copilot, CodeWhisperer, and others were marketed as productivity boosters. But many developers are realizing they:
- Suggest incorrect or outdated code
- Distract from the actual thought process
- Increase keystrokes as you constantly delete wrong completions
- Burn massive compute resources per suggestion
Traditional autocomplete (IntelliSense, TabNine-classic, etc.):
- Is more predictable
- Runs locally
- Has near-zero latency
- Doesn't try to outguess your logic
For many developers, Copilot is just noisy autocomplete with worse UX and a bigger carbon footprint.
4. AI Customer Support Is Backfiring
Companies like Klarna, Air Canada, and Frontier Airlines rushed to replace human agents with AI chatbots. What followed?
- Customer frustration
- Lawsuits from AI hallucinations
- PR disasters
- Quiet reversals to bring humans back
AI might answer basic FAQs, but once a conversation requires empathy, context, or escalation, it fails — often spectacularly.
Saving a few cents per interaction isn't worth losing a customer's trust forever.
5. What Are We Actually Gaining?
We already had Stack Overflow for debugging. Grammarly for writing. Zapier for automation. Static analysis for code. What's new with AI?
- It's faster — when it works.
- But it's wrong — often.
- And it's wasteful — always.
Many users spend more time correcting hallucinated code, rewriting clunky blog drafts, or debugging broken workflows than if they'd just done it manually.
AI promises to save time, but usually shifts the labor from creation to correction.
6. The Feedback Loop of Doom
If AI-generated content keeps flooding the internet:
- Future models will be trained on synthetic, low-quality data
- This leads to degradation in quality (known as model collapse)
- Which further reduces trust in AI outputs
When AI learns from AI, the whole ecosystem begins to rot.
TL;DR: The AI Boom Is Hitting a Wall
- AI isn't scaling because the compute cost and unreliability are too high
- AI agents are glorified toys — not task solvers
- Copilots distract more than they help
- Customer support bots are making things worse
- The content explosion is setting us up for self-sabotage
We're not against AI. But we're against pretending that it's already the solution.
It's not.
At least, not yet.
If you're looking for real productivity, sometimes a traditional tool — or a human — still wins.
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