AI in production 2026: Ford rehires engineers, Zuck admits, and an unexpected ending
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“That would produce a high-quality product” - The most expensive statement of Ford in 2026
“Mistakenly we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product.”
This sentence is not from a young startup. Not from a founder on Twitter. But from Charles Poon - VP of vehicle hardware engineering at Ford. One of the largest manufacturing corporations in the world, existing for over 120 years.
Ford has just re-hired 350 veteran engineers - “gray beard engineers” - after their AI quality control system failed. They had retired, but were called back to train AI tools.
And the best part? Ford is not an isolated case.
Zuck’s Internal Meeting: “AI Agents Haven’t Advanced as Expected”
This week, Mark Zuckerberg stood before the entire Meta staff and said: AI agent development “hasn’t accelerated in the way” executives had expected.
This is Meta - the company that has:
- Laid off 8,000 people (10% of its workforce)
- Transferred 7,000 people to AI groups, including “Agent Transformation”
- Burned billions of dollars into AI infrastructure
And now, Zuck has to admit to the entire company: AI agents aren’t ready yet.
One of the largest AI spenders on the planet is saying this. Not a tech blogger. Not a dev on Reddit. The CEO of Meta.
Microsoft: “Copilot must earn the right to exist”
The same week, Jacob Andreou - EVP of Microsoft - sent an internal memo. The message reads like a warning.
Copilot “stripped out what wasn’t working.” Copilot Podcasts was killed. Copilot Labs was killed. They merged consumer and enterprise apps into a single app. And they focused on “real work” instead of “intelligence for intelligence’s sake.”
In plain language: Copilot has not yet brought enough value to exist.
After so many years of development, so many billions of dollars invested, so much marketing hype - they have to admit internally that the product is not good enough. And they are cutting out features that no one uses.
Also this week, Microsoft launched a $2.5B AI deployment company. They send engineers to enterprise clients’ departments to help build AI into workflows. An acknowledgment that a chatbot alone is not enough. Value only comes when real people sit alongside, set up, and fine-tune.
The hype cycle: Which phase are we in?
expectation
^
| AI Agent hype peak
| /\
| / \
| / \
| / \
| / \
| / \
| / \ === WE ARE HERE ===
| / \ Ford rehire, Zuck admit,
| / \ Microsoft "earn the right"
| / Initial \_________
| / excitement \______
| /______________________________________\____> time
| Trough of Slope of
| disillusionment enlightenment
I’m not saying AI is a scam. I use Claude Code, Hermes Agent, and a bunch of other AI tools every day. They help me reduce 30-40% of the time spent on boilerplate code. I’m writing this blog thanks to AI.
But there’s a dangerous misconception happening: confusing “useful tool” with “replacement for expertise.”
Why AI Fails in Production?
All three stories above have a common point: misplaced expectations.
Ford thought AI could replace quality control experts. Someone who understands the context of a part, knows when a slight tolerance leak is okay because the next assembly will compensate for it. Twenty years of experience cannot be encoded into training data.
Meta thought AI agents could automate customer service, internal workflow, marketing. But forgot that the context of each business is different. The same problem, different company, different data, different process.
Microsoft thought Copilot just needed to chatbot-ize everything. But forgot that real value comes from integration into workflow. From custom setup. From people who know what they are doing.
AI doesn’t fail. Misplaced expectations fail.
A Backend Engineer’s Perspective
I’m writing this while using Claude Code to debug a bug in the menu service. It’s helpful. It suggests where I need to look. It generates test cases quickly.
But it has also made mistakes with conventions. It suggests Python-like patterns in Go code. It hallucinates function names that don’t exist.
I’ve learned one thing: AI is best when paired with a skilled person.
It’s not a replacement for domain knowledge. It’s an accelerator for those who already have domain knowledge.
Counterargument: “But won’t newer models solve this?”
Yes. AI will get better. Claude Mythos, GPT-5.5, the next model is always better than the previous one.
But the problem for Ford, Meta, Microsoft is not that the “model is not intelligent enough.” It is that “AI cannot replace the context of the organization, processes, and experience.”
And that problem - in my opinion - the next model will not be able to solve either.
Why? Because that context is not “knowledge” that can be learned from training data. It is institutional memory. It is the relationship between teams. It is unwritten rules that the entire organization implicitly understands.
LLM can read 10K documents. It cannot understand what is not written down.
Bottom line: Where are we?
I think we are at the intersection of a major change:
- AI tools are not going away - They are too useful. I’m not going back to coding by hand.
- But expectations are being adjusted - Ford, Meta, Microsoft are the first signs.
- True value = AI + Expert - The best people will be those who know how to use AI effectively. Not that AI replaces people.
The future is hybrid. Not “AI agents automatically doing everything.” And not “AI is useless.”
But: engineers using AI to do what engineers shouldn’t do - boilerplate, grepping through 20 files, generating test cases, formatting code. And engineers focusing on what AI can’t do: design decisions, trade-off analysis, understanding business context.
What do you think?
I wrote this article not to stir up controversy, but to hear from you.
Have you ever encountered an AI tool failing in production? Or are you someone who believes that AI will completely replace developers within the next 5 years?
Comment below. I want to hear your story. 🦞