From Handsome Horses to Actual Unicorns: Why AI Expertise Is Different
Series: Why AI Unicorns Don’t Exist
Part 1 of 4
📚 Unicorn Series Navigation
| Part | Title | Link |
|---|---|---|
| Part 1 | From Handsome Horses to Actual Unicorns | (You are here) |
| Part 2 | The Great Reset (Why ‘5 Years LLM Experience’ Is Impossible) | Read → |
| Part 3 | Stop Chasing Unicorns, Start Building Orchestras | Read → |
| Part 4 | Building AI Teams That Actually Work | Read → |
Why the “AI expert” job posting asks for something that actually doesn’t exist—and why this time is different from every previous tech wave.
The Job Posting
I found this job posting last week. It’s real:
Required Qualifications:
- 5+ years of experience in solution architecture, with at least 3 years focused on AI/ML projects
- Deep understanding of AI/ML technologies, including traditional ML, deep learning, and generative AI
- Experience designing scalable, production-ready AI systems
- Strong knowledge of cloud architectures (AWS, Azure, or GCP) and AI/ML services
- Expertise in data architecture, data pipelines, and data governance
- Experience with MLOps, model deployment, and monitoring strategies
- Experience leading technical teams and managing stakeholder relationships
Preferred Qualifications:
- Master’s or PhD in Computer Science, AI/ML, Data Science
- Experience with enterprise AI platforms and tools
- Certifications in cloud platforms (AWS Solutions Architect, Azure AI Engineer, Google Cloud Architect)
- Experience with AI ethics, responsible AI, and regulatory compliance
- Background in multiple industries and diverse AI use cases
- Knowledge of edge AI and IoT architectures
- Experience with conversational AI, computer vision, or NLP solutions
- Understanding of cost optimization for AI workloads
Let me translate this:
One person who is simultaneously:
├─ Solutions Architect (5+ years)
├─ AI/ML Specialist (3+ years)
├─ Deep Learning Expert
├─ Generative AI Expert
├─ Cloud Architect (AWS + Azure + GCP)
├─ Data Architect
├─ Data Engineer
├─ Data Governance Specialist
├─ MLOps Engineer
├─ Technical Team Lead
├─ Stakeholder Manager
└─ That's 11 distinct roles.
Plus "preferred":
├─ PhD holder
├─ Triple cloud certified
├─ AI Ethics expert
├─ Regulatory compliance specialist
├─ Edge AI specialist
├─ IoT architect
├─ NLP expert
├─ Computer Vision expert
├─ Conversational AI expert
└─ 9 more specializations.
Total: 20 specializations in one person.
Here’s another one I found the same week:
Required:
- 5+ years of experience in AI-related roles
- Expert proficiency in Python for AI model development
- Experience building and scaling user interfaces with React
- Deep understanding of API design, implementation, and deployment
- Hands-on experience with AI and ML frameworks (TensorFlow, PyTorch, scikit-learn)
- At least ten years of work experience in IT areas including: IT architecture, infrastructure, databases, cloud development, software architecture, business analysis, DevOps, project and product management
And at the bottom:
“This is an exempt position, so you may have to work hours that exceed the standard 40-hour work week.”
So they want an AI architect who is also a React frontend developer, with 10+ years of broad IT experience—who will also work overtime.
Because one person doing the work of five people should also work extra hours.
The Problem Isn’t Ambition
The problem isn’t that companies are asking for a lot.
The problem is that what they’re asking for has fundamentally changed.
In every previous tech wave, the “unicorn” job posting was an exaggeration. The skills were ambitious, but achievable with time and effort.
This time, it’s not an exaggeration.
It’s a mathematical impossibility.
Let me show you why.
Act 1: The Cloud Engineer “Unicorn” (2010–2015)
Ten years ago, everyone said cloud architects were unicorns.
Companies needed people who knew:
- Traditional infrastructure
- AWS/Azure/GCP
- Distributed systems
- DevOps practices
- Security and compliance
They seemed impossibly rare.
What actually happened:
The skills built on existing knowledge. A systems engineer with 5 years of experience could learn cloud platforms in 6–12 months. The fundamentals transferred. The mental models were similar.
Experienced Systems Engineer
+ 6–12 months cloud training
+ Hands-on project experience
= Competent Cloud Architect
Training time: ~1,000 hours
Gap: Bridgeable ✓
Cloud engineers weren’t unicorns.
They were handsome horses. 🐴
The market eventually caught up. By around 2015, cloud expertise was common.
Act 2: The Data Scientist “Unicorn” (2012–2020)
Then came the data science boom.
“Data Scientist: The Sexiest Job of the 21st Century” — Harvard Business Review, 2012
Companies wanted people who were:
- Statisticians
- Programmers
- Business analysts
- Visualization experts
- Domain experts
What actually happened:
A software engineer could learn statistics and ML basics. A statistician could learn Python. The paths crossed.
Software Engineer or Statistician
+ 12–18 months focused learning
+ ML fundamentals (scikit-learn era)
+ Business domain exposure
= Competent Data Scientist
Training time: ~1,500 hours
Gap: Bridgeable ✓
Data scientists weren’t unicorns either.
They were handsome horses with nice saddles. 🐴✨
By 2020, bootcamps and master’s programs produced data scientists by the thousands. The “unicorn” became common.
Act 3: The AI Expert “Unicorn” (2023–Present)
Now look at what is being asked for:
AI Expert Requirements:
├─ Everything from Cloud Engineer ✓
├─ Everything from Data Scientist ✓
├─ Deep learning theory
├─ Transformer architectures
├─ LLM-specific techniques
├─ Vector databases and embeddings
├─ Prompt engineering
├─ Agent frameworks
├─ RAG architectures
├─ Research paper literacy
├─ MLOps and deployment
├─ Probabilistic system thinking
└─ "3+ years focused on AI/ML projects"
Let’s do the math:
Cloud Engineer Path: ~1,000 hours (6–12 months)
Data Scientist Path: ~1,000 hours (6–12 months)
Deep Learning: ~1,000 hours (6–12 months, maybe more)
Transformers/LLMs: ~700 hours
RAG/Agents/Tools: ~400 hours
Research Literacy: ~500 hours
Production Systems: ~400 hours
Probabilistic Thinking: Ongoing (mindset shift)
Conservative Total: ~5,000 hours (my guess, not official)
At 20 hrs/week alongside a job: 5+ years
And while you're learning, best practices are changing underneath you.
This isn’t just “more skills.”
It’s a different category entirely.
Why AI Is a Paradigm Shift
1. Deterministic → Probabilistic
Cloud engineering:
def deploy_server(config):
server = create_instance(config)
return server # Same config = same result. Always.
AI engineering:
def generate_summary(document):
return llm.complete(document) # Same document = different result. Every time.
This isn’t just a skill gap. It’s a mental model shift.
- Debugging deterministic systems: “Find the bug, fix the bug.”
- Debugging probabilistic systems: “Why did it work yesterday but not today?”
Most engineers have spent their entire careers in deterministic systems. The shift to probabilistic thinking takes years, not months.
2. Documentation → Research Papers
Cloud engineer learning path:
Read AWS documentation → Follow tutorial → It works as described
Time: Hours to days
AI engineer learning path:
Read "Attention Is All You Need" (2017)
→ Understand Q, K, V matrices and self-attention
→ Requires linear algebra and information theory
→ Implement in practice
→ Make it robust in production
Time: Weeks to months, per paper
Research literacy is now required, not optional.
And new papers come out daily.
3. Stable Knowledge → Constant Obsolescence
Cloud Knowledge:
├─ AWS EC2 (2006) → Still relevant in 2025
├─ Kubernetes (2014) → Still dominant
├─ Docker (2013) → Still standard
└─ Learn once, use for years ✓
AI Knowledge:
├─ BERT fine-tuning (2019) → Largely obsolete
├─ Early RAG patterns (2023) → Already evolving
├─ Prompt techniques (2023) → Change monthly
├─ "Best practices" → Moving target
└─ Must relearn constantly ✗
You can’t “become an AI expert” the way you could become a cloud expert.
By the time you master the current best practices, they’ve already shifted.
The Visual
Skill Requirements
↑
│
Actual 🦄 │ AI Expert (2024)
Unicorn │
│ ↑
│ │ Paradigm shift
│ │ (not trainable in 12 months)
│ │
Handsome 🐴✨│ Data Scientist (2015)
Horse w/ │ ↑
Saddle │ │ Skill addition
│ │ (trainable)
Handsome 🐴 │ Cloud Engineer (2012)
Horse │
│
└────────────────────────→
Time to Learn
Cloud → Data Science: Add skills ✓
Data Science → AI Expert: Change how you think ✗
A Personal Note
I will be honest about my own position in this landscape.
I have a deep learning research background. Enterprise systems experience at IBM. Hands-on AI system work. Active research paper reading. Cloud infrastructure knowledge.
For years, I deliberately tried to build breadth across research and industry—an unusual combination.
When I look at these job postings, I estimate I meet maybe 50–60% of what they are asking for, at the depth they imply.
After years of focused effort.
If someone who deliberately built this unusual combination cannot fully meet the requirements, then the requirements are not “high standards.”
They are fantasy.
What’s Actually Happening
When companies post impossible requirements:
- Qualified people don’t apply — They know they honestly meet only half of it.
- Overconfident people do apply — They don’t know what they don’t know.
- Companies hire the overconfident ones — They “check the boxes.”
- Projects fail — Because checking boxes is not the same as having depth.
- Everyone concludes “AI is hard” — Instead of “we defined the role incorrectly.”
The system is broken.
Not because people aren’t trying hard enough.
But because what’s being asked for doesn’t exist at scale.
But Here’s What Most People Miss
Everything I’ve said so far could still be dismissed as:
“AI is just harder. Give it time and the market will catch up, like cloud and data science.”
I don’t think that’s true.
There is a deeper problem that makes AI expertise uniquely impossible, not just difficult:
- The entire field reset in 2022.
- Everyone became a beginner again.
- Even the “experts” had to start over.
That is the next part of this series.